Future of business analytics trends in 2022 and beyond

Via selecthub.com: Source

By Jason Keller

During the past few years, the future of business analytics seemed bright.

Cloud analytics was finally taking off against a market stacked to the brim with on-premise business analytics software. Flat, static dashboards were being ditched for more robust and agile interactive dashboards. Spreadsheets got sent packing in the face of heavily augmented and data-informed reports. Self-service analytics hit the ground running, giving more and more people the ability to utilize complex analytics without a complex degree.

On the other hand, the European Union’s General Data Protection Regulations (GDPR) finally went into full effect, and Facebook was rocked by the massive Cambridge Analytica Scandal, which had a ripple effect on the tech industry as a whole.

In brief, 2018 and 2019 were watershed years for business analytics trends, and 2020 will continue on much the same course. As businesses stop asking themselves “do I need analytics?” and start asking themselves “how can I get analytics?” we’re going to see a major acceleration of all present trends across the board for 2020, along with a few new ones. As the cloud continues to loom ever-present (no pun intended), trends from years prior will continue to grow in the age of privacy-conscious cloud analytics.

The world of business moves fast, and technology moves even faster. So we’ve compiled a list of some of the top business analytics trends for 2020 and beyond to help you stay agile in the analytics space.

Key Takeaways

• Data quality management is set to become one of the most important digital trends.
• Predictive and prescriptive analytics are changing the analytics game.
• Self-service is shaping the future of digital analytics by lowering the barrier of entry into the world of BA.
• Privacy and personalization are going to butt heads into 2020 as consumers become increasingly privacy-conscious, yet more demanding of their personalized data.

A Guide to This Article

• Data Quality Management to Become More Prominent
• Data Management, Security and Accessibility to Scale Up
• Citizen Data Scientists
• Digital Privacy — Not Just Scaremongering
• Augmented Analytics to Take Center Role
• The Cloud Continues to Reign
• Predictive and Prescriptive Analytics
• Embedded Analytics on the Rise
• Artificial Intelligence and Automation Augmenting the Consumer Experience
• The Future is Personal(ized)
• Final Words

Data Quality Management to Become More Prominent

As we collect more and more interaction, business data quality management is going to become even more important than ever. Not sure what data quality management is? Don’t worry. While it might just be the most important trend emerging in business intelligence sphere for 2020, it’s still relatively new at the larger scale. Make no mistake, however. DQM is picking up steam rapidly and will come to shape the future of business analytics as a whole.

Managing your data’s quality doesn’t just mean having high-quality data to start with (though that is important), it means cleaning and preparing data, distributing it across your enterprise, and then managing that data across its lifespan.

Essentially, as the data becomes useful (and eventually useless), it’s your job to look over it, track it, and make sure it’s being used appropriately. Good data quality management practices also mean being very careful and intentional with the good data that you have available to you. We’ll cover data security and privacy in another section.

Keep in mind that DQM is still being refined as a definition and a process. Your agency will likely take an appropriate amount of time to start practicing data quality management. Be warned though: you should make it a top priority because it impacts all levels of your business. Your analysts will be the ones who really kickstart quality management practices in your company.

DQM is a critical factor in complying with the latest regulations and consumer demands for data. Stripping away bad data and maintaining the good data can put companies in good standing with their consumers, and make following codes of ethics such as the GDPR much easier.

Collecting as much data as possible and making sure to define the context that the data was collected in has become a staple of DQM. According to Gartner, poor data quality management cost businesses $15 million dollars in 2017. That cost will only continue to climb as more businesses adopt analytics strategies without data quality management techniques

Data quality management is the next big trend in business analytics. We made a guide show you what that means.

To recap, data quality management is…

• Cleaning and preparing data
• Harvesting lots of good data
• Effective distribution of data
• Management of distributed data
• Contextualizing and distributing data

Data Management, Security and Accessibility to Scale Up

The past few years have been a minefield for business analytics and business intelligence as a whole. The future of business analytics seemed wrapped up in regulations and data scandals.

