Why Data Is Only As Good As Its User(s)

Why Data Is Only As Good As Its User(s)

Big data. It’s a term that’s been all the rage over the last several years. And, it’s a term that’s actually deserved the hype. As Dov Seidman reminds us in Forbes, “Big Data is currently revolutionizing our approaches to medicine, development work, education, advertising and shopping.” Seidman adds, “It is an incredibly powerful tool—one that allows you to take a broader perspective, factor in all relevant information, and make decisions that reflect a fuller comprehension of the situation at hand.”

The problem, however, is “that some businesses see automation as a one-size-fits-all solution, removing humans from the very tasks that need them most.”

Shvetank Shah, Andrew Horne, and Jaime Capellá state a similar belief in an article for the Harvard Business Review. “Investments in analytics can be useless, even harmful, unless employees can incorporate that data into complex decision making.” In fact, Shah, Horne, and Capellá conducted research that discovered “there’s an odds-on chance that someone in your organization is making a poor decision on the basis of information that was enormously expensive to collect.”

In other words, data is only as beneficial as its users. And, here are five main reasons what that’s the case.

It Takes Time to Analyze Data

Analyzing data takes time to gather, comprehend, and prepare. In fact, a survey conducted by IDC “found that over 40% of IT advisers required more than two days to prepare financial data for reporting.” The survey also interestingly found that “the majority of business users found the slow and fragmented nature of their data systems to be perfectly OK.”

Amanda Reinhart asks in Business 2 Community “why is this a problem?” Reinhart answers that question with the following example;

“Consider a potential interaction with a customer or supplier. Having to switch awkwardly between transactional and analytical applications means a contact center agent would have to physically look away from one set of data to pull up another. Even then, any analysis or recommendation that person might offer will have been based on data gathered well before the time of the conversation.”

Using a single data management that can be used as both a “systems of record” and “systems of decision” should be embraced by more users.

Our Minds Are Already Made Up

Even if users welcomed a single data management system it wouldn’t change the fact that we can be stubborn. Carlos Melendez makes this point perfectly clear on InfoWorld when discussing how UPS is using big data “to inform how drivers actually make deliveries.” Melendez adds that UPS was "aiming for 70 percent of its U.S. drivers to use the system by year-end." Melendez speculates that UPS didn’t aim for 100% because the drivers may “think they can make better decisions than the big-data predictive analytics routing algorithm.”

As Melendez further explains;

“Put another way: how many times have you decided to take a detour on your way to get somewhere, because you knew you'd hit traffic and, based on past experience, you thought you'd have a better chance of arriving on time if you took an alternate route? Or second-guessed the GPS because you know that computers are only as smart as their programming?”

Even though data could provide beneficial insights, we’re sometimes too accustomed to our old ways and less willing to believe a new technology.

Analysts Lack Relevant Expertise

Remember Google Flu Trends? Martin Wilcox said that this was “the prototypical example of the power of big data” since it used “a group of Data Scientists with little relevant expertise were able to predict the spread of flu across the continental United States.” The Forbes articles goes on to say, “We now know that GFT systematically over-estimated cases – and was likely predicting winter, not flu.”

Wilcox ultimately asks, “What can we learn from all of this about the business of extracting insight and understanding from data in business?”

“Plenty. If you are a bank that wants to build a propensity-to-buy model to understand which products and services to offer to digital natives, then leverage clickstream data. But use it to extend a traditional recency / frequency / spend / demography-based model, not replace it.

If you are an equipment maker seeking to predict device failure using, “Internet of Things” sensor data that describes current operating conditions and are streamed in near real-time, you can bet that a model that also accounts for equipment maintenance and manufacture data will out-perform one that does not.

And if you are leading a big data initiative, you should prioritise integrating any new technologies that you deploy to build a Data Lake with your existing Data Warehouse, so that you can connect your 'transaction' data with your 'interaction' data.”

The Data Hasn’t Been Customized

Harvard University professor Gary King gave Linda Tucci of SearchCIO an example of using big data “to discover causes of death in parts of the world where no death certificate is issued.” King says, “One way to collect such data is to have researchers go household by household and do what's called a ‘verbal autopsy.’ What were the symptoms the deceased exhibited before dying -- bleeding out the nose, stomach pains?” “This works great,” Kind adds, “until you try to turn the verbal report into a diagnosis and find you won't necessarily get the same cause of death from one physician to the next.”

"In public health, they don't care about you -- they care about what everybody died of," King also said. In fact, the approach is ineffective across numerous fields. "Once we realized that, we realized we needed to come up with a different method for estimating the percent in the category that had nothing to do with an individual's classification."

Data is Boring

DeZyre argues that “Storytelling is data with a soul. Data Scientists are extremely good with numbers but numbers alone are not sufficient to convey the results to the end user.” And that’s important because people find data “dry and boring.” However, we all love a good story. That’s why users should be able to tell a story with data.

For example with our invoicing product we were able to analyze over 1+ million invoices and give people some tips on how to get paid quicker.

Data scientist can achieve that by doing the following;

  • Know your business's problem, define the question, and what it matters.
  • They should have in-depth with the current business environment.
  • Must be able to study the implications from various perspectives.
  • Should be able to identify any roadblocks.
  • “Present the business impact of the presented solution through a resolution to the story.”
  • Must adapt the data to their audience.

With effective storytelling data scientists can help their organization make actionable insights.

 


 

About the Author

John Rampton is an entrepreneur, investor, online marketing guru and startup enthusiast. He is founder of the online payments company Due.

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