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Seven Steps to Creating an Effective Data Governance Framework

Take these steps to help your team mitigate data-related risks at your firm.
Christopher Bloechle
"Data, not oil, is today's most valuable resource.” The Economist

An increasingly digital, data-centric asset management environment, a robust data governance program is a key differentiator. Data governance defines who does what in data management for a firm. It cannot be resolved in just one area of the firm; instead, it requires a concert between IT and business to manage data as an asset indeed. A robust and defined data governance program should outline who makes the decisions and who is accountable for each data management activity in the firm.

Although firms require broad-reaching data governance now more than ever, many firms hesitate to take the necessary steps to achieve it. It may often seem like an impossible task due to the complex nature of today's information environments, usually comprised of maintaining an intricate web of internal systems and often rely on data from various external and third-party sources during their day-to-day operations.

Inadequate data governance can have severe consequences for a buy-side firm from the front to the back office. Insufficient data quality, coupled with a lack of responsibility for crucial data objects, may result in reporting issues, akin to incorrect or missing data items for P&L reporting. Managers require enhanced analytics that improves operations, and to achieve this, asset managers must focus on being a data-first company.

Step 1: Prioritize areas for improvement. Firms should objectively assess where improved data governance will bring the most immediate benefit to the firm and establish a foothold there. This approach sets a firm foundation for taking data governance across other areas of the business. Do not try to boil the ocean by tackling all data sets and data flows at once. Even a universe comprised of small data sets can have many nuances requiring a significant investment in time and resources from your data strategy team. Identify the core datasets that influence decision making throughout your Firm and start there.

Step 2: Maximize information availability. If your coworkers do not know what data exists, you should not be surprised if they create their own data set(s) and processing pipeline(s) to get their work done. Make sure people know what data exists by creating a list and publishing it to a readily accessible location. Over time, this list should evolve into a Data Dictionary complete with data set names, fields, owners, descriptions, and any other ancillary attributes relevant to your consumers. For many firms, information and data exist in a myriad of applications, legacy file structures, partner systems, or other outside sources. Firms must leverage integration technologies and best practices to ensure that any and all data is easily accessible.  

Step 3: Create clearly defined roles, responsibilities, and rules. Organizations should determine who does what with data by creating formal roles, obligations, and procedures that teams follow when working with information. The best place to start is with your Firm's business users, who can provide practical insight into the Data itself: such as what problems exist, how Data is used, what it should look like, and ultimately what the impact is if quality issues continue or degrade. These users can also help suggest rules and guidelines for maintaining information integrity. From there, firms should create formalized plans for the ongoing, proactive rule-based cleansing to keep the data intact. This would be managed by a data committee or standards board made up of business and IT representatives from departments across the Firm.

Step 4: Ensure information integrity. Once you have agreement on a set of rules, you need to implement procedures to ensure adherence to them. Enhancing and ensuring the quality of enterprise data is a critical step. This can be done by:

  1. Profiling, to compare information to predefined quality metrics as a means of identifying "good" and "bad" data
  1. Parsing and standardization, to validate and correct industry-standard and organizational-standard attributes within the Data
  1. Enrichment, to extend and enhance existing data with new and complementary information where appropriate
  1. Monitoring, to uncover areas in need of process improvement and guarantee data quality on an ongoing basis


Step 5: Establish an accountability infrastructure. Processes alone will not ensure the integrity of data—people do. Thus, it is essential to establish an accountability infrastructure that assigns "owners" to each information asset and defines policies and workflows that hold people accountable for them. Good progress on your data management strategy will regress back to data silo’s and duplication if data consumers cannot count on reasonable SLA’s from owners.  

Step 6: Adopt a master data-based culture. By focusing on the effective management of master data, Firms can foster improved data governance by facilitating global identification, linking, and synchronizing information related to these critical entities across all heterogeneous sources throughout a firm. A single source of truth (SSOT) for each data set is created and ensures that everyone in an organization bases business decision on the same data.

Step 7: Develop a feedback mechanism for continued process improvement. Finally, for any data governance program to be successful, a feedback mechanism should be built into the process that allows for continual assessment and alteration of data governance activities. Monitoring information assets over time provides a clear picture of how initiatives are performing and provides a way to identify both successes and failures in the process, so corrective action can swiftly be taken as needed.  

Although there can be substantial functional and tactical obstacles to establishing a data governance framework, the firms who reach this level of data maturity consistently report that the benefits and overall payoff are well worth the implementation. An incremental approach can facilitate the successful implementation sustainable data governance that will meet both immediate needs and your Firm's future requirements. A few years ago, a data management and governance strategy may have been considered luxury. Now, not having one is a liability.

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