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The best of data governance

Executive summary Obtaining value from business analytics and cloud services requires perspective and discipline. Grant Thornton LLP believes that data governance drives effective analytics and you can’t be effective in one area without the other. Mastering internally owned content is an important step to providing the ability to evaluate external sources of content as part of the big data evolution.

A good place to start is with your structured financial chart of accounts data. Managing internal financial structures and extending to other operational areas — product definition, customer grouping, HR employee roll-ups and sales organization definition — enables you to achieve value-driven predictive and prescriptive analytical functions. The key to mastering data is not necessarily in the data itself, but how you relate it to the proper business context of what the data represents. Without the structures and hierarchies that are used for categorization and reporting, a piece of transactional data has no connection to the meaning of how your company uses the data. For example, an employee’s name can be used for HR purposes, while the same employee’s name can be used as part of a sales team that covers branch locations or pursues new customers.

Proper governance will enable change in the organization and alignment of processes to support business analytics. However, too much discipline can be restrictive and can sacrifice the agility and ability to support varying requirements across the enterprise. There is a point of optimal data governance that you should aim for (see figure above).

Data governance should be applied to both financial and nonfinancial areas and be part of the roadmap for getting value from big data (see figure below). Managing business hierarchies and data relationships with effective controls is the first step to good business analytics. The structured data sets, which cross financial and operational functions, will bring the proper context when merged with external customer data and related unstructured sources of information. 
Road to big data
This white paper highlights data governance practices that were applied to leading companies and varying business functions to provide for structured data to be related to unstructured data. We’ve applied these practices and related solutions for over a dozen years, and evolved with enhancements in technology, corporate governance processes and government regulations. The best of data governance is intended to leverage existing investments in infrastructure platforms, business intelligence (BI) and data warehousing to provide the right level of governance to expand into big data.   

Data governance framework
Leading companies take the time to put a data governance framework in place. A framework should be relevant to both their business strategy and application footprint, and enable process improvement. To use analytics for competitive advantage, you must balance procedures that enable higher-quality data controls with maintaining agility for varying business stakeholders. A company’s framework should allow for data to be optimized for both corporate standards and local/divisional use.

Grant Thornton’s position on data governance enables interdependent solution components to provide for disparate operational processes and performance management applications to work together as one unified solution. As such, a company with various business-specific applications can utilize data governance principles to bring the pieces together into one single solution, while providing for a distributed change management solution across a diverse set of business stakeholders.

Furthermore, a data governance framework establishes strategies, objectives and policies for effectively managing an organization’s data. It consists of the people, processes, structure and architecture required to manage the availability, usability, integrity, consistency and auditability of secure data. A well-implemented data governance framework will transform data to information and ultimately knowledge, which will enable repeatable fact-based decision-making.
Data governance framework
A data governance framework has the following characteristics around the data:
  • Includes definitions of term, metrics, items, customers and related elements (Structure)
  • Distinguishes dimension values from the analytics of hierarchy definition (Structure)
  • Makes/collects/aligns rules (Process)
  • Assigns accountabilities to resolve issues (Process)
  • Organizes data stewards and governance bodies (People)
  • Monitors/enforces compliance while providing ongoing support to and change management for broad stakeholders (Architecture)

The need for data governance
We have worked with companies that struggle with operational issues related to growing the business, compliance and regulatory pressures, and restructuring the organization. Implementing data governance can help these companies deal with the issues by helping them manage change more effectively.
We recommend the move to formal data governance when one of these five major changes occur:
  1. Traditional management cannot address cross-functional activities as the organization has grown.
  2. An organization’s data systems get so complicated by numerous functional activities that multiple definitions of common business entities begin to permeate the data systems.
  3. Regulation, compliance or contractual requirements call for formal controls to ensure data integrity and avoid risk.
  4. An organization’s data architects, service-oriented architecture teams, or other horizontally focused groups need the support of a cross-functional program that takes an enterprise (rather than siloed) view of data concerns and choices.
  5. Organizations wish to empower decentralized management of the business under a parameter-based overall governance framework.

