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the work from home rebellion or revenge of the introverts
The Work-From-Home Rebellion, Or Revenge of the Introverts

the work from home rebellion or revenge of the introverts

I got my second Pfizer Covid shot today, which means that I’m now part of the growing post-Pandemic population. Having been more or less in quarantine from the middle of March of 2020, I’m more than ready enough to leave behind the masks, to visit the coffeeshop and the barber and the gym, to be able to go into a restaurant without trying to eat around a 3-ply paper facial covering.

Concerns remain, of course, including strains of COVID-19 that seem to be resistant floating around, but we are also reaching a stage where the health care system is less and less likely to be overwhelmed by the virus, in whatever form it takes, which has always been the primary purpose of the lockdown. Here in Washington State, most restrictions should probably be dropped by the middle of June 2021.

However, in the next several months, it is very likely that the following scenario will be repeated over and over again. The company that you work for sends out email notices saying that, with the pandemic now in the rearview mirror, workers will be expected to return full time to the office, or face being fired.

Some people will do so without reservation, having missed the day-to-day interactions of being in an office. Many of them will be managers, will be older, and will likely be looking forward to being able to see everyone working productively just like the old days. While a broad oversimplification, let’s call them Team Extrovert. They thrive in the kind of social interactions that working in the office brings, and they enjoy the politics that comes from having a group of people forced to work in the same area daily.

The members of Team Introvert, on the other hand, are awaiting such emails with dread. For the first time in years, they’ve actually managed to be very productive because there have been far fewer unnecessary interruptions of their day, they could actually work later into the evening, could do so in an environment that they could control (more or less) and in general, could make the most of the time that they had.

Going back to work was going to mean giving up that flexibility. It will mean once again finding babysitters to get their kids to daycare or school, and dealing with a medical emergency will mean hours not working. It will mean losing a couple of hours a day in commuting on top of the eight-hour days that they work, will mean that if they are in meetings all day they will also have to spend the evenings getting the work done they couldn’t get done during the day. It will mean dealing with the unwanted sexual attention, the disapproval of the office gossips, the uncompensated late nights.

Evaluating Assumptions

The last year has been a boon for work researchers, as one study after another has field-tested several key assumptions about the work-life balance:

the work from home rebellion or revenge of the introverts 1

False: Technology Is Too Immature

“The technological underpinnings necessary to work from home were insufficient to being able to support it.”

Zoom came out of nowhere as a free-to-use telepresence platform to largely dominate the space in under a year. Market leaders in that space stumbled badly as they failed to recognize the need to be able to provide no-cost/low-cost telepresence software in the first months of the Pandemic. Microsoft pivoted quickly to provide similar software Teams for organizations that worked with their increasingly ubiquitous online Office suite, and Slack picked up the slack (in conjunction with a whole ecosystem of other tools) to fill out the collaboration space.

One significant consequence that’s now fully coming into fruition: – an online transcription service using machine learning-based algorithms, has now partnered with Zoom to enable auto-transcription (including figuring out who is speaking) and embedded call-to-action items into their generated output. This means that one of the most onerous tasks of conducting a meeting – creating an accessible record of the conversation – can happen automatically.

The upshot of this is that due to the pandemic, online teleconferences have suddenly become searchable and by extension manipulatable. The impact of this on businesses will be profound, if only because every meeting, not just those few that can afford to have a stenographer present, creates a referenceable record. This also has the potential to make meetings more productive, as it can help a manager identify who is actually providing valuable input, who is grandstanding, and who is being shut out. This form of collaboration is much harder to replicate in person.

False: Collaboration Requires Proximity

This is the watercooler argument. Collaboration, or so goes the story, is centered around the watercooler, where people can meet other people at random within the organization, and through conversations, learn about new ideas or work out problems. Admittedly, today the watercooler is more likely a Keurig coffee machine or it’s equivalent, but the idea – that collaboration occurs due to chance encounters between different people when in informal settings – is pretty much the same.

The problem with this is that it is a bit simplistic. Yes, ideas can come when people meet in informal settings, one of the reasons that conferences are actually pretty effective for stimulating new ideas, but the real benefit comes primarily due to the fact that the people involved are typically not in the same company, or in many cases not even in the same industry. Instead, during these encounters, people with different viewpoints (and different cultural referents) end up discussing problems that they have, and the approaches that they used to solve similar problems in different ways.

The key here is the differences involved. This is one of the reasons that consultants can be useful, but the more they become embedded within an organization, the less valuable their contributions. They are valuable primarily because they represent a different perspective on give problem, and it is the interaction that consultants have with a given, established group that can spark innovation.

Collaboration tools, such as Slack, provide both a way for disparate people to interact and provide a record of that interaction that can be mined after the fact. This kind of chat is largely asynchronous while being more immediate than other asynchronous communication channels such as email. Not surprisingly, programmers and technical people, in general, take to this form of collaboration readily, but members of Team Extravert (who tend to do better with face-to-face communication) often avoid utilizing this kind of collaboration because it doesn’t work as well for establishing social dominance, and because it isn’t synchronous.

False: Working in the Office Is More Secure

The idea that working in an office would be more secure than working remotely is a compelling one, since even a decade ago that would likely have been true. However, several changes, some pre-pandemic, some intra-pandemic, have changed that landscape dramatically.

For starters, by 2020, a significant number of companies had already begun moving their data infrastructures to the cloud, rather than using on-prem services. Sometimes the reasons came down to cost – you were no longer paying for physical infrastructure – but part of it also came down to the fact that cloud service providers had a strong incentive to provide the best protection they could to their networks. Most of the major data breaches that took place in the last ten years occurred not with large scale providers such as AWS or Azure, but with on-prem data storage facilities.

Additionally, once the Pandemic did force a lockdown, all of those supposedly secure on-premise data stores were left to sit idle, with skeleton crews maintaining them. The Pandemic hastened the demise of stand-alone applications, as these became difficult to secure, forcing the acceleration of web based services.

The global decision to move towards https: in 2017 also had the side effect of making man-in-the-middle attacks all but non-existent, and even keystroke analyzers and other sniffing tools were defeated primarily because the pandemic forced workers to distribute, making such hacking tools much less effective. Similarly, one of the biggest mechanisms for hacking – social hacking, where spies would go into company office buildings and note passwords and open ports or would conduct dumpster diving in company trash bins, was foiled primarily because the workforces was distributed.

Hacking still exists, but its becoming more difficult, and increasingly, companies are resorting to encrypted memory chips and data stores, and not social engineering, to keep data secure. Now, it is certainly possible that an enterprising data thief could make a surreptitious run of laptops at the local coffeeshop, but again, this is a high cost, low profit form of hacking.

the work from home rebellion or revenge of the introverts 2

False: Remote Workers Are Less Productive Than Onsite Workers

This is the tree falling in a forest principle: If people are not being watched over, how do you as a manager know that they are actually working rather than browsing the Internet for porn or otherwise wasting time. The reality is that you don’t. The question is whether you should assume that they will do the latter without some kind of oversight.

One of the most significant things to come out of the Agile movement is the shift in thinking away from allocation of hours as a metric of completion to whether or not the work itself is being done. DevOps has increasingly automated this process, with the notion of continuous integrated builds, which works not just for developing software but for assembling anything digital. In general, this means that there are tangible artifacts that come out of doing one’s work consistently, and a good manager should be able to judge whether someone is working simply by looking at what they are producing.

Indeed, this notion of an audit trail is something that remote workers are well aware of. With most productivity tools now tied into some kind of auditable dashboard, a manager should be able to tell when there are problems without in fact actually needing to see the people involved. However, for some managers this ability is a two edged sword, as their managers have access to the same dashboards, the same drawdown charts, and the same bug reports, rendering their oversight role redundant. This points to a bigger problem.

A number of recent studies have shown that when people have clear goals and targets, direct oversight can actually be counterproductive, because the workers, far from doing their best work, become worried that they are being judged unfairly for taking risks that could provide benefits. Put another way, this heavy oversight makes it harder for them to concentrate, which is what they are being paid for in the first place. In this light, the worker is branded a criminal who is setting out to deliberately steal from or sabotage their employers. Not surprisingly, this can become a self-fulfilling prophecy, as these employees leave to find better employment under less stringent oversight, causing disruption in their wake.

