Last year around this time we prepared a list of Top 7 Business Intelligence and Analytics Buzzwords for 2015. In this post we refer to this list again – not because we are feeling sentimental but rather to make revisions and update the list for 2016. With some of the predicted buzzwords we hit the jackpot to the extent that has gone even beyond our expectations. Other buzzwords were less trending then we thought. Let’s take a look on the 10 business intelligence buzzwords that will be present in the Business Intelligence world everywhere and will turn our eyes in 2016.
Three years ago Thomas H. Davenport and DJ Patil were prophetic when they announced in their Harvard Business Review article that data scientist is the sexiest job of the 21st century. Today data science practitioners are like celebrities of business intelligence – we all know they exist and we can read about them everywhere but we don’t know what exactly they do. In a world where the amount of data is growing exponentially, there is a massive demand for people who can make sense of all these numbers. The problem is that data science remains a blurry term. It’s a medley of analytics, computer science, modeling and statistics – more a skill set of a group of people then an upgraded IT guy. There are definitely not many people with this attractive combination of scientific background, computational and analytical skills as well as entrepreneurial thinking. Moreover, there aren’t many university programs offering degrees in data science, there is also little consensus on where the role fits in an organization, how data scientists can add the most value, and how their performance should be measured.
In today’s business environment more and more companies are making use of various self-service BI tools. Employees are empowered to track KPIs and prepare advanced business reports faster than ever before. They can easily share their insights with executives and update data on the fly. So if all businesses are so data-driven, why failed business decisions haven’t become extinct? Why so many businesses still pay a high price for the wrong moves?
The answer is simple: the human factor. Even when provided with most accurate and timely reports, many executive still would rather go for their gut feeling and ignore the reports. Among most common reasons why business leaders ignore data-driven decision-making are overreliance on previous experience, confirmation and optimism biases or group think.
Just like all terms on this list, smart data is a celebrity business intelligence buzzword, but it would be difficult to find a company who actually applies it. Some characterize it as big data made clean – as opposed to volume, velocity and variety that are the attributes of big data, smart data is all about value and veracity. The purpose of smart data is to filter out the noise and hold the valuable data, which can be effectively used by the enterprise to solve business problems. It’s a turn from quantitative to qualitative data analysis. Thanks to smart data we should be able to notice in what way various events are interrelated. This essentially means that the focus should not just be to collect a vast amount of all possible data, but also contextualize, trying to understand why and how something happened and what would be the consequences. On a deeper level it’s about a move from data management culture (struggling to manage all kinds of data) to data-driven dashboard culture (leveraging all the value behind the data).
We’ve already recognized it as the biggest Buiness Intelligence and Analytics Trend of 2017. Predictive analytics is the practice of extracting information from existing data sets in order to forecast future probabilities. Applied to business, it is used to analyze current and historical data in order to better understand customers, products and partners and to identify potential risks and opportunities for a company. Without doubt it’s a big technological advancement, but the extent to which it is believed to be already applied is vastly exaggerated.
The commercial use of predictive analytics is a relatively new thing. The accuracy of the predictions depends on the data used to create the model. For instance, if a model is created based on the factors inherent at one company, it doesn’t necessarily apply at a second company. The same may be true about a model for one year compared to the next year within the same company. Approaches need to take this dynamic nature into mind. Moreover, as most predictive analytics capabilities available today are in their infancy — they have simply not been used for long enough by enough companies on enough sources of data – so the material to build predictive models on is scarce.
Last but not least, there is the human factor again. The psychological patterns behind why people make decisions cannot be boiled down to simple logic and very often are complex and unpredictable.
Theoretically, this business intelligence buzzword implies the possibility to access analytics as the data come into a system. Practically, experts provide a more concrete time frame for what constitutes real-time analytics, such as suggesting that real-time analytics involves data used within one minute of it being entered into the system. Although it’s indeed a very effective technology, we cannot leave unsaid that there is still some shift in time. Examples of effective use of real-time analytics would be any continually updated or refreshed results about user events by customer, such as page views, website navigation, shopping cart use, or any other kind of online or digital activity. These kinds of data can be extremely important to businesses that want to conduct dynamic analysis and reporting in order to quickly respond to trends in user behavior.
