This blog series will focus on Data Governance in Financial Institutions. Our first post introduces data governance fundamentals. It will be followed by a discussion of root cause analysis, metadata, the five stages of data governance deployment, and a final blog that crafts a business case for data governance.
Today’s industry leaders recognize data among their top enterprise assets. According to Gartner, the leading global research firm, 20-25% of enterprise value is directly attributed to the quality of its data. However, Financial Institutions (FIs) often underutilize this key business driver by not establishing a formal data strategy.
Let’s look at some of the challenges to building a data strategy, opportunities for implementing a data strategy, critical components of a successful DG program, and the aspects of data that can be governed. We also want to discuss some potential consequences of poor DG implementation and the most important step to mitigate the risk of it occurring in a Financial Institution.
What is Data Governance?
Data Governance (DG) serves as the framework for defining the who, what, when, how, where and why of your formal data strategy. Through the collection of policies, roles, and processes, DG ensures the proper definition, management, and use of data towards achieving enterprise goals.
Challenges of Building a Data Strategy
Too often, the largest hindrance to building a data- and analytics-driven enterprise is the enterprise itself. For historical reasons, data tends to be siloed within internal business units, resulting in disparate collections of overlapping yet inconsistent data. Given that data is built and accumulated over time in various places in the organization, often via mergers and acquisitions, it can be difficult and time-consuming to gather and use the data.
Without a transparent view of enterprise-wide data, credible decision making becomes nearly impossible. More time is spent gathering and consolidating the data than analyzing it. The goal, then, of DG is to break down the silos in which data becomes segregated and foster a holistic approach towards managing common data. Common data creates a shared understanding of data information and is of paramount importance when sharing data between different systems and/or groups of people.
With the proper implementation of DG standards (data naming, quality, security, architecture, etc.), a firm can realize a variety of optimization-based benefits.
Data Strategy Opportunities
An enterprise that properly implements and executes DG creates opportunities for enhanced productivity.
For example, if an enterprise works with large data sets, having defined naming standards allows for data consistency across all commonly used domains (i.e., Customer, Transactions, Employee, etc.) within the enterprise. This results in increased productivity and a competitive advantage relative to other firms.
As DG improves operational efficiencies, FIs can expect increased customer satisfaction rates, attracting both a loyal following from current customers and new prospects.
Critical Components of a Successful Data Governance Program
FIs have a lot of information as part of their normal business processes so it may be difficult to identify what data needs to be governed.
It is important to note that not all data needs to be governed. There are two types of data that do not need DG: department-specific data and application data not needed for regulatory reporting or cross-department communication.
However, there are three key types of data that should be governed to provide reliable information that can be leveraged across all departments of the FI:
- Strategic data is unique and usually created within the company, providing a competitive advantage to the firm. A few examples include data about customer insight, market insight, and risk models.
- Critical data ‘materially affects’ most external reporting, risk management, and/or supports critical business functions. This includes financial data, supply chain data, and counterparty data.
- Shared data is used in multiple business processes in which the definition, quality, and format needs to be synchronized. For example, customer data for marketing, customer service and sales, and counterparty data for risk management and pricing.
Critical Data Aspects
Beyond the data itself, there are multiple aspects of data that are critical to govern. A successful program will consider the following:
Data Ownership: The possession of and responsibility for information
Data Handling: Ensuring that research data is stored, archived or disposed of in a safe and secure manner
Data Allowable Values: Some data types let you specify that a property is restricted to a set of values
Meta Data: A set of data that describes and gives information about other data
Data Storing: The recording of information in a storage medium
Data Architecture: The structure of an organization’s logical and physical data assets and data management resources
Data Quality: The state of qualitative or quantitative pieces of information
Data Definitions: The origin of a field that references a data domain and determines the data type and the format of data entry
Data Reporting: Collecting and formatting raw information and translating it into a digestible format to assess business performance
Poor DG Consequences
A word of caution: There is such thing as poor DG implementation. If your program is poorly built, the enterprise will suffer.
Building inefficient processes, for example, can delay timelines for tasks like data retrieval and data analysis.
An inferior DG implementation may also create compliance issues. If the program is difficult to understand, enterprise employees may disregard your guidelines.
Overall, if DG is applied within internal silos, it cannot be optimized across the organization. The segregation of data that internal silos create needs to be broken down to achieve the goal of managing common data.
How to Mitigate Poor DG Risk
The entire FI must “buy in” to a DG program to be most effective. Without assistance from both data practices and business functions in the rollout of DG program initiatives, the program will likely fail. It is the responsibility of the business, IT, and internal operations facets to be fully engaged and coordinated within the implementation of DG program initiatives.
What’s Next?
Now that we have outlined what a successful Data Governance program includes, it is time to discuss Root Cause Analysis. Our next post in this series will discuss how to find root causes in FIs and recommend actions to solve problems that you may face when implementing a DG program.
RESOURCES:
“ELearningCurve.” Information & Data Management Courses & Certification Online, https://ecm.elearningcurve.com/category_s/213.htm.
Data Ownership, https://ori.hhs.gov/education/products/n_illinois_u/datamanagement/dotopic.html.
Data Handling, https://ori.hhs.gov/education/products/n_illinois_u/datamanagement/dhtopic.html.
“Administering and Working with Oracle Enterprise Data Management Cloud.” Oracle Help Center, 24 Nov. 2021, https://docs.oracle.com/en/cloud/saas/enterprise-data-management-cloud/dmcaa/property_allowed_values_102x340a19b7.html.
“Metadata.” Wikipedia, Wikimedia Foundation, 23 Dec. 2021, https://en.wikipedia.org/wiki/Metadata.
“What Is Data Storage?” IBM, https://www.ibm.com/topics/data-storage.
Olavsrud, Thor, and Senior Writer. “What Is Data Architecture? A Framework for Managing Data.” CIO, 24 Jan. 2022, https://www.cio.com/article/190941/what-is-data-architecture-a-framework-for-managing-data.html
“What Is Data Quality? Definition and Faqs.” OmniSci, https://www.omnisci.com/technical-glossary/data-quality
“Data Definitions.” IBM, https://www.ibm.com/docs/en/ecm/10.1.3?topic=objects-data-definitions.
“What Is Data Reporting and Why It’s Important?” Sisense, 21 May 2021, https://www.sisense.com/glossary/data-reporting/