AI for Influencer Marketing: How It Is Transforming the Market

                               Image Credit: Instagram.com

Influencer marketing is human-centric. So, how can artificial intelligence (AI) for influencer marketing ever work? 

Surprisingly, the interplay of AI and humans is making influencer marketing more scientific and data driven.

To put things in perspective, consider these facts:

Shudu Gram who campaigned for Rihanna has 202K followers. Lil Miquela, Prada and Nike’s star influencer, has 2.4M followers. What sets Shudu and Lil apart is that they are virtual influencers powered by computer-generated imagery or CGI.

This means consumers don’t really care if their favorite influencers are humans or not. If AI-powered influencers excel at storytelling, there’s no reason they can’t win hearts as well as human influencers.

That’s how much the influencer marketing industry has transformed since the advent of AI.

In this post, I’m going to dig into four more disruptive applications of AI in influencer marketing. 

AI Helps With Influencer Discovery

When you want your brand’s content to stand out on oversaturated social media platforms, you need an influencer with a flair for storytelling and the right brand affinity. 

And that brings us to the crux of the problem faced by 61% of marketers: influencer discovery.

Image via Mediakix

In every major domain, there are hundreds if not thousands of influencers, many of whom are just bots or fake accounts. So, how do you evaluate an influencer’s authenticity?

Manually scouring their profiles to check their engagement and credibility is impossible for us humans. Hiring an influencer marketing agency for an audit may be heavy on your pocket.

Enter AI-based influencer marketing tools.

These tools can assess thousands of profiles in minutes. Equipped with big data functionality, they can collect and assimilate massive data from multiple touchpoints. They can even create detailed personas for each influencer on your radar, allowing you to compare them side by side.

Powered with predictive analysis, AI tools can pick winning content creators even before they create content for you.  They understand your audience even better than you do, thanks to their persona-building capabilities. Moreover, they keep “listening” to audience sentiment across channels and look out for red flags in your brand mentions.

Using these insights, they can predict with near accuracy how your audience will receive a piece of content. When you combine that super-power with their precise influencer evaluation, you’ve got yourself a machine that can spot a brand-influencer mismatch from miles away.

Some powerful tools like trendHERO also help you spot influencers who have got fake followers. As a result, you can avoid those influencers and partner with genuine ones who can help in taking your campaign to the next level.

Image via trendHERO

Also, the best part about working with tools is that you don’t have to worry about prejudice or subjectivity. Likewise, AI-based tools evaluate influencers objectively only on the basis of hard data and historical patterns. This enables you to make data-driven decisions with confidence.

AI Helps Measure Campaign Performance

Measuring campaign performance is another gray area for marketers. For years, they have been leveraging influencer marketing without having clarity about its ROI. As a result, they’ve had a hard time justifying their expenditure in front of their management. 

If you think that replicating brilliant influencer marketing campaigns can guarantee success and lock in ROI, think again. With unpredictable social media users, the move can backfire massively.

That’s where AI-driven influencer marketing tools come into play.

These tools can forecast campaign ROI with unbelievable accuracy. They can even drill down into each influencer’s performance, overall and per post. Using historical campaign data, these tools can predict your campaign and influencer performance even before you get started.

Not only that, but these tools can identify channels and strategies with maximum ROI-generating potential. By eliminating the guesswork from influencer marketing, these tools can help you generate good leads from your campaigns. You can fix budgets and targets and count on your influencers to deliver on them.

Once that happens, influencer marketing becomes a potent weapon in a marketer’s arsenal. 

AI Tools Assist Influencers with Content Creation

It’s well accepted that influencer-driven content strategies increase engagement rates exponentially.

But when AI is added to the mix, what you get is unbeatable content that is tailored to each platform and audience group.  As discussed at length before, AI tools can decipher an influencer’s tone by studying their past content. Artificial neural networks (ANN) enable these tools to analyze video and image attributes deeply.  Likewise, natural language processing (NLP) enables these tools to analyze user comments and figure out how they feel about different types of content.

Putting two and two together, these tools can determine if your influencer can cater to your audience’s tastes and needs satisfactorily. They can provide “intelligent” suggestions to fine-tune content copy to perfection. 

AI tools can go really granular in their analysis. They can predict minute elements such as typography and CTA copy that appeal to your target audience. When you target the right people with the right content through the right channels and tools, you can cut through the noise and get noticed on overcrowded social media platforms.

In a Gist

Brands and marketers who fuel their influencer marketing with AI find their ROI steadily climbing northwards. AI tools can not only arm them with information to make data-backed decisions but also reduce time and resources spent on repetitive tasks. I’m sure you’ll now agree that AI is reshaping the future of marketing in a big way. 

So, how do you apply AI to influencer marketing? Share your experiences and insights in the comments below. Perhaps, I can provide you with some optimization tips of my own.

Source Prolead brokers usa

The Main Trends in Mobile App Development That Will Dominate in 2021

In the last few years, mobile apps have constantly changed our lives. And due to their great popularity and usability, they represent a significant opportunity for learners and businesses. According to Statista, mobile apps are expected to generate approximately USD 189 billion in revenue. Moreover, many experts have already stated that the mobile app development industry is one of the fastest-growing industries and shows no signs of slowing down in the future.

With recent technological advancements and new inventions coming almost every day, it is not wrong to believe that 2021 will be the year of mobile apps and that entrepreneurs and companies will have more opportunities to do business in the future. After our team of business analysts conducted extensive research, we have identified and listed below the most promising trends in mobile app development that will dominate in 2021.

The Augmented Reality and Virtual Reality Era is Just Beginning

AR and VR are cool! There is no doubt about it. But in 2021, their use will no longer be limited to gaming applications. Tech giants are already developing lots of new applications for both. For example, both Google and Apple released new AR demos on their latest devices, proving that AR/VR will change the game shortly. These technologies are also expected to be used on social platforms for branding and targeting potential customers through AR/VR apps beyond the screen.

Snapchat and Instagram, for example, have already launched their AR filters that can transform a human face into various fun digital characters.

