“Nobody needs to be told that data is becoming more complex,” writes Adrian Bridgwater (@ABridgwater). “Every user realises that all forms of information are becoming richer, faster and more fine-grained.”[1] That trend is only going to accelerate as the Internet of Things (IoT) matures and more devices get connected and start generating even greater amounts of data. “The ‘big data’ we have today,” writes Daniel Burrus (@DanielBurrus), “will be nothing compared to the abundance of data and information available to us in the near future — data generated by hundreds of millions of devices, things like wearable tech, smartphones, and anything that’s part of the Internet of Things.”[2] Analyzing that data (i.e., figuring out what results you want to get out the data) can be daunting. Daunting though it may be, Burrus insists your company needs big data analytics. He explains:
“Data analytics is the science of extracting patterns, trends, and actionable information from large sets of data. While often used interchangeably with the term ‘business intelligence,’ it’s useful to distinguish the terms. Think of business intelligence as the ways in which companies use data to improve their management and operations. Data analytics involves improving your ways of making sense of that data before acting on it; further still, you can slice and dice the data to extract insights that allow you to leverage this data to give you and your organization a competitive advantage.”
Admittedly, there is a fine distinction between data analytics and business intelligence; but, in the end, both terms involve analyzing data to improve a business’ bottom line. Burrus adds, “Improving your capacity to analyze this data works at multiple stages — from collection processes to organizing and communication techniques such as modeling and visualization. Yet, whereas data once required a large team of skilled analysts to be made useful, today there are a number of enterprise level tools for running high speed data analytics on massive amounts of data.” Atop that list of tools is cognitive computing. Accenture analysts state that cognitive computing will provide the “ultimate long-term solution” for many business challenges.[3] Because cognitive computing systems can ingest and integrate both structured and unstructured data, they are a good fit for the flood of data that the IoT is going to generate. The amount of data is going to be so immense that the term “big data” will sound quaint. Or, as Dan Graham, General Manager at Enterprise Systems, puts it, “Big data is six year old news. The Internet of Things data is bigger. It’s the hot new trend. It’s a flood of data. Billions of sensors never sleep, never stop talking. They send more data in an hour than millions of people in a day. Terrified yet? Don’t be. No corporation wants terabytes flooding their network. It’s mostly noise that gets deleted anyway. Most IoT data is repetitive. And it goes stale in a few days. So filtering it, deduplicating, and compressing often shrinks terabytes to gigabytes.”[4] Cognitive computing systems can help do that. Graham notes, “It takes considerable effort to eliminate boring data without actually losing any information. Many IoT compression algorithms still need to be invented. Not all data will succumb. But most sensor data will.”
As a side note, Graham discusses how cutting edge technologies can capture and leverage tribal knowledge. “The loss of top data analysts is driving corporations to collaborative analytic tools to retain tribal knowledge,” he writes. “New hires should not spend half their time backtracking, guessing, and searching for the right data. Veteran analysts should not spend hours and days tutoring recent hires.” To learn more about the importance of capturing tribal knowledge, read my article entitled “Cognitive Computing can Help Retain and Leverage Tribal Knowledge.” Graham’s bottom line is that new algorithms are being developed that are going to improve big data analytics in the years ahead. “New algorithms will see what people cannot,” he writes. “And they never miss anything, never take a coffee break. Algorithms will detect anomalies and emerging trends long before the [business executive] needs to know.”
Burrus predicts that the Internet of Things will usher in an era of digitization that will force companies to transform into digital enterprises in order to survive. He calls it “the Hard Trend of unstoppable digital disruption.” He adds, “Disruption … will happen to you and your industry in wave after wave.” That’s why he insists that your company needs advanced data analytics. “By harnessing the plethora of data available,” he writes, “you can put your company ahead of disruptions in your industry, leveraging the data to augment your competitive position relative to others in your field. … With exponentially increasing amounts of data accumulating in real-time, there’s no reason why you cannot turn data into a competitive advantage.”
Of course, turning data into a competitive advantage is easier said than done. Bridgwater notes that the days of managing a database containing names, phone numbers and addresses are long past. Today’s complex datasets involve analytic layers that include things like geospatial analysis, voice analysis, text analysis, and sentiment analysis. Bridgwater continues:
“Industry has plenty of reasons and use cases for making data analytics more complex. Data is no longer just data; we have hot data, cold data and archived data. Hot data is needed now, often in real time. Cold data is needed a little bit later and it may not matter how long later, as long it comes after hot data. Archived data is neither hot nor cold, but it was once at least both.”
All of this complexity leads to need for artificial intelligence, machine learning, and cognitive computing. Bridgwater concludes:
“Artificial Intelligence (AI) is starting to sound a lot less artificial if we start to understand the heuristics involved behind the way we deal with information. Heuristics are methods for problem solving based on learned experiences and logical guesswork rather pre-established mathematical formulas. Using many of the methods, channels and tools discussed here we are reaching a point where computers can start to make decisions that look after us. Yes we already have aircraft autopilots that operate within a comparatively (to our brains) defined physical world, but we are talking about deeper and more complex level of human understanding through analytics. The world is getting better, probably.”
Big data analytics are essential for future business success, but Annie Pettit (@LoveStats), Chief Research Officer at Peanut Labs, reminds us that companies still need to do some heavy lifting to ensure they provide the desired results. “The simple reason we continue to fail at big data is that we fail to create concrete and specific research plans and objectives as we do for every other research project,” she writes. “Do you want to succeed at big data? Then stop treating it like a magical panacea and do the work. Do the hard work.”[5] Bob Violino (@BobViolino) reports companies that have put in the hard work are starting to see results. “Big data initiatives are delivering generally positive early results for those organizations that have launched projects, according to research from IT industry association CompTIA. Nearly three quarters (72%) of the 402 business and IT professionals surveyed said the results of their companies’ big data initiatives have exceeded expectations.”[6] Even so, Violino notes, “While the early returns might be encouraging, the study also shows that much more work needs to be done to harness and make use of data.”
Footnotes
[1] Adrian Bridgwater, “Complex Data Analytics, Put Simply,” Forbes, 26 October 2014.
[2] Daniel Burrus, “Why Your Company Needs Data Analytics,” Business 2 Community (B2C), 25 October 2015.
[3] “From Digitally Disrupted to Digital Disrupter,” Accenture, 2014.
[4] Dan Graham, “Buckle your Seats Belts. 2016 is the Year of Awesome Analytics,” Information Management, 28 December 2015.
[5] Annie Pettit, “This Is Why You’re Failing at Big Data,” Huffington Post The Blog, 28 October 2015.
[6] Bob Violino, “Big Data Initiatives Paying Off, For the Most Part,” Information Management, 6 January 2016.
