Top Data Roadblocks for Digital Businesses
Part one of a series on smart data and data value
Data has become the lifeblood of digitally driven businesses, as organizations look for new ways to improve customer experiences, increase operational efficiency, and create data-driven products and services. But as many companies have discovered, leveraging data is easier said than done. Data is hard to harvest efficiently, hard to secure, and the quality of the data is often suspect.
Organizations certainly have enough of it. According to an article on InsideBigData.com, the digital universe is doubling every two years and will have expanded 50-fold from 2010 to 2020. Notes the article, “Human- and machine-generated data is experiencing an overall 10 times faster growth rate than traditional business data, and machine data is increasing even more rapidly at 50x the growth rate.” And while consumers are responsible for the bulk of the data growth today, IDC says that by 2025 enterprises will account for 60% of new data created.
Some companies are already reaping the benefits of using data in new ways. By the end of last year, IDC says,“revenue growth from information-based products” was “double that of the rest of the product/service portfolio for one-third of all Fortune 500 companies.” And that’s just a taste of what is possible, adding fuel to the digital flame. As Harvard Business Review notes: “Data is no longer the domain of tech companies or IT departments — it is fast becoming a centerpiece of corporate value creation ... Data contributes not only to brand equity, but to what constitutes product and service delivery.”
Roadblock One: Quality
But for many companies, attaining data-driven value creation is easier said than done. Organizations struggle to leverage existing data, to say nothing of using it to launch new digital initiatives. In an article about data strategy, Harvard Business Review says, “Cross-industry studies show that on average, less than half of all organization’s structured data is actively used in making decisions–and less than 1 percent of unstructured data is analyzed or used at all.”
With apologies to the Ancient Mariner, “Data, data everywhere, nor any information to make decisions.”
Part of the problem may be issues with data quality. In a study examining the quality of corporate data that was spelled out in Harvard Business Review, researchers conclude that, “on average, 47 percent of newly created data records have at least one critical (e.g., work-impacting) error … In today’s business world, work and data are inextricably tied to one another. No manager can claim that his area is functioning properly in the face of data quality issues.”
Estimates on the cost of bad data are eye-popping. According to research outlined in the MIT Sloan Management Review, bad data costs companies 15 percent to 25 percent of revenue. “These costs come as people accommodate bad data by correcting errors, seeking confirmation in other sources, and dealing with the inevitable mistakes that follow,” researchers say. “Fewer errors mean lower costs.”
Roadblock Two: Time
The time involved in gathering and grooming data for analysis presents another roadblock, particularly as agility remains a key success factor in the data-driven digital age. A recent survey of data scientists revealed they spend close to 80% of their time collecting data sets and cleaning and organizing data. That leaves precious little time for analysis and coming up with innovative ways to put data to work.
Indeed, even mundane data collection scenarios run into timing bottlenecks. Consider this data challenge as described to TechTarget by Bill Gillis, CIO of the Beth Israel Deaconess Care Organization in Boston:
“In an accountable care, risk-based contract environment, the goal is to improve patient care, reduce cost, and improve overall experience. What we're trying to do is, instead of the traditional way of doing analytics in a risk environment, is look at claims data. Our biggest challenge is that claims data tends to be 90 days lagged from when the event occurred. So, if you think about that from a care perspective, [you can have] a patient who could be having a diabetic incident at his physician's practice and when you're looking at that 90 days later, that patient probably ended up in the emergency room. There's no way to really track and trend that or put a care management team to them.”
“What we've been trying to do is look at real-time clinical data coming out of electronic medical records. So, every night, as physicians see their patients and they sign off in their notes, we're getting that data back in our data warehouse. It's massive amounts of data, but we take that, look at it, analyze the patients and try to generate reports and give information back to teams that will allow them to interact with the patients and head off any incidents and control costs.”
It is all about the data, Gillis says, and the challenges are getting more intense as the data swells.
But the potential for data to drive remarkable change in this digital age is profound. “We’re in the middle of a tremendous transformation process,” says Martin Hofmann, Volkswagen’s chief information officer, in a Wall Street Journal interview. “Everyone is talking about digitalization, and Volkswagen as a group is moving from being a traditional automotive producer to a digital company, with electric vehicles, autonomous vehicles, mobility services, robotics, all of that. In the past, IT was a support function, a back-end function, a cost factor. It was never seen as a big value-add. Now, (we’re) moving to the forefront.”
Data is what drives it all, that makes it possible. And the secret to solving these roadblocks is to make your data smart. We’ll explore smart data in Part Two of this five-part series.
~Written by John Dix. John is an IT industry veteran who has chronicled major shifts in IT since the emergence of distributed processing in the early ‘80s. An award-winning writer and editor, he was the editor-in-chief for NetworkWorld for many years and an analyst for research firm IDC.