The world of data management seems to cycle through trends and jargon as quickly as teenagers tear through fads (frosted tips, really?). But while teenage trends tend to be as frivolous as they are fleeting, the difference is that data trends typically emerge as part of the evolution of data. Ten years ago, for example, the age of “big data” was really just getting started, its exponential expansion predicted but not yet realized. Today, however, the normalization of big data has arrived, with organizations of every size able to consider the potential that big data has for their growth and profitability. What was once a buzzword has now become an integral part of the data lexicon.
Similarly, the term “data-driven” is gaining tremendous momentum of late. It seems that everyone is talking about the importance of being a data-driven organization. But what does that even mean in the age of big data? Today, a data-driven organization is one that takes every opportunity to leverage their data as an enterprise asset. They realize data is a critical business resource that must be deployed to business users across all lines of business. Organizations are realizing that data is an asset (like a valued balance sheet item), it’s a service (think offering “data products” to other parts of the business), and it’s a responsibility (think GDPR, privacy, etc.). With easy access, understanding, and empowerment, those users can turn that data into actionable insights and make impactful business decisions.
Data-driven organizations understand that data will provide the keys to innovation and competitive advantage, and accordingly invest in the technologies, strategies and processes that enable fast and easy data analysis. Yet while most companies aspire to create a data-focused organization, many continue to fall significantly short of that goal.
To foster a data-driven culture, organizations need to embrace truly driving data and viewing governance as a critical mechanism to do. Organizations also need to craft an environment in which data is the center of the enterprise ecosystem, across every department and every level of management. Everyone must be focused on how this very valuable asset can be leveraged at every level and opportunity, from CEO to middle management to entry level employees. Organizations need to be data driven through their driven data.
Establishing a data-driven culture is a huge undertaking and is best accomplished through a comprehensive data governance program. Creating a successful enterprise data governance program requires time and effort from top leadership, but it also must be built from the ground up. You need both executive-level advocacy and grassroots support, which requires a continued and concerted effort from data owners, stewards, and users across the entire organization.
To start, executive leadership must establish a data governance committee or appoint a Chief Data Officer (CDO) or data evangelist. Next, executive leadership needs to allocate an appropriate budget for the resources and technologies required to ensure ongoing success in an increasingly competitive marketplace. Once a budget is approved, organizations must combine the right strategies with the right solution to help increase collaboration, improve accountability, ensure user trust in data and enhance ROI on data assets. Messages from the top must emphasize that this is a new paradigm and being data driven does not have an “end,” rather an opportunity to continuously improve.
The next step in building a data-driven culture is getting buy-in from every line of business and level across the organization. This requires an ongoing campaign to communicate the importance of data across the enterprise and explain the benefits that embracing a data-driven culture can provide. This is when each line of business must recognize their role in the overall picture and help outline what data they will be responsible for governing. By incorporating data governance within each line of business, it helps engage and invigorate the parties involved by sharing “skin in the game.”
Once everyone is involved, organizations must engage all parties and clearly define varying roles and responsibilities among data owners, stewards, curators and users to ensure full understanding of data assets. Beyond communication, collaboration must also be a key tenet of this data culture. That way, you ensure common understanding and underscore shared accountability across teams. When users have confidence in both their data knowledge and the quality of data, they will deem it trustworthy and dependable for making important business decisions.
Finally, while a data-driven culture starts and ends with people, you must give them the tools to pay more than lip service to the concept, all while supporting processes to help govern.
Establishing a data-driven culture through data governance requires an all-inclusive solution suite with multiple capabilities that can adapt and strengthen the organization’s ability to properly execute the governing committee’s vision. It should deliver complete transparency into an organization’s data landscape, from the data available, its owner/steward, lineage and impact usage, to its associated glossary definitions, synonyms, and business attributes. All users need to have the ability to easily define, track, and manage all aspects of their data assets to make important business determinations (Do I trust the data? Is this the right data? Where did this data come from?).
The solution suite should promote a community approach, bringing people and data together. After all, data governance is only as valuable as the amount of resources contributing and consuming the assets (i.e. economies of scale). It should clearly define ownership and accountability for every data asset in the organization, so every user knows who to ask when they have urgent questions about data.
Another factor is that the solution suite should also ensure how it manages data quality at both the metadata and record level. It should embody building a user’s trust across the data supply chain (cradle to grave), from quality checks that include data profiling, completeness, consistency, conformity, reconciliations, and timeliness, to machine learning algorithms for self-learning to continuously improve data integrity.
Being data driven through driven data is a long journey that does not have an ending. It is one that feeds the survival of the modern organization and can drastically change the relationship between organizations and the informed decisions that are made.
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