Managing a remote workforce is different, and can be especially difficult when one’s definition of management comes down to ensuring that workers are engaged and on task. There are comparatively few jobs, especially in-office jobs, that require 9 to 5 engagement. People need time to think, to plan, and to re-engage in interrupted tasks. This is especially true when dealing with mental activities. Context switching, reorganizing thoughts and concentration to go from one task to another, takes time, and the more the need for concentration, the longer such context switching takes.

One interesting phenomenon that has taken hold during the Pandemic has been that many businesses now concentrate their meetings during the middle of the week, rather than scheduling them at random through the week. arguably, this can be seen as creating a longer weekend, but in practice, what seems to happen is that people tend to use their Mondays and Fridays (or one of the weekend days) as days to concentrate on specific tasks without interruption. This helps them accomplish more with less stress. Workers still may provide a written report at the end of the day (or the week) summarizing what they’ve done, but this becomes part of a work routine, rather than an interruption, per see.

False: On-Site Work Improves Company Morale and Employee Loyalty

I’ve attended a few company picnics, holiday parties, and corporate retreats over the years. They are supposed to enliven morale and increase esprit de corps, but, in reality, they are fields filled with social land mines where you interact as little as possible with anyone that is not in your immediate group for fear of running afoul of a Senior Vice President or, worse, the Head of Human Services.

Company culture is an artificial construct. It does have its place, but all too often the putative company culture is set out in an employer’s handbook that everyone is supposed to read but few actually do. The actual company culture is mostly imitative, where one follows the whims and actions of the senior stake-holders in the company, even if these are potentially toxic.

Ironically, as the pandemic fades as a factor, corporate get-togethers may actually replace being in the office as the dominant mode of interaction. There are signs that this is already happening, especially as corporations become more distributed.

the work from home rebellion or revenge of the introverts 3

False: Everyone Wants To Return To The Office

Managers tend to be extroverts. They prefer face-to-face interaction, and in general are less likely to want to read written reports, even though these usually contain more information about how the projects are going. In some cases, written reports also make it harder to achieve deniability in case something does go wrong, though in this case you could argue that this is just a sign of bad management.

However, a significant percentage of the knowledge worker-based workforce are introverts. Introverts make up roughly 30% of the population, but that 30% corresponds to writers, designers, artists, programmers, analysts, architects, librarians, scientists, musicians, and other people who spend much of their time working in comparative solitude. When you factor this in, the number of people who are actually likely to be in offices probably comes closer to 55-60% of everyone who was in an office before.

This changes the dynamics of returning to the office (not returning to work itself, as none of these people deliberately stopped working) considerably. Most were more productive in the last eighteen months than they have been in years. Because they could work with minimal interruption (and that mostly asynchronous) and because they could control their environment, those largely introverted workers were able to use their skills more effectively.

Additionally, without the need to hire locally, companies could hire people anywhere. This had some unintentional side effects, as workers in San Franciso and New York migrated in droves towards smaller communities that were within an air-flight commute if need be, but were no longer forced into paying seven digits for a small house. A lot of companies made such positions contingent upon onsite-post-covid, but that provision may be impossible to enforce, especially as tightening labor markets in this critical sector make it a no-brainer for many people to opt to work elsewhere that has a more liberal work-from-home policy.

A recent Linked-In study made the startling discovery that, in their survey of members, if given a choice between working from home and a $30K bonus to return, 67% said they would prefer the work from home option. While this isn’t necessarily scientific, it hints at the reluctance that many people have to going back to the daily grind.

Are We At a Tipping Point?

As workers come reluctantly back to the office, there are several points of uncertainty that may very well derail such efforts.

  • The pandemic is not yet over. One of the principle reasons for the initial lockdown was to keep the healthcare system from being overwhelmed. In those places where the lockdown was followed closely, this never happened. In those places where it wasn’t, the healthcare system was overwhelmed. With multiple variants still arising, and a vaccine that likely still needs additional tweaking, the prospect of the pandemic continuing (or flaring back up) for at least another year is not out of the question.
  • Social resistance. Ironically, there are now many people who have taken to heart the social restrictions, and it will still be months, if not years, before, the habits ingrained into people during the pandemic are overcome.
  • The threat of lawsuits. If a person goes back into the office, contracts COVID-19, and is left dead or crippled, is the employer liable? This really hasn’t been tested in the courts yet, and until it is, employers will be reluctant to be the first ones to find out.
  • Worker mobility. Similarly, a tightening job market and two demographic trends are seeing many people retiring in 2021, fewer people entering the job market, and the natural consequences of ignoring employee loyalty playing out with more defections to new opportunities.
  • Tech Trends. The technology for doing work from anywhere is clearly in the direction of distributed work. This means that financially, the benefits of bringing people in-house are far outweighed by distributing that same workforce.

the work from home rebellion or revenge of the introverts 4

What Do Managers Do Now?

If your company has not already been planning a post-pandemic strategy, hoping that things will return to normal, it’s likely getting almost too late to do so. Things will not return to the way that they were pre-pandemic, simply because the pandemic has accelerated where we would most likely have been ten years from now to today. This means that the post-pandemic organization is going to look (and act) very different than what it was in 2019.

There are several things that can be done to make the transition as painless as possible for all concerned.

Is The Office Even Required?

A number of companies had been skirting the edges of going fully virtual even before the pandemic, and as the lockdowns dragged on, decided to make the jump to a facility-less existence. The move cut down considerably on business costs, and when the need to meet in person (or meet with clients) came up, these same companies would rent out hotel conference space at a considerable savings. These companies and others like them are crunching the numbers to see if having the extensive physical presence is really all that necessary any more.

Evaluate Worker Safety

The pandemic is not going away. Rather, we are reaching a stage where COVID-19 is becoming manageable and survivable for most, albeit still a very dangerous disease for some. Before workers can return to the office, establishing a vaccine screening protocol should be considered carefully, with workers who are not yet vaccinated being considered high risk for return, and social distancing protocols should likely be taken into account for some time even when lockdowns are rolled back. It may also be worth bringing back employees in staggered tranches, with enough time between these (at least a month) to determine whether or not the virus is still spreading locally.


Identify those positions that absolutely must be on premises at all times, those that need to be on premises 2-3 days a week, and those that can work comfortably remotely. Be critical about your assumptions here: a facilities manager needs to be available or have back up, but an analyst or programmer usually does not. If someone is already working remotely and are at a physical remove, leave them there. After six months, re-evaluate. Chances are pretty good that you might you need fewer people onsite than you think.

Make Management and Reporting Asynchronous

To the extent possible, make tracking and reporting something that does not rely solely upon face-to-face meetings. Whether this involves utilizing Zoom or Teams-like meetings more, Slack, various DevOps tools or distributed office productivity tools, take advantage of the same tools (many of them part of the Agile community) that your teams are already using to communicate in an auditable fashion. Additionally, every day, each person should include a log entry at the end of the day indicating where they are, what they are working on, and what issues are needed. It is the responsibility of the managers to ensure that if conversations DO NEED to happen, that they are facilitated.

Move Towards 3-2-2

Move as many of your meeting as possible towards the center of the work week – Tuesday through Thursday – then treat Monday and Friday as concentration days, with minimal meetings but higher expectations. Demos and reports should always be held during a midweek-block. Similarly, identify core hours for availability during the week for team collaboration.

Improve Onboarding

Onboarding, whether of a new employer to the company or to a different group, is when you are most likely to see new hires quit, especially if badges or other access issues delay the process. Identify an onboarding partner within the group who is responsible for helping the newbie get access to what they need in a timely fashion, and who can help these new hires get ramped up as fast as possible. While this is useful for any new recruit, it’s especially important in distributed environments.

Buddy Up

When possible, buddy up coworkers in the same team so that they are also in the same time zone. If your operation is on the US East Coast but you have workers in Denver and Seattle, then those workers should be paired off. This provides an additional channel (and potential backup) for communication, while at the same time keeping people from having to work awkward hours because of time zone differences. This holds especially true for transnational teams.

Eliminate Timesheets

You are hiring people for their technical or creative expertise, not their time in seat. You can track hours through other tools (especially agile ones) for determining how long tasks take for planning purposes, but by moving to a goal oriented, rather than hour oriented approach, you reward innovation and aptitude rather than attendance.

Make Goals Clear

On the subject of goals, your job as manager is to make sure that the goals that you want out of your hires are clear. This is especially true when managing remote workers, where it is easier to lose sight of what people are doing. You should also designate a number two who can work with the other team members at a more technical or creative level but can also help ensure that the goals are being met. This way, you, as a remote manager, can also interface with the rest of the organization while your number two interfaces with your team (this is YOUR partner).

