Getting the Most Out of Your Asset Management Data: 4 Steps to Creating a Data Strategy

Data packs. Data lakes. Data warehouses. You are probably hearing terms like this lately, followed by someone telling you that your asset management firm needs one or all of them. It’s also likely that you either: a) know little or nothing about them, and/or b) don’t know why you need them.

And just to clear the air before moving forward, data packs are sets of data from intermediaries, so there are many data packs that are put into a data lake or a data warehouse.

There is no doubt: More than ever before, data is king in asset management. And there is plenty of data to be had. The problem, however, isn’t the data itself; it’s knowing how to get to it, manage it, and make it useful. This has always been an issue, and as the amount of available data and the need for it increases, the issue is becoming a serious problem. And software or databases don’t cut it. Without a meaningful, actionable data strategy, you are not only losing out on an extremely valuable asset, but you are also missing opportunities, both big and small.

Here are four steps to building a reliable data strategy that yields powerful benefits:

Step 1: Automate the acquisition of the data

If you can’t get to and process data, it is no good to you, so the first step is to optimize the data acquisition process. This is an extremely important step particularly for larger firms that deal with large volumes of data—and typically they’re doing it manually. This is very inefficient and, in most cases, yields inaccurate data. Put processes in place to automate and pull that data in. One recommendation: Don’t try to do a lot of pre-processing of the data up front; just pull it in in its raw format and in a form you can access later. The next step will take care of processing the data appropriately.

Step 2: Unify the data

After acquiring the data sets (also referred to as data packs in asset management), the next step is to process it into a data set that is more unified across the organization in terms of schema or layout, including file formats, fields, and so on. This is also the time when you match that data against records that already exist or that are related within the organization.

If you’re an asset management firm, for example, you have relationships within the firm to individual brokers that are represented as contact records, and you have wholesalers that are talking to those brokers and building relationships. You need to be able to match those records against the broker. This means you need to match–at the field level–the contact to individual traits, which can then be digested into the organization. This allows you to see that information in the context of that contact within CRM, the data warehouses, analytics, and and machine learning analytics. So, all of these pieces come back together into a unified format within the organization that can be used to gain powerful insights.

Don’t dismiss or underrate these first two steps. Data packs are comprised of transactional data that comes in from partners;  but those data packs are all in different formats with varying levels of quality and content. Therefore, just the action of importing, processing, and matching up that data is a major task for most firms, and there is not much discipline around how it’s done or how often. It’s crucial to understand the importance of getting these two steps in place—and not underestimating the effort it can take.  

Step 3: Make the data digestible

The next step is to make the data digestible. The asset management industry is struggling with how to get meaningful data around the actual trades that occur versus anecdotal information around activities such as visits to a broker. That part is easy for CRM, but when you start matching that data with transactional data, you get meaningful insights, like understanding the real value of an individual broker. You know the other trades the broker is making that are outside your portfolio, potentially, which means you have an opportunity to upsell that broker and educate them on other products they aren’t currently buying from you.

Step 4: Make the data usable

So, now that you have all the data components in place inside your firm, the final step is to make it usable and decide what you want to do with it. For example, if you’re working with a specific broker to whom you have associated this trade data, you need to know not only how much business you are doing with that broker, but also if there are any trends or movements of that data across the broker’s trade. Maybe the broker is changing attitudes towards a specific asset class. You need to understand why they’re  changing, is that change impacting the trades within your organization, and determine if there is a product you offer that the broker does not currently know about—one that is inside the asset class the broker is moving to. Those very specific types of insights can be gathered from data once it has been gathered, unified, and made digestible.

Having  a data strategy is cost effective for firms of all sizes

Right now, some of the much larger firms are using a data strategy, but most are not—and mostly because of cost. Data is very expensive to buy from partners, and the overhead of processing and making it meaningful is so high that it just doesn’t seem to be worth the price. If the data was in a consistent format across partners, this would not be an issue, but in asset management, that is not the case.   That’s why the right data management strategy can change the game.

Want to learn more? Read about data visualization services.

Looking for a partner who understands the challenges of making data useful inside your firm, with experience working with other asset management firms to do the same? Contact AKA Enterprise Solutions.

By | 2019-09-03T14:20:27+00:00 January 3rd, 2019|Business Intelligence (BI)|0 Comments
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Contributor: Michael Quattlebaum

As Vice President of AKA's CE (Customer Engagement) practice, Michael has 20+ years of experience in technology and financial services, focusing on a business-centric approach to problem solving. His expertise in functional and technical design enables him to convey confidence to end users as well as C-level executives.

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