Every organization wants to turn their stores of data from business byproducts into valuable assets. As big data has grown and technology has rapidly evolved, companies of every size have explored ways that data analysis can help them leverage their data for strategic business decision making and competitive differentiation. A critical tool for making this a reality is enterprise data governance, which ensures the health of any data supply chain as information is deployed and processed across an organization’s data ecosystem.
The challenges of data governance include process standardization, tracing data lineage, and data protection assurance, as organizations seek to improve data understanding, ensure appropriate usage, safeguard compliance and manage policies. As organizations formulate their plan and execute on their vision, they must also build collaboration among diverging lines of business, gain buy-in from upper management, and evangelize a data-driven culture to promote communication and accountability. In any organization, this is a demanding undertaking.
Among all the data governance challenges, a major stumbling block continues to be the failure to adopt modern data technologies. Data is no longer the concern of an isolated few IT professionals, yet many organizations still use highly technical tools that leave business users behind. Or they employ spreadsheets and piece together disparate tools for various capabilities. With so much data at stake, tools and spreadsheets only compound governance challenges as more data stacks up every day. If businesses want to stay competitive in today’s data-driven world, they must adopt the right technologies.
State-Of-The-Art Data Governance Technologies
When organizations rely on spreadsheets and separate, specialized tools for tasks like data governance, data quality and analytics, IT becomes the de facto data governance team because they are the only ones with the technical skills to use the tools. Although business users are those best positioned to understand data and apply it for maximum impact and advantage, they are left trying to translate technical jargon and reliant on IT resources.
Data governance technologies have evolved to recognize the need for a business-centric approach to data management, and eliminate some of the challenges of legacy tools. Today, organizations can adopt business-oriented solutions that integrate capabilities and afford business users self-service options to serve their data needs. These tools enhance business user control over data and help solve business impacting questions.
Ideally, an enterprise data intelligence platform should integrate:
Data Governance Capabilities: Delivering a business-friendly perspective requires data governance to help translate the IT department’s technical lingo into easy-to-understand business terms to eliminate any ambiguity among business users struggling to perform critical business functions. The solution must enable tracing of both business and technical data lineage to give data consumers a clear view of their data as it moves along the data supply chain. The solution suite should also foster collaboration by delivering business users an all-inclusive view of their data landscape to easily define, track and manage all aspects of their data assets. IT will still retain an important role managing access and usage of data across the enterprise.
Data Quality Capabilities: Ensuring data quality is about establishing trust in data. Business users should have a data management solution that establishes data quality scores through governance so they may apply and analyze data for business planning, strategic decision-making and day-to-day business operations. The solution suite should ensure data remains complete, accurate, relevant and consistent across complex data supply chains to ensure that business users have continued confidence in data integrity and will appropriately utilize all data assets available.
Analytical Capabilities: Turning data into insights at high speeds requires elimination of manual processes and implementation of automation wherever possible. A solution suite with analytics capabilities should include machine learning algorithms to monitor data quality while maximizing governance efforts, ultimately enabling data governance to improve data integrity automatically. This minimizes data preparation time and focus on data analysis to make smarter business decisions at a rapid pace.
These three core data management capabilities—governance, quality, and analytics, should be seamlessly integrated and available to business users with an intuitive, self-service interface that promotes data utilization, data understanding and data consumer empowerment. Together, this will foster a data-driven culture and help drive generation of critical business insights.
Benefiting from Modern Data Governance
For too long, data governance has fallen on the IT department’s shoulders because of their technical expertise, or been relegated to a project within a specific department. But with the recognition of data’s value and the importance of putting business first, and the evolution of data management tools to include integrated and self-service options, that is no longer the case. With a comprehensive solution suite, business users and IT can collaborate across diverging lines of business to deploy data for maximum competitive advantage, ultimately improving profitability and increasing market share.
If you would like to learn more about using modern data governance technologies to overcome common data governance challenges, download the data sheet below.
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