Hold Both Online and Offline Team Building Exercises

Start with the assumption that everyone is remote, whether that’s a desk in the same building, a coffeeshop, their house, or a beach with good wifi access. Periodically engage in activities that help promote team building, from online gaming to coffee klatches but that don’t require physical proximity. At the same time, especially as restrictions ease, plan on quarterly to annual conventions within the organization, perhaps in conjunction with conferences that the organization would hold otherwise. Ironically, it is likely that these meetings will become more meaningful because in many cases the only real contact you otherwise have with the organization is a face in a screen.

Don’t Penalize Remote Workers Or Unduly Reward Onsite Ones

Quite frequently, organizations have tended to look upon remote work as a privilege rather than a right, and it is a privilege that comes at some cost: if you’re not in the office, you can become effectively invisible to those that are. This means that when establishing both personal and corporate metrics, that you take this bias, along with others, into account to determine who should be advanced. Additionally, if your goal is near to full virtualization, it’s worth taking the time to identify who is most likely to be against such virtualization and understand their objectives. There are people who want to build personal fiefdoms who see virtualization as being deleterious to those ends. That too can contribute to corporate culture, and in a very negative way.


There are signs that many parts of the world are now entering into a period of overemployment, where there will simply not be enough workers to fill the jobs that are available. The pandemic has forced the issue as well, accelerating trends that were already in place by at least half a decade if not more. Because of this, company strategists who are relying upon the world going back to the way things were before are apt to be shocked when they don’t.

Planning with the assumption that work from anywhere is likely to be the dominant pattern of employment is likely to be a safe bet. For most organizations it provides the benefit of being able to pull upon the talents of people without having to physically disrupt their lives, giving them an edge against more traditional organizations that can’t adapt to that change. With better productivity and collaboration tools, the mechanisms increasingly exist to make remote work preferable in many respects to onsite work, but it does require management to give up some perceived control and discomfort with adaptation. Finally, the workers themselves may have the final say in this transition, voting with their feet if they feel there are better opportunities with more flexibility elsewhere.

Kurt Cagle is the Community Editor of Data Science Central, and the Producer of The Cagle Report, a weekly analysis of the world of technology, artificial intelligence, and the world of work.

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the environmental toll of nfts
The Environmental Toll of NFTs

the environmental toll of nfts

  • The Non-Fungible Token (NFT) craze is an environmental blitz.
  • What’s behind the massive energy use.
  • Solutions to the problem.

The high environmental toll of NFTs

A couple of weeks ago, I wrote an article outlining the Non-Fungible Token (NFT) process. For a brief moment, I considered turning some of my art into NFTs. That moment soon passed after I realized the staggering environmental toll behind buying and selling NFTs. While fads come and go with little impact (anyone remember Cabbage Patch dolls?), NFTs are unique in that they not only cause environmental harm when they are first bought and sold, but they continue to consume vast amounts of energy every time a piece is resold. In other words, while purchasing used goods, like clothing or housewares, has a fraction of the environmental impact of buying new, the same is not true of NFTs. Every time any NFT transaction takes place, whether it’s for a newly minted piece or for a resale, massive amounts of energy are required to fuel the transaction.

Why do NFT Transactions Consume So Much Energy?

The enormous environmental costs associated with NFTs is tied to the way the network they are built on is secured. Ethereum, the blockchain which holds the NFTs, uses a compute intensive Proof-of-work (PoW) protocol to prevent double spending, economic attacks, and other manipulations [1]. PoW was designed to be computationally inefficient: Basically, the more complexity involved in creating one, the higher the security [2].

The validation of ownership and transactions via PoW is based on search puzzles of hash functions– cryptographic algorithms that map any-size inputs of any size to a unique output of a fixed bit length. These challenging puzzles, which must be solved by networks, increase in complexity according to the price of cryptocurrency, how much power is available, and how many requests there are for new blocks [3]. As NFTs take off, demand surges and the entire systems struggles to keep up, fueling demand for more and more warehouses, more cooling, and more electricity consumption. 

 Many organizations and individuals have attempted to ballpark the exact carbon footprint of NFTs, and most of those paint the process in a poor light. A single NFT transaction has been estimated to have a carbon footprint equal to the energy required to:

  • Keep the lights on for 6 months (or more) in an art studio [4].
  • Produce 91 physical art prints [5]
  • Mail 14 art prints [6],
  • Process 101,088 VISA transactions [7],
  • Watch 7,602 hours of YouTube [7],
  • Drive 500 miles in a standard American gas-powered car [8].

Although it is challenging to ascertain the exact environmental cost of NFTs, much work has been reported on the more established Bitcoin, which runs on similar principles. For example, Elon Musk’s recent dabbling with PoW-based Bitcoin used so much energy in just a few days, that is negated the amount of carbon emissions reduced by every Tesla ever sold [9].  

Digital artist Everest Pipkin, writing on the state of cryptoart in a blog post, states

“This kind of gleeful wastefulness is, and I am not being hyperbolic, a crime against humanity” [10].

What is the Solution?

Steps have been taken toward more energy efficiency. For example, Ethereum is attempting to move to a more energy efficient consensus mechanism called proof-of-stake (PoS). However, this is faltering out of the starting gate. A post on the Ethereum website states “…getting PoS right is a big technical challenge and not as straightforward as using PoW to reach consensus across the network.” [11]  In other words, while we wait  (potentially for years) for Ethereum to “get in right”, we’re busy polluting the atmosphere like it’s 1972.

Some digital artists have attempted to make their transaction carbon neutral by planting trees or creating sustainable farms, but their efforts have backfired. For example, artist John Gerrard recently created a “carbon-neural” NFT video piece called Western Flag [12]. The carbon-neutrality was a result of investment in a “a cryptofund for climate and soil”. However, Gerrard’s piece caused more buzz for NFTs, fueling more creations by more artists—most of whom did not even attempt to offset their transactions by planting trees [9]; Not that planting trees to alleviate emissions guilt works anyway. Critics have equated tree planting offset schemes as nothing more than a fig leaf [13]. 

the environmental toll of nfts

“Robert” by Zac Freeman.

The real solution? Pass this fad by. Instead, support artists who create sustainable art, like assemblage artist Zac Freeman. Freeman, a resident artist at CoRK arts district in Jacksonville, Florida, creates art in the real-world from found objects: throwaway items like used Lego bricks, paper clips and plastic bottle tops.

“If I can get my art in front of 10,000 people and get them to think about disposable goods and cultural consumerism,” says Freeman, “I’ve achieved my goal.”

For the environmental cost of a single NFT transaction, you can get Zac (or any other artist) to ship you 14 prints. Or, for the price of one animated flying cat with a pop-tart body [14], you can commission Zac to create assemblage pieces of your entire extended family. I know which option I would choose.


Carbon Footrprint Image: By Author

“Robert” by Zac Freeman. Used with the artist’s permission.

[1] Proof of Work PoW

[2] The Unreasonable Cost of Cryptoart

[3] The Carbon Footprint of Bitcoin

[4] NFTs Are Hot. So Is Their Effect on the Earth’s Climate


[6]  What Are NFTs, And What is Their Environmental Impact?

[7] Ethereum Energy Consumption

[8] NFT Climate Change.

[9] Non-fungible tokens aren’t a harmless digital fad – they’re a disas…

[10] Can fashion ever be sustainable?

[11] Ethereum Upgrades.

[12] Western Flag Spindletop Texas

[13] Tree-planting to offset carbon emissions: no cure-all

[14] Why an Animated Flying Cat With a Pop-Tart Body Sold for Almost $60… Source Prolead brokers usa

mern vs mean which stack to use in 2021

mern vs mean which stack to use in 2021

Bothe MEAN and MERN are full stack frameworks with Java coded components. The difference is that MEAN uses Angular JS while MERN uses the React JS developed by Facebook. Both aids developers to make reactive and intuitive UI. To understand which stack is the better one, we need to understand the underlying differences between them.


  • MEAN: Components include Mongo DB, Angular JS, Express, and Node.
    MERN: Components include Mongo DB, React JS, Express, and Node.
  • MEAN: JavaScript development stack.
    MERN: Open source JavaScript library.
  • MEAN: Uses Typescript language.
    MERN: Uses JavaScript and JSX.
  • MEAN: Copnent based architecture.
    MERN: None.
  • MEAN: Regular DOM.
    MERN: Virtual DOM.
  • MEAN: Steep learning curve.
    MERN: Better documentation.
  • MEAN: Bidirectional data flow.
    MERN: Unidirectional dataflow.