Some examples of AR and VR trends

• Disrupting mobile AR
• AR in marketing and advertising
• AR in healthcare
• AR in manufacturing

Smart Things – The New Era of Mobile, Connected Bright Things

The words “smart objects” or “smart things” were initially coined by relatively new technology: the Internet of Things. Also known as IoT, it is essentially a network of physical objects with sensors, electronics, and software, all connected in the network itself. For example, Samsung, Xiaomi, Bosch, Honeywell, and many other major brands already have a significant market share. Recent trends in IoT app development include Kisi Smart Lock, Nest Smart Home, Google Home, etc. IoT is generally considered one of the game-changing technologies in the world of mobile app development. The global IoT market expects to generate a revenue of USD 1.335 trillion by 2021.

Future IoT trends

• Smart homes and intelligent zones
• Routers with more security
• Self-driving cars
• IoT in healthcare

AI and Machine Learning

Both AI and machine learning have penetrated quite deeply into the mobile application market. AI has mainly manifested itself in chatbots, while Siri, the combination of machine learning and artificial intelligence, has become an integral part of mobile app innovations. In 2021, AI and machine learning power will not be limited to chatbots and Siri. Many organizations have already begun to embrace the development of AI applications to increase profitability and reduce operational costs in various forms. In fact, according to IDC, over 75% of workers using ERP solutions will now harness the power of AI to develop their skills in the workplace. It means that not only are AI and machine learning embedded in today’s mobile applications, but they also offer a significant opportunity for future innovation.

Future trends in AI and MI you should watch for

• AI-enabled DevOps through AIOps
• Artificial intelligence chips
• Machine learning
• Neural network interoperability

Beacons – A Market Worth Millions

Beacons are not an innovation anymore. Several sectors, including museums, hotels, and healthcare, are now using beacon technology in their applications. We think it is fair to say that beacon technology has become more understandable to ordinary users. However, their applications will not be limited to 2021. Beacons have a much greater capacity than that. For example, beacons combined with IoT in retail can help users by providing them with valuable information about sales and other current offers they may find nearby.

Future trends in beacon technology 

• Beacons for mobile payments
• Artificial intelligence chips
• Machine learning
• Beacon scavenger hunt

Cloud – the must-have for future mobile applications

While many still consider the cloud a luxury option, this will no longer be the case by 2021. The world is already beginning to realize the benefits and opportunities of the cloud. For example, reduced web hosting costs, improved upload capacity, and streamlined business operations are just some of the benefits offered by the cloud. Today, many security-related issues are solved through the cloud, making mobile application development more secure, fast, and reliable. 

In addition, using cloud technology such as Dropbox, AWS, SlideRocket, and many others, it is now possible to develop robust applications that run directly in the cloud. This means that we should also expect to see other equally powerful applications that require minimal storage on the smartphone in 2021.

Cloud computing trends in 2021

• Quantum computing
• Hybrid cloud solutions
• Evolution of cloud services and solutions

Mobile Wallets – The Game Changer for Mobile Banking 

There is no doubt that demand for mobile payment app development is on the rise, and with security being the main concern for developers, the use of mobile wallets will only increase in 2021. Frictionless payment methods are what today’s customers like to see in the mobile applications they use.

So, by 2021, mobile wallets and the integration of payment gateways offering the highest level of secure encryption will become commonplace in all types of mobile applications.

 Mobile banking trends to look out for

• Over 2 billion mobile wallet users
• More secure mobile wallets
• Contactless payment

Blockchain – Things Beyond Bitcoin and Smart Contracts

Since its inception, blockchain development has opened up a world of new and more exciting possibilities in the IT industry. In 2018, we all saw the use of blockchain technology used to create cryptocurrencies and smart contracts. However, in reality, blockchain is more valuable than you can imagine. For example, decentralized mobile apps can develop using blockchain. Decentralized mobile apps, or DApps, are essentially apps that not only belong to no one but also can’t be shut down and have no downtime. Simply put, the blockchain expects to contribute more to the mobile app industry by making the mobile app itself decentralized, just as the bitcoin blockchain did for money.

Future trends in blockchain technology

• Asset tokens
• Blockchain as a Service (BAAS)
• Trading on cryptocurrency exchanges
• Cryptocurrencies and games

Wearables – The Essential Accessory of the Future

There is no denying that the wearable electronics market is increasing. According to Statista, the value of wearables expects to reach more than $44.2 billion by the end of 2021. It means that there is a precise volume of investment in the wearables market but that in the future, the word “wearable” will be as redundant as the word “smartphone” is currently. Currently, the main control panel of any wearable device is the smartphone and means that to create a wearable device, the devices need to be paired with it and need to be nearby. However, according to UNA co-founder Ryan Craycraft, our smartphone will no longer be the central hub shortly. Apps developed by wearable devices will have a more ubiquitous connection directly to the web and perhaps even to our bodies.

Future trends in wearable electronics for 2021

• Wearable technology takes the top spot in fitness trends for 2021.
• The rise of wearables is leading to a decline in sales of traditional watches.

On-Demand App Development – The Most Successful Business Model of Modern Times 

The on-demand business model was once considered an inevitable bubble in the mobile app world. Today, however, on-demand services are the future. Almost every industry has embraced the on-demand model and no sector will abandon this successful business model in 2021. To date, 42% of the adult population uses at least one on-demand service. And there is no sign that this on-demand trend is going away anytime soon. Overall, the on-demand trend is here to stay, and their competitors will surely crush companies that don’t adapt.

 On-demand trends in 2021

• Greater focus on the B2B sector
• More industries will embrace on-demand applications

2021 and Beyond…

We believe that keeping up with the latest trends and technologies is the key to keeping up with the ever-changing demands of customers and competitors. We hope we’ve shared some great insights on mobile app development trends for 2021 in this blog. While it’s hard to determine the exact benefits of all the mobile app development companies for your business, don’t hesitate to reach out to experts if you do. We guarantee that your app will stand out in the mobile app market.

Source Prolead brokers usa

9 Top-Notch Programming Languages for Data Science & Machine Learning

Have you ever had a question about which programming language is best for data science and machine learning? To become a data scientist or a machine learning expert, you will have to learn various programming languages. So, in this article, we will be talking about the best programming languages you should learn to become a data science or machine learning expert. 

