How do companies keep track of the overflow of data in a technology driven market? How can data be turned into an asset? How does one discern good data from unusable data? If you have a genuine interest in organizing data for profit, then perhaps you should consider the role of Data Governance Manager.
All Things Data states that the “Data Governance Manager is responsible for the implementation and oversight of the Company’s data management goals, standards, practices, process, and technologies.” A Data Governance Manager is a communicator that is in charge of organizing and streamlining the process with which data is collected, shared, utilized, protected, cleaned, and stored; all the while maintaining efficiency and saving the company from inaccurate data handling, unusable data, and potentially saving lots of time and money by keeping the data congruent with the company goal. A Data Governance Manager will define terms like “revenue” that often vary from company to company, in an effort to coordinate different departments within a business. Eric Sweden, in his report Data Governance – Managing Information as an Enterprise Asset, states that Data Governance:
“[r]efers to the operating discipline for managing data and information as a key enterprise asset. This operating discipline includes organization, processes and tools for establishing and exercising decision rights regarding valuation and management of data. Key aspects of data governance include decision making authority, compliance monitoring, policies and standards, data inventories, full lifecycle management, content management, records management, preservation, data quality, data classification, data security and access, data risk management, and data valuation.”
A Data Governance Manager must be a team leader with a clear focus.
- “Most companies do not combine data governance and Master Data Management (MDM) when getting started, so they fall short in addressing the people and process issues that cause data quality issues in the first place. Oracle’s Data Governance Manager simplifies and streamlines data stewardship tasks and is a core tool for improving data quality – a key to MDM success.” Kelle O’Neal, Managing Director, First San Francisco Partners LLC.
- “Data quality software without data governance wastes valuable time and resources, and is destined to deliver results below expectations.” Information Managers: Deliver Trusted Data With a Focus On Data Quality, Rob Karel, Forrester Research.
Serving as a liaison between both internal and external technical and business groups, the Data Governance Manager should bring positive results stemming from their governance of the methods used by data stewards and scientists handling data.
The primary goals of Data Governance include: Increasing consistency and confidence in decision making, decreasing the risk of regulatory fines, improving data security, maximizing the income generation potential of data, designating accountability for information quality, enable better planning by supervisory staff, minimizing or eliminating re-work, optimize staff effectiveness, establish process performance baselines to enable improvement efforts, acknowledge and hold all gains. The Data Governance Manager is essentially a team leader.
Data Governance Managers are skilled at managing efficiency in people, processes, and technology to enable an organization to utilize different kinds of data. Companies compile different kinds of data in the form of Master Data (customers, products, suppliers, locations), Transaction Data (payments, purchase histories), Reference Data (market specific data), Metadata (metrics, calculation formulas, units and definitions of a data entity), Analytical Data (data residing within a data warehouse, data marts), Documents and Content (claim forms, medical images, maps, video files), and new forms of Big Data. Delegating the use of structured and unstructured data, the goal of the Data Governance Manager should be coordinating this information across departments and deciding which information could be best utilized as an asset for the company.
What does it take to be a Data Governance Manager?
Listed below are the qualifications and concepts that anyone interested in become a Data Governance Manager should be familiar with:
- Education: Besides having a Bachelor’s or Master’s degree in Information Technology or Business Administration, one must have a proven track record of success in database development and administration. In compiling basic documents for inter-office dispersion, a thorough experience with MS Office, including: Excel, PowerPoint, Word & Visio, and with Unix and Windows OS experience is implied.
- Communication: Data Governance Management is a cross-functional skill set that depends on coordinating information and data between departments. Once the company goal is determined, the Data Governance Manager must act as liaison, communicating through reports, meetings, company newsletters, or memos the best use of data in each department from business owner, to IT, all the way to the consumer. In this sense, the Data Governance Manager must have a solid technical writing foundation. If utilization of the data does not reflect the business model, then all the effort of collecting, storing and managing the data is for naught. According to the 2009 Oracle report, “How Technology Enables Data Governance,” the communications framework “is a guideline and process that drives stakeholder awareness and helps maintain momentum and participation.” As a Data Governance Manager, it is up to you to formulate a system of decision rights and accountabilities based on the definitions set by the company which describe who can use which information, when, how, and utilizing what methodologies. Stakeholders may not need the same data as the IT department, and so on, so organizing and relaying information appropriately and involving others in the process is a big part of communication.
- Data Analysis: A Data Governance Manager will analyze and orchestrate teams to organize data available to their business. Inspecting, cleaning, understanding, and transforming data is part of the creative process. Keep the big picture of the business model in mind and analyze with focus and purpose to transform quality data into an asset. Take a few classes on statistical analysis and data analysis. Key analysis tools to be familiar with are Trillium, DataFlux, Business Objects, ROOT, PAW, JHepWork, KNIME, Data Applied, R, DevInfo, and Zeptoscope Basic.
