Artificial Intelligence to Take User Experience to the Next Level

Just imagine that you walked into a restaurant and are welcomed by the hotel staff. Later, one of the waiters directs you to a convenient seat, tells you their special dishes, and helps you order food by understanding your preference. He makes sure that you are attended well, serves you good food, and asks your feedback before you leave. It is a perfect example of a decent user experience. A bad user experience is similar to an endless spiral staircase in which you simply go on climbing the steps but are unable to reach the hotel. You go on moving around the entire stairs, but nothing comes up till the end.

In the present digital world, the significance of user experience (UX) design has even more increased, wherein user satisfaction is given prime importance. Whether it is a mobile app, website, or customer care the key motive of a good user experience is to satisfy the customer. UX design trends and requirements keep on progressing with time. We live in an era where the applications of Artificial Intelligence (AI) are booming at a rapid pace. 

Amalgamation of Artificial Intelligence and User Experience

In recent years, AI has been revolutionizing the design sector by facilitating enhanced user experience. It is a trending buzz these days mainly among several businesses that collect heaps of data. It makes use of highly advanced technologies to enable machines to offer human-like behaviors autonomously. From automating processes to improving efficiency, the potentials of AI are unlimited. From driving a vehicle to assisting doctors in a surgery, AI has proven to be a game-changing technology in various sectors; this has triggered a sense of worry in peoples’ mind that one day this technology might overcome humans in every possible field.

With no industrial sector left untouched, AI is set to revolutionize the current digital world at an accelerated pace. So, how can one use artificial intelligence to make enhanced websites and UX designs?  

There are a wide array of methods and tools that use artificial intelligence for enhanced website designs to produce satisfying and sophisticated user experiences and eventually surge conversion rates and revenues.

In the past, UX teams used to use metrics and tools like A/B testing, heat maps, usability tests, and data to comprehend the ways to boost customer engagement in their web products. In the world of big data, loads of pragmatic and actionable data generated regularly can be used to identify and understand user behavior patterns and ultimately enhance user experience.

What are the Benefits of AI for Enabling Enhanced User Experience? 

AI has significantly revolutionized user experience by enhancing designers’ capabilities, delivering smarter content, and thus improving the support to customers. AI offers various benefits such as—

• Reduces manual labor by eliminating repetitive and monotonous tasks, hence boosting productivity.
• Empowers designers to offer better design choices based on various factors
• Increases conversion rates with advanced level of personalization and relevant content to individual users
• Enhances competences of data analysis and optimization
• Makes design systems more vibrant and interactive

Facilitates a more personalized user experience

• Helps in understanding customer behavior and expectations hence, offers customized recommendations and content to the users. For instance, users get movie recommendations on over-the-top (OTT) platforms or video recommendations based on their earlier behavior on YouTube.
• Helps in enabling services such as customized emails for each user by considering their behavior patterns on each website or any other online platforms. It helps in delivering relevant emails to potential customer, and thus boosts conversions.
• Chatbots are another example of AI integrated customer support. These chatbots facilitates human-like conversations by chatting with the users and directs with their issues or queries.
•       It facilities 24/7 assistance to users. It also makes sure that business processes run uninterruptedly and smoothly.

Future of AI in UX Designs

We are heading toward a more digitally connected world wherein every task involves smart gadgets. AI has taken user experience to the next-level by making it more interactive, convenient, and appealing. In simple words, it pampers users with customized products and services in their entire journey on any online platform. In the coming years, more frequent use of Internet of Things (IoT) technology will enhance the user experience and make it even better.

The user experience design trends will keep on evolving with time. Having websites and applications that can transform as per the user requirements in real-time or apps suggesting movies or music playlist as per user’s current mood can soon be a reality with more use of AI techniques in digital platforms.

However, this doesn’t mean that AI will take designers’ jobs by completely replacing their roles in UX designing. Instead it will assist designers in offering better designs that meet user expectations. Businesses are required to be updated with the continuously evolving, innovative, and advanced technologies to adapt with them to stay competitive and at the forefront. We live in an age where we are extremely dependent on technology and it appears that this dependence is only going to increase in the coming years. So, who is a robot in reality? We might think that we are in control of AI; however, is it a puppet string that is being controlled by technology developed by the humans.

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How a good data visualization could save lives

Ignaz Philipp Semmelweis (1818-1865) was a Hungarian physician and scientist, now known as an early pioneer of antiseptic procedures.

Described as the “saviour of mothers”, at 1846 Semmelweis discovered that the incidence of puerperal fever could be drastically cut by the use of hand disinfection in obstetrical clinics (this fever was common in mid-19th-century hospitals and often fatal).

He observed that in clinics where midwives worked the death ratio was noticeably less than in clinics with educated doctors.

