Increasing Your Data AUM

Data Governance for Asset Management

Every company needs some kind of data governance strategy. They might not call it that. Depending on their size and business model, they might not even need a Chief Data Officer. (Gasp!) But to keep a business running, you have to keep your ducks in a row. Naming ducks and keeping them organized is the essence of data governance.

For asset management companies, good data governance and engineering are especially critical. The unique mix of data use cases in asset management magnifies the risks and opportunities of applied data science. Other companies might be able to pick and choose their data governance battles. Asset management has it all: performance analytics and forecasting; home-grown and acquired data sets; upline and downline channel partners — not to mention multi-agency regulatory oversight and a demand for ever-faster and transparent decisions. Asset management companies risk falling seriously behind their peers and rivals if they don’t have at least a long-term strategy that checks all the boxes.

Your Requirements = My Requirements

For mid-level asset management companies, in particular, one principle is absolutely critical: You must accept your customers’ and partners’ requirements as your own. A lot of service delivery companies tell their clients, “Your success defines our success.” In asset management, this is simply objectively true. Investment firms and RIAs don’t demand insight into performance details just to test you. They do it because their ownclients and regulators are demanding it of them. To succeed and grow, you have to enable and support their success. Investment is a close-knit industry, and the best way to expand your client base is to inspire trust and loyalty. This means prioritizing your clients’ user experience alongside your own operational needs, keeping in mind that transparency is currency.

The good news is that getting your clients’ uses and views right will drive out other requirements and opportunities, making your whole operation more client‑focused and more effective. In asset management, for instance, information about the buying and selling patterns of fund wholesalers is publicly available. Many large firms are extremely predictable, making the same kinds of sales and purchases at the same time each year; others respond in measurable ways to leading market indicators. Of course, aggregating, analyzing, and visualizing take effort. But the intelligence to be able to anticipate your clients’ trading patterns—and redirect your sales teams accordingly—can yield dramatic savings in the first year, as well as ongoing efficiencies as those predictive algorithms get refined.

A similar “bucket-sharing” approach is important for internal data requirements, too. McKinsey touches on this in their article, “Achieving Digital Alpha in Asset Management,” when they note that successful firms have “erased the traditional boundaries between their operations and technology groups, combining their budgets and development strategies,” and “made their operations and technology capabilities central to their competitive strategies.”

A Single Data Cosmos

Uniting operational and analytic data requirements within a single data cosmos requires leadership that thoroughly understands the company’s revenue mechanisms and can assess and foresee the profit potential of new data wells and analytic tools. Ideally, this is a single person. (See “Chief Data Officer: Rule Maker or Rain Maker?”) Practically speaking, though, it can also be achieved by making sure your data stakeholders include people who are directly responsible for revenue—and that these people are tightly integrated into the data governance mission, not just spectators. When all of this works well, data stewardship will emerge simultaneously, and nearly organically, from both the technical and revenue‑making sides of the business.

It’s also important to seek out, bring onboard, and keep onboard “minor” stakeholders who will personally reap the benefits of the “unified data cosmos” in their work. Engaging these people early ensures that their processes and requirements are accurately reflected in the analytical models. Keeping them engaged, through rapid iteration and consistent messaging, helps assure the long-term success of data changes by building their skills, allaying their uncertainty, and giving them a view of their success in the new data cosmos. Practically speaking, it also helps the company recognize and allocate budget for data changes across the full breadth of the organization.

It boils down to this. In asset management — as in other industries — implementing good data governance isn’t about making and enforcing rules. It’s about creating leverage and unleashing organizational lift.

A final thought. Where in this picture is compliance? In the highly regulated financial sector, it is tempting to make compliance the chief driver for data governance, especially if the organization has been sanctioned in the past. However, it’s extremely difficult to track measurable ROI against a sanction that has been avoided—and the more successful the changes are, the harder it gets. But in a “data cosmos” focused on organizational lift, good governance begets good data—and, as a result, regulatory compliance falls out almost free of charge.

Chief Data Officer: Rule maker or rain maker?

In his book Infonomics (a nice conference giveaway at last summer’s MITCDOIQ Symposium, by the way), Gartner VP Doug Laney makes some important claims about how companies should regard their data.

Notably, he states that all companies should be actively seeking to monetize their data, especially the “dark” data that resides in their systems but isn’t used or needed to meet core business objectives. This is the precious dross that everyone is in search of: the data “slag” that serves no purpose for the organizations that produce them, but which can be incredibly useful to others. Laney suggests that a natural home for this monetizing activity is in a company’s Analytics function, which makes sense: why not let the people who already massage data for business use take the lead on finding new ways to use it?

At the same time, Laney confirms — or at least he doesn’t contradict — the view of many senior data practitioners that most companies need someone in the role of Chief Data Officer to make sure the company’s data is protected. This view coincides with the common understanding that the CDO’s main responsibility is to shepherd their company through the culture shift of transforming into a data-centric or “data-driven” organization.