Data breaches prompted major conversations in security and ethical handling of data, such as Facebook’s breach of 87 million accounts in the Cambridge Analytica scandal, or Equifax’s breach of customer data back in 2018.

Unacceptable from an ethical standpoint, these breaches, along with continuing headlines about hacks and stolen accounts have done damage to the enterprise of business analytics, jeopardizing its future, not to mention your enterprises bottom line. A 2019 report from IBMdiscovered that a data breach can cost, on average, 3.92 million USD globally. If you’re based in the United States, that number goes up to 8.19 million USD. The healthcare industry is hit the hardest, with average costs upwards of 6.45 million USD across the globe. The average size of a data breach is usually 25,575 filed.

This is why data management, more accurately known as data governance, is so important. So what is data governance? It’s defined by the Data Governance Institute as “the exercise of decision-making and authority for data-related matters,” which, in simpler terms, just means making wholesale decisions about your data and what to do with it.

Unsure how that could be a trend?

Agencies will continue to monitor, secure, and refine the data that they collect, as we move forward into a future marred by reports of major security breaches and endpoint securitychallenges, as well as database debates.

Another challenge that the industry will face is user access to data. As per stipulations in the GDPR, customers need to be able to request access to their data at will, and within a reasonable amount of time.

Companies looking to be in compliance with the GDPR (i.e., any companies that serve customers in Europe) need to revamp and redefine how they store their data and what kind of data they store. The truth is, if someone comes knocking for their data, you’re legally and ethically obligated to turn it over and delete it (upon request). When you consider this, e can only expect regulations to grow tighter in the coming years.

Data is only powerful when it can be read correctly, and to be read correctly, it must also be accessible to more people. Companies will need to toe the line between data security and data accessibility, which isn’t an easy feat to accomplish while staying agile and competitive.

Citizen Data Scientists

In terms of business intelligence vs business analytics, things are actually getting simpler than they were before. This sounds counter-intuitive to the complex world of business intelligence and business analytics, right? Well, the people taking advantage of business analytics aren’t always data scientists. In fact, Forbes and SelectHub writer Louis Columbus says that companies with fewer than 100 employees are two times more likely to adopt a business intelligence solution. And it’s unlikely that a data scientist is among those 100 employees, given that they’re so expensive to hire.

In order to adapt to the explosive adoption rates of business analytics, market forces would dictate that the future of business analytics is in the hands of the non-skilled analysts, the decision-makers and managers. In a surprise to nobody, self-service BI is adapting to all kinds of different platforms, user-levels and deployments.

The end result? We‘re seeing businesses that no longer need IT teams to access their data. We’re seeing analytics being served in the cloud (read on for more info on the cloud, or you can read this article for a deeper look into cloud analytics) as opposed to complex and costly on-premise installations. What a self-service BI solution hosted off-site might lack in its algorithms and customizability, it makes up for in speed, responsiveness, reporting and ease-of-use — and that’s a major draw for lower-level users.

Business analytics dashboards are going to progress with or without data scientists, but data scientists themselves are not going away any time soon. Shaku Atre, keynote speaker and president of Atre Group Inc, says quite the opposite: There will be more data scientists as time goes on.

“If anything there will be an increase in data scientists. Software is getting more sophisticated, but it is nowhere near the human brain’s inference drawing capabilities. In order for the software to have that capability, it will need more data and not only more high-level data, but more granular data. And that is where a data scientist is absolutely needed,” Atre said. “People have been naïve to think that new technology is going to solve all the problems in no time.”

Mike Galbraith, Vice President of Technology Strategy & Solutions at ThoughtFocus, echoed Atre’s sentiments, but with a slight adjustment: Data departments are going to become redundant as the proliferation of data continues, but data scientists themselves aren’t going away.

“Yes, I do think data scientists and other dedicated roles in the business analytics area will continue to be prevalent in the coming year, but that trend for dedicated teams and roles will start to tail off as automation technologies and AI services evolve, as companies begin to get a handle on the data flowing in and around their businesses and as skills and methods within organizations mature,” he said.