Dr. Semmelweis started his work to identify the cause of this tremendous difference. After some research, he found that at “clinic 1” doctors performed autopsies in the morning and then worked in the maternity ward. The midwives (clinic 2) didn’t have contact with corpses.

Dr. Semmelweis hypothesized that some kind of poisonous substance was being transferred by the doctors from the corpses to mothers. He found a chlorinated lime solution was good to remove the smell of autopsy and decided it would be ideal for removing these deadly things.

During 1848, Semmelweis widened the scope of his washing protocol, to include all instruments coming in contact with patients in labor, and used mortality rates time series to document his success in virtually eliminating puerperal fever from the hospital ward.

Semmelweis had the truth but it was not enough.
“Doctors are gentlemen and a gentleman’s hands are clean.” – said an american obstetrician Cahrles Meigs and this phrase shows us a common opinion that time.

It is dangerous to be right in matters on which the established authorities are wrong.”
Voltaire

Semmelweis paid dearly for his “heretical” handwashing ideas. In 1849, he was unable to renew his position in the maternity ward and was blocked from obtaining similar positions in Vienna. A frustrated and demoralized Semmelweis moved back to Budapest.

He watched his theory be openly attacked in medical lecture halls and medical publications throughout Europe. He wrote increasingly angry letters to prominent European obstetricians denouncing them as irresponsible murderers and ignoramuses. The rejection of his lifesaving insights affected him so greatly that he eventually had a mental breakdown, and he was committed to a mental institution in 1865. Two weeks later he was dead at the age of 47—succumbing to an infected wound inflicted by the asylum’s guards.

Semmelweis’s practice earned widespread acceptance only years after his death when Louis Pasteur confirmed the germ theory.

Semmelweis’s data was great – truthful, valuable, and actionable, but the idea failed.
Let’s try to understand why.

1. Semmelweis published his “The Etiology, Concept, and Prophylaxis of Childbed Fever” in 1861. But over 14 years, from inventing to publishing, the medical community misinterpreted and misrepresented his claim.
Lesson 1: keep your data clear and timely.

2. Semmelweis was not able to understand why people wouldn’t accept his advice. He insulted other doctors, became rude and intolerant.
Lesson 2: always remember about cognitive bias named “curse of knowledge” – know your audience, strive to understand them. And look for open-minded allies.

3. He felt emotion instead to evoke them.
Lesson 3: Use logic and reason to make your job, but use narratives to show it.

4. Semmelweis had his data only as data tables. Not many people can read that.
Lesson 4: good data visualization is the key.

Let’s try to use this – I’ll show the same data but in other way:

Data and data analysis are the flesh and blood of the modern world, but this alone is not enough. We must learn to present the results of the analysis in such a way that they are understandable to everyone – “sticky” idea and clear visualization are the keys.

More information and code in my repo at GitHub.

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How COVID-19 Is Accelerating Smart Home Automation

Modern technologies have tapped varied sectors and the residential sector is the most prominent one. The growing influence of technologies like the Internet-of-Things (IoT) and others across a large number of applications at home have increased the demand and popularity of home automation to a considerable extent. The rising popularity of these systems will prove to be a game-changer for the smart home automation market.

Smart home automation refers to the automation of common household activities and processes. These automation systems create a centralized process where all the devices are connected and operated accordingly. The expanding urbanization levels and the rising rural-to-urban migration have led to an increase in the adoption of these systems. From a luxury to a trend, home automation has come a long way. The smart use of data in determining the system automation magnifies the convenience quotient, eventually boosting the growth prospects of the smart home automation market.

The COVID-19 outbreak has brought about a tectonic shift in terms of technological advancements across the smart home automation industry. The stay-at-home orders coupled with the threat of contracting the virus through the surfaces have served as a breeding ground for the development of smart home automation technology.

Stay-at-Home Orders Accelerating the Influence of Smart Home Technology

Due to the ongoing threat of virus transmission, many countries imposed strict stay-at-home orders. This factor forced many individuals to stay at home for longer intervals, serving as a growth accelerator for the smart home automation market. Smart automation gained traction as the use of technology at home increased with more time spent at home. All these factors brought golden growth opportunities for the smart home automation market.

Automation in Scheduling Certain Home Activities

Touching varied surfaces frequently can increase the risk of COVID-19 transmission. Scheduling the function of turning lights on and off is done automatically through home automation technologies. It will minimize the transmission risk to a considerable extent. Thus, this factor bodes well for the growth of the smart home automation market

Door Lock Management

Smart home automation can enable individuals to attend unauthorized visitors through a video door phone. It is necessary for security purposes and will also guard against virus transmission. Smart doors unlock automatically and prevent individuals from touching the surface of the door that decreases the risk of transmission. These aspects ring the bells of growth across the smart home automation market.