In fact, we haven’t arrived to real-time knowledge of the audience and 100% real-time targeting yet. In the not so distant future programmatic advertising will be adjusted dynamically to suit the audience. Different people watching the same show on different TVs, laptops, or mobile devices will see different advertisements. As opposed to RTB which refers to the purchase of ads through real-time auctions, programmatic advertising software will allow advertisers to buy guaranteed ad impressions in advance from specific publisher sites.
The VMware survey conducted in March 2015 revealed that while the first tentative steps are taking place in moving towards enterprise mobility, companies are by no means over all the hurdles. CIOs continue to rank mobility as one of their highest priorities, but in fact 40% of organizations surveyed said they were in an early stage of their business mobility journey, compared to 22% who confessed advanced mobile analytics adoption. Companies plan to upgrade infrastructure to support a mobile business model (77% of respondents), introduce new mobile customer-facing apps (70%) and rebuild critical apps for mobile (69%), but it is still a matter of the future.
Intelligent Decision Automation (IDA)
Intelligent decision automation — the combination of artificial intelligence (AI) and automation — has already become one of the most used business intelligence buzzwords of 2015. Intelligent automation systems sense and synthesize vast amounts of information and streamline decision making processes. They are capable of aggregating, extracting, and analyzing complex information such as human speech or unstructured text. The automated analysis and decision process can be embedded in a workflow that includes humans reviewing and approving machine decisions. IDA has vast potential applications: from collecting, analyzing, and making decisions about textual information to guiding autonomous vehicles and advanced robots.
In fact, the buzz around IDA is exaggerated. So far the systems can only handle simple tactical (i.e., single customer/situation) decisions, but as AI is applied more widely to model and learn, IDA will definitely find a wider use. Initiatives like Google making its machine learning software (TensorFlow) open source can only lead to the acceleration of the use of AI in decision making.
Other commercial examples of the IDA application include marketing systems that presents offers to customers based on their profile analysis, a credit card processing systems that identifies and blocks fraudulent transactions, and an e-discovery system that classifies documents according to their meaning and relevance to the pending case.
Personal analytics personified by wearable devices has already been a business analytics buzzword for the last couple of years. Most prominent examples include popular Apple Watches or Fitbit wristbands, already proudly owned and showed off by some tech savvys. The future belongs to embedded computing, and fashion combined with analytics, for better health monitoring, improved intellectual and fitness performance or just for fun. However, wearables still face some obstacles that prevent them from being as popular as tablets or smartphones. Such barriers include high prices, lack of functionality, attractive aesthetics and favorable mindset of potential users. Consumers still don’t understand how a wearable might really benefit them or don’t feel the need to measure and record their every bodily function. Moreover, most of the devices still need to connect with a smartphone or tablet for most of their functionality.
Visual Business Analytics
With so many vendors of visual business analytics out there, including datapine, we couldn’t leave this trend out. Visual analytics is a form of inquiry in which data is displayed in an interactive, graphical manner. The approach uses data visualization technologies to help business professionals identify trends and patterns in the data they are working with. Packaged visual analytics software tools are appropriate also for non-technical users as they include drag-and-drop options for setting and modifying analytical parameters. Visual business analytics tools integrate, unify and standardize data coming from different data sources and make it possible to perform analysis on large, complex data sets.
Experts stress that it’s data marked by volume, variety and velocity or data that you cannot read with your own eyes as the size of data itself becomes a part of the challenge. Calling big data the business intelligence buzzword of 2015 or 2016 is an understatement; it’s a buzzword of the decade. Oil companies, telecommunications companies, and other data-centric industries have had big datasets for a long time. And as storage capacity continues to expand, today’s “big” is certainly tomorrow’s “medium” and next week’s “small”. At some point, present techniques for working with data always become inefficient and companies need to develop new strategies.
We hope you enjoyed our list of business intelligence and analytics buzzwords of 2016. We also hope that the next years will bring us new trending words, because we can’t stand looking at these ones anymore. Let’s keep fingers crossed for new advancements in business intelligence.