Here’s a question for you: Is data governance part of your overall business strategy? If yes, well done – your asset management firm is likely on a good track to become a data-driven company.

But if not, well, read on.

Why is data governance critical?

Just like your investment portfolio and customer relationships, your data needs careful and rigorous management. Disjointed and disorganized data puts you at risk of breaching regulations and overspending on storing and managing duplicate data. And inaccurate data means you can’t be sure you are making the right decisions for your business.

To tackle these issues around data, your asset management firm should have a comprehensive data governance program that combines aspects of organization, process and technology. Such a program requires investment, but a lot of firms now agree the costs are justified.

Where should you start?

In a recent Accenture research study, 55% of asset managers surveyed said they have a data management initiative underway that aims to enhance data governance and quality.

From working with clients on multiple data governance strategy and implementation engagements, we’ve found that it is important for a program’s structure and approach to reflect the specific needs of your organization. A good starting point is to choose a meaningful use case that involves changes across the enterprise, including in the business and technology functions. The program should be robust enough to address real issues with a framework of measurement, monitoring and reporting, while not being overly onerous to maintain.

 55% of asset managers have a data management initiative underway to enhance data governance and quality.

In practice: How data governance helps one asset manager improve decision making

Take a recent project where we helped an asset management client through the strategy and launch phases of its data governance framework.

The initial focus of the program was on verifying that the data being reported was accurate and wouldn’t introduce reputational risks or the possibility of incurring regulatory fines. However, the benefits ultimately realized from the program went much further: reductions in both operational costs and risks through the creation of common artifacts shared across the organization.

By automating key manual tasks involving integrating data, the client was able to focus its resources on value-adding activities and managing risk more effectively. It ultimately achieved higher quality outcomes and improved business decision-making.

Is it worth it? Let’s look at some of the benefits I’ve seen asset managers achieve

1. Reduced data management and storage costs

While the business case for data governance can sometimes be difficult to articulate, cost reduction is invariably a tangible outcome of a successful program.

Buying third party data is a significant expense for asset management firms, yet they have limited bargaining power with the largest, industry-standard data providers. Our experience with clients has indicated that data optimization – driven by better governance in procurement and market data demand management – could generate a cost reduction up to 8%. By centralizing data procurement and validation, a firm can streamline processes, boost efficiency and reduce operational costs by eliminating data management redundancy.

Meanwhile, the rapid migration to relatively inexpensive cloud storage has had an unintended consequence: asset managers are creating and paying for the storage of vast amounts of data that is not readily accessible and is rarely used, commonly referred to as “dark data”. Firms can break this habit by using data governance for data retirement and lifecycle management, thereby ensuring their cloud estate is both usable and cost-efficient.

2. Faster and simpler data management

I regularly see asset managers set up a data governance organization (DGO) with a top priority being consistent firm-wide standards for business data transparency, data protection and audit integrity.

Adopting a consistent set of standards can allow the DGO to incorporate and adopt best practices while remaining agile in the face of industry and regulatory changes. Having a DGO helps a firm to mitigate risk when taking on large-scale data transformation initiatives. Additionally, a DGO capability – with its associated data dictionary and controls – might accelerate the mobilization of new initiatives between two and four weeks per project. Increased data quality also helps the governance organization spend less time addressing ongoing integration activities and data issues. This helps firms become much more agile and responsive to changing business and industry demands.

By combining effective governance with the implementation of an enterprise data management platform, I’ve seen firms capitalize on this new strategic asset, and position themselves for quicker development and delivery of new data sources and services to the marketplace.

3. Enhanced ownership and accountability

Effective data governance helps a firm establish a business information and data ownership model.

Asset managers use various names and models when defining the business data catalogue or information model, along with the associated ownership roles within their data management and governance structure. Firms can build iteratively towards this governance goal by standardizing the information model and business terminology – or “ontology” – in each new project and dataset brought into the firm.

The model should hold data owners and stewards accountable for overall content, delivery and quality within their respective business units. This way, the business can define clear data usage rules, thereby reducing IT exposure and minimizing risk when making data available for consumption by the business and various systems.

4. Increased data quality

A strong data governance program supports and amplifies the firm-wide commitment to establishing and maintaining a data quality framework.

Establishing this capability creates trust among users that the data they use has been validated and is fit for its business purpose. The result? It allows the consumers of data to focus on their core competencies and means they won’t create siloed data management teams to support their business needs.

5. More effective risk management, privacy and end-to-end transparency

Data governance is the common factor that makes risk and privacy policies easier and more effective.

Personally identifiable information (PII) is a major component of enhanced data governance, enabling the firm to better understand what data needs to be protected. In addition to PII, data transparency and privacy should be extended to all domains across the investment lifecycle.

I also talk with clients about the importance of the data governance framework addressing both short-term data agility and long-term data stability needs. In this highly sensitive cybersecurity environment, firms should ensure the overall security and safety of data, as well as transparency and audit integrity across all business-critical data assets.

Shifting drivers – from cost and regulation to technology

In the past, cost and regulatory requirements were main motivations for asset management firms’ investments in data governance. But today, data governance should also adapt to the ongoing disruptive changes in technology.  Existing programs should be rapidly extended to take on new challenges such as the emergence of intelligent automation, machine learning and cloud migrations.

For more Accenture insights on data governance, data strategy and innovating with advanced technologies, read our latest report, “The Power of Data-Driven Asset Management”.

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