Our article was published in Barron's on November 8, 2019
Think about when you made wrong decisions for your firm. I bet those decisions were based on reports containing incorrect data. It’s frustrating when this happens once. After several occasions, it’s time to do something about it.
An unanticipated consequence of AI and data analytics popularity is the increased concern about data quality within these technologies. Your client relationship may be enhanced by the nice-looking graph, risk profile explanation, and the simulated projected account value; but, if those results were based on incorrect data, your client experience has just dropped a few notches.
The phrase “big data” doesn’t translate to quality data. Many firms understand this and are elevating data governance responsibilities on the priority ladder by creating a chief data officer role. Data accuracy and consistency across all technologies are part of this role.
If you don’t have the capacity for a full-time data governance role, several data audits can determine if your data is clean or needs additional scrubbing. Let’s start with your CRM. You can apply this process to all your technologies.
Data Quality Audit – A few queries can show you missing (blank) data fields, inaccurate or outdated data, and inconsistent data fields with multiple meanings depending on the client or advisor. Catching a simple mistake such as a salutation field containing “Mr. & Mrs. Smith” to a recent widow can prevent an embarrassing moment. Are blank data fields the result of entering data in the Notes field, where it’s difficult to retrieve? Are custom fields updated regularly? What documentation exists on each data field’s definition and who is allowed to alter them?
Data Consistency Audit – Firms using best-in-breed technologies find data fields have inconsistent usage across technologies, yet don’t have documentation explaining those differences. Using your CRM data fields, audit how those fields are populated in other technologies, including how and why differences occur. For example, Household (Client) and AUM may have different meanings in CRM, portfolio accounting, rebalancer, and planning software. If the same custom field in multiple technologies has a different meaning, perhaps renaming the field will avoid confusion. If your data-flow diagram resembles a bowl of spaghetti, a data library may help your firm understand data characteristics such as the data source, where, and how they are used.
Spreadsheet Data Audit – A lot of data ends up in Excel because the CRM data fields don’t exist, are used for different meanings than what staff needs, or staff understands Excel better. Firms forget that Excel is the most-used software in the firm and contains the underlying data behind many of your reports. Questions you should be asking:
- Why isn’t the data in the CRM (or other appropriate software?)
- What do we do with the data - is this a one-off need, or does it apply to our client base?
- How many versions of the same spreadsheet exist with different data in each?
As you plan for 2020, think about taking a closer look at your data before getting excited about big data.
Need help in getting your data house in order? Contact us for more information.