As self-service BI becomes more and more prolific, the function of data specialists will become smaller and smaller, according to Galbraith. But they’re not going away anytime soon, he said.

“The specialist titles will become more generalized as the roles and skills become more pervasive within the business function and a companies organization become more data-driven. For instance, as organizations transform to become more data-driven and their cultures change to institutionalize a more a digital way of doing business, business analysts in particular functional areas, whether in finance or supply chain or business development or manufacturing will soon have a good grasp on the technical tools and methods that make up the foundation of data analytics,” Galbraith said.

Many BA and BI platforms (especially big data analytics) are reliant on things that data scientists are especially skilled at. One of those things is R integration, which allows for a wide variety of statistical models and graphical techniques to be applied to data sets. That’s not something your average BA user can do although it’s not always essential. R is great for big data and high-level analytics, but most business analytics dashboards are well configured to be readable by your average user.

“What we’re starting to see is very business user-friendly business intelligence platforms that can be highly automated and are starting to incorporate some data science tools that don’t require a data scientist with a Ph.D. to utilize,” said Ryan Wilson, CTO for Build Intelligence. Wilson echoed Galbraith and Atre’s thoughts on self-service BI, saying that the role of the analyst will be taken up by more and more non-technical employees.

“This is going to lead to more and more companies incorporating data-driven business at every level of the business. As this happens I think we’ll start to see everyone becoming a bit of an analyst which will start to shift the role of a dedicated analyst to running, maintaining, and extending these platforms and tools in most organizations,” Wilson said.

Digital Privacy — Not Just Scaremongering

Brian Acton, the co-founder of WhatsApp, a now-Facebook-Owned messaging service, is advocating for users to delete their Facebook accounts. The European Union’s General Data Protection Rules went into effect in 2018. Google was recently fined $57 million for privacy violations. The word on everyone’s lips is “privacy,” and in the realm of BA, privacy has to be a concern from day one.

Consumers are becoming more and more aware of their personal data and how valuable it can be to both enterprises and criminals. Hence legislation like the GDPR, and the California Consumer Privacy Act, which sets a precedent for consumer’s rights to privacy, and in some cases (like that of Google) gives the legislating body the ability to leverage punishments against offenders.

Atre again chimed in with her thoughts on consumer data privacy, and more specifically, the GDPR. She says that the United States should learn a thing or two from Europe

“Take a look at the European Union. We all have to learn from them as they implement GDPR,” she said. “The US agencies should watch what the agencies in the EU are doing and follow their implementations and avoid their mistakes.”

More and more, our conversations are turning towards personal data, database security.

Augmented Analytics to Take Center Role

Augmented analytics isn’t just a buzzword anymore. Rather, it’s the future of business analytics. Gartner predicts the deployment of AI in business analytics to be the second most important technology trend in the forthcoming year. In fact, by the end of 2020, they predict that more than 40% of data science tasks will be augmented and automated.

Augmented analytics is the practice of deploying automated algorithms to not just process large sets of data, but to also make predictions and prescriptions based on that recommended data. Augmented analytics looks a little like machine learning and artificial intelligence, which opens up a world of potentials for businesses. AI working alongside data scientists, parsing data, alerting us to potential discrepancies in numbers long before the human eye catches it isn’t just a concept for science fiction anymore.  thinks that the rise of augmented analytics will play a large role in the proliferation of other data trends as well. such as DQM and governance.

We’re growing past the days of the passive reports that tell us what did happen and are now evolving into the dynamic world of live updating. Augmented reports now tell us what will happen. As the internet of things continues to grow, our pool of data will also exponentially grow, meaning that self-learning tools like AI and augmented analytics will have even more information to learn from. This also means that they will get another area to serve, making their continued development and adoption a priority.