Python is the most popular and the most used programming language in the field of data science. Python is considered as one of the easiest languages to work with, its simplicity and a wide choice of the library just make it more convenient. 

Python is an easy-to-use open-source language and supports various paradigms, from structured to functional and procedural programming. Python is the number one choice when it comes to machine learning and data science.

You can’t talk about Data Science without mentioning R. R is again considered as one of the best languages for data science because it was developed by statisticians for statisticians to deal with such needs.

R is typically used for statistical computing and graphics. There are numerous applications of R in data science and have multiple useful libraries for data science. R is very handy in conducting ad hoc analysis and for exploring data sets and plays an important role in Logistic Regression.

JavaScript is an object-oriented programming language that is used in data science. There are hundreds of Java libraries available today that can cover every kind of problem that a programmer may come across. 

Java is a versatile language that can manage multiple tasks at once. It also helps in embedding things from electronics to web applications and desktops and can be easily scaled up for large applications. Popular processing frameworks like Hadoop also run on Java. 

This elegant programming language is comparatively new, created recently back in 2003. It was initially designed with the purpose to address issues with Java but nowadays Scala is applied in numerous places ranging from web programming to machine learning. 

As the name suggests, it is an effective and scalable language for handling big data. In modern-day organizations, Scala supports functional programming, object-oriented, and as well as synchronized and concurrent processing. 

Structured Query Language or SQL, is a domain-specific language that has become a very popular programming language for managing data. Although SQL is not exclusively used for data science procedures, knowing SQL queries and tables is really helpful for data scientists to deal with database management systems. SQL is remarkably convenient for storing, manipulating, and retrieving data in relational databases. 

Julia was developed for speedy numerical analysis and high-performance computational science which makes it an optimal language for data science. One thing that makes Julia undisputed is its speed. It is extremely fast and can work even faster than Python, R, JavaScript, or MATLAB.

It can quickly implement different mathematical concepts and excellently deals with matrices. Julia can be used for both front-end and back-end programming.

Julia comes with various data manipulation tools and mathematical libraries. Julia can also integrate with other programming languages like R, Matlab, Python, C, C++, Java, Fortran, etc. either directly or through packages.

Perl is widely used to handle data queries. Perl supports both object and procedural-oriented programming. Perl uses lightweight arrays that don’t need a high level of focus from the programmer and it is proved to be very efficient as compared to some other programming languages. 

The best part about Perl is that it smoothly works with different mark-up languages like XML, HTML and also supports Unicode.

C++ has a unique spot in the data scientist’s toolkit. There is a layer of a low-level programming language on top of all modern data science frameworks and that programming language is C++. You could say that C++ has a very big role in executing the high-level code fed to the framework. This language is very simple yet extremely powerful. And guess what? C++ is one of the fastest languages out on the battlefield. And as it is a low-level language, it allows the machine learning and data scientists practitioners to have a more extensive command of its applications.

Some of the biggest pros of C++ are that it enables System programming and helps to increase the processing speed of your application. Though knowing C++ isn’t essential for data science, but it helps you to find the solutions when all other languages fail.

MATLAB comes with native support for the image, sensor, video, binary, telemetry, and other real-time formats. It offers a full set of machine learning and statistics functionality, plus a few advanced methods like system identification, nonlinear optimization, and thousands of prebuilt algorithms for image and video processing, control system design, financial modeling. 

Well, if you look at it there are hundreds of programming languages in the world today, and the use-case of each language depends on what you want to do with it. Each of them has its own importance and features. So, it’s always up to you to choose the language based on your objectives and preferences for each project. 

To become an expert in data science or machine learning, learning a programming language is a crucial step. Data scientists should consider the pros and cons of the various programming languages before making a decision for their projects. Now that you know about the best programming languages for data science and machine learning, it’s time for you to go ahead and practice them! 

Source Prolead brokers usa

Could Machine Learning Practitioners Prove Deep Math Conjectures?

Many of us have solid foundations in math or have an interest in learning more, and are passionate about solving difficult problems during our free time. Of course, most of us are not professional mathematicians, but we may bring some value to help solve some of the most challenging mathematical conjectures, especially the ones that can be stated in rather simple words. In my opinion, the less math-trained you are (up to some extent), the more likely you could come up with original, creative solutions. Not that we could end up proving the Riemann hypothesis or other problems of the same caliber and popularity: the short answer is no. But we might think of a different path, a potential new approach to tackle these problems, and discover new theories, models and techniques along the way, some applicable to data analysis and real business problems. And sharing our ideas with professional mathematicians could have benefits for them and for us. Working on these problems during our leisure time could also benefit our machine learning career, if anything. In this article, I elaborate on these various points.

The less math you learned, the more creative you could be

Of course, this is true only up to some extent. You need to know much more than just high school math. When I started my PhD studies and asked my mentor if I should attend some classes or learn material that I knew was missing in my education, his answer was no: he said that the more you learn, the more you can get stuck in one particular way of thinking, and it can hurt creativity. That said, you still need to know a minimum, and these days it is very easy to self-learn advanced math by reading articles, using tools such as OEIS or Wolfram Alpha  (Mathematica) and posting questions on websites such as MathOverflow (see my profile and posted questions here), which are frequented by professional, research-level mathematicians. The drawback by not reading the classics (you should read them) is that you are bound to re-invent the wheel time and over, though in my case, that’s the best way I learn new things.

Professionals with a background in physics, computer science, probability theory, pure math, or quantitative finance, may have a competitive advantage. Most importantly, you need to be passionate about your own private research, have a lot of modesty, perseverance, and patience as you fill face many disappointments, and not expect fame or financial rewards – in short, not any different than starting a PhD program. Some companies like Google may allow you to work on pet projects, and experimental research in number theory geared towards applications, may fit the bill. After all, some of the people who computed trillions of digits of the number Pi (and analyzed them) did it during their tenure at Google, and in the process contributed to the development of high performance computing. Some of them also contributed to deepen the field of number theory.

