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Data Governance: Maximizing the Value of Data

Insight

Data Governance: Investing for the Future

Data has evolved from being an ancillary byproduct of business operations to a critical and core aspect of company operations. This shift has led to a significant increase in the value of data as an asset. Since data is much more valuable, it deserves its own strategy and management, thus necessitating its own governance. Building an organization that emphasizes a strong data governance program is paramount when faced with internal and external pressures. A strong data governance program allows an organization to navigate through regulatory disruption, operational transition, and innovation and digital transformation. An organization’s foundation is sound when its strategy, people, processes, technology - and even its culture - are aligned with its data. 

Defining Data Governance

Data governance has been defined a number of ways through various frameworks and publications: “Data Governance is a collection of practices and processes which help to ensure the formal management of data assets within an organization.” (Dataversity) [1] “Data Governance includes the people, processes and technologies needed to manage and protect the company’s data assets in order to guarantee generally understandable, correct, complete, trustworthy, secure and discoverable corporate data.” (Data Strategy & The Enterprise Data Executive) [2] “Data governance is the overall management of data availability, relevancy, usability, integrity and security in an enterprise.” (IBM) [3] “Data governance is a system of decision rights and accountabilities for informationrelated processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.” (The Data Governance Institute) [4] Sia Partners defines data governance as:

A holistic set of policies and procedures which standardize the way an organization uses data in order to improve business outcomes.

When defining data governance it is important to distinguish between data governance and data management. Data management is a high-level term that outlines the processes used to plan, determine, facilitate, construct, acquire, maintain, secure, use, retain, recover, control, and purge data. Data governance is the set of rules that data management must comply with in order to be effective and efficient. According to the widely-used Data Management Body of Knowledge (DAMA DMBOK), data governance is the central knowledge area in the Data Management Framework (the DAMA wheel). The framework emphasizes data management as a business function and includes generally accepted best practices for each of the knowledge areas. [5]

Benefits of Data Governance

Data governance is important in the same way sales, production efficiency, or cost reduction is important: it increases the value of an organization. The better the organization is at governing data, the greater the likelihood of positive outcome, all else equal. According to Gartner, data constitutes 20-25% of enterprise value. [6] Realizing this value is a key objective of data governance. Data is useless and can even destroy value if used inappropriately. For example, pricing data that has been mapped to the incorrect product curve can lead to misinformed decisions. Or, a derivatives contract with incorrect settlement instructions can lead to late or missed payments. A strong data governance program safeguards against these incidents by improving data Data Governance Data Architecture Management Data Development Database Operations Management Data Security Management Reference & Master Data Management Data Warehousing & Business intelligence Management Document & Content Management Meta-data Management Data Quality Management Data Governance vs Data Management The DAMA DMBOK2 Guide Knowledge Area Wheel 4 quality. The antithesis is also true. Data, when governed incorrectly (or not at all), can lead to missed revenues, increased costs, and higher risk. [7] Data, when governed effectively, accelerates decision making, increases revenue, streamlines costs, improves risk management, and strengthens capabilities. [8] Examples of these benefits include the below:

Accelerated Decision Making

  • Improved evidence-based, strategic,and investment decisions by:
    • Quickly acquiring and analyzing large sets of data
    • Decreased reporting errors
    • Easily accessing uniform, reliable data
    • Improved standardization, increasing confidence and transparent communication

Increased Revenue

  • Heightened business intelligence and advanced customer analytics drive revenue growth by:
    • Introducing new products
    • Enhancing customer service
    • Optimizing marketing techniques

Streamlined Costs

  • Reduced duplicate data acquisition through central data control
  • Minimized integration points across applications enabling data synergy
  • Reduced data storage costs due to decreased duplicate records and outdated or inoperable data

Improved Data Privacy and Risk Management

  • Improved compliance with GDPR and CCPA through data privacy, security, and controls protocol
  • Increased effective and efficient management of regulatory, financial, and operational risk by ensuring data privacy, security, integrity, accuracy, consistency, completeness

Enhanced Capabilities

  • Increased transparency through comprehensive business process documentation
  • Increased efficiency, collaboration, and productivity enabling superior knowledge management
  • Augmented digital workforce through RPA, AI, Big Data

Example Applications of Data Governance

Numerous processes, tasks, and projects across the organization benefit when companies prioritize and invest in data governance. Data governance is imperative for large, complex organizations. Smaller companies benefit as well, as establishing a governance strategy early mitigates costs and streamlines processes. A strong data governance office allows for seamless and dynamic response to regulatory compliance, operational transition, and digital transformation.

