Data. Data. Data. Warehouses and lakes and packs…oh my! You’re likely hearing terms like these and thinking “What are we supposed to be doing?” Does your organization actually need any or all of these things, and more importantly, what do they even mean to you?
Let’s start by clarifying the terminology. Data packs are, quite simply, sets of data from intermediaries. Those data packs are then put into a data lake, a vast pool of undefined data, or into a data warehouse, for more filtered and structured data.
There is little doubt that data rules the roost in just about any industry. There is a plethora of data to be had. The problem does not lie in the data itself, it’s having the know how to get to it, manage it, and mold it into useful form. Though this has always been an issue, as the amount of available data and the need for it has increased, the issue has become a more serious problem. Existing software or databases just don’t cut it and without a deliberate, actionable data strategy, you are missing out on the ability to use an extremely valuable asset, and you are losing opportunities, big and small.
Here are four steps to constructing a dependable data strategy that delivers powerful benefits:
Step 1: Develop a plan for automating the acquisition of the data
Optimizing the data acquisition process is essential, because if you can’t get to the data to process it, it does you little good. This step is particularly critical for larger organizations that sift through large volumes of data—quite often a manual procedure. Not only is this extremely inefficient, but in most cases, it yields inaccurate data. Processes must be put into place that automate this function and pull that data in. We recommend that you not do a lot of “pre-processing” of your data up front. Pull data in in its raw format and store it in a form that you can access later. Step 2 will take care of the actual processing of the data properly.
Step 2: Unify the data
After you have acquired the data sets (also called data packs), the next step requires processing the data set so that it is more cohesive across the organization in terms of schema or layout. This includes file formats, fields, etc. This would also be the point in which you match the data against records already in existence or those that are related within the organization.
Let’s take the asset management industry as an example. Asset management firms have existing relationships within the firm to individual brokers. These are represented as contact records. You have wholesalers who are then talking to those brokers and building relationships. That asset management firm needs to be able to match those records to each broker. What this means is that you need to match, at the field level, the contact to individual traits. This can then be digested within the organization, allowing you to see that information in context of that particular contact within your systems (ERP, CRM, analytics, data warehouses, machine learning analytics, etc.). At that point, all of these pieces merge together into a unified format within the organization. And that information can be used to achieve powerful insights.
It would be a mistake to dismiss or undervalue these first two steps. Data packs (sets) consist of transactional data that is acquired from partners; but those data packs are all in various formats with varying levels of content and quality. Consequently, just the mere action of importing, processing, and matching up that data presents a major task for almost any organization. Unfortunately, there is typically not much discipline regarding how it’s done or how often. It is imperative that you understand the significance of getting these two steps in place and the major effort that is required to undertake this process.
Step 3: Get the data into a digestible format
Making the data digestible is the third step. Using asset management again as our example, the industry is grappling with how to go about getting meaningful data as it pertains to the actual trades that occur as opposed to the anecdotal information about activities, such as visits with a broker. That anecdotal part is simple for any CRM. It’s the ability to actually start matching that data with transactional data that allows for meaningful insights, like understanding the true value of an individual broker. With that kind of knowledge, you recognize the other trades the broker is conducting that are outside your portfolio. This could potentially mean you have the prospect to upsell that broker and inform them of additional products they are not currently purchasing from you.
Step 4: Put the data into a usable format
Once you have all of the data components in place within your organization, the final step is making that data usable and then deciding what it is that you want to do with it. As an example, if an asset manager is working with a specific broker with whom he or she has connected this trade data, they not only need to know how much business they’re doing with that broker, but also if there are any trends or changes of that data across the broker’s trade. It’s possible that the broker is changing attitudes regarding a specific asset class. Asset managers need to be able to recognize why they are changing and whether that change is having an impact on the trades within the organization. They need to be able to determine if there is a product the firm offers that the broker may not currently be aware of—one that is within the asset class that the broker is moving to. These specific, detailed types of insights can be discerned from data once it has been collected, unified, and put into a digestible format.
A data strategy is cost effective for firms of any size
Currently, many of the larger companies are using a data strategy, but the majority are not—and that is mostly because of cost. Obtaining data from partners is very expensive, and the overhead expenses of processing and making that data meaningful is so high that it is cost prohibitive to many. If the data was maintained in a consistent format across partners, however, then this would not be an obstacle. Unfortunately, in asset management, that is not the case which is exactly the reason that the right data management strategy can be a game changer.
Want to learn more? Read about AKA Enterprise Solutions’
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