Data governance strategy is something every organization has started to talk about - though levels of approach or maturity down that path vary. Customers will often ask us “How do I sell a long-term data governance strategy program to our executive team and show them the ROI?” Truthfully, a lack of data governance strategy is absolutely costing your business a great deal of money. But it’s not the lack of the program itself that is the cause - it’s everything that feeds into why you need to have a strategy in the first place.
Without data governance, your operations will suffer from the classic “garbage in/garbage out” syndrome — except today, this familiar problem has become exponentially more complex and impactful. For example, in manufacturing, not having a process for maintaining data integrity in your spare parts inventory could result in buying parts you don’t need. Or in retail, excess inventory is sitting on shelves with no system for updating the SKUs still in stock - resulting in future, unjustified high-volume re-orders. The cost resulting from this lack of data governance could run into millions of dollars per year.
What falls under the data governance umbrella?
Data governance is defined as the exercise of authority and control over the management of data assets. Governance processes and policies are designed and implemented to result in accurate, high-integrity data in your organization. It’s the superset of concepts that include metadata management, data quality, master data management and reference data management. Think about it quite literally as the governing body providing oversight of your data operations. Data governance strategy is the programmatic approach to how data governance can be achieved. Data Governance ensures that the right data models and systems are being utilized, that enterprise data policies and processes are effectively implemented and enforced, and that management and oversight are effectively applied to protect the integrity of the enterprise data assets over time.
Grappling with the costs of data governance strategy errors and omissions
Errors and omissions in data governance strategy come at a cost. These include both direct costs and opportunity costs. If you haven’t devised a coherent or well-implemented data governance program, you’re already paying these costs, perhaps without realizing it.
If data governance strategy is delegated to individual business units, often divergent data governance strategies that operate within organizational silos will proliferate policies across the organization. Let’s remember that every operating unit (including IT) owns or uses their own systems of record with no guarantee that any systems are adequately integrated or talk to each other. Individuals could be completing manual data migration operations to move data from one system to another or potentially loading external data which could be out of date or prone to duplication. This all matters because disaggregation and a lack of data quality within the enterprise leads to inefficient work and avoidable errors.
In the spare parts example, different unique identifiers for the same part could exist in different corporate divisions. This could lead to a missed opportunity to get a better price on the part through volume purchasing. It may not sound like such a big deal, but if you have the same problem in multiple locations within your business, you can begin to estimate the impact that this data governance deficiency could be costing you.
What does a data governance strategy begin to address?
- Data Integration Challenges—transactional and source data tends to gather in “silos” across the enterprise. A comprehensive data governance and master data management plan can be put in place to effectively manage this data and to ensure one version of the truth with integration across the organization. Effective master data management will work to create a golden master record that can then be syndicated throughout all corporate systems without duplication. Without effective data management, multiple corporate divisions may never realize they are ordering or servicing the same part due to multiple versions of the same part circulating throughout the organization. Achieving effective data management and governance requires a fair amount of analysis, planning and technical implementation, but the results that will be obtained will far outpace the cost and organizational inefficiency that could be incurred by not having a roadmap to perform these steps.
- Driving data-driven decision making—Without question, the effective usage of data for decision making is arguably the most important goal for many corporate strategic technology initiatives, including the use of analytics. Executives want to make decisions based on the most accurate and timely information available. They require insightful analysis and informational data visualizations to drive strategic decision making.
- Reducing inefficiency and risk due to poor data quality—Gartner estimates that the average financial impact of poor data quality on organizations is $9.7 million per year. Companies that do not practice effective data quality management risk sending out erroneous reporting externally and providing inaccurate information in reports that may drive key management decisions. Some organizations report that 20-25% of daily work output is wasted by the need to reconcile and overcome data quality deficiencies.
- Missed revenue opportunities—The effective use of data and analytics can help maximize revenue growth and enable positive outcomes at companies that employ information to better understand opportunities for their customers and markets. Without effective data governance, the use of sophisticated customer analytics becomes problematic as poor data quality and a lack of understanding of key data elements introduce irregularities to customer analytic models. The employment of an effective data governance strategy must factor in the value of having accurate customer and market data in order to facilitate growth.
- Security and compliance - Data security and privacy compliance are increasingly important to organizations in highly regulated industries like healthcare, financial services, and insurance. A number of regulations mandate effective data governance and metadata management procedures to achieve compliance. The European Union’s GDPR regulation and comparable laws like the California Consumer Privacy Act, require careful management of personal data. Non compliance with these regulations could result in stiff fines and penalties for violations. Additionally, breaches of the public trust can cause incalculable damage to corporate goodwill with customers. An effective data governance program will enable the creation of the corporate Data Privacy Office and will ensure compliance with these regulations.
Where should you start?
Data governance strategy enables business accountability for data assets while collaborating and partnering with information technology. It represents a cross-enterprise effort that requires buy-in from data stakeholders within each corporate domain. At the end of the day, comprehensive data governance enables data democratization and effective data-driven decision making. It enhances corporate efficiency and minimizes risk. In today’s convoluted technology landscape, it is a MUST for managing the key asset in any organization, their data.
If you are curious how Clarity Insight’s approach to data governance strategy is different and why large enterprise organizations are turning to us to help build their strategy to support their data management initiatives, let’s talk.