Contactless Elevators

Existing elevators can be transformed into contactless elevators by simply integrating smart home automation. For instance, ElSafe, a contactless elevator solution provider installs a technology wherein a person can scan a QR code and tap the ‘start’ button on his/her phone. This factor eliminates the need for touching the surface of the elevators and prevents transmission risk.

Apart from the rising popularity of the COVID-19 pandemic, a variety of advantages are responsible for the growth of the smart home automation market. Some of the major ones are as follows:

Comfort: Smart home automation helps in creating a comfortable atmosphere around a home. They offer intelligent and adaptive lighting solutions that increase the growth opportunities.

Control: Smart home automation helps individuals to control many functions in a better way across their homes. This aspect churns profitable growth for the smart home automation market.

Savings: Smart lights and intelligent thermostats help in saving energy and cutting utility costs over time. Home automation technologies are also important for water-saving measures.

The smart home automation market is expected to grow at a rapid rate and will witness extensive technological advancements in the coming years. The players are consistently involving in the process of developing affordable and excellent smart home automation technologies.

GetMore Information about Smart Home Automation by TMR

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Why Open Data is Not Enough

Periods of crisis create a greater need for transparency. In the age of Open Data, this observation is more true now that everyone can access massive amounts of data to help make better decisions. 

We all would like to know more about the impact of a stimulus policy on public health issues. For example, there are so many questions we ask ourselves every day in the age of COVID-19 — how is the vaccination campaign evolving? How many new contaminations per day? Are the intensive care units saturated?

The goal of Open Data is to put public data into the hands of citizens, which ultimately will improve our functioning democracy. In the U.S, the OPEN Government Data Act requires federal agencies to publish their information online as open data, using standardized, machine-readable data formats, with their metadata included in the Data.gov catalog. However, the idea of “data-for-all” is still a long way off.

Open data… for whom? 

The term Open Data refers to data made available to everyone by a government body. For public offices, the opening of data makes it possible to engage citizens in political life. A rich legislative framework has been put in place to institutionalize the publication of this data.

Yet, data must not simply be available and accessible, but must also be able to be reused by anyone. This implies a particular legal status and also technical specificities. When data is normalized to facilitate integration with other data sets, we are now talking about interoperability.

Interoperability is crucial for Open Data, but it concerns users who have the technical skills to manipulate the data tables. For the general public, however, this criterion has only a limited impact and for the uninitiated this all still depends on the goodwill of data experts. 

A matter of experts?  

No offense to tech lovers, but the right to  public information does not date from the digital revolution. At the birth of the United States, Thomas Jefferson wrote within the Declaration of Independence that “governments are instituted among men, deriving their just powers from the consent of the governed.” It is the responsibility of government bodies and public research institutes to keep the interested public informed.

So is Open Data really a revolution? Those who usually consult this public data are experts in their fields: economics, law, environmental sciences or public health. The big challenge is helping all citizens understand the data. For this, digital tools are an invaluable help. Governments have the responsibility to make information hidden in the jungles of tables and graphs immediately accessible. In the U.S., one such challenge is to inform the public about the evolution of the pandemic in an accessible manner. We’re now living in a world where the data that connects to public affairs needs to be accessible to more than just the experts.

The challenge of shared information 

Does the average citizen need an avalanche of statistics, however interesting they may be? Some may reason that as long as experts and decision-makers have access to the data, then everyone is in good hands. Others may believe that artificial intelligence will soon exempt us from the tedious exercise of interpretation. For instance, smart cities and their connected objects already promise algorithmic self-regulation; smart traffic lights will lessen traffic; and  autonomous cars will direct motorists to the least congested roads. The monotonous voice of on-board GPS will soon be a bad memory. 

It may be tempting to envision this technocratic utopia where algorithms and experts hold the reins of society. However, we choose to bet on democracy, where citizens, well-informed by Open Data, will vote for the common good. Data alone cannot drive the best decisions, but it is a compass that helps guide citizens towards the most just political choices. It is important to put it in everyone’s hands. 

Charles Miglietti is the CEO and co-founder of modern BI platform Toucan Toco, which is trusted by more than 500 global clients to build insight culture at scale. He was previously an R&D engineer at Apple and a software engineer at Withings.

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DSC Weekly Digest 14 June 2021

Inflation is not one of the more usual topics one thinks of when talking about artificial intelligence, data science and machine learning. Yes, economic modeling is increasingly done using these technologies and certainly the field is ripe for exploration given the evolution of AI tools, but all too often there is a chasm between the technical and the political/economic that people are uncomfortable jumping so it’s worth understanding how one impacts the other.