Mind the Gap

Hanging between these two positions is something of a gap. Analytics for monetization demands an aggressive, forward-looking stance. In contrast, the stance typically described for the CDO “data shepherd” is protective, governance-oriented, and compliance-focused. So the question is: what should the relationship between these two positions be?

One view is that the Chief Data Officer should be the rule maker, setting the standards and boundaries for collecting and storing data across the company, and optimizing data-sharing to meet the business needs of the company. This puts the CDO in good company with other members of the C-suite who fulfill this kind of protective, provisioning role — CFOs, CIOs, General Counsel, etc. The monetizing team, in contrast, are the “rain makers“. Their work is not limited to current business processes. Instead, they have permission to comb through all the company’s data looking for new patterns. They are also expected to look beyond the current interests of the company and query the market — much like traders on the stock market floor — for new ways to use otherwise stale data.

Does this model sound familiar? Depending on the size and maturity of your company, it might. There’s an inherent problem with this structure, though. Can you see it? Again, depending again on the size and maturity of your company, I’ll bet you do.

The problem this structure poses is that it effectively pits the rainmakers against the rule‑makers. Their respective responsibilities give them both “jurisdiction” over the same company asset — data — but with radically differing priorities. Over time, no matter how friendly and collaborative individuals try to be, the structural tension between revenue and rules can easily devolve into the old familiar rift between profit center and cost center. And this is serious — because when that happens, the cost center is always at a disadvantage.

The bottom line is this: a Chief Data Officer whose responsibilities are tied only to strategic goals of compliance and efficiency is already compromised.

Yes, and…

Bearing all this in mind, the alternative might seem obvious. To head off a conflict between the goals of compliance/efficiency and the goals of future profit, the CDO’s role simply has to encompass both. It’s an ambitious stance, though, and more challenging than it might look.

One challenge in bringing these goals together lies in the different backgrounds of today’s business data experts, who already cross a range of disciplinary lines including data science, information governance, business analysis, systems development, program implementation, and more. A more serious challenge lies in setting the right corporate expectations about what the CDO should do. This includes not only the functions and capabilities under their control, but also how they relate to the CEO and the Board.

If the Chief Data Officer is to create value for the enterprise, their counterpart in the C-Suite cannot be the CFO, CIO, or General Counsel. Instead, they must be expected to behave like the Head of R&D (in science & technology) or Upstream Exploration (in oil & gas): actively involved in prospecting, testing, and growing new sources of revenue, while also protecting the safety and integrity of the company’s data.

Now What?

So, CDOs (and CDOs-in-waiting), ask yourself this: “How does my company view my role?” If you find that your goals are being consistently construed as protecting your business, then I encourage you to push to expand that view — for the good of your company and your own career. The skills involved in finding and creating value in data will persist long into the future, even as the technology continues to evolve.

Core. Collaborate. Delegate.

Is your enterprise data strategy keeping up with the changing digital economy?

Pop quiz: A researcher—or maybe an analyst or a paralegal—approaches your department with 2 TB of data they need to house for immediate access. What do you do?

We’ve all been there. The options are often not pretty.

House it inside? …On whose budget?

Outsource it? …With what security guarantees?

The solution is almost always more complicated than they expect. One thing is sure, though: between cheap storage and free cloud solutions, if you don’t work with them, they will go on without you.

The role of enterprise data strategy is to unlock the value of information for an enterprise. It’s the underpinning for a framework of capabilities that, when executed together, let a company acquire and maintain accurate, consistent data to meet their business requirements—and put roles, principles, and tools in place to increase the value of the information.

As a discipline, enterprise data strategy encompasses policy and governance, master data management, data and system architecture, security and access controls, lifecycle and archiving. That’s a lot of moving parts. The key to a successful enterprise data strategy is to not only protect the company’s interests but also to create business value through deliberate strategic alignment with corporate objectives.

For instance, the basic corporate mandate to increase shareholder value could drive a goal of better performance on strategic investments. We know better forecasting supports better investment decisions. What supports better forecasting? How about cleaner and more timely data? A good enterprise data strategy ensures that even the humblest data quality project aligns with strategic corporate goals such as investment, customer satisfaction, and operational improvement by providing measurable improvements in key business indicators such as forecast accuracy, client churn, transaction costs, or cycle times.

The key to a successful enterprise data strategy is to not only protect the company’s interests but also to create business value through deliberate strategic alignment with corporate objectives.

So how can you ensure that your enterprise data strategy is aligned with your company’s best interests? And how can you make practical decisions in the moment that preserve your alignment while still meeting the immediate needs of the business?

Strategic alignment can be obtained by following a 3-part mantra:

Core.
Collaborate.
Delegate.

  • Core: Identify your central competencies—the things you can control that contribute to the strategic goals of your company.
  • Collaborate across the organization to make sure your core competency is contributing to the organization’s success.
  • Delegate, either to another part of the organization or to a vendor, things that are not part of your core competency.

