By Christina Robbins, Digitech Systems
Does better data really lead to a more intelligent business? Today’s business environment is characterized by an increasingly rapid pace, limited resources and a glut of information. Whether it’s stacked in boxes filled with paper files or consuming gigabytes on a corporate or cloud network, many organizations have more data than they can really manage. As data becomes more difficult to manage, it also becomes more difficult to filter for the information that we need to make important decisions — dumbing down our organizations.
It stands to reason that understanding what information we have, where to locate it, and when to delete it as redundant, obsolete or trivial (ROT) has the potential to aid decision-making. With the right information in our hands at the right time in a decision-making process, we become dramatically more likely to recognize smart strategies and move in a direction that leads to a healthy organization. Sounds like business intelligence to me.
Data is growing
The amount of data you manage is growing in size, complicating your ability to effectively manage it. You’ve heard this story before. IDC now forecasts that by 2025, the global datasphere will exceed 163 zettabytes (a trillion gigabytes), roughly 10 times the size it was in 2016. And we will continue to fall further behind in our ability to sort what is important, to secure everything that is sensitive, and to locate key data in our moment of need. IDC analysts David Reinsel, John Gantz and John Rydning note, “As data grows in amount, variety and importance, business leaders must focus their attention on the data that matters the most. Not all data is equally important … .”1
Key information is still hard to find
While many options have hit the marketplace in the last five years that can help you sort through electronic information to find what is valuable, information stored on paper is still being neglected in the decision-making processes of most organizations. AIIM reports that only 25 percent of companies run paper-free and fewer than half report that paper use is declining.2 Yet paper-based information is harder to find and sort through to locate the data specific to our needs. Further, for many organizations, electronic information remains fragmented. Sixty-one percent of workers access four or more systems regularly and more than one in 10 access at least 11 systems daily — simply to do their jobs! IDC further explains that the average knowledge worker spends about one-quarter of their time managing information, yet we only find what we need 56 percent of the time.3
As a result, many of us focus on information that is easier to access — though potentially less valuable — in our efforts to inform decisions. We’re rushed by a frantic pace and too many tasks than we can reasonably handle, and we’re content to collect some related information and to blindly hope that it’s the best available so we can guess our way to successful outcomes. Ouch!
Artificial intelligence (AI) gets you to what you need
IDC identifies AI as one key to helping businesses get a handle on their information in a way that speeds information processes and leads to better decisions.4
The term “artificial intelligence” has resurfaced as a catchphrase in recent years, but buyers should be cautious about rushing to implement AI. Not all types of AI add value for businesses — especially if there is a poor match to business needs. Further, all software can claim to be AI on the basis of a much broader definition of the term formerly in use. You don’t want to find yourself duped by decades-old algorithms that simply don’t leverage the latest innovations. Today’s breakthrough AI technologies fall into three categories:
1. Natural language processing (NLP) – You may remember the voice-activated phone trees of the early 2000s that could distinguish one or two word responses to simple questions to try to route you to the appropriate person. Those were early attempts at NLP, designed to help computers speak and understand audio cues. Today, NLP helps us interact with computers much more naturally — whether we’re asking Alexa for the score of the game, interacting with a Watson-driven interface to answer tax questions, or even ordering a drink from our robotic bartender.
2. Robotics – no doubt about it, our robots (often driven by an NLP interface) are getting smarter, and robotics is leading to interesting applications in manufacturing, inventory management and even retail.
3. Machine learning – Machine learning AI algorithms allow systems to evaluate problems involving very large data sets much more quickly than most of today’s linear software programming. These AI engines can explore an exponentially large number of inputs to arrive at a previously unknown output through the recognition of patterns and categories of data. The engines even learn and adapt; they are not taught. This is the area of AI that promises dramatic breakthroughs in our ability to intelligently manage data and processes.
So, how does AI really help solve our data problem? Arvato is a service provider specializing in the development and implementation of custom technology solutions for more than 150 million customers around the globe. They focus on complex business problems that simply don’t have easy solutions, so they work closely in partnership with breakthrough technology providers. They recently helped a large software client automate processing for more than 63,000 licensing contracts annually, using an AI-driven forms processing engine. Why was AI needed? Each licensing contract was unique; Troy Brown, director of innovation and tools for Arvato, described them as “extremely complex” with a “high degree of variability.” They were simply too tough for a standard OCR-driven processing engine to handle. But machine learning algorithms helped the company churn through millions of pages of data much more quickly to locate and extract important information and to categorize the contracts for management. Automation saves the client more than 10,500 hours of productive time annually.
Brown explained that converting the information into a usable, accessible format was critical to the project’s success. He said, “You can’t automate something you can’t read or look at. … Being able to extract the data with AI has enabled us to automate the entire business process for our client, saving them more than $3 million annually.”
In this case, AI helped cull through the mountains of data, extract the elements of highest importance, and make them accessible to decision-makers throughout the client’s business. Overall, the project raised the ability of the organization to make smart choices.
As the amount of data continues to scale up year after year, it becomes more and more difficult to put critical information in front of decision-makers at the time it is most needed. Therefore, we should begin today to establish systems and processes that help us secure, organize and manage the information we already have, and AI — particularly machine learning technologies — should be a core component of our strategies. The longer we delay, the harder it will be to recover.
1 Reinsel, D, John Gantz, and John Rydning. (2017, April). Data Age 2025: The Evolution of Data to Life-Critical. Framingham, MA: IDC.
2 Larivee, B. (2016). Paper Free: Are We There Yet? Silver Spring, MD: AIIM.
3 Schubmelh, D. (2014). The Knowledge Quotient: Unlocking the Hidden Value of Information Using Search and Content Analytics. Framingham, MA: IDC.
4 Reinsel, et al. (2017, April) ibid.
Christina Robbins is marketing manager at Digitech Systems LLC.
This article originally appeared in the May 2018 issue of Workflow.