Both tech has high class features and immense functionality. The slight upper hand that MERN enjoys is in the learning curve. MERN is easier to grasp because the learning curve differs between Angular JS and React JS. Let us take a deeper dive into the benefits of MEAN and MERN stacks to understand the power of each of these stacks fully.



  • All types of applications can be developed easily.
  • Various plug ins and widgets have compatibility with this stack. For development that has a stricter time frame, this comes in handy.
  • The functionality skyrockets due to the availability of plug ins.
  • Developers enjoy community support since the framework is open source.
  • Real time testing is possible with the built-in tools.
  • A single language is used for back end and front end. This increases coordination and gets applications to respond faster.


  • Front end and back end are covered by a single coding script.
  • The entire process can be completed using only JAVA and JSON.
  • Seamless development through the MVC architecture.
  • Real time testing through built-in tools.
  • Runs on an open source community and the source code can be easily modified.

According to Hacker Rank development skill report, 30% of developers went with Angular JS while 26% stayed with React JS. The report also mentions that 30% of the programmers wanted to learn React JS and 35.9% of developers prefer to develop using React JS, thus MERN stands slightly above MEAN when it comes to popularity.

As far as we know, in terms of ease of understanding and popularity, MERN is at the forefront now. Let us take a detailed comparison to understand who will win the race in 2021.


Scalability, Security: Both MEAN and MERN are equally secure. However, in terms of scalability, MERN is at the forefront.

  1. MVC: For enterprise level apps, a complete architecture needs to be maintained. MEAN is the better option for this.
  2. UI: For an advanced and simple UI, MERN is the go-to stack. MERN facilitates user interaction.
  3. CRUD: For CRUD (create, read, update, delete), MERN is the ideal stack. The React JS handles data changes quickly and has a good user interface as well.
  4. Support: The Angular JS in MEAN supports HTTP calls and unites the back-end. Enterprise level app development will require third party.
  5. libraries. On the other hand, React JS improves functionality through its supplementary libraries. MEAN scores slightly above in this section.

MEAN enhances the experience through the use of third party extensions while MERN would require additional configurations to do this.

In aspects of the learning curve, UI, scalability, and CRUD, MERN stack scores more than MEAN stack. However, in the aspects of community support and MVC MEAN stays ahead. In terms of security both are at par. However, the application of the stacks depend entirely on the business needs.

MEAN is more affordable, and is the first choice for startups and SMEs. Switching between clients and servers is easier. For real time web apps, MEAN is definitely the best choice. In MERN, the Virtual DOM enhances user experience and gets the developer’s work done faster. A stable code is maintained by React JS due to a unidirectional data flow. For coding for Android and IOS using JavaScript, MERN is definitely the way to go.


Companies like Accenture, Raindrop, Vungle, Fiverr, UNIQLQ, and Sisense among others use MEAN in their tech stacks. Brands such as UberEats, Instagram, and Walmart use MERN stack. Both the stacks provide an incredible user experience. Stability and scalability can be achieved with both stacks.

From this we can conclude that enterprise level projects require MEAN over MERN. MERN makes rendering UI simpler. Both are reliable for a quick front end development.

MEAN is good for large scale application. MERN is good for faster development of smaller applications.

At Orion we have an excellent team that can help you with all your MEAN and MERN stack development needs.

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dean of big data 2021 2022 data analytics trends
Dean of Big Data: 2021-2022 Data & Analytics Trends

dean of big data 2021 2022 data analytics trends

I’m starting to see the big consultancies and advisory services coming out with their lists of “what’s hot” from a data and analytics perspective.  While I may not have the wide purview of these organizations, I certainly do work with some interesting organizations who are at various points in their data and analytics journey.

With that in mind, I’d like to share my perspective as to what I think will be big in the area of data and analytics over the next 18 months.

  • Contextual Knowledge Center. A contextual directory, on AI steroids, that facilitates the identification, location, reuse, and refinement (including version control) of the organization’s data and analytic assets (such as workflows, data pipelines, data transformation and enrichment algorithms, critical data elements, composite metrics, propensity scores, entity or asset models, ML models, standard operating procedures, governance policies, reports, dashboard widgets, and design templates).  It upon an organization’s data catalog by integrating contextual search, Natural Language Processing (NLP), asset scoring, graph analytics, and a decisioning (recommendation) engine to recommendation data and analytic assets based upon the context of the user’s request.
  • Autonomous Assets. These are composable, reusable, continuously-learning and adapting data and analytic assets (think intelligent data pipelines and ML models) that appreciate, not depreciate, in value the more that they are used. These autonomous assets produce pre-defined business and operational outcomes and are constantly being updated and refined based upon changes in the data and outcomes effectiveness, with minimal human intervention.  This could apply to almost any digital asset including data pipelines, data transformation and enrichment algorithms, process workflows, AI / ML analytic models (like Tesla’s Fully Self Driving or FSD module), and standard operating procedures and policies.  Yea, this is probably one of my top 3 topics.

dean of big data 2021 2022 data analytics trends 1

  • Entity Behavioral Models: These Analytic Profiles capture, codify, share, re-use, and continuously-refine the predicted propensities, patterns, trends and relationships for the organization’s key human and device (things) assets…at the level of the individual asset. This is the heart of nanoeconomics, which is the economics of individual human or device predicted propensities.  It is Entity Behavioral Models or Analytic Profiles that drive the optimization of the organization’s key business and operational use cases.

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  • AIOps / MLOps. This is an emerging IT field where organizations are utilizing big data and ML to continuously enhance IT operations (such as operational task automation, performance monitoring, load balancing, asset utilization optimization, predictive maintenance, and event detection, correlation, and resolution) with proactive and dynamic insights.
  • DataOps. An automated, process-oriented methodology to improve model quality and effectiveness while reducing the cycle time in the training, testing and operationalizing data analytics. DataOps is an integrated suite of data management capabilities including best practices, automated workflows, data pipelines, data transformations and enrichments, and architectural design patterns.
  • Data Apps / Data Products. Data apps or data products are a category of domain-centric, AI-powered apps designed to help non-technical users manage data-intensive operations to achieve specific business and operational outcomes.  Data apps use AI to mine a diverse set of customer, product, and operational data, identify patterns, trends, and relationships buried in the data, make timely predictions and recommendations with respect to next best action, and track the effectiveness of those recommendations to continuously refine AI model effectiveness.
  • Software 2.0. An emerging category of software that learns through advanced deep learning and neural networks versus being specifically programmed. Instead of programming the specific steps that you want the software program to execute to produce a desired output, Software 2.0 uses neural networks to analyze and learn how to produce that final output without defining the processing steps and with minimal human intervention. For example, Software 2.0 using neural networks can learn to differentiate a dog from a cat versus trying to program the characteristics and differences between a dog and a cat (good luck doing that!).
  • AI Innovation Office. The AI Innovation Office is responsible for the testing and validation of new ML frameworks, career development of the organization’s data engineering and data science personnel, and “engineering” of ML models into composable, reusable, continuously refining digital assets that can be re-used to accelerate time-to-value and de-risk use case implementation. The AI Innovation Office supports a “Hub and Spoke” data science organizational structure where the centralized “hub” data scientists collaborate with the business unit “spoke” data scientists to engineer (think co-create) the data and analytic assets. The AI Innovation Office supports a data scientist rotation program where data scientists cycle between the hub and the spoke to provide new learning and development opportunities.
  • Data Literacy, Critical Thinking, and AI Ethics. AI will impact every part of your organization not to mention nearly every part of society.  Consequently, there is a need to train everyone on data literacy (understanding the realm of what’s possible with data), critical thinking (to overcome the natural human decision-making biases), and ethical AI (to ensure that the AI models are treating everyone equally and without gender, race, religious, or age biases). Everyone must be prepared to think critically about the application of AI across a range of business, environmental, and societal issues, and the potential ethical ramifications of AI model false positives and false negatives.  Organizations must apply a humanistic lens from which to ensure that AI will be developed and used to the highest ethical standards. 

dean of big data 2021 2022 data analytics trends 3

Well, that’s it for this 2021.  And if we can avoid another pandemic or some other major catastrophe, I’m sure that next year will be even more amazing!

