You have likely heard many data related terms being thrown around— data packs, data lakes, data warehouses— but what do they mean? And are they important to your business? If someone is telling you that your business needs one, or even all of these, then where do you begin?
For starters, let’s define the terminology. Data packs are sets of data acquired from intermediaries. These many data packs or sets are then stored in a data lake or a data warehouse, depending on what the information is and how it has been sorted.
There is very little doubt that we are operating in a world where data is top dog in most industries. And there is data to be found everywhere you turn. The problem exists not in the data itself or the amount of it; it lies in the ability to get at it and manage and utilize it in a meaningful way that benefits your organization. While this is not a new issue, the seriousness of the problem has increased as the amount of available data and the need for it has increased. Current databases and software don’t have what it takes and without a dedicated, workable data strategy, you are losing out on a valuable asset and missing opportunities and insights that are crucial to successful business operation in today’s market.
To build a reliable data strategy, start by incorporating these four steps that will help harness the potent benefits of meaningful data:
Step 1: Automate the data acquisition process
What good is that data if you can’t get to it? The first step is streamlining the acquisition of data. This step is particularly important especially for larger organizations that constantly cope with substantial amounts of data and are usually processing it manually. The manual processing of information is not only inefficient but also tends to deliver inaccurate data. Putting processes in place that automate the data collection is one key recommendation: Additionally, do not try to do too much pre-processing of the data up front. Simply pull data in in its raw form and in a format that you can access later. The second step of the process will take care of handling the data appropriately.
Step 2: Unify the data
Once you have acquired the data sets (also called data packs), the second step is to process the information into a data pack that provides a more unified schema, or layout, across the organization. This includes file formats, fields, and so on. At this point, you would also want to match any data you have against records that are already present or that correlate within your organization.
For example: Within an asset management firm, there are relationships in the firm for individual brokers that are represented as contact records. You also have wholesalers that are interacting with those brokers and are building relationships. The ability to match those records against the broker is key. Matching, at the field level, the contact to individual traits, can then be assimilated into the organization. This facilitates the ability to see that information in the framework of that contact within CRM, the data warehouses, analytics, and within machine learning analytics. In the end, all of these pieces merge into an integrated format within the organization that can later be used to gain advantageous insights.
These first two steps are key to an effective data strategy. Data packs contain transactional data that comes in from partners. That data is received in all different formats with varying degrees of quality and content. The basic action of importing, processing, and then matching up that data is a daunting task for any firm. There is usually very little discipline surrounding how this process is done or how often it’s done. It is crucial to understand the significance of getting these two steps in place, while understanding the effort it can take.
Step 3: Put the data in a digestible format
The third step is to make the data digestible—I’ve got it; now how do I use it. Following our asset management example, this industry constantly struggles with how to get meaningful data pertaining to the actual transactions that occur as opposed to anecdotal information such as visits to a broker. That particular part is easy for CRM. It is the ability to match that basic data with transactional data that provides meaningful insights into a market, like understanding the actual value of a specific broker. You may have knowledge of the other trades the broker is making outside of your portfolio, which means you then have the opportunity to “upsell” that broker by educating them on different products they are not, at present, buying from you.
Step 4: Make the data usable
Once you have all the data components inside your firm in place, the last step is to put that data to practical use. For example, when asset managers are working with individual brokers to whom they have linked certain trade data, they then need to know how much business they are doing with that broker, are there any trends, and what type of movement is occurring across the broker’s trade. Possibly the broker is shifting attitudes in the direction of a specific asset class. The asset managers need to understand why they are changing and if those changes are impacting other trades within the organization. They need to be able to determine if there is a product that they offer that the broker may not presently know about. Is there one they offer that is inside the asset class that the broker is moving to? Those very detailed types of insights can be ascertained from data once it has been cohesively grouped and made digestible.
Firms of all sizes need—and can afford—a data strategy
Data strategies are currently being used by much larger organizations, but most are not utilizing any plan due in large part to cost. Data is very expensive to obtain, and the overhead costs involved in processing the data and making it meaningful might not seem worth the price to some. If the data packs were in a consistent format across partners, this would not pose such a problem; however, within asset management, that is not the case. That could all change, and that is exactly the reason that the right data management strategy could change the game forever.
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