Admit it: Your big data is just a big waste of money and server storage space.
No matter how much of it you collect, no matter how many analytics programs you run, no matter how many data scientists you have pouring over the bits and bytes -- big data is not boosting the bottom line or making your business smarter.
Youāre not the only one facing this problem, according to recent articles in the Harvard Business Review and McKinsey. But donāt blame big data for that. Itās all your fault.
Youāve been focused on analyzing the data rather than extracting intelligence from it and taking quick action on that information. Big data would be more than happy to help you achieve these goals, but only if you use it correctly.
It's possible to transform your big data into an amazingly accurate soothsayer using process-centric and process-embedded AI and machine learning. In order to do so, you have to overcome the three biggest challenges in the quest to monetize big data: complexity, consumerization and continuity.
I'll dig deeper into each of the three challenges in future articles, but first, here's an overview of the aforementioned challenges.
Challenge No. 1: Confronting Complexity
A recent article in Forbes reported that data scientists devote up to 80% of their time collecting and massaging data before even trying to extract anything useful from it. Some even termed these tasks as ādata janitor workā and ādata wrangling.ā
Trying to get value out of vast amounts of complex data is a serious challenge facing businesses. A recent big data survey by NewVantage Partners cited āability to develop greater insights into their business and customersā as the primary driver behind their big data initiatives as well as āfaster time-to-answer, faster time-to-decision and faster speed-to-market.ā We know that organizations that manage to do this benefit greatly.
Challenge No. 2: Creating Consumerization
Organizations want to extract intelligence from their big data as quickly and easily as consumers get the information they need from the apps on their smartphones. In other words, businesses want āconsumerizedā data. The resounding success of the IBM-Apple partnership is a significant harbinger of the increasing need for AI-powered corporate apps that can institutionalize analytics-driven decision making.
Think about that for a moment. Our phones can sift through mountains of data, select the most applicable information and display it in a usable, appealing fashion along with personalized suggestions for further action. This is AI at work. And they accomplish this in real time.
How can businesses work with their big data in the same fashion? Analytics firms need to consumerize the data, providing businesses with the information they need right now, in a consumable format for effective decision making.
Challenge No. 3: Constructing Continuity
Until now, organizations mainly relied on analytics to understand the drivers of past performance or the root cause of a problem, but organizations are increasingly demanding insights from analytics that enable them to predict their future. Predictive and prescriptive insights are big dataās real value.
But as anyone who has seen a time travel movie or studied quantum physics knows, the predictable future is not set in stone. If something shifts, the whole scenario changes. A new product is released, a clever business maneuver is made, the global economy coughs, and suddenly, you are in uncharted territory. All the predictions around product demand, consumer perceptions or consumer sensitivity to pricing are no longer optimal because thereās suddenly a new kid on the block and the picture has changed -- again.
Creating continuity between the present and the rapidly changing future is the third challenge that faces organizations today. While meeting all three challenges is critical to extracting the most value from your big data, continuity is in many ways the most important, as it underpins the self-learning architecture of the analytics that are ushering in the era of sophisticated AI, machine learning and deep learning.
Welcome To Tomorrowland
Every business today is functioning in a complex, dynamic environment. Nothing is static anymore. Some businesses may move faster, and some slower, but no matter what the industry is, business is in a constant state of motion, always trying to compete. This has consequences for big data.
As new data flows into the ecosystem, insights need to be continually recalibrated. Big data analytics must continuously update the insights so that stakeholders are served with predictions that are based on the latest and greatest data.
Thereās a saying that dates back to the early days of computer science: āGarbage in, garbage out.ā It's a phrase coined in 1963 by George Fuechsel, an IBM technician and programming instructor. Fuechsel was referring to a computerās remarkable ability to grind out vast amounts of utterly incorrect information when fed erroneous data. As sociologist William Bruce Cameron once said, āNot everything that can be counted counts, and not everything that counts can be counted.ā (Cameronās quote is now widely, and wrongly, attributed to Einstein.)
Any big data initiative that doesnāt reduce complexity, enable consumerization, provide users a peek into the future and rapidly recalibrate as the future unfolds is simply not optimal. The bottom line is this: You must be smarter than your data. The purpose of your data is to keep you way ahead of the curve. If your data is moving faster than your predictions, your insights about the future will prove no more realistic than a ride through Disney World's āTomorrowland,ā now also known as the "Future That Never Was."
