With the standardization of big data and rapid expansion of data sources, the scope of data ingestion continues to outpace the ability of business units to analyze it and gain the needed insights to drive business decisions. Beyond the rising costs to store data, ever-growing digital data sources encourages the capture of smaller data islands, which also hinder an organization’s effort to make decisions. While data users may believe they can ingest data and start realizing business insights, in most cases, large data capture does not correlate to an organization’s ability to curate and leverage critical data assets. Before data can be of real value, users need to, first, understand and manage it properly. Failure to do so leads to poor data quality, unreliable analytic outcomes and flawed insights. To avoid this situation, it is critical that data collected and used to make strategic business decisions is managed well.
How can this management of data be accomplished? With proven data governance. When businesses lack easy-to-understand, readily accessible data, it discourages the usage of data among users. If users do not understand and trust their data, they cannot gain insights to inform leadership on growth strategies, investments, operational trends and customer-centric initiatives. Beyond bringing order to organizations data systems, effective data governance also helps businesses identify the relatively small subset of data that is vital to their competitive health, and not only manage it, but also monetize it through various channels such as product innovation or cost-savings operational models.
Fostering Data Governance Success Across an Enterprise
Managing data governance is on the same strategic level as managing other business assets, such as finance and human resources. For example, within the typical finance department, everyone has defined roles to ensure budgets and profits are the center of attention. However, because data flows across an enterprise, there is usually no single person in charge of an enterprise’s data landscape. Yet to be successful, the same strategic rigor applied to finance must be applied to data and data governance. To ensure that level of success, data governance efforts start with a Chief Data Officer (CDO) or head of data governance who, working with other executive leaders, establishes budgets,forms a team and selects the resources and technologies required to assure ongoing data and data governance success.
Everyone involved in data governance works to create a framework where data is the focal point of the business environment across diverse lines of business. Staff, from C-level executives to analysts, must each have a role in establishing the processes and technologies required to leverage data as an enterprise-wide asset. We view this creation of processes and frameworks as a collaborative effort across the organization to ensure that everyone is on the same page. This ongoing process evolves with the addition of each new team and data set, compliance mandate, business process improvement, M&A and more.
To ensure success, it is vital for the CDO to prioritize high-value data projects that will lead to additional profit or cost-savings—for example, a project that derives more comprehensive and accurate insights on customer data to drive customer service, retention and growth. Or, a plan to efficiently comply with the GDPR process and provide a single repository for all personal data to avoid hefty GDPR fines.
Combining Modern Tools with a Comprehensive Data Governance Strategy
Once the need to execute a data governance framework and process has been identified, technology tools are a critical support for both people and process. Tools which enable organizations to automatically assimilate data into all aspects of the business, while integrating an array of data management capabilities including metadata management, data quality and analytics—encouraging collaboration across different departments, automating manual processes, increasing user trust in data, and ultimately, increasing ROI.
Metadata management, coupled with enterprise data governance capabilities, provide complete transparency into the details of an organization’s data landscape, including the data available, its location, the data owner/steward and data lineage. It also provides users with unified and quickly accessible glossary definitions, synonyms and business attributes for data, so they may easily define, track and manage data needed to make important strategic decisions.
Data quality capabilities should include data integrity checks for completeness, conformance and validity. As well as company-specific integrity checks to ensure that key data, especially customer-interfacing data, maintains acceptable quality levels.
Analytic capabilities automate data quality improvement such as data blending and cleansing with machine learning algorithms for self-learning, while promoting faster data acquisition and shortening the time for analysis and collaboration among enterprise users.
Data governance not only provides a framework to turn analytics into actionable insights, but it also automates manual IT processes, creating time for other tasks. Starting a data governance program might seem like a tall task, however, it is the price of entry for any company who wants to remain viable. By combining the right people, processes and technologies, businesses can develop profitable data governance programs that will help them thrive in our new data driven economy.