In my case, it was never my goal to prove any big conjecture. I stumbled time and over upon them while working on otherwise un-related math projects. It peeked my interest, and over time, I spent a lot of energy trying to understand the depth of these conjectures and why they may be true. And I got more and more interested in trying to pierce their mystery. This is true for the Riemann hypothesis (RH), a tantalizing conjecture with many implications if true, and relatively easy to understand. Even quantum physicists have worked on it, and obtained promising results. I know I will never prove RH, but if I can find a new direction to prove it, that is all I am asking for. Then I will work with mathematicians who know much more than I do, if my scenario for a proof is worth exploring, and enroll them to work on my foundations (likely to involved brand new math). The hope is that they can finish a work that I started myself, but that I can not complete due to my somewhat limited mathematical knowledge.

In the end, many top mathematicians made stellar discoveries in their thirties, out-performing their peers that were 30 years older despite the fact that their knowledge was limited because of their young age. This is another example that if you know too much, it might not necessarily help you.

Note that to get a job, “the less you know, the better” does not work, as employers expect you to know everything that is needed to work properly in their company.  You can and should continue to learn a lot on the job, but you must master the basics just to be offered a job, and to be able to keep it. 

What I learned from working on these math projects: the benefits

To begin with, not being affiliated with a professional research lab or the academia has some benefits: you don’t have to publish, you choose your research project yourself, you work at your own pace (it better be much faster than in the academia), you don’t have to face politics, and you don’t have to teach. Yet you have access to similar resources (computing power, literature, and so on). You can even teach if you want to; in my case I don’t really teach, but I write a lot of tutorials to get more people interested in the subject, and probably self-published books in the future, which could become a source of revenue. My math questions on MathOverflow get a lot of criticism and some great answers too, which serves as peer-review, and they even point me to some literature that I should read, as well as new, state-of-the-art yet unpublished research results. On occasions, I correspond with well known university professors, which further helps me not going in the wrong direction. 

The top benefits I’ve found working on these problems is the incredible opportunities it offers to hone your machine learning skills. The biggest data sets I ever worked on come from these math projects. It allows you to test and benchmark various statistical models, discover new probability distributions with applications to real-world problems (see this example), new visualizations (see here), develop new statistical tests of randomness and new probabilistic games (see here), and even discover interesting math theory, sometimes truly original: for instance complex random variables with applications (see here), lattice points distribution in the infinite-dimensional simplex (yet unpublished), or advanced matrix algebra asymptotics (infinite matrices, yet unpublished)  and a new type of Dirichlet functions. Still, 90% of my research never gets published. I only share peer-reviewed, usually new results. The rest goes to garbage, which is always the case when you do research. For those interested, much of what I wrote and that I consider worth sharing, can be found in the math section, here.

Source Prolead brokers usa

DSC Weekly Digest 24 May 2021

You must have data scientists in your organization! Data’s the next oil! Data scientists will tell you what all this data means!! If you don’t want to be left behind, hire your experts now, before they’re all gone!!!

The hyperbole in the tech press about data scientists has exceeded a fever pitch, with the upshot being that there are a lot of young (and not so young) people with PhDs in data analytics that expect to be swept up to nosebleed salaries the moment that the ink on their diplomas dries. The reality, however, is considerably more muddled.

A data scientist, at the end of the day, is an applied mathematician. Their focus may be either in statistical analysis or in solving complex differential equations, typically through the use of specialized graphs called kernels. Most data scientists are also subject matter experts in a given domain, and the tools that they use may or may not be consistent from one domain to another. An economist, for instance, uses a very different set of notations than a biological researcher or an experimental physicist. Because of that, generalists, those who know the tools but not necessarily the domains, may very well not be what your organization needs if you are expecting subject matter expertise.

Analysts may or may not be data scientists. An analyst has domain expertise and the ability to both understand a situation at a strategic level and to make recommendations about how best to proceed in that area to maximize the goals of the organizations. They are, in essence, modelers, and such models can both make sense of past activity and, with predictive analytics, suggest future activity. However, this is all dependent upon having the data that’s needed when it’s needed, and upon having a clear set of objectives about what specifically needs to be modeled – and why.

This means that effective data management means going beyond the data in your databases, and building, through inferences, data – knowledge – about the world outside of the organization’s walls. It means strategic investment in solid data sources or the willingness to invest in data gathering operations, and it means keeping a much of that data contextual as possible. If your organization is not willing to make that investment, then don’t hire data scientists.

I do not believe the hype that if you are not a data-driven company that you will fail. Some, perhaps many organizations aren’t data-driven, not in any meaningful sense. This is not to say that if you are a data-driven organization you can get by without good analysts who are adept with the tools and understand their domain. It’s just important to recognize that a data scientist, or a whole department of them, is not going to transform your business into a data-driven one if the strategic will to do so isn’t there. Data science efforts usually fail not because of poor data science, but due to poor strategic management.

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

Source Prolead brokers usa

Do’s and Don’ts for Leaders to succeed in digital transformation

When it comes to technology projects, 54 percent of businesses worldwide say digital transformation is their top priority.

Digital transformation has many advantages, including increased sales and stock values. However, financial gains take precedence over all other considerations, as these are the company’s primary objectives.

Companies like Target, Best Buy, and Hasbro were quick to recognize the value of digital technology and have leveraged its full potential. However, some businesses failed to implement the right digital transformation strategy, which eventually led them to shut down their operations.

According to Harvard Business Review, 52 percent of Fortune 500 firms from 2000 are no longer in business now. The reason behind? They couldn’t keep up with how the world was changing, and they couldn’t keep up with the latest tech trends.

Hence, I’ve listed some practical Do’s and Don’ts for new leaders to implement digital transformation in this article.

Do’s and Don’ts for making the journey towards digital transformation a success

Things new leader need to focus for persuasive digital transformation – Do’s

Map Out A Clear Strategy

To create a solid digital transformation plan roadmap, you must first conduct a thorough business analysis. Simultaneously, you can concentrate on your objectives and assess how they will impact your current business model. Determine the digital transformation vision and consider how it can enhance customer service and company culture.

Customer engagement, employee empowerment, operational optimization, and business model change should all be part of a successful digital transformation roadmap.

Evaluate Expense

As a leader, you can begin crunching numbers once everyone is on board. How much expense do you have to devote to transform your business digitally?

