Businesses often believe that transforming enterprise data into analytical insights requires deep data science expertise. As a result, many organizations default to IT to prepare data for everyone across the enterprise. However, leveraging data for business purposes frequently requires a solid understanding of organizational data from a business standpoint. Business users from varied departments are in the best position to choose the right operational data for analysis to generate actionable insights to enhance organizational growth, increase revenue and better serve customers.
As important as data is today, it is only valuable if it’s understood by business users so it can be converted into improved business outcomes. Ideally, IT and business will collaborate to analyze the right data at the right time to generate timely and meaningful insights. If IT is working in a black box, the output can fail to meet the needs of business users, confusing them with overly technical outputs that may not use the best enterprise data for analysis.
A common approach is to create a data catalog. The data catalog is great for inventorying technical assets in the enterprise. It can store the metadata in the catalog and allow users to search and find the information they need. However, most organizations make the mistake of dumping everything into the catalog. This tends to lead to more confusion, and prevents the organization from choosing the right data to base decisions on.
This is why it is critical for business and IT to work together on this on their data catalog initiative. The business can identify the key goals, metrics, and objectives they have and align the key business terms with those. IT will then know the important technical assets they need to connect to and include in the data catalog. This will allow the organization to generate actionable business intelligence and processes to foster collaboration and create a business-friendly data governance model.
Business-friendly data governance is about increasing the understanding of enterprise data by cultivating a culture of open communication among different lines of business and IT resources. Data understanding and collaboration promotes the enterprise goal of increased data utilization for increased analytic insight and improved business decision-making. Despite this, expanding participation across the organization remains a common business challenge.
If organizations want to ensure that data users appropriately and effectively leverage data, they need to implement an enterprise data governance strategy. The program must begin with data accountability, assigning data ownership for individual assets. It must establish policies for data access, and ensure that data is available and accessed appropriately. Finally, it must encourage a community approach to data understanding, bringing IT, diverging lines of business and data together. When disparate teams work together to define and document data, it builds consensus among data assets, and eliminates confusion business users face when analyzing data.
With full participation across the enterprise, data governance can deliver complete transparency into an organization’s data landscape. Business users are now empowered to easily and quickly define, track, and manage all aspects of their data assets and quickly derive meaningful insights to make critical business decisions.
Ideally, the data governance solution should provide data consumers with self-service tools to simplify data utilization. It should provide business users with simple interfaces, clear visualizations, and navigable workflows. With the right data governance model and technologies, organizations can bring greater company-wide engagement to bolster business outcomes.
By fostering a business-friendly data governance program, organizations can realize a host of benefits. For example, a bank holding company implemented an extremely technical data governance tool for an exceptionally large data environment. The technical expertise it required made it anything but business-friendly.
The company’s primary challenge was that data lineage could not be derived from glossary items, but instead needed to come from more technical metadata resources. The technical aspects of the tool made it impossible for business users to navigate. The company realized they needed a more flexible governance solution to overcome data lineage and metadata challenges and help business users understand and manage data assets.
The bank holding company implemented a much more business-friendly data intelligence platform. The platform provided enterprise data governance to serve all data users, including data stewards, data quality control officers, executive management and business users. The solution’s zero-code customization empowered business users to own, manage and maintain an adaptive data governance framework. This provided visibility into the broad organizational data landscape.
The platform delivered a fully integrated view of the company’s data environment, including workflow capabilities to assign work items to the corporate and business data stewards. This included capturing data quality scores and metrics from their in-house quality tool. Business lineage was built and managed by business users without coding, using simple drag and drop functionality. The results were a business-friendly data governance program where business users could derive deep insights for immediate decisions.
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