Three Common Barriers To Building a Strong Data Culture
The greatest barrier to data success is not technology, but the business culture. As I wrote last month, the days where top leaders ruminate in isolation over key decisions, are over.
To succeed, organizations must evolve and change their current business processes and how they work with data. Here are three top barriers that can hamper a successful data culture, and how organizations can overcome them.
Overcome fear of data
If employees are afraid of data through misplaced concerns or unfamiliarity with the environment, they will consciously or unconsciously avoid using it whenever possible. This will have the effect of dampening data initiatives before they even get started, ultimately sabotaging the effort to establish a data culture.
Training and discussions are essential to alleviate this fear of data, says data consultant Vanessa Lam. Rather than handing licenses of tools to new analysts and assuming their skills have prepared them to deal with the organization’s data, Lam recommends providing short education sessions on the tools offered by the organization, its data, and data best practices.
Data is tricky, and there are specific ways it can and cannot be used, she explains. Hence such training sessions aren’t about rote learning, but about communicating nuances such as when and how to ask questions, as well as an introduction of relevant resources to help users understand and rectify common data issues.
“Up front data education is beneficial to provide context and resources before new analysts have a chance to develop bad habits... These training sessions make them feel as if they are a part of the analyst community and help them feel comfortable discussing data issues with other analysts across the organization,” she explained.
Address mistrust of data early
Another issue is mistrust of data, which arises when insights turn out wrong due to incorrect data or erroneous interpretations. This culminates in a vicious cycle of increasing caution and a greater likelihood of subsequent analyses being binned the moment an error is detected. Over time, wider margins of error are added with greater hesitancy to leverage data, eroding its value.
The solution is communication, says Lam, who advocated a formal system of tickets and informal feedback. She wrote: “If analysts are not aware of known issues and fixes in the works, every new problem they encounter becomes an unknown unknown. They assume small errors are indicative of larger ones and spend their time checking the data instead of doing analyses.”
Communication is a two-way street and is not limited to users bringing up potential issues or problems with data to the operational team. Instead, the data team should also communicate issues that they identified and rectifications they are making to the data – even when no one is complaining about them.
Finally, familiarity with data builds confidence. This means establishing a culture of discussing data during office hours, including a conscious attempt to incorporate data in all decisions, starting from the top.
Break the barriers to data access
Sharing data among stakeholders and team members within the same organization might seem obvious. But the reality is that data sharing across lines of businesses can be difficult and even risky from a compliance point of view. For this reason and more, employees typically end up putting data in silos to avoid the risks inherent to data sharing.
According to Glan Jackman of data access platform Immuta, efficient data management is a central component of successful data initiatives. While one would expect them to say just that, Jackman argues that with the creation of standardized, centralized processes for the injecting, classifying, storing, and organizing of data, businesses can ensure that data is accessible and used appropriately.
In other words, democratizing data access does not mean giving access to all, and instead is inextricably linked to data governance. The onus is on businesses to create an environment suited for data sharing through the establishment of proper data management and a strong data management framework. Only when business units and teams are assured that proper controls are in place can we expect silos to be bridged.
Finally, we have written before about how the cloud can simplify data-related sharing and processing. While this is true, it is worth noting that as organizations turn to multiple cloud platforms, the result could be a patchwork of cloud-based capabilities that don’t scale consistently, says Jackman. To be truly effective, data access and control should extend across all cloud platforms.
It takes the entire organization to build a robust, forward-looking data culture. But with proper training, open communication, and robust data sharing, businesses can get off to a good start.
Paul Mah is the editor of DSAITrends. A former system administrator, programmer, and IT lecturer, he enjoys writing both code and prose. You can reach him at [email protected].
Image credit: iStockphoto/lerbank