The Cloud Continues to Reign

Use of the cloud has become a staple in most business. They store customer records, important documents, and collaborate in a shared digital space. And now the six elements of analytics — data sources, models, processing applications, computing power, analytic models and data storage — will be located in the cloud. Cloud-based tools like SAS Business Analytics already offer a viable off-site solution, with more to come as the cloud becomes increasingly sophisticated. Gartner even predicts that by 2021, a “no-cloud” policy will become as rare as a “no-internet” policy is today.

Cloud analytics is an agile, responsive and robust solution to on-premise business analytics. The benefits are numerous, including pre-configured algorithms, storage options, real-time data and more. There are shortcomings to the cloud of course. As with anything cloud-related, the data is out of your hands. If there’s a breach, you can’t secure it yourself. If your internet goes out, you’re largely locked out of your data. If their services go down, they’ll take your data with them until it can be restored or retrieved, though many companies opt for stipulating required uptime in their contracts.

Predictive and Prescriptive Analytics

Among analysts, predictive and prescriptive analytics are the most exciting topics in the business analytics realm currently.

“By combining the power of cutting-edge machine learning and artificial algorithms with massive data sets, a variety of industries are making significant advances across a wide range of domains,” says HortonWorks.com author Ryan Wheeler.

But not all analytics are created equal, though all types can be useful. Unlike their counterpart descriptive analytics, prescriptive analytics and predictive analytics are future-oriented, and are, unsurprisingly, shaping the future of business analytics, says Ryan Wilson.

“In my opinion, it’s all industries that should consider implementing business analytics! There’s a lot of tools and platforms that are really business user-friendly to use which makes them accessible to even small businesses that aren’t going to have data scientists or dedicated analysts on staff,” he said.

With the rise of self-service BI and powerful reporting platforms, more and more enterprises are able to take control of their analytics needs. They’re able to analyze all different forms of analytics — chiefly predictive, prescriptive and descriptive

“These modern business intelligence platforms have a large capacity to increase the value of all the existing software and tools businesses use, as well as increasing the value each employee brings to the business,” Wilson said.

Predictive analytics is the branch of advanced analytics that predicts future trends and outcomes based on current data and its projections. This can be extremely useful when the user is able to capitalize on results and draw their own insights. Its predictions are based on past data, and so an acceptable margin of error is included. Predictive analytics are useful in identifying risks, opportunities and better understanding customers and products.

Predictive analytics provides more in terms of sheer diagnostic information than say its counterpart prescriptive analytics. Examples range from managing customers in fast food lines to your next visit to Disneyland. It works by assessing past data and making future predictions, allowing you to act on those predictions as you see fit. But unlike prescriptive analytics, predictive analytics don’t say why something is happening.

Prescriptive analytics, on the other hand, prescribe reasons as to why something happened, based on historical and projected data. Sound impressive? Maybe that’s why it’s called the final frontier of analytic capabilities, and definitely part of the future of business analytics.

“Where prescriptive analytics come into play is they’re looking out for ‘symptoms’ of something being out of balance and ‘prescribing’ an action to take to correct/resolve the symptom. Sometimes this can include the why something happened, but oftentimes it can be valuable even without it,” Wilson said. “For example, prescriptive analytics might tell you that one of the registers at a store isn’t performing as well as you’d expect based on its performance historically and it’s performance relative to the other registers. This might ‘prescribe’ that you check that register for a few things. Is there a problem with the register? Does it need maintenance? Does it need cleaning?”

This fusion of descriptive, predictive and augmented analytics is what’s making waves in the business analytics world. The power of this business analytics tool is its ability to work with past data, make predictions on new business developments, and then inform users as to why those developments will take place. It’s your crystal ball, so to speak if the soothsayer were also telling you clear and defined reasons as to why something will happen in your future.

By gathering intel on the crucial “why” of new developments, you’re better equipped to generate your own insights. Imagine you know why your online sales are dropping. Though your analytics suite is recommending a course of action, you also see another story in the data — abandoned shopping carts, for example. This allows you to not only take suggestions from your software but understand its reasoning and devise your own strategies.