Inflation is oft-misunderstood because it isn’t really a “thing” per se. Rather, inflation occurs when the price of a particular good or service increases relative to either its own past price or the price of other goods or services. Ordinarily, inflation tends to rise by 1-2% a year, primarily as a reflection of an increase in money supply as the population grows. This is fairly benign inflation as wages should go up at roughly the same level as commodity costs.

Where problems arise is when commodity or service inflation rises faster than wage inflation (labor costs). This commodity/wage inflation ratio is typically fairly elastic – commodities can increase in price for some time before workers can no longer afford even basic goods and services, which in turn usually result in businesses failing and recessions occurring. As the economy recovers, wages tend to get renegotiated, especially when companies cannot hire enough workers at the old price points and have to raise hourly wages.

However, things are different this time around. For starters, the pandemic was global in nature, which meant that employment plummeted globally as well, and a large number of companies disappeared nearly overnight. This means that there are far more people who are now renegotiating contracts with employers in a period of very high demand. Additionally, because so many employees that did survive had to work from home, they saw first-hand that they did not need to work in an office and could in fact do all of their work nearly as well as (or in many cases better than) they could when working from an office.

They also discovered the art of the side hustle – creating virtual storefronts, becoming virtual celebrities that live off Google ad revenues from their media ventures, writing content for multiple clients, and otherwise taking advantage of the Internet to become producers rather than just consumers. This was work that met their personal needs, gave them a stake in their own products, and increasingly took them outside of the 9/5 walls of the corporation. Put another way, employers are no longer competing just against other companies, but increasingly with their potential employee’s own gigs.

One other wild card is the roles that AI and machine learning play in this equation. Commodity prices are rising in part because of supply chain disruptions, but those supply chain disruptions have to do primarily with a lack of people at critical points in a system where employers have been trying to eke out every potential performance gain they could through automation.

Automation causes wages to fall because you need fewer people to do things the automated process replaces. It can make processing commodities somewhat faster as well, but you are still limited by the laws of physics there (indeed, most performance improvements in commodity processing have come about because of improved material sciences understanding, not automation per se). Improved data analysis allows you to better eke out some performance gains, but increasingly it will be the skills and talents of the people who work for you (or increasingly with you) that will determine whether you succeed or fail as a business.

AI is not a magic panacea. It is a powerful tool to help understand the niche that your organization fills and it can expand the capabilities of the people that you do employ, but ironically we may now be entering a protracted period where the gains that came from automation are balanced out by the need for qualified, well-trained, creative and intelligent workers who increasingly are able to use the same power of that automation for their own endeavors. This should be a sobering thought for everyone, but especially those who expect things to return to how it was pre-pandemic.

These issues and more are covered in this week’s digest. This is why we run Data Science Central, and why we are expanding its focus to consider the width and breadth of digital transformation in our society. Data Science Central is your community. It is a chance to learn from other practitioners, and a chance to communicate what you know to the data science community overall. I encourage you to submit original articles and to make your name known to the people that are going to be hiring in the coming year. As always let us know what you think.

In media res,
Kurt Cagle
Community Editor,
Data Science Central

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Making Sense of Data Features

Spend any time at all in the machine learning space, and pretty soon you will encounter the term “feature”. It’s a term that may seem self-evident at first, but it very quickly descends into a level of murkiness that can leave most laypeople (and even many programmers) confused, especially when you hear examples of machine learning systems that involve millions or even billions of features.

If you take a look at a spreadsheet, you can think of a feature as being roughly analogous to a column of data, along with the metadata that describes that column. This means that each cell in that column (which corresponds to a given “record”) becomes one item in an array, not including any header labels for that column. The feature could have potentially thousands of values, but they are all values of the same type and semantics.

However, there are two additional requirements that act on features. The first is that any two features should be independent – that is to say, the values of one feature should in general not be directly dependent upon the same indexed values of another feature. In practice, however, identifying truly unique features can often prove to be far more complex than may be obvious on the surface, and the best that can be hoped for is that there is, at worst, only minimal correlation between two features.

The second aspect of feature values is that they need to be normalized – that is to say, they have to be converted into a value between zero and one inclusive. The reason for this is that such normalized values can be plugged into matrix calculations in a way that other forms of data can’t. For straight numeric data, this is usually is sample as finding the minimum and maximum values of a feature, then interpolating to find where a specific value is within that set. For ordered ranges (such as the degree to which you liked or disliked a movie, on a scale of 1 to 5), the same kind of interpolation can be done. As an example, if you liked the movie but didn’t love it (4 out of 5), this would be interpolated as (5-4)/(5-1) = 3/4 = 0.75, and the feature for (Loved the Movie) when asked of 10 people might then look like:

Other types of data present more problematic conversions. For instance, enumerated sets can be converted in a similar fashion, but if there’s no intrinsic ordering, assigning a numeric value doesn’t make as much sense. This is why enumerated features are often decomposed into multiple like/dislike type questions. For instance, rather than trying to describe the genre of a movie, a feature-set might be modeled as multiple range questions:

• On a scale of 1 to 5, was the movie more serious or funny?
• On a scale of 1 to 5, was the movie more realistic or more fantastic?
• On a scale of 1 to 5, was the movie more romantic or more action oriented?

