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  • Dejan Kostadinov Petrov

Business analytics trends in 2020 and beyond



It has been a while since data became one of the primary tools at the disposal of any forward-looking business. Even small companies can no longer expect to fight for their share of the market without collecting and analyzing data. If they do not do it, their competitors certainly will and it will give them a competitive advantage. Meanwhile, both the sheer amount of data produced, collected, and processed by businesses and the number of types of data that one has to consider is growing all the time. It is obvious that business analytics has to evolve to meet these new challenges — and it is very likely that we are going to see some significant moves in this direction in 2020. In this article, we will cover some of the most important business analytics trends to look out for this year.

1. Wide application of AI

Business intelligence started with people manually studying the data gathered in the course of running businesses, making connections between seemingly unrelated things and drawing conclusions from these interconnections. As the amount of data to process and make sense grew, it became difficult to do without software. Today, analytics are unthinkable without the help of complex software tools that make it possible to scan huge amounts of data and find correlations in them. However, we are quickly approaching the next frontier. The amount of information we now generate in a single year is staggering — 90 percent of all the data currently existing has been produced over the last two years. The tools we have right now are simply insufficient to process the amount of data an average business has to deal with every day. This means that we need to move to a wider and deeper application of AI technology.

2. Combining reporting with business intelligence

Traditionally, reporting and business intelligence have been viewed as two separate processes pursuing two individual purposes. Reporting usually has a narrow focus on a specific set of data over a period of time. In other words, it gives a clear picture of how a certain process was happening and is currently happening. Business intelligence deals with more extensive data interrelations, sometimes using multiple systems to collect information. Its purpose is to analyze this data and single out trends, which can provide insights that can be used to improve the situation. The thing is, we are about to see more and more companies moving away from pitting business intelligence vs. reporting and going toward combining them into a single procedure with the help of newly emerging tools.

3. Automation

According to Gartner, by the end of this year, more than 40 percent of data science tasks are going to be fully automated and will not require any human interference whatsoever. As the demand for high-quality, high-precision analytics grows, the lack of specialists to cover all the positions becomes more and more obvious, which leads analytics companies to look for ways of retaining or improving the quality of their output without hiring extra employees. Throughout 2020, we are likely to see many new tasks automated to deal with the lack of experts with sufficient skills.

4. Affordability of predictive analytics

With artificial intelligence and machine learning becoming ever more pervasive and ubiquitous, the technology is more available to a wide range of businesses, including small and mid-sized companies. As more and more AI-based solutions become commercially available, a business willing to implement AI in its analytics will not have to develop its own program but will be able to get a ready-made solution at a reasonable price.

5. Data protection

Since GDPR came into effect in 2018, data protection has become a primary concern for businesses worldwide. In addition to the usual problems with gathering and processing an ever-increasing amount of data, now they have to find ways to deal with this flow in ways that not just provide valuable insights and improve business processes, but also comply with legal requirements and follow the criteria listed in GDPR.

6. Natural language processing

Providers of analytics solutions today face the need to create new types of interfaces that would allow non-data experts to interact with them. Most people do not know SQL; currently, a business needs a dedicated data specialist to simply extract the necessary information from the database. Even if someone knows exactly what they need, most people lack the necessary skills to find the required data on their own. Natural language processing gives an opportunity to generate analytical queries using simple language accessible to everyone. It is already possible today; however, its application is still limited to relatively simple queries. Anything more complex than “Show me sales by product” still requires the knowledge of SQL. Further application of NLP has enormous consequences, as it frees data scientists from a variety of simpler jobs and lets them focus on their core competencies.

7. Blockchain

Blockchain influences many different areas of life and IT, and business intelligence is no exception. It is particularly important here, as the efficiency of analytics is, to a large extent, dependent on how much it can trust the information it uses. In the future, blockchain can potentially be used to ensure the immutability of data across a network of multiple participants. It will immediately notice if something in the networks gets changed, which will be incredibly useful in tracking false reports and fake news.

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