We live in a fast-paced world, where today’s seemingly groundbreaking technology is quickly obsolete tomorrow. For example, take the evolution of mobile phone technology. One day we were all enamored with our trusted mini flip phones, and the next we couldn’t live without our smartphones, those handheld computers that do far more than make phone calls and send text messages.
The same applies to data management tools. Just months ago, the majority of organizations leveraged disparate tools for various capabilities such as data governance, data quality and analytics. In addition many of these tools were deployed in silos across the enterprise. As the business side of organizations began to view data as an asset for operational and strategic impact, demands for more integrated and advanced technologies increased. Organizations wanted solutions that could leverage data in all aspects of the business.
Businesses today understand that data will provide the key to innovation, with the right commitment to increasing data value and proper investment in the right technologies, strategies and processes to enable fast and simple data analysis. To accomplish this goal, organizations must create an environment in which data is the center of the business universe, across every department and every level of management.
To build a data-centric culture and encourage enterprise-wide utilization of data as a primary asset, organizations must first begin with a unified focus on data governance across the data supply chain. Regardless of position or job role, employees from C-level executives to interns must zero in on how to leverage data at every level of business. Creating a successful enterprise data governance program requires both executive-level advocacy and grassroots support, which means a continued and united effort from data owners, stewards, and users across the entire enterprise.
Data governance efforts start at the top, by appointing a Chief Data Officer (CDO) to lead the data governance initiative. The CDO will establish a team and work with other executive leaders to allocate an appropriate budget for the resources and technologies required to ensure ongoing governance success. Once a budget is approved, organizations must combine the right strategies with the appropriate solution to help increase collaboration among diverging lines of business, improve accountability, ensure user trust in data, and ultimately, enhance ROI.
Once a team is established, a strategy has been developed, and tools have been chosen, organizations must execute a strategy and implements the processes to ensure proper data access and accountability. All parties must be engaged as varying roles and responsibilities are defined among data owners, stewards, and users to ensure full understanding of data. By encouraging collaboration, businesses ensure will reinforce a common understanding of data terms and underscore shared accountability across teams. When users have confidence in both their data knowledge and the quality of data, all areas of the business will have increased confidence in their data. But beyond this strategy, they must also have the proper technological solution to help streamline the process.
Assimilating data into all areas of business and building a data-driven culture requires an all-inclusive solution suite with integrated data management capabilities. A foundation of data governance will provide transparency into an organization’s data landscape, including the available data, its location, the data owner/steward, and data lineage. Users across the enterprise will have unified and quickly accessible glossary definitions, synonyms, and business attributes for data, so they may easily define, track, and manage all aspects of their data assets to make important business decisions.
The data intelligence platform should also be easily navigable and promote self-service to encourage both data utilization and collaboration. The user interface and functionality should be intuitive, clearly defining data ownership and featuring simple workflows so everyone, including business users, know where to go when they have urgent questions about data. This union of people, process, and technology is the foundation for a data-driven business and solid data governance.
As a part of a data governance program, data quality capabilities are also essential to conducting data integrity checks that include completeness, conformance, and validity. Advanced checks go beyond basic quality dimensions to ensure data is properly transformed as it flows between and across systems, and that the data remains accurate. Finally, analytics can leverage machine learning algorithms for self-learning to continuously improve data quality.
By combining the right technologies with the right approach, businesses can successfully integrate their data assets throughout all aspects of business for a competitive advantage.
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