A feature set then is able to describe a genre by taking each score (normalized) and using it to identify a point in an n-dimensional space. This might sound a bit intimidating, but another way of thinking about it is that you have three (or n) dials (as in a sound mixer board) that can go from 0 to 10. Certain combinations of these dial settings can get you closer to or farther from a given effect (Princess Bride might have a “funny” of 8, a “fantasy” of 8 and an “action oriented” of 4). Shrek might have something around these same scores, meaning if they were described as comedic fantasy romance and you liked Princess Bride, you stand a good chance of liking Shrek.

Collectively, if you have several such features with the same row identifiers (a table, in essence), this is known as a feature vector. The more rows (items) that a given feature has, the more that you’ll see statistical patterns such as clustering, where several points are close to one another in at least some subset of the possible features. This can be an indication of similarity, which is how classifiers can work to say that two objects fall into the same category.

However, there’s also a caveat involved here. Not all features have equal impact. For instance, it’s perfectly possible to have a feature be the cost of popcorn. Now, it’s unlikely that the cost of popcorn has any impact whatsoever on the genre of a movie. Put another way, the weight, or significance, of that particular feature is very low. When building a model, then, one of the things that needs to be determined is, given a set of features, what weights associated with those features should be applied to get the most accurate model.

This is basically how (many) machine learning algorithms work. The feature values are known ahead of time for a training set. A machine-learning algorithm uses a set of neurons (common connections) between a starting set of weights, testing the weights against the expected values in order to identify a gradient (slope) and from that recalibrate the weights to find where to move next. Once this new vector is determined, the process is repeated until a local minimum value or stable orbit is found. These points of stability represent clusters of information, or classifications, based upon the incoming labels

Assuming that new data has the same statistical characteristics as the test data, the weighted values determine a computational model. Multiply the new feature values by the corresponding weights (using matrix multiplication here) and you can then backtrack to find the most appropriate labels. In other words, the learning data identifies the model (the set of features and their weights) for a given classification, while the test data uses that model to classify or predict new content.

There are variations on a theme. With supervised learning, the classifications are provided a priori, and the algorithm essentially acts as an index into the features that make up a given classification. With unsupervised learning, on the other hand, the clustering comes before the labeling of the categories, so that a human being at some point would have to associate a previously unknown cluster to a category. As to what those categories (or labels) are, they could be anything – lines or shapes in a visual grid that render to a car or a truck, genre preferences, words likely to follow after other words, even (with a large enough dataset such as is used by Google’s GPT-3, whole passages or descriptions constructed from a skeletal structures of features and (most importantly) patterns.

Indeed, machine learning is actually a misnomer (most of the time). These are pattern recognition algorithms. They become learning algorithms when they become re-entrant – when at least some of the data that is produced (inferred) by the model gets reincorporated into the model even as new information is fed into it. This is essentially how reinforcement learning takes place, in which new data (stimuli) causes the model to dynamically change, retaining and refining new inferences while “forgetting” older, less relevant content. This does, to a certain extent, mimic the way that animals’ brains work.

Now is a good point to take a step back. I’ve deliberately kept math mostly out of the discussion because, while not that complex, the math is complex enough that it can often obscure rather than elucidate the issues. It should first be noted that creating a compelling model requires a lot of data, and the reality that most organizations face is that they don’t have that much truly complex data. Feature engineering, where you identify the features and the transforms necessary to normalize them, can be a time-consuming task, and one that can only be simplified if the data itself falls into certain types.

Additionally, the need to normalize quite frequently causes contextual loss, especially when the feature in question is a key to another structure. This can create a combinatoric explosion of features that can be better modeled as a graph. This becomes especially a problem because the more features you have, the more likely your features are no longer independent. Consequently, the more likely the model is likely to become non-linear in specific regimes.

One way of thinking about linearity is to consider a two-dimensional surface within a three-dimensional space. If a function is linear, it will be continuous everywhere (such as rippling waves in a pond). If you freeze those waves then draw a line in any direction across them, there will be no points where the line will break and restart at a different level. However, once your vectors are no longer independent, you can have areas that are discontinuous, such as a whirlpool. that flows all the way to the bottom of the pond. Non-linear modeling is far harder because the mathematics moves towards generating fractals, and the ability to model goes right out the window.

