Transforming enterprise data into analytical insights that boost organizational growth, increase profit and better serve customers is key in today’s digital economy.
Yet some of the most recognizable brands in the world are missing opportunities to take advantage of their data.
Their biggest obstacle?
Overreliance on IT. Too many businesses rely exclusively on IT resources to prepare data for everyone across the company because they believe data prep and analytics requires deep data science expertise.
In reality, leveraging data to enhance operational and enterprise initiatives requires a strong understanding of organizational data from a business perspective before data science can add value. Business users are best positioned to select the right information to analyze in order to generate actionable and meaningful insights—provided that they are educated data consumers. Data is only valuable if it’s easily understood by business users, so it can be transformed into improved business outcomes.
To generate timely and meaningful insights, organizations can no longer depend solely on the IT department for data needs. Instead, they must implement an overarching data governance strategy that encourages business and IT collaboration.
Building a Data Governance Strategy
Business users require self-service data capabilities so they can pull their own data for business intelligence. To put data directly into the hands of business users, data governance efforts often start with a data catalog to make data easily digestible. The catalog is ideal for storing metadata. It allows users to search and quickly discover the data they need. However, without an underlying strategy, most organizations make the mistake of dumping all of their data assets into the catalog without defining or categorizing it. The results are an overload of information that causes confusion across the company and prevents business users from selecting the right data to drive decision-making.
To avoid a disorganized data catalog, it’s critical to start with a data governance program that 1. Defines a strategy for business and IT to work together on data-driven initiatives, and 2. Focuses on critical data and key business objectives among the various lines of business.
Data governance is about increasing the understanding of enterprise data in order to support the generation of actionable business intelligence and processes, and to foster collaboration and accountability across business departments and IT resources. Data understanding, collaboration and accountability build data trust and utilization, and improve the quality of analytic results.
Data governance establishes data accountability by assigning data ownership for individual assets, and by setting policies for oversight to ensure that data is accessed and used appropriately. Building a data catalog brings together IT and disparate lines of business (along with their disparate data). When different departments work together to define and document data in a data catalog, they must create consensus on definitions and categorize data assets to eliminate confusion. Overall, data governance stimulates a collaborative, enterprise-wide approach to managing data.
Data governance also helps organizations maintain high-quality data throughout its entire lifecycle. By scoring and monitoring data quality and by establishing data integrity controls throughout the data supply chain, governance ensures that all users have accessible, accurate data at their fingertips.
With a thorough strategy that ensures data quality and enables full cooperation across the enterprise, organizations can shift their focus toward technologies that deliver complete clarity into an organization’s data landscape.
Executing a Strategy with Modern Data Governance Technologies
Keeping data governance efforts on track is a challenge, especially as an organization ingests more data. These efforts require an all-inclusive tool with integrated data management capabilities. A tool with robust data governance and metadata management capabilities provides transparency into an organization’s data landscape, including the available data, its location, the data owner/steward and data lineage. Armed with these capabilities, business users across the organization are empowered to quickly find glossary definitions, synonyms and business attributes for data. They can then easily define, access and understand data assets to make impactful business decisions.
It is critical to select a tool that is easily navigable and thus, encourages both data utilization and collaboration. An intuitive user interface and functionality allow businesses to clearly define data ownership and to ensure that everyone, business users included, know where to go when they have pressing questions about their data. This merging of people, processes and technology is the foundation of solid data governance.
The tool must also incorporate data quality capabilities to assure data accuracy, completeness, conformance and validity. Advanced quality checks go beyond basic quality dimensions to ensure data is properly transformed and integrity is maintained as it flows between and across systems. Analytics capabilities also leverage machine learning algorithms for self-learning to continuously improve data quality.
By first developing a comprehensive data governance strategy and combining it with the right technologies, businesses can successfully integrate data assets throughout the business and quickly derive meaningful insights to make critical business decisions.
Reuben Vandeventer, Chief Data Officer in Residence at Infogix.
Reuben Vandeventer has been driving progress and helping to shape the data space for more than a decade, across many industries including medical device, pharmaceutical, insurance and asset management. As the data space has rapidly evolved, his background in science, statistics and finance has created a robust foundation to create real economic value for organizations in new and innovative ways. Throughout his career, he has served in key strategic leadership roles for some of the top financial services organizations in the world. In these roles, he has developed a repeatable way to analyze, study and design data communities (people that are accountable and care about data), revolutionizing the respective organization’s approach to pragmatic data strategy.