Three common barriers to creating a strong data culture

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The biggest barrier to data success isn’t technology, it’s corporate culture. As I wrote last month, the days of senior leaders mulling over key decisions in isolation are over.

To succeed, organizations must evolve and change their current business processes and the way they work with data. Here are three key barriers that can get in the way of a successful data culture and how organizations can overcome them.

Overcome fear of data

If employees are fearful of data due to misplaced concerns or unfamiliarity with the environment, they will consciously or unconsciously avoid using it whenever possible. This will have the effect of stalling data initiatives before they even begin, ultimately sabotaging the effort to establish a data culture.

Training and discussions are key to alleviating this fear of data, says data consultant Vanessa Lam. Rather than licensing tools to new analysts and assuming their skills have prepared them to handle the organization’s data, Lam recommended offer short training sessions on the tools offered by the organization, its data and data best practices.

Data is delicate and there are specific ways to use it and not to use it, she explains. Therefore, these training sessions are not about rote learning, but about communicating nuances such as when and how to ask questions, as well as an introduction to relevant resources to help users understand and fix issues with current data.

“Initial data training is beneficial in providing context and resources before new analysts have a chance to develop bad habits… These training sessions make them feel like part of the analyst community and help them feel comfortable discussing data issues with other analysts in the organization,” she explained.

Dealing with data distrust early

Another problem is data mistrust, which occurs when information turns out to be wrong due to incorrect data or misinterpretations. This results in a vicious cycle of increasing caution and a greater likelihood that subsequent scans will be pooled the moment an error is detected. Over time, wider margins of error are added with greater hesitancy to exploit the data, which erodes its value.

The solution is communication, says Lam, who advocated a formal system of tickets and informal comments. She wrote, “If analysts aren’t aware of known issues and ongoing fixes, every new issue they encounter becomes an unknown unknown. They assume that small errors are indicative of larger ones and spend their time verifying data instead of doing analyses.

Communication is a two-way street and is not limited to users reporting potential issues or issues with data to the operations team. Instead, the data team should also communicate issues they identify and corrections they make to the data, even when no one is complaining.

Finally, familiarity with the data builds trust. This means establishing a culture of discussing data during office hours, including a conscious attempt to integrate data into all decisions, starting at the top.

Break down barriers to data access

Sharing data between stakeholders and team members within the same organization may seem like a no-brainer. But the reality is that sharing data across lines of business can be difficult and even risky from a compliance perspective. For this reason and more, employees usually end up putting data in silos to avoid the inherent risks of data sharing.

According to Glan Jackman of the Data Access Platform Immuta, effective data management is central to successful data initiatives. While one would expect them to say exactly that, Jackman argues that with the creation of standardized and centralized processes for injecting, classifying, storing and organizing data, companies can ensure that the data is accessible and used appropriately.

In other words, democratizing access to data does not mean giving access to all, but is on the contrary inextricably linked to data governance. Businesses have a responsibility to create a suitable environment for data sharing through the establishment of proper data management and a strong data management framework. It is only when business units and teams are assured that proper controls are in place that we can expect silos to be bridged.

Finally, we have already written about how the cloud can simplify data sharing and processing. While this is true, it’s worth noting that as organizations shift to multiple cloud platforms, the result could be a patchwork of cloud-based capabilities that don’t scale cohesively, says Jackman . To be truly effective, data access and control must span all cloud platforms.

It takes the whole organization to build a strong, forward-looking data culture. But with the right training, open communication, and strong data sharing, businesses can get off to a good start.

Paul Mah is the editor of DSAITrends. A former system administrator, programmer and professor of computer science, he enjoys writing code and prose. You can reach him at [email protected].​

Photo credit: iStockphoto/lerbank


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