This is the realm of deep learning, and even then only so long as you stay in the shallows. Significantly, re-entrancy seems to be a key marker for non-linearity, because non-linear systems create quasi-patterns or levels of abstraction. Reinforcement learning shows signs of this, and it is likely that in order for data scientists to actually develop artificial general intelligence (AGI) systems, we have to allow for “magical” emergent behaviors that are impossible to truly explain. There may also be the hesitant smile of Kurt Goedel at work here, because this expression of mathematics may in fact NOT be explainable, an artifact of Goedel’s Incompleteness theorem.

It is likely that the future of machine learning ultimately will revolve around the ability to reduce feature complexity by modeling inferential relationships via graphs and graph queries. These too are pattern matching algorithms, and they are both much lighter weight and far less computationally intense than attempting to “solve” even linear partial differential equations in ten-thousand dimensions. This does not reduce the value of machine learning, but we need to recognize with these machine-learning toolsets that we are in effect creating on the fly databases with lousy indexing technology.

One final thought: as with any form of modeling, if you ask the wrong questions, then it does not really matter how well the technology works.

Enjoy.

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How To Build An Amazing Mobile App For Your Startup?

It isn’t every day that you are blessed with app ideas to make money. But when you are, the worst thing you can do is launch it without the right resources and knowledge.

To build a mobile app for startup is more than just getting a team of tech-savvy people to make a product that appears as a tile on your phone. It is about developing your idea and prepping that idea for the market.

If your app idea has potential, but you dont know how to code, what legal stuff to take care of, or even secure funding to execute all of it; here is a brief custom mobile app development for startups guide on things to take into consideration.

Will Anyone Pay for Your Idea

When you come up with an app idea you may feel that it is the most brilliant idea in the world, and it probably is but do you know to what scale?

One of the primary reasons businesses fail is because nobody needed their product in the market they launched. Imagine finding out everything you built and invested time, money, and effort into was useless.

Therefore the very first thing you must do is a market analysis for how viable your product/ application is. Will it fly or flop?

Finding out that a product has great potential and staggering demand in a market is the green light you need to put things into motion.

What is Your Competition Doing

While you are researching a market need for your product keep up the habit and do a competitive landscape analysis. This is super insightful and a time for you to absorb details. Learn what they did that worked for them, and what they did that ended up as massive fails. It’s like second-hand valuable experience on how to run a business built on an app like yours.

More often than not market analysis also brings to light market opportunities that your competition is neglecting and is up for grabs.

Finding Your Brand

Most people make the mistake of using a business’s logo interchangeably with its brand. Building a powerful brand image is perhaps the best way to survive in a cutthroat market for the long term.

To build one, you must figure out what differentiates your application from others. Once you figure that out, the next step is to make sure that you have a uniform design so that every time a potential customer sees your promotional material they associate it with your business and everything it stand for.

Do You Have a Plan?

A solid app business plan will take you places. By projecting and outlining everything up to 5 years post launch, you are in a better position to stick to your goals. Moreover as a young startup a plan also offers you milestones with set deadlines that you must work for. They help you gauge the performance of your business and breed confidence in your decision making.

Money, Money, Money

To find an app idea investor can be time consuming and stressful but is not an impossible feat. If you completed all the right steps, there are bound to be several investors who see how your product is headed for greatness.

A great way to accumulate and summarize everything you have learned from market research is to share highlights in a pitch deck. A pitch deck is also how you can show off your existing structure, your chosen business plan, and your capability as an entrepreneur and a leader. All of these things can make or break a startup and potential investors want to know these things.

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Managing DevOps Teams In a Remote Work Environment

The remote working triggered by COVID-19 is leading to a series of interesting and positive business outcomes. An area where we are witnessing considerable impact is the practice of DevOps or the art of accelerating and improving technology delivery and operations processes. At the heart of DevOps, it is all about culture, people and technologies coming together.[i] The practice seeks to drive collaboration between development and operations teams, bringing them closer right from conceptualization, planning, design, deployment to operations. With COVID-19 imposing remote working, DevOps is witnessing a significant increase in adoption. Cloud has become critical for decentralized DevOps teams, providing a new set of challenges that need to be addressed.

The challenges are significant. For organizations that do not (yet) have a complete cloud-native DevOps program, Freddy Mahhumane, the Head of DevOps for South Africa’s financial services leader, the Absa Group, has some advice: “Don’t just pick AWS or Azure or Google Cloud and migrate. If an organization is in early stage of cloud adoption, come up with a hybrid strategy that keeps some processes running in house and on premises while you learn in the cloud environment.” This gives the required confidence to Development and Operation teams. But that is increasingly turning into a luxury with COVID-19 around. Working remotely in cloud environment has become necessary—and will soon become the default mode.