Consideration of the company’s overall objectives is essential to effective data governance. Understand what your business needs from its data, then use that knowledge to create a data governance framework that directly ties into those needs. Governing data requires a rethink of your operating model, with new roles, responsibilities and processes emerging. 

Evolution of data relationship management
Oracle Data Relationship Management (DRM), a leading data governance solution, has been around for more than 15 years and had its beginnings in the pioneer days of master data management. DRM was originally developed to allow very acquisitive companies to deal with the high rate of change that goes along with constant reorganization. While these companies initially used spreadsheets to do the premerger planning and postmerger mapping, they quickly realized that without a repeatable, business-user driven process, the success of the acquisition was at risk. The initial release of DRM provided this acquisition framework, but its configurable, multidomain model allowed it to also be used for ongoing master data and hierarchy maintenance as well. By its second release, it was a full-blown governance application that was being used to manage all master data domains and hierarchies at the enterprise level.

Today, the average DRM customer manages 9+ data domains within DRM, including the chart of accounts, products, employees and customers. A few years ago, DRM was expanded to include a formal data governance module that fully supports the RACI matrix for data governance standards, and allows requestors, stewards and approvers to participate in workflows around master data and hierarchy change management.1

Grant Thornton’s Business Analytics team has been using dimensional modeling concepts and functions that are now part of Oracle’s DRM for over a dozen years, since deploying a global solution at one of the world’s largest alumina manufacturers. We implemented a data governance solution to manage financial, customer, and vendor change control procedures, while reducing audit costs and providing controls to reduce risk. The client benefited from the solution with (a) the ability to close the books in three business days and be the first to report earnings on Wall Street; and (b) a two-day reduction in days sales outstanding and over $1 million in annuity savings, among other results. Since this initial project, the Grant Thornton team has implemented more than 100 Oracle DRM solutions. We draw from that experience to describe the common functions that have been used in leading data governance solutions.

What makes good data governance?
  • Common terminology
  • Integrated applications
  • Mapping
  • Hierarchies
  • Change management and workflow
>>Explore further

Data governance in action
  • Chart of accounts
  • Acquisition onboarding and scalability
  • Sales and organization hierarchies
>>Explore further

Conclusion
A data governance solution provides for effective change management within departments and across the organization.  Organizations can be agile when dealing with growth strategies, including product acquisition and business unit reorganizations. Alignment of people, process and technology can be achieved effectively and in a timely basis with proper data governance protocols in place. The journey to big data begins with mastering your internal content so that your organization has the context to discover trends when external variables are added to the competitive equation. Using information as an asset requires a strong foundation. Without the right data governance, success with analytics may be both expensive and ineffective. The effective implementation of data governance techniques and tools (such as Oracle Data Relationship Management) can provide that necessary control and governance for master data management, while supporting the need for flexibility. With data relationship management, you can achieve both agility and financial reliability through alignment.

Opportunities and benefits can extend beyond the finance function as business measures and hierarchies exist everywhere. Then once governance is established with fundamental structured data, you are on your way to reaping the benefits of advanced analytics — competitive advantage, performance improvement and growth.

The best of data governance, and its principles, will provide the direction to establish the procedures to reduce risk while enabling analytics to drive organizational outcomes.

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About the authors
Joseph Coniker is a principal with Grant Thornton and national practice leader for business analytics. Coniker has over 20 years of experience, including 15 years in Oracle solution delivery. His projects have resulted in over $1 billion in working capital improvements, including account receivable collection, vendor payment, inventory optimization and cost allocation reduction — all of which served as components to delivering increased product and customer profitability, using information as an asset. Coniker is a graduate from New York University Stern School of Business and Harvard Business School.

Doug Cosby, in his role as vice president of software development at Oracle, manages the team that designs and develops the Oracle Data Relationship Management product. He was the founder of Razza Solutions, the company that originally created the DRM product, and has been working in the master data management space for 20 years.

1 See section "Change management and workflow" under What makes good data governance? for more information on RACI.