It’s essential to remember that digital transformation is a long-term project. It’s a futuristic approach to how you do business that will affect all aspects of your company.

On the other hand, the budget should be measured, keeping all of the business areas in mind that will benefit from digital transformation. By specifying goals and defining the scope of the process, you can implement the best digital transformation strategy.

Research and Incorporate New Technology

As a business leader, you must remain informed on emerging technology and trends to expand and meet consumer demands. Digital transformation services are not a one-time fix but rather a process that leads to a commitment to digital relevance. However, each business must be careful when introducing new technology, as not all of them can best fit its objectives.

An effective cognitive computing system will aid in digital transformation and place business on the road to being a successful digital enterprise.

“ According to Research and Markets – From 2019 to 2025, the digital transformation market is projected to rise at a CAGR of 23%, bringing the total market value to $3.3 trillion.”

Assess The Progress

Once you’ve started implanting your digital transformation journey, keep track of your progress or monitor essentials steps. Adopting the pinnacles of lean thinking will enable you to track your progress. This involves providing value to the end-user, cultivating a culture of continuous improvement, reducing redundancy through operations (e.g., via automation), and concentrating on people, process, and output iteration.

Points a leader should evade while implementing digital transformation – Don’ts

Overlook The Security

With more and more apps migrating to the cloud, it’s imperative to know what experts thought about cloud storage security. Surprisingly, one out of every five (22%) individuals believes that data kept in the cloud is not as secure as data saved locally. Furthermore, 30% of the participants believe that data is not currently stored securely within the organization.

Hence, during the digital transformation journey, a leader must closely watch the data security aspects. Don’t think of your network as a security barrier. Using the same network you used in 2010 isn’t going to help you. And don’t expect to find your ideal frameworks and diagnostics tools on the first try.

Investing In Unwanted Tech Or Tools

Don’t be over-enthused and purchase or invest in all the tools that come across during the process. Understand your business requirements and accordingly look for technology, tools that would help run the business operations smoothly.
It’s also unnecessary to use a paid software or service, initially looking for free tools in the market. This will assist you in understanding the effectiveness, features of the software. As a leader, you must ensure that every process, tool, or strategy built is efficient, reliable, and scalable.

Stop Experimenting (once digital transformation is done, they stop)

Never stop experimenting with trying new strategies or technology. Several digital transformation agencies enable the business to move ahead by introducing cutting-edge tech solutions on a timely basis. It’s a fact that tech is ever-evolving; the new tech that was talk-of-the-town today will be labeled ‘old-school’ the next day.
So don’t get satisfied or stop once you’ve started the journey towards digital transformation.

Let The Past Data Go To Waste

These technologies have the advantage of being data-driven from the start. The adoption of digital technologies is part of a more significant shift toward a data-driven organization in which information is shared across the ecosystem. Consider how production performance data could be valuable to both the line manager looking to increase efficiency and the engineers looking to ensure quality.

Because every interaction in the digital world from buying things to learning new food dish – is based on data. And data has become a critical pillar for digital transformation. This data allows you to set baselines and benchmarks for your digital transformation and serves as a valuable indicator of success.

Conclusion

Managers struggle to understand what digital transformation consulting means for them to pursue opportunities and prioritize efforts. It’s no wonder that many C-suite leaders anticipate major business disruption, significant new technology expenditures – a complete shift from physical to virtual channels, and the acquisition of tech start-ups.

Some businesses have succeeded in responding to the digital challenge by making significant changes to their distribution processes, production channels, or business models. In contrast, many others have done so by taking a more incremental approach that keeps the fundamental value proposition and supply chain largely untouched.

Source Prolead brokers usa

Create a Data-driven Operation with Analytics Infusion

When everyone has access data analytics, they have the context to make the best decisions for their team, their department, and the company – in real time.

Facilitating decision making throughout the enterprise starts with commitment from the top

Many organizations today are accumulating data faster than they know what to do with it. But it’s not how much you have; it’s the quality and what you do with it that produces the real value. Businesses that have come to grips with the fact that acquiring data is the easy part are quickly realizing that transforming that data into insights, while challenging and at times risky, is well worth the effort. A data-led approach provides the foundation to a culture that infuses analytics throughout the business and beyond.

Yet, even as self-service data analytics have become standard practice, the impact of data has not been fully realized. According to a recent Harvard Business Review (HBR) survey, 89 percent of participants believe analyzed data is critical to their business innovation strategy. Results also noted data analytics as improving the customer experience and operational efficiency – but not leveraged to fuel innovation and new business opportunities. The takeaway? Too many organizations are not taking full advantage of data and analytics, creating competitive opportunity for those who dare to think differently.

Respondents in the HBR survey cited a lack of employee skills or training and inferior data quality as impediments to analyzed data utility. Training a diverse workforce in a broad range of roles and departments to use specialized BI technology is not easy. Convincing them to work outside their comfort zones can be downright impossible. But, to become ‘data-driven,’ everyone must participate.

Infusing existing workflows, processes, and applications with analytics is a seamless way to solve the participation challenge – increasing automation and resulting in a streamlined user experience with no complicated tools or technical training required. Embedding analytics within the employee’s standard workflow puts the information front and center, empowering timely decisions from within that same application. This ability to ‘stay within one’s comfort zone’ can boost analytics adoption, laying the strategic foundation to a data-driven culture.

In a data-driven culture, where every employee has access to the data they need within their own workflows, data analytics delivers significant benefit. This infusion of analytics offers insights and promotes strategic decision making on the spot. But to do this right, the C-suite must lead. Placing emphasis on data literacy and decision making throughout the organization is the precursor to infusing insights into each employee’s daily workflow.

While it is not necessary to become a data scientist to lead a data-centric organization, a fundamental knowledge of basic data principles is certainly invaluable to executives. These include an understanding of the insights required, recognition that clean data is valuable data, and the capacity to pinpoint data gaps. With this level of data know-how, leaders can reshape how decisions are made throughout the operation – and beyond (i.e., suppliers, partners, and customers). Establishing corporate priorities via consensus enables leadership to define how data will be leveraged and choose technology that drives adoption of the company’s data strategy. With harmony among the leadership team, goals are set, measured, evaluated, and adjusted. Metrics can encompass anything from revenue generation to improving customer experience and identifying demand for new products and services.

