Don't overlook data quality when developing your data-driven culture. If people don't trust your data, they'll have problems using it.
In this age of information, many leaders strive for a more data-driven culture, but they're often missing an important component to realize this goal. To improve the analytic capability within your organization, you'll need to change the way your workforce thinks about data.
If left unmanaged, about 75% of your workforce does not put a high enough value on data to support a data-driven culture. Although you may be passionate about data-driven analysis, and your data scientists certainly hold data in high regard, the rest of your workforce will need to be persuaded.
As Aristotle teaches us, there are three methods of persuasion: ethos (credibility), pathos (emotion), and logos (logic). All three are important, but today we'll focus on ethos. If you're trying to build a data-driven culture, data credibility is an aspect you can't ignore.
The foundation of high-quality data
You must give your organization a good reason for trusting your data, and it starts with data quality. I've worked with a number of organizations on building a data-driven culture, and this is the number one reason why people are reluctant to rely on data. If people are unsure about the quality of the base data going into your systems, all trust will be lost with what's coming out of your system. This is where your data scientists come in very handy.
First, remember that data scientists are more than number crunchers — they're data professionals. Data quality is fundamental to good data management, so your data scientists should be intimately familiar with how to make it work. Your ETL (Extract, Transform, Load) should include data cleansing and integrity rule checking; good Master Data Management (MDM) should be in place for dimensions that source from multiple places; and database constraints (e.g., referential integrity) must be evaluated every time new data is loaded into the system.
Furthermore, leverage your data scientists' analytical skills to develop robust data quality metrics and use them to help educate the workforce about the realities of your data measurement methods.
SEE: Job description: Data scientist (Tech Pro Research)
I'm working with a large oil and gas client to help them improve their inspection practices. An interesting aspect of measuring pipe thickness is that sometimes a reading can be higher than an earlier reading, indicating that the pipe has actually grown in thickness. As you know, that's not physically possible, so this is often interpreted as bad data quality. However, what's actually happening is measurement error, which is a very normal part of data collection. Good metrics and a little bit of education can help debunk the myth of bad data.
Giving data power
Another aspect of data credibility comes with the power it has over your organization. Think of your data like any other leader or manager that requires some kind of power over your employees to make things happen. It may seem odd to think of data as having the same kinds of power issues as leaders and managers, but it does. And although data cannot posses legitimate power (i.e., the data is not anybody's manager or boss) it can wield both reward and coercion power.
I don't advocate using coercion power as much as reward power, but they're both effective. Sales organizations have used the reward power of data for many decades. In many cases a sales person's entire compensation is based on his or her sales numbers. Whenever I help companies with organizational change, I often use measurement as a means to reward employees for desired behavior. This makes people care about the data, which is what credibility is all about.
Another interesting form of power that your data can hold is expert power. Consultants like me rely on expert power to exert influence over others, because we don't typically have formal, reward, or coercion power. Having knowledge or skill in an area that others want or need gives our advice credence. Data works the same way, especially data that has evolved into information, knowledge, or wisdom. When the uninitiated or even skeptic initially queries your data it will be met with criticism. However, when your data is proven to be accurate and beneficial, it will reinforce a behavior to continue its use. The more people find your data helpful, the more power it will hold to influence their behavior.
Your data's credibility is integral in building a data-driven culture — the ethos in Aristotle's rhetorical triangle. And although installing data with a human-like quality like ethos is a bit anthropomorphic, it's very appropriate in engendering a culture where data has significant meaning.
Data credibility starts with data quality. If people can't trust your data, they'll have problems using it. Part of this is education, so take care to debunk mythical data quality issues. Once your data can be trusted, grant it some power. Use it as a basis for reward and possibly punishment, and build an expert data system that people learn to rely on.
In a truly data-driven culture, the data becomes more credible than even the leaders who use it. This is what you should strive for.
- Big data's billion-dollar quality problem: 3 tips for sidestepping it (TechRepublic)
- Farm out big data chores so employees can focus on analytics (TechRepublic)
- Why CXOs ignore data quality problems (TechRepublic)
- Scary and fascinating: The future of big data (ZDNet)