Over the past few years, stories about big data have ranged from articles declaring big data a big flop to those insisting big data is a big boon to business. I daresay, however, few companies today would be willing to give up their big data projects. Brian Johnson predicts, “Over the next 5 years every mid-large US enterprise will adopt some set of modern technologies such as Artificial Intelligence, Machine Learning, Internet of Things, Big Data, and Advanced Analytics to remain competitive.”[1] Of those technologies, big data and advanced analytics will prove to be the bedrock supporting other modern technologies.
Advance analytics lie at the heart of the Information Age
Johnson notes, “Intelligent enterprises are defined by their use of data to achieve desired outcomes faster and with less risk through better, cognition, automation, and integration.” Most everyone is aware enterprises of all varieties are collecting data from multiple sources and for varied purposes. Data, however, is only useful when it is analyzed. When the World Economic Forum declared data a resource as valuable as oil, it did so knowing the real value comes from analysis. Data lying unanalyzed in a database is no more useful than oil lying unrefined in storage tanks. The value derived from all of the modern technologies mentioned by Johnson — artificial intelligence (AI), machine learning, and the Internet of Things (IoT) — comes from a combination of data and analysis. Generating and storing data is the easy part. Ronald Corker, Sr., observes, “The world’s information systems started generating 2.5 exabytes of data every day roughly a decade ago. Since then, that number has grown. Today, big data is growing increasingly important for enterprises. As the field matures, business leaders are looking for more effective ways to manage their information assets.”[2]
Corker further observes, “Business leaders are increasingly dependent on big data analysis to keep up with continually changing and growing consumer demands. Enterprise leaders want to harness this information to drive profits. However, forward-thinking leaders must take the right steps to produce meaningful results with their big data initiatives.” Although keeping up with consumer demands is critical, advanced analytics can do much more to make a business more efficient and effective. The key to success is knowing what results you are looking for and what type of analytics will help you achieve those results. Mike Brody (@ExagoInc), Co-founder and CEO of Exago Inc., explains, “A recent survey by NewVantage Partners reveals that 97 percent of executives are investing in analytics projects. What this figure does not reveal is that each of those projects is unique, incorporating different tools in pursuit of individual goals. Business analytics are a powerful tool, but only if you embrace the type that is right for you.” He goes on to note, “Analytics come in four distinct types, and each builds off the other. Imagine a pyramid, with each level supporting the next: descriptive, diagnostic, predictive, and prescriptive.”[3]
Types of analytics
Descriptive Analytics. Alexander Bekker, Head of Database and BI Department at ScienceSoft, notes descriptive analytics is the simplest analytics to conduct and answers the question of “what happened.”[4] He adds, “Descriptive analytics juggles raw data from multiple data sources to give valuable insights into the past. However, these findings simply signal that something is wrong or right, without explaining why. For this reason, highly data-driven companies do not content themselves with descriptive analytics only, and prefer combining it with other types of data analytics.”
Diagnostic Analytics. Diagnostic analytics, Bekker notes, allows a company to use historical data, measured against other data, to answer the question of “why something happened.” He explains, “Thanks to diagnostic analytics, there is a possibility to drill down, to find out dependencies and to identify patterns. Companies go for diagnostic analytics, as it gives a deep insight into a particular problem.”
Predictive Analytics. “Predictive analytics,” Bekker writes, “tells what is likely to happen. It uses the findings of descriptive and diagnostic analytics to detect tendencies, clusters and exceptions, and to predict future trends, which makes it a valuable tool for forecasting.” David Weldon (@DWeldon646), editor in chief of Information Management, notes, “As businesses look to cash in on the hype of predictive analytics, artificial intelligence is becoming one of the main drivers behind those efforts.”[5] John Crupi, vice president of IoT analytics at Greenwave Systems, told Weldon predictive analytics can pay big benefits in the area of preventive maintenance. He explained, “AI models can continuously learn as more data arrives and the models refined to achieve increasing accurate results. Predicting abnormalities in minutes or hours in advance can save companies millions of dollars by avoiding catastrophic failures and downtime.”
