There’s a growing number of tools that you can use to analyze data for a business. But you may not be overly confident in the results if you don’t take the data’s blind spots into account. There’s no single way to do that, but we’ll look at some possibilities here. The first thing to keep in mind is that a blind spot generally represents an “unknown unknown.” In other words, it’s a factor you didn’t take into account because you didn’t think, or know, to consider it.
- 1. Start Locating Your Dark Data
- 2. Pay Attention to Data Stored on Mobiles and in the Public Cloud
- 3. Fight the Blind Spots That Stem From Confirmation Bias
- 4. Get Rid of Blind Spots in Statistical Models With Machine Learning
- 5. Assess Whether a Lack of Adequate Tools Makes Blind Spots
- Banishing Blind Spots Is Good for Business
1. Start Locating Your Dark Data
When many business analysts talk about blind spots in data, dark data comes into the conversation. Dark data is also called “unclassified data,” and it’s information your business has but does not use for analytical purposes or any other reason related to running the business. If you don’t have any idea how much dark data your company has, what kind of information it entails, and where your company stores it, that unawareness could cause blind spots. More specifically, having an excessive amount of dark data could mean you spend more time searching for data than analyzing it. Or, dark data could open your company to regulatory risks if you cannot retrieve requested information during an audit. Similarly, some dark data contains sensitive information that hackers might try to get. If they’re successful, you may not know a data breach took place until months later ? if at all. Fortunately, there are specialized software options that can discover the data your company has ? dark or otherwise ? and clean it so that you can eventually use the data to meet your business analysis goals. Instead of being overly concerned about the business investment required for that software, think of the risks to your company if you continue to ignore your unclassified data and the blind spots it causes.
2. Pay Attention to Data Stored on Mobiles and in the Public Cloud
It’s increasingly common for people to use smartphones and tablets during their workdays. Some of them do it especially frequently if they take part in fieldwork or visit clients at their homes. Vanson Bourne conducted a study for Veritas to find out more about dark data at the company level and ended up looking at mobile data, among other things. The study results revealed several fascinating conclusions. First, it showed that, on average, 52% of data within organizations is unclassified and untagged. Veritas asserted that this issue constitutes a security risk because it leaves potentially business-critical information up for grabs by hackers. The research also showed that data stored on mobile devices is particularly likely to be unclassified. It found that only 6% of companies polled classified all their data stored on mobile devices. Additionally, 67% admitted that they had classified less than half of their mobile device data. Other interesting findings related to data stored in the public cloud. That’s another weak point for companies that want to tackle their blind spots. A mere 5% of companies said they had no dark data in the public cloud, and 61% reported classifying less than half of it. These conclusions indicate that if you want to gain ground in identifying and resolving blind spots, your company’s mobile devices and public cloud are worthy places to start.
3. Fight the Blind Spots That Stem From Confirmation Bias
Natural human behavior can also cause data-specific blind spots. For example, the first math classes people took started training them to look for answers in the data they received. But the better approach to take is to figure out which important questions to ask before they seek out data. That’s because, if people already expect a particular conclusion, such as the one that supports their hypothesis, they’ll start ignoring data that doesn’t match their expectations. The phenomenon described above is called “confirmation bias,” and it explains why people so firmly latch onto certain pieces of evidence while pretending that other bits don’t exist. So, one thing you can do to consciously avoid falling into a confirmation bias trap that could lead to blind spots is to question all available data ? even if it backs up the things you expect to see. You don’t want to participate in a major oversight that happens because you fixate on data that favors your beliefs without accounting for the information that doesn’t. Whenever you look at data, ask yourself: “Is there something I’m missing?” or “Is there another way I should interpret this?” Forcing yourself to think critically is a practical way to steer clear from the blind spots your mind tries to create.
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4. Get Rid of Blind Spots in Statistical Models With Machine Learning
Data-related blind spots could also exist in your statistical models. RiskSpan is a company that built a machine learning algorithm that can flag error-prone parts of a statistical model and indicate which associated outputs may be unreliable. It also found that applying machine learning in this way can prevent the model’s accuracy from degrading over time. This way of using machine learning is still in its early stages. But if you suspect blind spots may be compromising the effectiveness of your statistical models, a custom-built machine learning algorithm could help reduce that issue.
5. Assess Whether a Lack of Adequate Tools Makes Blind Spots
Your company may also have blind spots to conquer if it doesn’t have the tools necessary to measure ROI appropriately. A 2015 infographic from DialogTech argues that marketers could encounter blind spots if they lack methods for tracking the return on investment (ROI) from click-to-call customers ? those who get information about a company on their mobile devices, then directly call them afterward. DialogTech’s statistics showed that only 21% of companies considered themselves effective at measuring mobile ROI. The data also showed that by 2019, 162 billion phone calls would come from people who initially encountered companies on mobile channels. Mobile searches and social media were the top drivers of such activity. Moreover, the cost-per-lead metric went down by more than $100 when marketers could calculate mobile ROI. That’s because they could accurately see which leads came from mobile devices versus other channels, and then adjust their spending accordingly. Aim to consider whether your company might have blind spots because it’s not tracking leads as well as it could, or if your tools fall short in other ways. Taking an internal poll to find out which kinds of data workers wish they could measure is a solid starting point for finding your blind spots and determining how to invest in resources that minimize them.
Banishing Blind Spots Is Good for Business
It should now be evident why taking steps to find and eliminate blind spots makes good business sense. The more aware you are of potential blind spots when working with data, the easier it is to feel confident about the results ? and you’ll feel even better-equipped when showing the data to superiors or decision-makers.