Technology, while necessary, can also impose limits to data visibility. Add the rapid amassing of more data, and the issue intensifies. Analytics infusion instead puts data and actionable intelligence in front of those who need it, when and where it’s needed. Sure, democratizing data is a bold move but leaders who understand the inherent value of a data-informed organization know the benefits outweigh the risks.

With an authentic data culture, leaders can introduce more efficient processes that guide innovation and new business opportunities. And while technology certainly plays a role, it’s the combination of culture and people supported by smarter processes – infused with analytics at the point of need – that powers strategic decision making at every level of the company.

Source Prolead brokers usa

Optimize Costs without Jeopardizing Growth via Data Cleansing Services

With the advent of data-spitting technologies, the opportunities to collect information from within and outside the organization grow tremendously. From edge sensors and social media platforms to customer relationship management (CRM) and enterprise resource planning (ERP) software—every file is generated in different formats wherein each data stream is unique. The integrity of data used for business decisions, thus, becomes doubtful.

A majority of organizations are grappling with data quality challenges. An IBM study states that poor quality data costs 3.1 trillion dollars per year in the US alone. It erodes a whopping 12% of the company’s revenue on average.

The reasons for gaps in data integrity are diverse—from abnormalities in terms of formatting and storage, poor acquisition methodologies to duplicates or incomplete records—everything under the sun has an impact on data. This highlights the importance of the data cleansing process for companies.

Data Cleansing—An Essential Business Mandate

Data-driven insights have been used as the major source of competitive differentiation for organizations. With COVID-19 gripping again, businesses are leveraging data-based insights to cope with the pandemic fallout. They are working on discerning new sources of growth while securing the company’s financial footing. This can be done efficiently when the data is clean and of assured quality.

Mentioned here are some of the industries where data cleaning plays a vital role:

1. Healthcare

Take the case in point: An organization comprising nearly 90 hospitals is committed to becoming the community resource to create insights, knowledge, and wisdom for the continuous improvement of healthcare in their area. To achieve this vision, the organization begins collecting information from its members and records it into a regional database. Through this collected data, the foundation can easily identify and address disparities related to health, diseases, gender, age, etc.—and transform this information into knowledge that can be put to use for the betterment of health programs in the community.

With good quality and clean data, this organization can make accurate analyses as well as offer evidence-based support to community programs, regional health partnerships, and various public health committees. This knowledge consequently translates into greater cost optimization, enhanced operational efficiencies, and reduced risks in healthcare.

Owing to the current pandemic landscape, governments, healthcare workers, and institutions are incorporating data-driven insights to make out of this situation and save people’s lives. Many providers are also refining their ACA readiness strategies to enhance the services offerings. Collaborating with reliable data cleansing and migration companies acts as an enabler in their quest of getting quality data without trading off its integrity. They leverage data quality tools that are HIPPA and FISMA compliant.

2. Manufacturing & Logistics

The companies dealing in the manufacturing and logistics vertical acknowledge the fact that inventory valuations depend on accurate data. Any type of anomalies, inconsistencies, or inaccuracies in the datasets can lead to delivery issues and an unsatisfied customer experience. Apart from this, configuring production machines and robots based on low-quality data leads to inefficient outcomes. However, associating with a reputed data cleansing company can help them efficiently streamline their processes, enhance bottom-line operations as well as gain big wins in productivity and profitability.

3. Banks & Financial Institutions

Incomplete or inaccurate data leads to regulatory breaches, sub-optimal trade strategies, and delayed decisions due to manual checks. Clean data translates into increased profitability and effective business for the banks and financial institutions. Not only do they gain confidence in their reports generated, but also get assured that their decision-making is supported by accurate information. They can easily stay compliant with the different data-related laws such as GDPR, CCPA, ADA, FCRA, etc.

Wrapping Up

The benefits of the data cleansing process are numerous for every business, irrespective of the industry verticals they deal in and these were just a few examples. Data cleansing services enables organizations to get hygienic data without trading its integrity. They can significantly optimize operational costs without jeopardizing the company’s growth. All that needs to be done is find the right data cleansing service provider!

Source: https://www.damcogroup.com/itesinsights/optimize-costs-without-jeop…

Source Prolead brokers usa

Mastering the Data Monetization Roadmap

Senior executives trained in accounting continue to struggle to understand how to determine the value of their data. The article “Why Your Company Doesn’t Measure The Value Of Its Data Assets” written by Doug Laney (by the way, why does the Forbes web site absolutely bury the reader in ads?) contains a telling comment from a senior accounting firm partner:

“… balance sheets and income statements which form the backbone of today’s accounting system now fail to capture significant sources of value in our economy. He said that the measurements we use don’t reflect all the ways that companies create value in the New Economy, and this lack of transparency results in undue market volatility and mere “guesstimates” by investors in valuing companies. Even the chairman of the AICPA stated that the accounting model is out of date and based on the assumption of profitability depending upon physical assets—an accounting model for the Industrial Age, not the Information Age.”

This paragraph reflects how a traditional accounting mindset, in the age of digital assets, is focused on the wrong valuation method – trying to represent value using an artificially-defined balance sheet that doesn’t capture how today’s companies are using data to create new sources of customer, product, and operational value.

Nothing says “We really don’t know how to quantify value in the digital age” better than Figure 1 where a significant percentage of the most valuable firms’ “value” is credited to nebulous intangible (non-physical) assets.  And the discrepancy in the creation of value between traditional physical assets and intangible digital assets is growing exponentially.

Figure 1:  Increasing Percentage of Most Valuable Firms defined by Intangible Assets

To properly reflect the value of their digital assets, executives must embrace an economics mindset where the value of an assets is determined from the use of that asset. This is critical given the unique economic characteristics of digital assets – they never wear out, never deplete, can be used across an unlimited number of use cases at zero marginal cost, and they can appreciate, not depreciate, in value they more that they are used (if properly engineered).