Core

Your core competency is the unique set of capabilities you have that deliver strategic value to your company and its customers.

Note that this concept is completely scalable. Executives and board members are responsible for making sure a company stays focused on its core value proposition. Directors and managers have a similar responsibility to understand their areas’ core value to the company and its customers, and to make sure that their teams or processes are actively contributing to the organization’s success.

Think about this question: “What do I (or we) do on a regular basis that contributes measurably to the overall success of the company?”

Think broadly: where do you see the effects of your work? What strategic aims does it support?

Think creatively: you’re not necessarily limited to your what you already do. What strategic aims could you impact, if you were able to?

Think practically and realistically: just because you can (or could) do something doesn’t mean you should. “Capability” means what you have the power to accomplish. If there are limitations on your capabilities, where are they coming from, and what would it take to remove them? If it’s too hard to remove them, then those capabilities are probably not part of your core competency.

It’s important to make sure that everything you define as part of your “core” contributes clearly to one or more strategic goals of the company. This will form the basis of your strategic corporate alignment. If a function or capability can’t be aligned, then it may be superfluous. If a particular activity or responsibility actually interferes with your ability to perform core functions, then it’s a good candidate for collaboration or delegation.

It’s important to make sure that everything you define as part of your core competency contributes clearly to one or more strategic goals of the company.

In the case of a strategic enterprise data capability, the core competency might include

  • Policy — Principles for enterprise data governance
  • Processes — Guidelines for implementing policies so that actual use meets governance principles
  • Technology — Scalable tools to enable capabilities
  • Taxonomy/Dictionary — Standardization and enhancement of data descriptions
  • Metrics — Monitoring and measurement of performance/impact

Collaborate

If “core” describes what you do for your organization, then “collaborate” defines how you do it. Collaboration is what you do to ensure that your work is actually contributing to the ongoing success of the organization.

With enterprise data strategy, collaboration might focus initially on clarifying how data are used and the controls needed to maintain high data quality. From there, it could extend to determining key data elements, codifying business roles, defining standards, and performing root-cause process analysis to identify strategic improvements.

Collaboration is what you do to ensure that your work is actually contributing to the ongoing success of the organization.

It’s important to note that collaboration doesn’t come “after” you’ve developed all the tools to support your core competency. Creation and deployment of a successful strategic enterprise data framework depend on harnessing independent expertise from across the organization through cross-functional collaboration. Workgroups wanting guidance on data governance don’t need to cultivate their own expertise in data quality root-cause analysis—any more than data analysts need to be able to read x-rays or climb communication towers. But the respective teams do need to know how to talk to each other and agree to work together for the good of the organization.

Ultimately, the goal of collaboration is to establish an efficient “fit” between different capabilities and competencies within an organization. Doing this well sustains all parties and can even lay the groundwork for new competencies to emerge down the road.

Delegate

Here is where things get interesting. The thing to remember is that delegation is not just a matter of passing something off to someone else. Successful delegation entails specifying a desired outcome to a responsible person or group, which is then tasked with delivering that outcome. For delegation to work well, you need to establish controls, identify limits on the scope of work, provide sufficient support, and stay up to date on progress.

We’ve already discussed ways to recognize possible activities for delegation. If there are limitations on success that you just can’t remove, then that activity could be a good one to delegate. If it’s in line with the same strategic goals you support, but carrying it out impedes your ability to do other work, then it’s an excellent candidate for delegation.

The key is to focus on results rather than procedures. In delegating, you’re drawing on someone else’s core competency to reach your desired outcomes. You also need to pay careful attention to how and when those outcomes plug back into your strategic framework, so that everyone moves together toward the same goal.

In delegating, you’re drawing on someone else’s core competency to reach your desired outcomes.

Delegation can be internal—for example, calling on business process owners within your organization to create processes in harmony with established guidelines and standards. In other cases, outsourcing can allow you hit a sweet spot of meeting your company’s mission and enabling business growth by keeping your internal resources focused on core strategic goals and leaving everything else in the hands of a vendor or strategic partner. If you do outsource, it’s important to do so dependably and judiciously, keeping your eye on the outcomes and the controls you set around them.

*  *  *

So let’s return to that researcher/analyst/paralegal with all the data…

Knowing your area’s core competency, and, conversely, understanding which activities are right for collaboration or delegation can allow you to suggest a path forward that is already aligned with your processes, compliance principles, and strategic goals. It can also allow you to speak meaningfully and realistically about the controls, costs, and service levels that would accompany that course of action. In the long run, being able to respond quickly and reasonably to this kind of request makes it less likely that groups within your organization will go their own way when faced with data needs. This makes compliance more likely, mitigation less necessary, and processes more efficient, and strategies more effective.

Core. Collaborate. Delegate. Succeed. Grow. Repeat.