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solving unified data management analytics challenges for business and it
Solving Unified Data Management & Analytics Challenges for Business and IT

solving unified data management analytics challenges for business and it

Organizations in every industry are leveraging data and analytics to overcome roadblocks on the path to innovation and progress, plan for the future, and position their organization to competitive advantage. 

The entire enterprise must become more connected than ever before for expertise to come together to drive a shared vision for innovative change. This means transitioning away from legacy systems and adopting a modern approach to business processes and operations, and data and analytics. 

Ronald van Loon is an SAP partner, and as an industry analyst and insider for over twenty years, has an opportunity to further investigate the challenges emerging in the unified data and analytics domain.

To get the most value from data, businesses need to strengthen their data culture, and this continues to be an evasive objective for numerous organizations. With so many new systems, processes, and individual ways of thinking and working, creating a connected data ecosystem can be complex. 

But with a unified data management and analytics solution, the world of business and IT can unite to drive data initiatives forward, enhance productivity, and overcome the unique challenges that are inherent to business and IT.

Data and Analytics Complexities Grow

Data is unquestionably increasing in value, but it’s simultaneously growing in complexity. Organizations have to tackle this complexity in order to benefit from the potential value of data. But traditional approaches to database, data warehousing, and analytics can take years of development and deployment before they produce any benefits, and even then, companies can face limitations when pertaining to real-time analytics, complex data sets, and data streaming.

Businesses are also reporting that they’re grappling with advancing and managing data as a business asset, driving a data culture, accelerating innovation via data, and using data and analytics to compete. 29.2% of businesses report having accomplished transformational business results, 30% report having built a meaningful data strategy, and only 24% believe their company was data-driven this last year. 

A few of the primary challenges impeding analytics progress include:

  • Lack of a strong analytics strategy, which may encompass under-utilized technologies, failing to set manageable goals that can provide quantifiable value, or lack of interaction and agility across data, IT, and business.
  • Unbalanced analytics programs that don’t account for diverse user needs as well as enterprise-wide standards, which can result in inefficiency and prevent analytics from being scaled.
  • Insufficient data governance and data hygiene that impacts data accessibility and often leads to data silos.
  • Myriad data sources, overlap, and obscurity due to the adoption of new processes and systems throughout numerous layers of the organization.
  • Legacy analytics initiatives that hinder organizations from developing, deploying, and scaling advanced analytics due to deficient features for collaboration, and limited artificial intelligence (AI), machine learning (ML), and big data capabilities. 

Companies can be further challenged in infusing data into the very DNA of their decision-making processes rather than just understanding or discussing the importance of including it. Ultimately, this puts a damper on creativity, curiosity, and an enterprise-wide data mindset that fosters dynamic, smart innovation across products and services. 

Business leaders need to approach data and analytics investments as an accelerant to ongoing business requirements, develop analytics capabilities according to relevant use cases and business problems, and build upon this foundation by strategically implementing new tools, technologies, and solutions.  

Business and IT Unified Data Management and Analytics Challenges

As data is one of the most powerful and critical assets an organization has, it must be available, accessible, and able to be leveraged by every user across the entire value chain. Business users have to be able to ask questions and get answers from their data and rely on it as a pillar of decision-making. This extends to both IT and business lines, though these two areas have distinctive roles, responsibilities, and purposes. 

If business and IT can work together and share their knowledge and experience when it comes to data, progress can be optimized across the enterprise. But business and IT have their own set of unified data management and analytics challenges that they face that they need to overcome.

Business challenges:

  • Trusted Data: Lacking quality data, outdated data, or duplicate data. 
  • Self-service: Overly complex systems and processes create barriers for business units who need simplified methods to get to the data that they need to make decisions. 
  • Ease of use: Having the capabilities to work independently and access the data that they want without contacting and/or burdening IT teams. 

IT challenges:

  • Hybrid Systems: Working on premise or in the cloud can be time consuming and overly complex. 
  • Heterogeneous Data: Siloed data from multiple sources is too spread out.
  • Security and Governance: The evolving landscape of security, privacy and regulatory environments are becoming complicated. 

When business and IT teams can’t effectively use data, they can’t work freely, make confident decisions, and leverage artificial intelligence (AI) and machine learning (ML) to help them transform data into solutions and meet the demands of evolving consumer behaviors, rapid transformation, and technological enabled workspaces. 

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An Answer to Business and IT Challenges

Organizations need flexibility, agility, and connectivity in order to enhance collaboration and use data-driven insights to drive intelligent solutions forward. With a unified data management and analytics strategy, organizations can overcome the common challenges that business and IT are facing across industries. 

SAP Unified Data and Analytics connects business and IT professionals and improves end-to-end data management to simplify data environments and help organizations grasp and maximize the true value of their data to its fullest potential. 

In order to develop a unified data ecosystem, organizations are moving to cloud database as-a-service solutions, using an intelligent platform like the SAP Business Technology Platform that connects disjointed data from IoT, cloud and big data, creating a single source of truth. This helps businesses resolve data trust challenges and simplify IT disparate data and hybrid system complexities. At the same time, IT can better focus their attention on governance and model data in secure spaces. Data and workloads essentially become more integrated and connected, which helps business and IT better collaborate. 

With intelligent cloud solutions, like SAP HANA Cloud, SAP Data Warehouse Cloud, SAP Data Intelligence Cloud, and SAP Analytics Cloud, organizations can choose what data to keep on premise and which to keep in the cloud, scaling as needed, migrating legacy systems where necessary, and start the process of building a modern infrastructure. Organizations can give data purpose with SAP Unified Data and Analytics.

An intelligent platform is going to simplify data accessibility for business teams while simultaneously providing visualizations and dashboards. This brings real-time data insights to life, enabling business teams to establish a meaningful connection to their insights, which solves the previously discussed decision-making and accessibility challenges. 

Unified Data Management and Analytics Optimization

Eliminating unified data management and analytics challenges ensures that organizations are able to deploy their forecasts, contextualize data and analytics, and share insights across business and IT to continuously grow and innovate. 

To learn more and stay current with the latest data and analytics trends and information, you can visit the executive corner industry pages with a focus on retail, public sector, or consumer packaged goods on or sign up for the SAP Data Defined: Monthly Bytes newsletter.

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will gpt 3 ai put authors out of work permanently
Will GPT-3 AI put authors out of work permanently?

will gpt 3 ai put authors out of work permanently

In a world of GPT-3 AI-generated content, are writers even needed? In a recent business experiment, I set out to answer this question.

If you’re wondering, who am I to tell you anything about GPT-3 AI? Well, I’m Lillian Pierson, and I help data professionals become world-class data leaders and entrepreneurs – to date I’ve trained over 1 million data professionals on the topics of data science and AI. I’m a data scientist turned data entrepreneur, and I’ve been testing out GPT-3 AI for about 3 months now in my data business, Data-Mania. 

As a data entrepreneur, I spend a TON of my time, energy, and financial resources on creating content. From podcast episodes to YouTube scripts, to emails and social media posts, content creation eats up a huge chunk of my week.

So when I heard about GPT-3 AI copy services, I was curious to know: would this be a useful tool in my business?

Would I be able to 10x my content production rates? Replace freelance writers?

Rather than simply buying into the online hype, I wanted to conduct my own research – and today, I want to share it with you. Whether you’re a data entrepreneur, data professional, or simply a fellow data geek who LOVES reading about the smartest AI companies, read on to get the full scoop on GPT-3 AI and how I believe it will shape the content writing industry. 

In this article, we’ll cover:

  • What is GPT-3 AI?
  • The pros of GPT-3
  • The cons of GPT-3
  • 3 guidelines to use GPT-3 while maintaining brand integrity
  • Will GPT-3 change the content writing industry? 

Let’s get started.

What is GPT-3 AI? 

First of all, what is GPT-3 AI? GPT-3 is a model for human-like language production written in Python. It uses large amounts of texts crawled from the web to create similar, but unique content. Since it was developed by OpenAI and released for public use in June of 2020,  there have been TONS of data entrepreneurs creating SAS products that run off of GPT-3. 

Some of the most common GPT-3 AI content services are and WriteSonic. I conducted my experiment using Writesonic. 

Pros of GPT-3 AI

Alright, let’s start with the good. 

1. Great for Product Descriptions

During my experiment, I have to say I was genuinely impressed by the product description snippets I was able to create using Write Sonic’s GPT-3 AI service.