Fortunately, the DevOps culture talks about working remotely from anywhere, anytime -hardcore practitioners should be able to walk into an Internet café anywhere in the world and work. And now, after a few months of distributed working enforced by the pandemic, DevOps leaders are finding ways to solve the challenges of collaboration, processes management, delivery and more. However, cloud adoption for systems lowers the risk by several magnitudes. It allows them to access the required infrastructure when compared to accessing it over an enterprise VPN or via secure end points that need expensive licenses. This, naturally, raises questions around security.

Part of the answer lies in the practice of DevSecOps that seeks to maintain compliance and security requirements. But the focus of security, as called out by Mahhumane, has traditionally been on hardware and application security. He too feels distributed/remote working requires organizations to also focus on the social aspects of security. For example, an employee working from home social engineering may share data or innovation ideas with friends or family. So, it is very essential to understand the social side of the security as well. There could be many more challenges. DevSecOps was born to secure our systems, processes and the places where we are producing information with enterprise infrastructure. Physical security of data is assured. Now, DevSecOps has to deal with remote and isolated team members in environments that may be difficult to monitor. What is required is DevSecOps with an additional layer of controls.

This, in the long-term, is a welcome trend. It shifts attention to security right to the front of the SDLC and will cause more robust security practices. For banks and financial services this is a healthy development. They need to deliver digital products quickly to stay ahead of competition and drive innovation. Better security practices will ease their anxieties over potential breaches of compliance and regulatory requirements while enabling product development at pace.

Leaders who prioritize security strategy will find that they can drive their large distributed ecosystems of IT vendors to deliver better. To enable this, Mahhumane advises organizations to develop a security team along with strong strategy within the organization to support vendors. The team’s goals would be to help vendors meet organizational goals. He also suggests that DevOps leaders should understand their infrastructure thoroughly. For example, a data center plays the role of hosting, and understanding the processes of a data center can help create better delivery.

Over the last several years, DevOps has shown that it is an evolutionary process, not a big-bang event. However, currently it is being forced to evolve more rapidly than it has done. Processes used for years for versioning, testing, monitoring and securing, must undergo rapid change. DevOps leaders need to question their existing practices so new ones may quickly evolve for an era of remote working.

Co-authors

Freddy Mahhumane, Head of DevOps, ABSA

Ashok MVN, Associate Partner, ITC Infotech

[i] There is no clear, single definition of DevOps. As DevOps depends on company culture, the definitions show subtle differences when articulated by different people.

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De-constructing Use Cases for Big Data Solutions

Big data analytics are used in the quite classy analytic techniques on tremendously big varied data groups that include organized, and unorganized data from many separate bases, and all over the various sizes from terabytes to exabytes.

Data pertaining to a massive amount of data in both the organized and unstructured formats may be referred to as big data. As cloud computing is becoming a common feature of many different companies all across the globe, this has become an essential tool for companies of every size. When it comes to one particular firm, big data has an extremely wide array of possibilities. Information technology is rising in popularity over the last decade, as organizations of all sizes understand its vital relevance.

By making use of knowledge that can be obtained via the use of big data, enterprises may use this information to make high-impact business choices that can give their company an edge over its competitors. Our ability to surpass our opponents in every area makes us a market leader.

While it may seem straightforward, a big data analysis method requires a number of sophisticated procedures that include scrutinizing enormous and various arrays of data. to get a better understanding of and emphasize recent landscape, structures, associations, hidden linkages, inclinations, and other prominent discoveries and data found in a given collection of data.

While there contain many components of Big Data analysis that may get used by small companies to proactively create data kind planning, there are also numerous methods that may be utilized by small firms to create simple decisions.

Whether it requires a third party to develop a solution or if it is essential to provide a dedicated staff with the required tools to handle such data, this will depend on the demands of the organization. A well-defined strategy for including fundamental classes unique to the platforms and frameworks a company employs to help its employees generate data-driven outcomes should be part of any preparation strategy.

There are a lot of big data in every aspect of our lives. What continuously is rising is the requirement to gather and retain all raw information and numbers, regardless of the current magnitude, acceptable to ensure nothing is overlooked. This leads to the generation of a vast amount of data in nearly every discipline. The need for a big amount of data and statistics to drive current company practices and overtake rivals is a top priority for IT professionals right now because of the vital part it plays in generating options, developing new approaches, and getting ahead of rivals. In the analytics of big data, there is a significant need for specialists who benefit to procure and inspect each of the ballistics data which is stored. This holds various chances for those who fill these roles.

enterprise-wide big data solutions Australia issues faced by customers are solved by Australia-based firms to assist their businesses to realize their digital potential. Among the big data business solutions, we provide our big data strategy, real-time big data analytics, machine learning, digital transformation management, and analytics solutions. As a result, since they think that any organization can become a data-driven company, it helps you to put in place a complete long-term strategy and shine the focus on big data analytics services.

