Nearly everyone, it seems, understands that organizations perform better when they make evidence-based and data-informed decisions. A data-driven or data-enabled culture is essential for improving performance and for fostering continuous growth, and these days it may even be the difference between profitability and, well, bankruptcy. The end goal of a culture of data would be widespread proficiency in leveraging data effectively to make informed decisions.
However, nearly all of the organizations we work with run into real difficulty on their path to developing a data-driven culture. This blog post will discuss this data culture and having data governance in place.
At all levels of the organization, employees are not sure how to utilize data. Advocates for data enablement encounter difficulties in aligning organizational culture with data-driven practices, and even when such alignment exists, organizations still face challenges in implementing these strategies effectively. Even when everyone understands the need, without a structured approach to integrating data into organizational operations, the effort may be doomed to fail.
Data-driven culture ought to impact business success by making it easier and faster to deliver what yout customers and partners need. Data should help managers understand what those constituencies desire, and data should help identify barriers and bottlenecks to providing desired goods and services.
In theory, organizations can analyze data in order to predict demand, anticipate the response to new features, even head off complaints, and then they can create and offer products and services quickly in response to patterns or insights discovered in data. Or perhaps they can focus on identifying problems more quickly and addressing them, which also makes constituents happier.
Maybe data-driven cultures can also foster lower attrition by understanding what employees need to succeed and be productive, and by better providing tools and mechanisms for them to operate more efficiently. Resources not spent handling employee turnover would then be available to focus on other priorities.
We recognize that it can be difficult to align data objectives with organizational goals.
Plenty of organizations do not have the right metrics in place, or they do not have a consistent understanding of what those metrics mean, how they are calculated or derived, or how to respond if metrics change. And if basic metrics are constantly in doubt, how can an organization develop and act on more sophisticated ones?
In data-driven organizations, stakeholders should be asked to provide a data justification for their choices, and managers should be asked to track and report on data metrics in their organizations.
We believe this process runs up and down the data chain. We constantly hear, even today, about requests for data where there is little if any justification for the request: not only no discussion about whether the data is appropriate for sharing or analysis, but also no discussion about how the requester intends to use the data, or what problem they are trying to solve.
Can an organization that does not fully understand its data be data-driven?
That requires figuring out what data the organization has, where it is, how fast it is growing, which data is needed and which isn’t, which data is lacking or misunderstood, and which data can be archived or disposed of. Understanding organizational data also involves knowing which data elements and sets are being used and for what purposes, and what data is available but isn't being used.
This is often where we start with clients who come to us for data governance and data intelligence assistance. How can you govern your data if you do not know what data you have? That is, if you do not have intelligence about your data?
When we develop a data governance roadmap, we meet with key data stakeholders, who help us understand what departments need, what they do not have, and what interferes with their success. A number of common themes tend to emerge: data sprawl, lack of data intelligence, no common vocabulary to talk about data, no strategy to align data operations with organizational goals, uneven data quality, data silos (often accompanied by, if not a byproduct of, outright uncertainty about data security, privacy, and sharing regulations). Sometimes we'll hear that leadership isn't really interested in data; but just as often, maybe even more often, we hear that leadership is growing quite frustrated due to the lack of useful data.
It is hard for us to imagine a real culture of data in an organization that hasn't already prioritized data governance. Data governance involves defining what data means and who is responsible for it, where it is stored and for long, what responsible access and legitimate use looks like, how much of it and in what form and under which circumstances it can be shared, how to secure it, how to maintain and ensure it's of high quality, and so on. Data governance artifacts include data steward job descriptions, business glossaries, data product and processing catalogs, inventories of systems and tools, security protocols and access policies, sharing agreements, quality standards and issue resolutions procedures, and so on.
We have suggested for a long time now that data governance and data intelligence efforts tend to be more successful when they focus on making data available and understandable, rather than on security and privacy. It is essential to secure data and to keep private data private, of course, but an organization that spends too much effort telling employees what they can not do is probably not one that encourages creativity and collaboration. Sometimes we will call this the work of data intelligence. Once you catalog your data, define its key features, and figure out who's responsible for it at which points, then security classifications, quality standards, and acceptable usage becomes easier to document and understand.
Is a culture of data going to emerge organically just because you've done some of the hard work of data governance? Probably not. A successful data governance framework helps to ensure that understandable and curated data, that has been responsibly and consistently handled, is available as a component of planning and decision making. Many things go into a data governance framework, and our solution, the Data Cookbook, helps you organize and relate your data intelligence assets, your data governance processes, and the data-related content that is becoming ever more essential for modern business enterprises.
IData has a solution, the Data Cookbook, that can aid the employees and the organization in its data governance and data intelligence efforts. IData also has experts that can assist with data governance, reporting, integration and other technology services on an as needed basis. Feel free to contact us and let us know how we can assist.
(Image Credit: StockSnap_VMDNU0TJK8_CurvyRoad_DataEnablement_BP #B1273)