All I needed to do was input the name of my product (in this case, it was my free Data Superhero Quiz) as well as add product info such as features and benefits. All I did was copy and paste some bullet points from my sales page and I was good to go. 

And wow! With the click on a button, I had ten high-quality product descriptions to pull from. The service was even suggesting some features and benefits I hadn’t even thought of. 

2. Unique and Anonymous

A big pro to using GPT-3 AI content is that everything it spits out is completely unique. There’s no need to worry about plagiarized content. Also, the service is totally anonymous – no one will know you’re using AI so there’s no need to worry about being judged. 

3. Good ROI on Your Time and Money

After reviewing the product descriptions created by Writesonic, I have to admit I liked them a lot better than the ones I’d written myself. Considering the fact they’d taken me a good 10-20 minutes to write, PLUS I’d purchased templates for $50 to speed up the process of writing them, the GPT-3 AI content is clearly better value. I had dozens of descriptions within just 30 seconds. 

Overall, if you are looking for a tool to help you quickly and easily create short content snippets (i.e. product descriptions!) you should definitely add a tool like or Writesonic to your toolbox.

Cons of GPT-3 AI

While I had some successes with GPT-3 AI, I also had some total failures. 

1. Lacks context

Unfortunately, GPT-3 is not great at generating content if it doesn’t have the context directly from you. 

I tried playing around with its article writing mode, which is still in beta.  

Essentially, you give it an outline and an introduction, and then it returns the entire article with all of the body copy.

While technically the information may be factually correct, it lacks context. It won’t have the context needed for YOUR particular audience, so it won’t be intelligible.

Information without context about WHY it matters to your customers is useless. They need to know why they should care and how what you’re sharing will actually have an impact on their life. Without that, you’re simply producing content for the sake of content, and adding to the noise. 

2. In some cases, it gets things wrong.

While in some cases the information might be garbled and lacking context, in other instances, the content GPT-3 AI provides could be flat out wrong. GPT-3 AI will lack the nuances about your industry that come naturally to you.

For example, when I was using Writesonic’s article mode, one of the headings was “What are the obligations of a Data Processor?”

However, the body copy that GPT-3 produced did NOT correlate with the appropriate heading. Rather than telling me the obligations of a Data Processor, it gave me content about the role of a Data Protection Officer. 

It brought up a totally different point. And while it may be related, if you had actually used this content on the web, it would’ve reduced your credibility and put your brand in a bad light.

In short, I would straight up AVOID GPT-3 AI for article-writing or long-form content. You could potentially use it as a research tool, to help you uncover relevant topics you may not have thought of, but always be sure to dig deeper into those topics and not rely on what GPT-3 gives you.

3 Guidelines To Make the Most of GPT-3

Here are three recommendations and safety guidelines for you to use in order to make sure that you’re protecting your brand integrity and the quality of the content you produce when working with GPT-3. 

1. Review GPT-3 AI Content Carefully 

GPT-3 is going to create a TON of content for you. It’s up to you to pick and choose what is valuable, and to make sure everything is factually correct and appropriate. 

 2. Add Personalization

Whatever content that GPT-3 gives you, you need to improve on it, add to it and personalize it for your brand. You know your customers better than anyone else.  I recommend seeing GPT-3 as more of a content research tool than as something to produce finished copy.

will gpt 3 ai put authors out of work permanently 1

3. Add Context

No one on this planet needs more random information. What we need is meaning and context. So while the creators of GPT-3 are correct in saying it produces ‘human-like text, it’s not able to add the context readers need in order to create meaning in their lives.

Content without context doesn’t compel readers to take action based on what they’ve read – all it does is overwhelm them. Information for the sake of information simply adds to the noise – which is something all of us online content creators should be trying to avoid at all costs

4. Listen to All Content Aloud

And last, but not least, rule number four is to listen to your end text aloud.

You want to make sure that whatever content GPT-3 AI spits out, you’re listening to out loud so you can make sure it’s conversational and flows nicely. It’ll also be an opportunity to double-check everything is factually correct.

My favorite tool to do this is a TTS reader. 

By following these guidelines, you’ll be able to ensure that you can safely increase your content production WITHOUT harming your brand’s reputation.

Will GPT-3 change the game for writers?

After reviewing the results from my business experiment, I STILL believe that there is a need for highly skilled content writers. However, the rise of GPT-3 AI demonstrates how AI is certainly changing the content marketing landscape. 

While I do believe GPT-3 may replace low-level, unskilled writers (who, let’s be real, probably shouldn’t be pursuing writing in the first place) businesses will continue to require writers who can deliver nuance, context, and meaning to their customers. 

At best, GPT-3 will become a tool that helps smart writers speed up their writing process and make their lives easier. They may use GPT-3 content as a starting point from which they can create highly personalized and meaningful content. 

At worst, the web could become flooded with GPT-3 AI generated that only adds noise to the already crowded internet, significantly contributing to the overwhelm people are already experiencing when trying to find high-value information online.

In order to create long-form, meaningful content, GPT-3 AI content tools still have a long way to go, but they show promise as a tool to speed up businesses’ content workflows. 

About the Author

Lillian Pierson, P.E.

Mentor to World-Class Data Leaders and Entrepreneurs, CEO of Data-Mania

Lillian Pierson, P.E. helps data professionals transform into world-class data leaders and entrepreneurs. To date she’s educated over 1 Million data professionals on AI. She’s also been delivering strategic plans since 2008, for organizations as large as the US Navy, National Geographic, and Saudi Aramco.

Get the Data Entrepreneur’s Toolkit (free)

If you love learning about this GPT-3 tool, then you’re also going to love our FREE Data Entrepreneur’s Toolkit – it’s designed to help data professionals who want to start an online business and hit 6-figures in less than a year.

It’s our favorite 32 tools & processes (that we use), which includes:

  • Marketing & Sales Automation Tools, so you can generate leads and sales – even in your sleeping hours
  • Business Process Automation Tools, so you have more time to chill offline, and relax.
  • Essential Data Startup Processes, so you feel confident knowing you’re doing the right things to build a data business that’s both profitable and scalable.

Download the Data Entrepreneur’s Toolkit for $0 here.

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time to leverage data trust best techs in australia
Time to leverage data trust: Best Techs in Australia

time to leverage data trust best techs in australia

Business intelligence has the potential to help small businesses, crisis management experts, or also business administrators evaluate and appreciate investment opportunities simply and straightforwardly. Furthermore, research is used in the placement of goods in the industry. In reality, knowledge management’s value cannot be comparable to that of any other business instrument. Analytics is a subset of marketing information, and it is the only tool that can help a company turn massive amounts of raw data into usable business information that can be used to make decisions. It is widely found that companies that specialize in data analytics outperform their competitors. Without a question, information has been an important weapon for higher management.

With time, the emphasis has turned to business intelligence, in-memory analytics, large data analytics, streaming analytics, and, most specifically, data science, however, both of these flavors are good at solving similar problems. With the passion of ‘Connecting with Strength, Core, and Insight,’ data analytics services Australia are leading data analytics providers, generating meaningful results.

Make use of Big Data architectures and IoT to help you achieve your goals

The concept of “data requirements” is crucial, but it is often ignored. There is no such thing as a universal Big Data approach that works for all. Rather, the data framework you choose can explicitly support your specific business objectives.

Working alongside a partner who is unable to adapt a personalized solution to the case is not a good idea. No two similar implementations can be the same, just like no two companies are alike. Look for the personalized choice to have a solution that fits right in with the company’s brand. The Internet of Things, or IoT, is the Big Data of the future, and it’s reinventing multiple businesses around the world. Businesses may obtain data straight from the original, without the need of middlemen or 3rd parties, by using the Internet of Things. This information is often very detailed. Internationally, there are already 2billion IoT-joined computers, with that number projected to increase to 63 billion by 2024. Much of such instruments can generate a large volume of data lines.

Data processing and cognizance are almost as essential as, if not more essential than, data storage when it comes to computers of Things. The Internet of Things has an abundance of evidence. And the amount of data is increasing every day. As a consequence, organizations can be faced with an enormous amount of data that they are ill-equipped to handle.

In this regard, choosing the best collaborator or supplier of data solutions is crucial. The Internet of Things (IoT) reflects an immense pool of quality that is only waiting to be exploited. However, you must be able to decide which data sources include this value while others do not.

This is an opportunity you cannot continue to pass up. You can install IoT hardware anywhere you could, or collaborate with a company that has the expertise and scale you require.