Regulatory Compliance

A key driver of data governance investment and program acceleration is the evolution of regulatory compliance standards. Imminent compliance deadlines for regulatory and data privacy initiatives, such as GDPR and CCPA, have created a sense of urgency to implement Data Governance programs. For example, GDPR requires companies to establish dedicated processes around personal identification data and understand the new definition of personally identifiable information. The regulation requires companies to report data breaches within 72 hours and provide consumers the ability to dispute data and demand portability, along with other requirements regarding company data. [9] An organization with ineffective or absent data governance will not be able to meet mandated compliance requirements and could incur heavy fines or even lose its license to operate. Financial services firms are especially focused on compliance and risk management given the potential for financial and reputational damage in the event of a failure.

Operational Transition

A comprehensive data governance strategy contributes to organizational agility and mitigates data silos that exist across functions and/or different business units. Strong data governance results in cost savings and allows companies to intuitively respond to market changes.

Solving the LIBOR Transition

With the London Interbank Offered Rate (LIBOR) terminating by the end of next year, organizations around the world are confronted with a significant data management challenge. LIBOR is embedded in a wide range of financial contracts, including derivatives, mortgage and retail loans, commodities, bonds, and cash products. LIBOR-based contracts are projected to be worth ~$400 trillion globally – with a significant percentage of contracts expiring after the deadline. All of these contracts will have to transition over to a completely different benchmark rate. The data management ramifications of the transition are massive.

LIBOR Transition Benchmark Study

Sia Partners and the law firm Cadwalader, Wickersham & Taft conducted a global benchmarking study to provide detailed market feedback on the status and plan of action for LIBOR transition. The study included over 75 organizations and identified leading practices for a successful transition as well as common challenges. The study concluded that leading organizations made the transition an imperative and allocated resources to the program. Companies that have strong data governance are at an advantage when it comes to the transition of contracts.

Impact of Comprehensive Data Governance

Recognizing the importance of data for this effort is key. Companies must assess where the LIBOR curves exist in systems and determine lineage from the centralized database to the various systems that are impacted by the LIBOR curves. LIBOR and other similar rates are rooted in organization processes, models, and systems. Most companies have various functions, business units, and geographies that compute interest rate curves based on LIBOR and leverage these curves in financial contracts. Thus, any decision concerning the LIBOR transition that is ideal for one aspect of the business, may have unanticipated adverse effects for other aspects of the business. [10]

A capable data governance program office addresses operational transitions from an enterprise view. Structured and disciplined data practices facilitate the integration of data across the organization from a centralized location. In the referenced LIBOR scenario, a firm that has a dynamic data governance program would be able to create a new benchmark interest rate curve in a central database and seamlessly integrate the new benchmark interest rate curve across the enterprise.

Innovation and Digital Transformation

Robust capabilities in data governance are foundational to digital transformation. Strong IT fundamentals start with the successful use and management of data. The current business environment demands we record, analyze, and leverage every piece of relevant data we discover. In previous years, access to capital and resources was the key differentiator across industries. Now, access to data and information can be added to competitive differentiators. Since data is critical to core business operations, it is necessary for an organization to invest in data governance as a fundamental IT pillar.

A robust data governance program ensures:

  • Quality data can be easily accessed, shared, and stored as digital innovation disrupts the market
  • Correct data and the corresponding algorithms are in the right location for impactful analysis and action

A data governance program facilitates clean data as well as useful data for reporting and automation. For example, automation of power plant maintenance requires accurate data and a robust model that produces actionable output on critical maintenance needed. [11] Work orders can then be created and schedules updated automatically. Also, without comprehensive governance, the integrity of historical smart meter data used to forecast retail electricity consumption may be questioned. Sound data is the base to business intelligence and advanced analytics. Innovation thrives on accurate, reliable data.

How Sia Partners Can Help

Developing and implementing a data governance strategy is an integral part of Sia Partners Enterprise Data Management (EDM) offering. Sia Partners leverages the DAMA wheel and provides concepts and capability maturity models for the standardization of activities, processes, best practices, roles and responsibilities, deliverables, and metrics. Using our resources and expertise, Sia Partners works with clients to develop a robust strategy and operating model that spans the entire organization and a data governance management office to drive initiatives to realize value.