We’ve all heard the aphorism, “Never make forecasts, especially about the future.” In recent times, the adage has been largely attributed to the famous New York Yankee Hall of Fame catcher Yogi Berra; but, similar citations can be found much earlier in history. With the advent of Big Data and advanced analytics platforms, forecasting about the future may get easier. A press release from the University of Córdoba notes, “Technology is taking giant leaps and bounds, and with it, the information with which society operates daily. Nevertheless, the volume of data needs to be organized, analyzed and crossed to predict certain patterns. This is one of the main functions of what is known as ‘Big Data’, the 21st century crystal ball capable of predicting the response to a specific medical treatment, the workings of a smart building and even the behavior of the Sun based on certain variables.”[1] The release announced researchers from the university had found a way to superior prediction results using less data. One of the researchers, Sebastian Ventura, from the university’s Department of Computer Science and Numerical Analysis, noted, “When you are dealing with a large volume of data, there are two solutions. You either increase computer performance, which is very expensive, or you reduce the quantity of information needed for the process to be done properly.” Being able to do more with less, is just one of the trends in the area of big data and advanced analytics that business leaders can look forward to seeing in the years ahead.
Big data and advanced analytics trends
Beverly Wright (@DrBDub), Chief Analytics Officer at Aspirent, notes, “Analytics have become an important part of the decision-making process for many companies in the past few decades, particularly with corporations using data assets as a core competency and point of origin.”[2] She is one of the brave souls willing to make predictions and identify trends about the future of big data and advanced analytics. Following are some of those trends and predictions:
Big data will get bigger. Some subject matter experts note so much data is being generated and stored simply calling it “big” no longer has meaning. They suggest returning to the simple term “data.” Pam Barker (@bakercom1) writes, “In 2019, big data will remain challenging because, whether we call it ‘big’ or not, data is freaking huge and still growing. However, the actual challenges and opportunities will be different next year, as will be the ways we deal with them.”[3]
Computing power finally matches the need. Dealing with the “freaking huge” amount of data being generated has required IT professionals to reconsider how to store and analyze it. Barker predicts a combination of cloud and edge computing will prove to be the answer. “Cloud cannibalism will not be a thing in 2019,” she writes, “despite earlier predictions that the cloud (and its bandwidth) would be consumed by the Internet of Things. Instead, both cloud and edge computing will continue to grow fast, thanks to the hair-raising speeds of big data growth.”
Content intelligence becomes a thing. Wright observes, “With the dynamic shifts of data collection methods, types, and availability of data on consumers, machines, and just about everything else, I suspect the future holds more creative types of data, especially unstructured elements such as audio, video, mood capture, and other types of data.” Analyzing this data will become increasing important; hence, the rise of content intelligence. An analyst writing for TG Daily explains, “As the content becomes more and denser, it is obvious that the tools for analyzing this content should be upgraded. The insights that users can receive from complex analytics programs are definitely worth the wait. Content Intelligence, a term that will be frequently used in 2019, covers all the tools that analyze the content stored online.”[4]
Data quality management increases in importance. Data quality has always been important; but, thanks to new regulations — like the EU General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) of 2018 — getting a handle on data quality is more important than ever. Sandra Durcevic, an analyst with Datapine, notes, “Data quality management is not only uprising in the BI trends 2019, but also growing to a crucial practice to adopt by companies for the sake of their initial investments. Meeting strict data quality levels also meets the standards of recent compliance regulations and demands. By implementing company-wide data quality processes, organizations improve their ability to leverage business intelligence and gain thus a competitive advantage that allows them to maximize their returns on BI investment.”[5]
Cognitive computing takes center stage. The TG Daily analyst notes, “Cognitive applications can handle both structured and unstructured data, which is a big deal considering the latest trends in terms of Big Data. Unstructured data can be pretty difficult to handle, but with the emerging of new solutions such as CC, this is a problem of the past. In fact, CC should receive as much data as possible in order to learn more things and become more accurate in decision making and analytics.” Durcevic adds, “AI and machine learning are revolutionizing the way we interact with our analytics and data management. The fact is that it will affect our lives, whether we like it or not. We are evolving from static, passive reports of things that have already happened to proactive analytics with live dashboards helping businesses to see what is happening at every second and give alerts when something is not how it should be.”
Analytic speed becomes even more important. A Durcevic notes, real-time analytics are gaining in importance. As a result, Joel Syder, a data analyst and IT consultant at AcademicBrits, reports interest in in-memory computing is rising. He explains, “Companies have been massively looking into this type of technology [in] an attempt to speed up their big data processing.”[6] Wright agrees. She predicts, “Analytics on demand and in real time may take over tradition static insights to meet the fast pace environments where they are applied, whether that’s a marketplace, research lab, or other.”
Leveraging predictive and prescriptive analytics will increase. Most analysts discuss four types of analytics. In order of sophistication, they are: descriptive, diagnostic, predictive, and prescriptive. Descriptive analytics tell you what happened. Diagnostic analytics tells why it happened. Predictive analytics reveals what could happen. And prescriptive analytics recommends what you should do next. Durcevic notes, “Predictive analytics indicates what might happen in the future with an acceptable level of reliability, including a few alternative scenarios and risk assessment. Prescriptive analytics goes a step further into the future. It examines data or content to determine what decisions should be made and which steps taken to achieve an intended goal. Prescriptive analytics tries to see what the effect of future decisions will be in order to adjust the decisions before they are actually made. This improves decision-making a lot, as future outcomes are taken into consideration in the prediction.”
Those are just a few of the trends experts have identified in the area of big data and advanced analytics. Other trends identified include a greater concern for ethics, privacy, and data protection.
Concluding thoughts
Despite growing concerns about how data is gathered, stored, analyzed, and used, advanced analytics remain valuable to businesses, governments, and researchers. Wright concludes, “The dynamic nature and improved capabilities for analytics continues to excite and enable companies and even individuals to do more and in better ways, and I’m looking forward to seeing what the future of analytics brings toward our decision-making processes in the coming new year.”
Footnotes
[1] Staff, “Big data used to predict the future,” EurekAlert!, 9 November 2018.
[2] Beverly Wright, “6 predictions for the future of analytics,” Information Management, 5 December 2018.
[3] Pam Barker, “5 big data and analytics trends to watch in 2019,” enterprise.nxt, 27 November 2018.
[4] Steven, “Big Data and Analytics Trends that Will Dominate 2019,” TG Daily, 21 November 2018.
[5] David Weldon, “10 top analytics and business intelligence trends for 2019,” Information Management, 4 December 2018.
[6] Joel Syder, “7 top trends driving big data analytics,” Information Management, 8 November 2018.