Big data analytics is significant by disrupting many sectors

Organizations may utilize big data analytics to make use of their data and utilize it to uncover new possibilities. As a result, a firm will be better about its operational decisions, more efficient at running its operations, and will have a greater return on investment and better-off consumers.  A Marketing Analytics platform is built for a crowdfunding platform in order to generate improved campaign performance.

To a rising number of businesses, big data is no longer a choice but an unavoidable development, as both the volume of structured and unstructured data is increasing fast, along with a vast network of IoT devices that collect and process it.

The significant prospects for corporate expansion provided by big data exist for every sector, including:

1. IT infrastructures that use data-driven systems enable firms to automate time-consuming tasks such as data collecting and processing.
2. Over time, the usage of big data has led to new possibilities and trends which may be utilized to modify goods and services to meet the demands of end-users or to improve operational efficiency.
3. Data-based decision-making: Machines learn on huge data, which serves as the cornerstone of predictive analytics software, and therefore allows computers to act proactively with educated conclusions.
4. In terms of cost reduction, big data insights may be utilized to optimize corporate operations, eliminating superfluous expenditures while simultaneously boosting productivity.

Conclusion

Since all of the aforementioned factors have been introduced, it is no wonder that the significance of data as a corporate priority continues to expand.

The use of data to operate a company is becoming the norm in the fast-paced, technology-driven society we live in today. If you aren’t using data to lead your organization into the future, you’re almost certainly destined to become a company of the past!

While advanced analytics has made it simpler to expand your organization with data, unfortunately, the recent breakthroughs in data analysis and visualization have led to a significant reduction in the need for more data. We’ve included a guide for researching your own business data above so you can get critical insights to further your organization’s progress.

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3 Industries Making the Most of Data Science Technology

In a world that is becoming increasingly reliant on technology, data science plays an extremely important role in various sectors. Data science is the umbrella term that captures several fields, such as statistics, scientific methods, and data analysis. The goal of all these practices is to extract value from data. This data could be collected from the Internet, smartphones, sensors, consumers and many other sources. In short, data science is all around us; that’s part of what makes it such an exciting field.

Even now that we know that data science can be found all around us, you may still be wondering what industries are making the most of this fascinating technology? Let’s take a look at three industries that are putting data science to use in amazing ways.

 Source: Pexels 

1. Environmental Public Health Industry

According to the January 2021 Global Climate Report, January 2021 ranked as the seventh warmest January in the world’s 142-year records. It also marked the 45th consecutive January with temperatures over the 20th-century average. This indicates that climate change remains a prevalent issue.

It’s a particularly concerning issue for environmental public health professionals, who are designated to protect people from health and safety threats posed by their environments. This includes epidemiologists, environmental specialists, bioengineers, and air pollution analysts to name a few.

Public health experts use data science in a number of ways to facilitate their work. For instance, an environmental specialist would collect data such as air and water pollution levels, tree ring data, and more. They would then analyze that data to inform themselves and the public of environmental health concerns. Bioengineers, on the other hand, use big data on the environment to influence the engineering of various medical devices, including prosthetics and artificial organs.

2. Lending Industry

Have you ever taken out a loan or used a credit card? For most of us, the answer is yes, meaning that we’ve inherently benefited from the data science technologies leveraged by this industry.

Data science in the lending industry is incredibly important. In order to grant you (or any applicant) a loan, the lending agency must conduct a risk analysis to assess how likely it is that you’ll pay back the loan on time. They do this by collecting your data and analyzing it to assess your financial standing and level of desirability as a candidate. Some of this data could include your name, your past borrowing history, your loan repayment history, and your credit score.

3. Marketing Industry

In order to market to your target demographic, data is key. You need to know who you’re marketing to, what they like, what they do, how old they are, and more. This is why data science is so crucial and highly utilized in various forms of marketing.

Consider affiliate marketing as an example. Affiliate marketing is a process in which an affiliate gets a commission in exchange for marketing another person’s or brand’s products. Data science is vital in this process. Data analytics are needed to increase return on investment (ROI), find new opportunities in foreign regions, and discover new resources to optimize campaigns.

Clearly, data science is a multifaceted field with uses across many industries. Environmental public health, credit lending, and marketing are just a few examples of sectors that leverage these incredible technologies. 

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