Prescriptive Analytics. “The purpose of prescriptive analytics,” writes Bekker, “is to literally prescribe what action to take to eliminate a future problem or take full advantage of a promising trend.” Mark van Rijmenam (@VanRijmenam), founder of Datafloq, writes, “Prescriptive analytics comes with some benefits you can leverage with Big Data, such as enhanced awareness of the impact of new technologies or techniques, improved utilization of resources and increased insight into patterns and habits of consumers. For example, you can use prescriptive analytics to determine the best social media engagement opportunities to take.”[6] He adds, “The future of prescriptive analytics will facilitate further analytical development for automated analytics where it replaces the need of human decision-making with automated decision-making for businesses. … With the expanding use and value of prescriptive analytics, it is driving the future of Big Data. Enterprise leaders can avoid risky business moves and reduce financial losses with the power of prescriptive analytics to evolve the logic of their business decisions. When you incorporate prescriptive analytics in your Big Data strategy, it can help you make business decisions faster to enhance efficiency and productivity of your enterprise.”
Applying the right analytic model to right situation
Brody observes, “[A good] analytics agenda starts with asking the right questions. This helps you fully understand your needs and wants, your strengths and weaknesses. ” At a minimum, he recommends a company asks itself the following four questions:
1. Do you have a single source of truth? I have written a number of articles about the challenges companies face when they maintain siloed data. Brody writes, “The first step of any analytics agenda is to clean up and integrate your data so that it’s consistent and reliable, because a company isn’t ready for even diagnostic reporting if it relies on conflicting data sources.” Cognitive computing platforms are excellent for helping a company create a single source of truth because they can integrate and analyze both structured and unstructured data.
2. Do you have a data analyst? Brody notes, “Data analysts are trained in statistical analysis and data modeling. Having one on staff is important for keeping data organized and actionable and for identifying and interpreting insights.” Unfortunately, data scientists are in short supply and many companies will find themselves relying on vendors of advanced analytic platforms for this expertise. Most cognitive computing platforms have embedded advanced analytics and interact with users using natural language processing. In many cases, this can alleviate the need for an on-site data scientist.
3. Do you have dynamic reporting? “Dynamic reporting, Brody explains, “generates reports and visualizations essentially on demand. That way, they incorporate the most complete and accurate information possible and give you the most relevant insights available.” As I noted above, cognitive computing platforms leverage natural language processing making dynamic reports more understandable for most users. Application interfaces can be tailored to ensure the right information is delivered in the format to the right user to maximize understanding.
4. Do you have a business plan? This might sound like a silly question; but the dot.com bubble earlier this century demonstrated that hope and enthusiasm don’t substitute for a solid business plan. Brody explains, “High-level analytics require high-level data. Predictive and prescriptive analytics are both possible, but the data and technology required are both expensive. Without the right financial incentive, the ROI on high-level analytics may not justify the investment. Companies must carefully weigh costs and benefits before pursuing more advanced analytics.”
Brody concludes, “The potential of analytics is endless, so it’s tempting to push projects forward and try to learn as you go. The better approach is to exercise caution and prioritize planning. The more that companies calibrate analytics up front, the more valuable they will be in the long run.” At Enterra Solutions®, we recommend a “crawl, walk, run” approach to advanced analytic projects to ensure maximum benefit and a good return on investment are achieved.
Footnotes
[1] Brian Johnson, “Building an Intelligent Enterprise with Artificial Intelligence (AI),” Medium, 29 August 2018.
[2] Ronald Corker, Sr., “How to Maximize Big Data Results,” B2C, 30 August 2018.
[3] Mike Brody, “Which Analytics Do You Really Need?” Entrepreneur, 7 October 2018
[4] Alexander Bekker, “4 types of data analytics to improve decision-making,” ScienceSoft, 11 July 2017.
[5] David Weldon, “AI may hold the key to success with predictive analytics,” Information Management, 14 February 2018.
[6] Mark van Rijmenam, “Why Prescriptive Analytics Is the Future of Big Data,” Datafloq, 5 October 2017.