- Data Stewardship: DataStewards create, maintain, and use data. According to The Data Administration Newsletter, it is responsibility of the Data Steward “to approve business naming standards, develop consistent data definitions, determine data aliases, develop standard calculations and derivations, document the business rules of the corporation, monitor the quality of the data in the data warehouse, define security requirements, and so forth.” The role of a Data Governance Manager is similar to that of a Data Steward, but he must oversee the compilation and use of the data found by all the Stewards. Imagine the Manager as a chess player in a timed game, trying to use the best strategy that efficiently wins the game; the company as the chessboard; the rules as the definitions set by the company; and Data Stewards as chess pieces. Data Stewards may include Data Definition Stewards that put business definition to technology-based data, Data Production Stewards that enter and modify data, and Data Usage Stewards that relates the business operations of data usage (quality control, accuracy).
- Business Acumen: Take some Business courses during college. As a supervisor, the Data Governance Manager will be a team leader and is expected to know how to impart information across the corporate structure effectively and efficiently. Data Governance Managers understand that data has a variety of functions between business and IT, and should bridge the gap by streamlining definitions and making sure that the time and effort of the Data Stewards and project teammates is not wasted superfluously, and that their data compilation is business worthy. To be an effective and efficient Data Steward you must understand the inner workings of the business, they are inseparable skills. Managing Data Governance Specialists, Data Stewards, Data Architects, subject matter experts, Business Analysts, and marketing analysts requires a strong work ethic and a skillset geared toward business management. If each cog isn’t well greased, the machine will cease to function.
- Big Data and Non-relational systems: As mentioned above, Big Data is an emerging business trend. Knowledge of SQL systems and Hadoop open source Framework are major requirements for employers that utilize unstructured data. Other crucial products on the market are Cassandra, Hive, Pig, MapReduce, Redis, MongoDB, and Riak. As a Data Governance Manager, the more familiar one is with the technologies to navigate the untapped seas of Big Data, the better prepared they will be to coordinate the data with their team of data stewards and integrate the resulting information with the business model. Big Data yields potentially large assets.
- Data Migration Tools Knowledge: In the fast-paced world of business technology, software updates, physical company migration and moving from one database vendor to another is expected. Building a framework for a business model takes time, so shifting corporate decisions and the addition of new databases will necessitate innovation and seamless data flow. Database migration will usually only require a testing cycle, Data Governance Managers should understand technology associated with Sysbase, MySQL, DB2 or SQL Server and Oracle, just to name a few.
- Data Governance Drivers: While a Data Governance Manager should always strive to improve data, often C-Level leaders will expect compliance with external data regulations including Sarbanes-Oxley, Basel I, Basel II, HIPAA, etc. Furthermore, a Data Governance Manager should be aware that each company assesses risks differently. Understanding the guidelines referenced by COBIT, ISO/IEC, 38500 and others will help you along the way.
- Data Modeling: As part of a Data Governance team, Data Modelers will take the organized framework set up by the manager, and create navigable pathways for data to be analyzed, whether it is conceptual, logical or physical. The Manager, therefore, should have a comprehension of a large spectrum of tools such as CA ERwin, Data Modeler, dbConstructor, DbSchema, DeZign, ERD, MySQL Workbench, Oracle SQL, PowerDesigner, Agile, ORM and UML diagrams, DDL, Bachman diagrams and platforms like IBM DB, Informix, MySQL, Oracle, PostgreSQL and Sybase. Navigation through systems like OLTP and/or OLAP using the above-mentioned technologies is to be expected. The structure and organization of data is crucial to its success as an asset.
- Data Integration: Data Integration or Enterprise Information Integration (EII) is the sharing of heterogeneous data sources under a single query interface. Many large organizations must share data. Think of hospitals that must share patient records and competing airlines that must share flight path data to avoid collisions. Problems such as duplicating data, semantic integration and definition errors between databases occur often; the Data Governance Manager addresses and finds solutions for shared database problems.
- Data Warehousing: While A Data Governance Manager is not a necessarily a Data Warehouse expert, one should understand the difference between ETL-based platforms and warehousing constructed from integrated data source systems. Stored data is complex, modeled across multiple operational systems and decision support environments. Data flowcharts, integrated source systems across mergers, coding, and consistency are foundational for warehousing. Be familiar with dimensional concepts as well as top-down, bottom-up and hybrid methodologies. It is up to the Data Governance Manager to qualify stored data and metadata (the data about the data) so that is economical to customers and business users alike.
In the merging worlds of Business Information and Information Technology, data is where the big money lies. Think of a Data Governance Manager like an editor at a major newspaper. The articles chosen by the editor must reflect the information that is most interesting to the subscriber, must perform to the standards set by the franchise and be error free, and should always strive to attract more business through quality and brand recognition. The Data Governance Manager similarly utilizes the information that best suits the interests of the company.