I introduced the Data Monetization Roadmap in “Introducing the “4 Stages of Data Monetization” as a guide to help organizations in their data monetization journey. The roadmap emphasizes that the driver of data monetization is in the use or application of the data to create value. That is, the value of data isn’t in possession but in the application of the data to create new sources of customer, product, and operational value.  

As organizations negotiate the Data Monetization Roadmap, they will encounter two critical inflection points:

• Inflection Point #1 is where organizations transition from data as a cost to be minimized, to data as an economic asset to be monetized. I call this the “Prove and Expand Value” inflection point.
• Inflection Point #2 is where organizations master the economics of data and analytics by creating composable, reusable, and continuously-learning and adapting digital assets that can scale the organization’s data monetization capabilities. I call this the “Scale Value” inflection point.

This pivot point is where the organization makes the transition from just capturing, storing, securing, and governing data to actually monetizing it. How do you get organizations to make that first pivot towards Data Monetization?  How can one help the business stakeholders to connect to and envision where and how data and analytics can generate value (see Figure 2)?

Figure 2: Data Monetization Roadmap Inflection Point #1

Navigating Inflection Point #1 requires close collaboration with business stakeholders to identify, validate, value, and prioritize the business and operational use cases where data and analytics can create new sources of value.  The Big Data Strategy Document in Figure 3 provides a framework for that collaborative engagement process.

Figure 3: Unleashing the Business Value of Technology

The Big Data Strategy Document decomposes an organization’s key business initiative into its supporting use cases, desired business outcomes, critical success factors against which progress and success will be measured, and key tasks or actions. The Big Data Strategy Document sets the stage for an envisioning exercise to help the business stakeholders brainstorm the areas of the business where data and analytics can drive meaningful and relevant business value. Yep, there is a lot of work that needs to be done before one ever puts science to the data.

So, now we’ve given the business stakeholders a taste of success in monetizing their data.  Interest is building and others across the organization are asking for help in monetizing their data.  Now it gets really fun!

The second inflection point occurs just as organizations are scaling their data and analytics success across the organization.  More and more business units are coming to the data and analytics team for assistance with their top priority use cases. But remember:

“Organizations don’t fail due to a lack of use cases; they fail because they have too many.”

The volume of use case requests starts to overwhelm the limited data and analytics resources.   And when the business units can’t get support in a timely enough manner, the business units get frustrated and seek outside solutions. And as these organizations go elsewhere for their data and analytic needs, some fatal developments occur:

• Data Silos. These are data repositories that pop up outside the centralized data lake or data hub.  And with the ease of procuring cloud capabilities (got a credit card anyone?), it is easy for impatient business units to set up their own data environments.
• Shadow Data and Analytics Spend. The growing presence of software-as-a-service business solutions make it easy for impatient business units to just buy their solution from someone else.  Consequently, money that could be invested to expand the organization’s data and analytics capabilities is now being siphoned off by one-off, point solutions that satisfy an immediate business need, but create longer term data and analytics debt.
• Orphaned Analytics. Orphaned Analytics are one-off Machine Learning (ML) models written to address a specific business or operational problem, but never engineered for sharing, re-use, and continuous refinement.  The ability to support and enhance these one-off ML models decays quickly as the data scientists who built the models get reassigned to other projects, or just leave the company.

The result: instead of creating data and analytics assets that can be easily shared, reused, and continuously refined, the organization has created data and analytics debt that drives up maintenance and support costs which quickly overwhelms the economic benefits of the data and analytic assets.  Welcome to Inflection Point #2 (see Figure 4).

Figure 4: Data Monetization Roadmap Inflection Point #2

What can organizations do to avoid the collapse of the economic value of data and analytics that can occur at inflection point #2?

• Data Lake 3.0: Collaborative Value Creation Platform. Leading organizations are transitioning the data lake from a simple, cheaper (using the cloud) data repository to an agile, collaborative, holistic value creation platform that supports the sharing, reusing, and refinement of the organizations valuable data and analytic assets (see Figure 4).

Figure 5: Data Lake 3.0:  The Collaborative Value Creation Platform

Data Lake 3.0 employs intelligent catalogs to help the business units find the data they need for their use cases.  The data lake also employs intelligent data pipelines to accelerate the ingestion of new data sources, and a multi-tiered data lake environment to support rapid data ingestion, transformation, exploration, development, and production.  And eventually, these modern data lakes will transform into contextual knowledge centers that not only help the business units find the data, but also provide recommendations on other data sources (and analytic models) that might be useful for their given use case.

• Data Monetization Governance Council. Another key to navigating Inflection Point #2 is the creation of a data monetization governance council with the teeth to mandate the sharing, reuse, and continuous refinement of the organization’s data and analytic assets. If data and analytics are truly economic assets, then the organization needs a governance organization with both “stick and carrot” authority for encouraging and enforcing the continuous cultivation of these critical 21st century economic assets (see Figure 5).

Figure 6: Data Monetization Governance Council

The key to scaling the organization’s data monetization capabilities is to thwart data silos, shadow IT spend, and orphaned analytics that create a drag on the economic value of data and analytics.  When the business and operational costs to find, reuse, and refine the data and analytic becomes greater than the cost to build your own from scratch, then that’s a failure of the Data Monetization Governance Council.

The Data Monetization Roadmap provides both a benchmark and a guide to help organizations with their data monetization journey.  To successfully navigate the roadmap, organizations must be prepared to traverse two critical inflection points:

• Inflection Point #1 is where organizations transition from data as a cost to be minimized, to data as an economic asset to be monetized; the “Prove and Expand Value” inflection point.
• Inflection Point #2 is where organizations master the economics of data and analytics by creating composable, reusable, and continuously refining digital assets that can scale the organization’s data monetization capabilities; the “Scale Value” inflection point.

Carefully navigate these two inflection points enables organizations to fully exploit the game-changing economic characteristics of data and analytics assets – assets that never deplete, never wear out, can be used across an unlimited number of use cases at zero marginal cost, and can continuously-learn, adapt, and refine, resulting in assets that actually appreciate in value the more that they are used.

