At the Kontagent Konnect user conference in late May, Williams made a presentation called the "7 Deadly Sins of Data Science," in which he outlined the common mistakes that organizations make when processing large amounts of data. There's a good chance you're familiar one or more of the Deadly Sins, which include Sloth, Negligence, Gluttony, Polemy, Imprudence, Pride, and, of course, Torpor.
We've summarized each transgression below. If your organization has sinned, now is the time to repent.
1. Sloth: Lazy Data Collection. If your data-collection skills are bad, the data you acquire probably won't help your organization much. "We see a lot of times that faulty measurements lead to faulty management," Williams told InformationWeek. "It's a garbage-in, garbage-out problem."
[ If you had all the data that's available, where would you put it? See Big Data Means Big Storage Choices. ]
2. Negligence: Misapplied Analysis. It's easy to make analytical errors as data starts to filter through your organization. "Not everyone is a data expert, and they can draw the wrong conclusions," Williams said. You must analyze the data rigorously to create simple, easy-to-understand reports.
3. Gluttony: Too Many Reports. A glut of information and good visualization tools often lead organizations to produce too many reports, including those with vanity metrics (e.g., a website's number of registered users) that cause you to miss important facts about your business or industry. "Whether you're doing this in-house or with third-party vendors, it's easy to spit out a lot of reports, a lot of data," said Williams. "Too much information can cloud your judgment, and that makes it hard to make decisions."
4. Polemy: Data Definition, Use Disagreements. If the people in your organization don't agree about what a report means and how to act on it, you'll end up in conflict. Unclear definitions, personal interpretations of what the data means, or uncertainty on how to act on data, can hamper an organization's ability to make decisions. So make sure that different groups within your company aren't going in different directions based on the same data. "It's shocking how often that happens," Williams said.
5. Imprudence: Jumping To Conclusions. When you dig through data and read reports, it's not uncommon to see things that cause alarm. Companies may jump to conclusions without examining data sufficiently. They may even change their business model for the wrong reasons, such as relying on other people's conclusions, misinterpreting data, or reading an industry benchmark and deciding they need to follow a so-called best practice. "We encourage people to verify, run their own tests, and then decide if something that has become common knowledge really works," said Williams.
6. Pride: Decision-Driven Data Making. Rather than running tests and using data to confirm or deny assumptions, this Deadly Sin is where you dig through data to confirm your preconceived notions. "We see this happen a lot throughout organizations, both at the executive level and within teams," Williams said. "People try to confirm what they believe; they dig through data to find it." But the best data-driven cultures have mantras like: "Data wins arguments," he said. Let the data speak the truth.
7. Torpor: Learning And Acting Slowly. "A critical factor is how quickly you act on data, and how quickly you learn from it. This is where a lot of companies fall short," Williams said. You should interpret data methodically, of course. And you'll want to develop a process to ensure that people aren't jumping to conclusions based on the data, or using the data to confirm what they already believe. But once a decision is made, you must act on it right away.
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