What are the reasons for the increasing demand for big data analytics supports?

First and foremost, if you see yourself with much more information than you know what to do about, you must take control of such an extremely useful resource. Creating the groundwork for gathering, managing, and analyzing this data would aid in the transformation of your business into a forward-thinking, additional perspective, and, most significantly, valuation enterprise. However, as with any good program, that once pillars and systems are in place, they often need upkeep and consideration to remain cutting-edge and relevant to the modern era.

It is highly beneficial to provide an urgent supportive role to manage specific patches, modifications, and enhancements to the company’s software to preserve and enhance its efficacy. All of this can be accomplished in the background, easily, and in line with best practices when you outsource the work to an analysis consultancy including Main Window, allowing you to concentrate on the projects and multiple stages that are most important to you. Many companies have agile teams, which means they only have one individual in charge of data. This guy, whether senior or professional in expertise, would frequently be tasked with managing the real deal framework, from monitoring to network management, insights development, troubleshooting, and a never-ending list of BAU items. When you move from an individual model to a squad of data engineers, experts, physicists, and architects, having tech expert testing on-demand help ensures that the analytics capabilities can skyrocket. A specialist would always function in the best way in the team, educating and bouncing thoughts off colleagues, looking out for answers quite quickly as compared with themselves.


Bottom line

Big Data is here to remain, which means the company would need to be prepared to store and successfully use ever-increasing amounts of data and this is the reason why flexible data storage is needed in any company as data analytics are a big game-changer for several industries.

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2021 analysis of leading data management and analytics market solutions
2021 Analysis of Leading Data Management and Analytics Market Solutions

2021 analysis of leading data management and analytics market solutions

There’s a lot of conversation in the industry about how data is key to unlocking powerful decision-making capabilities. Data can ignite a wildfire of change, creativity, innovation, speed, and agility across an organization.

But decision makers have to be completely confident in their data in order to leverage these kinds of influential capabilities. Data has to be trustworthy, unbiased, accessible, and timely for it to generate meaningful, analytics-driven insights. Companies need to derive purpose and value from both data and analytics, especially in this time of uncertainty, using a unified data management and analytics solution. 

Ronald van Loon is a SAP partner, and is applying his unique position as an industry analyst to take a deeper look into what different organizations are doing in the data and analytics space.

Cloud, artificial intelligence (AI), machine learning (ML), database and data management, application development, and analytics are pillars of transformation today. As organizations look to future-proofing their business, they have some critical decisions to make when it comes to unified data management and analytics solutions that meet their individual needs.

With this in mind, we’ll explore vendor differentiators to help executives better understand the market so they can develop and benefit from their data and modernize their data architecture to support changing and emerging requirements.

Emerging Data Management and Analytics Trends and Evolving Business Requirements

What are today’s organizations looking for in a data management and analytics solution?

  • Greater agility, simplicity, cost-effectiveness, and ease of automation to accelerate insights.
  • The capabilities to overcome challenges surrounding traditional on-premise architectures that inhibit organizations from meeting emerging business needs, including those pertaining to real-time analytics, complex data sets, self-service, and high-speed data streaming.
  • The ability to surpass pervasive data challenges through the strategic application of both existing and new technologies to drive next-gen analytics. 
  • The ability to move beyond cumbersome data warehouses that typically demand a multi-year commitment to build, deploy, and gain advantages.

This reflects a few critical trends that are supporting the movement towards a unified data and analytics strategy. Businesses are migrating or extending to the cloud, with 59% of enterprises anticipating cloud use to exceed initial plans because of the pandemic. Also, data lakes and warehouses will begin to assume similar qualities as the technology grows. Finally, according to SAP, companies will transition to “data supermarkets” to manage data consumption to clarify processes.

As a modern architecture, Data Management and Analytics (DMA) reduces complications related to chaotic, diverse data via a reliable model that includes integrated policies and adjusting to evolving business requirements. It utilizes a combination of in-memory, metadata, and distributed data repositories, either on premise or in the cloud, to provide integrated, scalable analytics.

Data Management and Analytics Solutions Per Vendor

DMA adoption is increasing as organizations make efforts to benefit from the next evolution of analytics, introduce more collaboration across teams and departments, and transition beyond data challenges. When evaluating a DMA solution, there’s a few key elements that organizations should keep an eye out for, including:

  • Self-service capabilities that allow business users to ask questions to support decision making, drive data intelligence and aid in rapidly ingesting, processing, transforming and curating data through ML and adaptive intelligence. 
  • Real-time analytics through the streaming of multiple sources, and performance at scale for diverse and large-scale project types. 
  • Integrated analytics to help businesses better manage various data types and sources. This extends to storing and processing voluminous sets of both unstructured and semi structured data, and streaming data.

Organizations must also be able to leverage their DMA solution to support analytics-based processing and transactions across use cases like data science investigation, deep learning, stream processing, and operational intelligence.

There are several vendors in the domain who are offering data and analytics solutions to suit a wide range of use cases, though the following is not by any means a complete list:


Microsoft’s Azure platform suite offers a range of cloud computing services across on premise, hybrid cloud, and multicloud for flexible workload integration and management. They also provide enterprise-scale analytics for real-time insights, and visualizations and dashboards data collaboration.


SAP offers a complete end-to-end data management to analytics solution with SAP HANA Cloud, SAP Data Warehouse Cloud, SAP Data Intelligence, and SAP Analytics Cloud. These solutions are SAP Unified Data and Analytics and they coordinate data from multiple sources to fast track insights for business and IT and give data purpose. 


Amazon Web Services (AWS) offers numerous database management services to support various types of use cases, including operational and analytics. They’re the largest global cloud database service provider, and offer cloud provider maturity, scalability, availability, and performance.


The Google Cloud Platform (GCP) includes numerous managed database platform-as-a-service solutions, including migration and modernization for enterprise data. They offer built-in capabilities and functionalities for data warehouse and data lake modernization, and both multi and hybrid cloud architectures. 


Snowflake’s Cloud Data Platform is a solution that offers scalability for data warehousing, data science, data sharing, and support for simultaneous workloads. It includes a multi-cluster shared data architecture, and enables organizations to run data throughout multiple clouds and locations.

Empowering the Data Journey with Unified Data and Analytics

Unifying data and analytics can be problematic for organizations across industries due to increasing data sources and types, messy data lakes, unexploited unstructured data, and siloes that impede insights. 

Both business and IT teams need trustworthy, real-time insights and fast, seamless access to data to make sound, data-driven decisions. But business and IT worlds are often fragmented when they should be harmonized, and respective data and analytics needs often conflict, which can prevent a data culture from flourishing. 

The business side stresses data accessibility and self-service, while IT wants to strengthen data security and governance. These competing needs have to be balanced to support interdepartmental collaboration and maximize data effectiveness and productivity.

The SAP Data Value Formula conveys how each component of the SAP Unified Data and Analytics, the foundation of the SAP Business Technology Platform (SAP BTP), works cohesively to give data purpose:

2021 analysis of leading data management and analytics market solutions 1

This enables organizations to leverage capabilities to develop, integrate, and broaden applications and gain faster, agile, valuable data-driven insights. When different data sources are brought together in a heterogeneous environment, with a hybrid system for cloud and on-premise, business and IT departments can better collaborate to work towards shared organizational objectives. Basically, the end-to-end data journey is supported to help transform available data into actionable answers.

Unite All Lines of Business 

All aspects of a business can benefit from unified data and analytics, from finance and IT to sales and HR. Siloes are eliminated to facilitate an organization-wide approach to data and analytics, business and IT are united to accelerate data-based decisions, and data journeys are charged with agility and high quality data.

You can register for the SAP Data and Analytics Virtual Forum to learn more about powering purposeful data, or sign up for the SAP Data Defined: Monthly Bytes newsletter to stay on top of the latest data and analytics trends and developments.

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why you need multi disciplinary integrated risk management
Why You Need Multi-Disciplinary, Integrated Risk Management

why you need multi disciplinary integrated risk management

This article is excerpted from my upcoming book Agile Enterprise Risk Management: Risk-Based Thinking, Multi-Disciplinary Management and Digital Transformation.  The book provides a framework for evolving your Risk Management function to make it operate in a nearly-continuous fashion, which will allow it to keep pace with the rate of change required to remain competitive today.

We are advocating for your transformation to a more agile organization.  In all likelihood, you’ve already begun—created internal collaboration capabilities and customer-facing, web-enabled services.  But you probably have a long, long way to go before you have reached an optimal level of business agility.