Yes, you could say that the Data Monetization Roadmap is the game plan for fully exploiting the Schmarzo Economic Digital Asset Valuation Theorem.  But that’s just me and that Nobel Prize in Economics talking…

Source Prolead brokers usa

Data Monetization: Turning Data into Profit-Driving Assets

Basic monetization mechanisms

Nowadays, information is one of the most valuable resources at the disposal of companies. Data monetization is the process that allows valuable data within companies’ business operations to be turned into new revenue streams. According to Gartner, integrating data and analytics into key business roles is among the top trends for 2021.

The increasing need to get valuable insights from raw data opens many opportunities for data monetization vendors. According to MarketsandMarkets research, the global data monetization market was valued at $2.3 billion in 2020. It is growing rapidly and is set to reach $6.1 billion by 2025.

Direct and indirect data monetization approaches

IoT devices and other digital technologies allow businesses to gather a massive amount of data that offers insights into consumer demographics, preferred products, sales performance, etc. There are two main data monetization strategies: direct and indirect.

Indirect data monetization involves leveraging these information insights within your business processes to predict demand, cut waste, segment customers and optimize price and supply chain, etc.

Direct data monetization implies exchanging data-derived insights for money or cryptocurrencies, directly turning them into income-generating assets. Direct data monetization is often part of long-term roadmaps, yet it always seems neglected, coming after everything else, including blockchain adoption, AI and hyper-automation in terms of priority.

Even though the market is expanding significantly, according to the BI Survey, only 25% of large organizations and 9% of small companies have actually launched data monetization initiatives. Companies are sitting on millions of dollars of potential revenue benefits from data monetization, and only a handful of them are actually making it a reality. Most of the time, the reasons why companies postpone monetization of their data are trivial, like the fact that they have never done it before.

Use cases for data monetization

There are many types of data that can be sold – from raw sensor data to insights obtained by analytics teams. Data that can be turned into a product differs greatly for each industry. For example, in insurance, customers’ claims histories are widely used for identifying fraud. In media and entertainment, anonymized customer data can be used to find behavioral patterns and target the right audience. Here’s a brief showcase with real-life examples of how companies monetize data products:

• DTN. The agriculture company focuses on subscription-based services for the analysis and delivery of real-time data. It has created a cloud-based data tool for sharing information like field-level weather and commodity prices with agricultural businesses.
• Vodafone. The mobile communications provider uses anonymized and aggregated mobile data to get insights about users’ mobility patterns. The gathered information can then be leveraged by the tourism sector to understand both national and international tourists’ behavior, or by the real estate industry for site planning.
  In fact, data monetization has been a lucrative business for many telecommunication companies for years, including T-Mobile, Swisscom and others. Another example is NOS SPGS — Portugal’s biggest communications and entertainment group which monetizes anonymized phone data once a traveler enters the country and use its infrastructure.
  

  “We are proving the power of data-driven phone record monetization as a new business model.” João Moreira, Head of Corporate and Public Administration at NOS SPGS

• Uber. With the user’s permission, the ridesharing service can sell location data to food and retail industry players. Other companies can leverage this data to provide discounts and promotions personalized to the specific customer.
• Michelin. The company’s main activity is manufacturing of tires; however, it also provides digital services and publishes maps and guides to help enrich trips and travels. In order to expand their offering for B2B customers and acquire new revenue streams the company sells both raw tire data (temperature, pressure, GPS, mileage) and insights to study driving behavior.
• Dunnhumby. This is an analytics company and subsidiary of Tesco. The majority of company profits comes from selling customer analytics insights, which helps retailers and brands to improve customer experiences. Nestle, Unilever, Metro, Danone are among many clients of Dunnhumby. The company’s annual revenue reached $444 million in 2019.
• Monsanto. In 2012 Monsanto (part of Bayer since 2018) bought the Climate Corporation — a data analytics platform developed to help farmers improve their productivity using gathered insights. In 2020, the Climate Corporation revenue reached $100 million. Nowadays, the company uses FieldView a platform to turn data generated on a farm into additional income.

Preparing for data monetization

Before starting on the path to data monetization, pay attention to the following data monetization strategy components:

• Licensing and unauthorized usage. You can ensure that your data cannot be resold legally by creating a proper license. But, it is quite a challenging task to identify those consumers that have breached the licensing term. Some data vendors find it one of the most difficult problems to address. Fortunately, there are many techniques for detecting unauthorized usage. Should you need any extra information on this topic, please let us know in the comments below and we’ll be happy to answer your questions.
• Data privacy. Each data vendor must follow new data protection laws, like GDPR, that enforce the protection of personally identifiable information.
• Competitive advantage. Selling the data that gives you an edge over your competitors is not a wise decision. Determining what data can and cannot be monetized is challenging but vital.
• Marketing. Without a doubt, creating a marketing strategy for data monetization is pivotal. It includes consumer and market analysis, review of your competition, and, of course, marketing mix (product, place, promotion and price), among other things. Please keep in mind that there are also ways to avoid explicit pricing of assets.
• Data quality. This is a vital component of any monetization strategy. It makes consumers trust their vendor, which can be the difference between having dozens of clients and having thousands of clients. And as with documentation, focusing on data quality results not only in a better customer experience but also in internal gains.
• Using data marketplace vs your own infrastructure. There are different pros and cons of using both methods. For example, publishing your data to a marketplace makes it easy for marketplace users to discover and requires fewer infrastructure resources, while distributing it on your own grants you much more flexibility in terms of pricing options, etc. Marketplaces include specialized companies like Ocean Protocol and Datarade, as well as software vendors like Snowflake. Informatica has also jumped on the bandwagon of data monetization.
• Scalability and availability. Make sure that your data is accessible to consumers all the time, and that it is distributed to all of them without any degradation in speed to provide a better user experience.
• Documentation. The importance of thorough documentation must not be underestimated. Not only does it greatly benefit the consumer, but it also adds to the internal understanding of the data.

Key takeaways

Many companies have large amounts of unused data. And while these companies are sitting on a gold mine, very few of them decide to launch data monetization initiatives. Data that helps to develop and deliver new insights can identify new industry winners by boosting profits and creating internal value. Do not overlook your opportunity!

Should you have any questions concerning data monetization, we will be happy to answer them.

Originally published at ELEKS’ blog. 

Source Prolead brokers usa