“These prescriptive analytics can be incredibly powerful at augmenting employees because they can suggest there’s a problem/symptom and suggest an employee take some action to correct/treat it. This starts making businesses more proactive rather than reactive to problems!” Wilson added.

Embedded Analytics Continues to Grow

Embedded analytics is on its way up in the business world. Embedded analytics is a new type of analytics that physically embeds analytics and dashboards into your software. Where business intelligence software is a standalone piece of software, embedded analytics is a component of other programs that enable analytics.

For example, Salesforce has built-in analytics, making it an embedded analytics product. Analytics dashboards and KPIs are sprinkled in throughout the product, giving users insights into the customer information and behaviors. Embedded analytics offer distinct advantages, such as empowering your users with the right kind of data. Unlike traditional business intelligence, embedded analytics gives users just enough data to make the right kind of decisions, without overwhelming them with data. More importantly, it gives them focused data.

Artificial Intelligence and Automation Augmenting the Consumer Experience

If you’ve received online customer support in the past few years, then it’s entirely likely that you’ve spoken to a chatbot rather than an actual human. Surprised? You shouldn’t be. Chatbots and AI, in general, are getting pretty good at their jobs. They’re getting so good, in fact, that Gartner predicts up to 15% of all customer interactions will be handled by AI by 2021.

But what do chatbots and AI have to do with the future of business analytics? Well, AI can only get good by feeding off of data to begin with. In fact, that’s how chatbots are created and tested. The Facebook AI research team at Stanford has been feeding their AI not with pre-canned responses, but with data scraped from conversations.

What’s even more impressive (or scary, depending on how you feel) is the inclusion of natural language processing in AI data. Natural language processing is the not-so-simple task of taking language samples — both verbal and written — and then processing them, stripping them down to their barest parts, and data mining them.

Where do BI and BA intersect with chatbots and AI? Well, natural language processing and scraping customer interactions are shaping up to be a powerful source of business intel.

Imagine you’ve got a customer on the other end of a chatbot. They’re pretty upset about a missed shipment. This chatbot is able to not only reply in a more helpful, intuitive way but is also feeding data back to your analytics solution. The chatbot can solve the customer’s issue all while learning more about the issue as a topic.

Now think of AI’s predictive and prescriptive powers being fed by live customer data. Some business analytics software solutions, such as Sisense, are already paving the way for the future of business analytics by combining data and automation.

Though it doesn’t look like a chatbot, it’s still learning in much the same way — off of provided data. Sisense will detect and report anomalous data for you, based off of current, projected or historical data.

Personal(ized)

If business analytics were a body, data would be the blood. It’s constantly flowing, transporting important information back to different parts of the body. Anyone could have seen the underpinnings of deeply personalized data coming from a mile away. Massive companies such as Sprint have already zeroed in on their customer’s needs and wants, leading to an enormous reduction in customer churn.

Whether it’s by creating personal customer home pages, sending out individualized content or more, business analytics is allowing companies to gain a deeper level of personalization over their customer intel.

Ironically, as business analytics data and BI reporting become more sophisticated, the reporting dashboards themselves are still stuck in the inflexible past. To remain agile and relevant in the future of business analytics, analytics dashboards will have to be more flexible and customizable. Some are already getting there, such as Board’s customizable dashboards, but as a whole, the industry will look to make customization and personalization a higher priority.

Final Words

Is the future of business analytics bright or grim? We say it’s bright enough to banish any doubts or darkness. With things like the inclusion of customization, AI, data quality management and more, the field of business analytics feels more alive and robust than ever before. And as companies continue to open the veritable floodgates of customer data, we’re also going to see a greater emphasis on security and privacy in our conversations about business analytics. So what do you think will be the next big trend in BA or BI? Let us know in the comments.

What is the future of business analytics?

What are the top trends of data analytics in 2022?

What is the future of business intelligence and business analytics?

What is next for analytics?