Wherever you are in the evolutionary process, ERM must evolve and become more agile at the same time or you can impair your ability to recognize and manage risks as they are created or transformed by your evolving business.

Why Multi-Disciplinary?

The disciplines mentioned earlier—Enterprise and Business Architecture, Business Process Management, Transformation Portfolio, Program and Project Management—as well as Scenario Analysis, Strategic Planning and Transformation Roadmapping, are intrinsic to your managing your company.  There are, or should be, planning, operating, quality-controlling, monitoring and performance management processes and practices associated with each of them.  In addition to informing, guiding and governing how you do what your company does, you collect a great deal of valuable, raw information in the course of executing them.

Enterprise Risk Management is an information-intensive discipline; if you cannot see things that should be addressed, you will not address them.  Sitting in a conference room trying to build an inventory of these things is a sure way to miss some of them.  Extracting what is passively generated from your governance processes and your day-to-day activities and experiences is a good way to be more comprehensive.  You’re already doing it, more or less, but you need to develop a focus on root sources of risks, which may not be obvious to you.  So, looking at your company through the lens of each of the disciplines you use to run it will provide perspective that can enable you to put together a (more) complete and deeply-nuanced picture of where you should focus your risk management efforts.

Why Integrated?

Risks arise from decisions you make and actions you take.  Ideally, actions you take—execution of Business as Usual (BAU) operations, have had their risks addressed via policies, practices, processes and procedures.  It should be OK, once these are running smoothly to lower the scrutiny level.  Decisions, other than those integrated and embodied in BAU operational processes, may occur regularly or irregularly and your risk management team must be present for you to manage the risks associated with them. 

In the case of higher-level decisions, such as an acquisition, you would expect risk management to be an intrinsic component of the analytical process and it probably is, up to a point.  In such a case, due diligence is an important risk management tool, perhaps your only opportunity to identify and assess non-obvious risks that don’t appear in the acquisition target’s financial statements or that may involve differences in governance or operational processes or culture.  One important lens that you can apply to focus due diligence is a taxonomy or ontology that you apply to the risks that you identify, analyze and treat.  Taxonomies and Ontologies are classification schemes that you use to qualify types of risks so that you can better understand and work to manage them.  What you see as a risk, with a presumed cause or source, your acquisition target may view as something entirely different, something which doesn’t rate mentioning to you or a prescriptive treatment in the course of their operations.

In the case of BAU-related risks, decisions crop up when (a) a case arises for which there is no prescribed action or (b) when there is a need to revise the business process.  If your risk management team is not integrated to the degree necessary to recognize and respond to either of these events, then your risk management comprehensiveness will slip, just that little bit.  Obviously, if you amass enough of these cases, your control over your risks can be seriously compromised.

Depending on a periodic review process to identify new or morphed risks is a bit like driving while looking in the rear view mirror.  It’s OK for a few seconds while you are on a straight road but fails spectacularly when a curve comes along.  The process you will go through while you compile your starting risk inventory, which may be pretty well stocked with risks from your existing risk register, will hopefully be a one-time thing.  However, many existing risk inventories are structured around avoiding undesirable outcomes more than they are identifying root causes.  Revisiting the risks in the register to refocus on source-of-risk and risk/reward analysis is an important task and crucial to reorienting your risk management posture.  Once you have refocused and developed intuition about risk sources, you can apply continuous risk management best by integrating your risk team in tight collaboration with operating units whenever decisions are being made.

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6 environmental public health jobs where data science is useful
6 Environmental Public Health Jobs Where Data Science Is Useful

6 environmental public health jobs where data science is useful

These days talk of Public Health may send a shiver down one’s spine. After over a year of the Coronavirus pandemic, the term almost feels like a buzzword, having overwhelmed, and oversaturated the media for months with no end in sight. Plus, public health is heavily discussed and debated amongst politicians and government officials on any given day, let alone during an election year amidst a global pandemic. All of this results in much of the population believing that public health refers strictly to government health programs. However, public health actually refers to everything in the environment, communities, and population that poses a threat to the health of the population. 

With climate change on the rise and the wellness of our planet on the top of many people’s minds, careers in environmental public health are more important than ever. From handling long-term challenges, like protecting natural resources, to more immediate problems, such as disaster management, environmental public health “focuses on protecting groups of people from threats to their health and safety posed by their environments,” according to the Centers for Disease Control and Prevention (CDC)

Data science skills are playing a crucial role in helping environmentalists model different stats and data together to forecast future environmental challenges. 

Let’s take a look at the top five environment public health jobs where having data science skills are useful: 


A very timely occupation, epidemiologists focus on investigating the origin and spread of disease in humans. They identify people and communities who are notably at risk and work to control or completely stop the spread. Furthermore, they develop ways to prevent these diseases from happening in the first place by working in labs and hospitals and educating the community at large as well as policymakers. Data management is essential for the epidemiologist especially experiences in R-software and other data visualization techniques.  

Environmental Specialist

These specialists regulate the containment and disposal of hazardous materials. They help to develop regulations at the federal, state and local levels and ensure that all waste streams are managed according to those regulations. These are the folks who inspect and interview any violators of the waste management system.

Regular data analysis is an integral part of the environment specialist job. Some of the critical data pieces that require further analysis are air and water pollution data, tree ring data, temperature records, etc.

Environmental Toxicologist

Toxicologists study the effects of toxic chemicals. This includes how toxic chemicals are metabolized by an organism, how they impact the ecosystem as they cycle through, and all lethal and non-lethal effects the chemicals have on an entire species. Some environmental toxicologists may also conduct testing on new chemicals before they’re released to the market, in order to ensure they won’t cause adverse effects in humans such as cancer or birth defects.

Looking at the past data is significant to track the effects of toxic chemicals. Hence, knowledge of data science is useful for this job.


Another timely speciality, Bioengineers can follow a number of different, yet extremely valuable, career paths. 

Those with a degree in bioengineering can go on to become Pharmaceutical Engineers. Pharmaceutical Engineers create effective (and safe) pharmaceuticals that can impact lives for the better, and in the case of Covid-19, save hundreds of thousands of lives. These specialized engineers develop, create, and test medications for the treatment of a wide variety of viruses, diseases, and injuries. 

Bioengineers can also go on to study medical device engineering, which is the development of new medical devices like prosthetics, artificial organs, and other breakthrough technology. Yet another popular career choice for bioengineering grads is a Medical Scientist. Medical Scientists promote population health through a combination of bioengineering and medical science and can carry out important duties including conducting research, clinical trials, and more.

Every bioengineering career option requires knowledge of data science. Big data is crucial to decode the human brain to promote a better healthcare system. 

Air Pollution Analyst

These vital analysts collect and analyze data from polluted air. They trace their data to the source of the pollutants and work to develop future techniques for reducing or altogether eliminating air pollution. Air Pollution Analysts hold humans accountable and control pollution outputs in order to preserve our atmosphere and maintain the quality of the air we breathe.

It is the responsibility of the air pollution scientist to examine data from polluted air. Besides, they compile different stats to create a detailed analysis.  

Environmental / Health Inspector

The health inspectors that most people are familiar with scope out your favourite local restaurant for any health violations to keep you safe. Environmental health inspectors scope out all businesses, buildings, parks, waterways and other settings to ensure they meet health and sanitation standards. They search for any potential health threats and produce ways to correct and prevent problems. Some may be responsible for inspecting food manufacturing plants, landfills, or underground storage. These inspectors also make sure any mass-produced food supply is as safe as possible.

In order to pursue any of the above Environmental Public Health jobs as a career, a minimum of a bachelor’s degree is required with knowledge of data science. This degree does not necessarily have to be in environmental public health specifically but could center on occupational health and safety, or a related scientific field like biology, chemistry, or even engineering. Depending on the position, some career options may require a graduate degree such as a Master of Public Health (M.P.H.), or perhaps even a doctorate. This field has a considerable technical and scientific nature, so for most, getting a master’s degree in environmental public health is a good idea in order to advance in the field.

Final Thoughts

No matter whichever public health career you choose, data science is an integral part of every environmental job. Career opportunities in the environmental public health sector are predicted to grow due to the continuously rising challenges presented by climate change and other factors. Maintaining a healthy environment is integral to increasing general longevity and quality of life and the knowledge of data science is helping to study the past data to improve the future. 

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