It’s no secret that the age of big data has arrived, and it also comes as no surprise that businesses of every size and across every industry are trying to use data for maximum advantage. Enterprises are leveraging their information to enhance strategic business decisions, optimize day-to-day business and financial operations, improve customer experience, enable digital initiatives and eventually increase revenue and growth. However, with data being amassed, disseminated, and deployed at such a fast pace, there is a very real danger that data assets can become data liabilities if they’re not correctly managed.
Data is an essential business asset for an organization, provided data consumers fully understand its meaning from a business context, can accurately gauge the quality of data, and can easily access and use it. This must be true not only for the IT department, but also for business users from various departments and lines of business.
Lack of access or degrading quality can both create data liabilities, but a robust data governance strategy can ensure that data remains a valuable enterprise asset. When data governance is done properly, all users are empowered to quickly find, acquire, and prepare the right data sources to be analyzed and acted upon, and they’re assured that the quality of those data assets are maintained.
Establishing a Data Governance Model
Data governance is about setting guidelines, policies, and processes around data management, deployment, decision-making, and quantifying data quality value. Historically, organizations may have documented information about their data on spreadsheets, SharePoint, or wikis, but as organizations continue to get inundated with data, this quickly becomes an unsustainable model of governance. In addition, responsibility for documentation is often relegated to IT, which historically can cause issues with access and understanding among business users.
Let’s look at an example. Mapping data lineage is an essential part of data governance. It helps users understand the source of data, where and how it is used, if it may have been altered, and where it has moved through the data supply chain. All of this information is critical to understanding how it might be used for analytics and for assessing data quality levels for reliability of derived results. However, data lineage is often technical and thus the mappings might not be easy to understand for a business user. Therefore it’s critical that the business has input or a complete understanding of the lineage for a successful approach.
Adapting Data Governance to Make Data Accessible to Everyone
Organizations need to adapt and take an alternative approach to data governance. One that is tenable and bridges the business and IT divide to help make data usable for everyone in the organization.
Making data readily available requires a collaborative approach to data governance combined with a solution suite that promotes collaboration. The solution suite should help engage all parties and outline various roles and responsibilities of data owners, stewards and users, to provide all data users with a clear understanding of their data assets. Business users then become empowered to quickly and easily define, track and manage all aspects of their data assets to help inform strategic business decisions.
If you would like to learn more about making big data consumable, download the white paper below that talks about data governance in the age of big data.
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