Using Data Aggregation to Streamline Firm Operations
Identifying where your firm stands on its path to becoming data driven can be more difficult than you might think. In a previous post, Malartu has outlined the four step process required for becoming data driven, here. Without breaking down the structure step by step and aligning your current practices with each stage of the MDMM, it is easy to overestimate the quality of your existing data aggregation processes. We regularly encounter firms that believe they are data driven, when in reality, they have yet to achieve data awareness.
What does it look like to lack data awareness?
Imagine this. You, the managing partner of an asset management firm, are sitting at your desk and get a request from an LP for information detailing the past 5 years of Q1 financial data for a specific company. Sure, this doesn’t happen often (or ever for some), but what if it did? Would you be prepared? You’ll probably send the request on to your analyst and hope for the best.
The analyst receives the request and puts their game face on. As they enter into the daunting and dusty DropBox of data from years past they quietly curse the name of the LP that asked for such old information.
On the hunt for data, the analyst successfully finds the requested spreadsheet from 2015, 2014, 2012, and 2011. But, where on earth is the spreadsheet from 2013? It can not be found in the chronological succession of the other four documents.
After hours of sleuthing skills that even Sherlock Holmes would envy, the analyst discovers that three years ago when the data for 2013 was stored in DropBox, there was a fat fingering error, and the elusive document at hand was incorrectly named 201W.
It shouldn’t be that hard
Let's move back to your desk, the managing director. It has now been 2 days since you made your data request. Meanwhile, the LP is wondering what on earth is taking so long to get a simple report, questioning your ability to run a tight ship. You are six feet deep in frustration because you know this isn’t a hard question to answer. It’s not why this LP invested in the first place. But it’s making you look bad nonetheless.
Now that the analyst has all 5 years of spreadsheet data retrieved from DropBox, it is time to pull for the specific metrics that the boss man/woman asked for. Once again, the hunt ensues. In cell D:564-569 the analyst strikes gold, the financial metrics from Q1. After going into the other 4 spreadsheets, pulling the correct numbers, and aggregating the information into another spreadsheet: Q1 Financial Data From The Depths of Hail, the report is ready for the boss. Maybe not. How about Q1: Financial Data from 2011-2015. Alright, ready to be sent.
You, the managing director, have your data from the analyst. Now, all you need to do is send it to your investor relations team to generate the report with comprehendable charts and tables. At this point, there is a hot debate about using mustard or sunshine yellow for the pie charts as opposed to skyline blue. The report design process takes another day.
Twenty-six employee work hours later, you have the Q1 2011-2015 data prepared. It is sent to the LP, and your typical day resumes.
When you need to get operational metrics from 13 of your portfolio companies aggregated for your new operating partner.
Bringing your firm into the modern age
This series of events is likely relatable for you and your employees. Accepted data aggregation practices within private equity are dated and cumbersome. Aligning your firm with stage one, data awareness, of the MDMM is the first step in breaking the free from the restraints described.
In being data aware, two goals must be achieved. First, a central location must be selected to organize existing data. Secondly, the data that is portfolio operator relevant, both internally and externally, must be identified.
Sifting through years of data can seem like a daunting task. One that you might be able to write off as not worth the effort when compared with how much you’re paying the person who would end up doing it. Before jumping to any conclusions, consider this: leveraging a tool powered by machine vision like the Malartu Reader. It turns any data table within an Excel file, PDF, PNG, or JPG into workable data within the Malartu system (or a csv file with our free tool). Financial statements, quarterly reports, budgets, and pitch decks can be securely sent to Malartu’s cloud platform, where the data is available to your team, securely, anywhere in the world.
In simpler terms, The Reader is used to turn something like this:
and turn it into a workable CSV like this:
Time, money, and risk of error are all optimized by reducing the fat fingering danger of data entry. Just upload and review the document for corrections. Pretty simple.
Having new tech available for use in updating data aggregation policy can increase the competitive advantage of your firm. However, the true value that this tech has to offer can only be realized when there are governance structures in place to encourage policy adoption. The tech paired with optimal governance structures offers the perfect one-two punch for landing into stage two of the MDMM, data proficiency, which we’ll discuss in a later post.
Data Maturity is a Process
There is a lot that your firm has to gain from becoming data mature. We hope that by breaking down common challenges associated with working through stage one of the MDMM, you feel more prepared to take your firm on the journey to being data driven. If you have any questions or would like help identifying where you are currently, reach out. We are excited and ready to help you become data driven.