4VCO logo
Change Management
Private Equity Operations

Why waste the time of your high value team members? Clean your Data!

Technology, supporting enterprise processes, the people, and ultimately “the organisation”. The POPIT model. It has done the rounds for 30+ years.

Let’s revisit POPIT. Here’s the classic visualisation of enterprise infrastructure:

Technology, supporting enterprise processes, the people, and ultimately “the organisation”.
The POPIT model. It has done the rounds for 30+ years.

Some types of business put People at the apex – those businesses which depend on rock star performers. Private Markets, in terms of “front office” activity, fits that view but that is another story.

This is a good rendering of the model. Made good by the “&” separating “I” from “T”. Information Technology is IT, right? The domain of the boffins and plumbers who make stuff happen: the “other”.

Information, on the other hand is recognisable to the business. (If it isn’t, we have a bigger problem to discuss).

So, information is useful to the business. No surprise. But “information” is the refined product of Data. If the data is telling you something, that is information. Without drifting into GIGO (garbage in, garbage out) and the jokes around cause and correlation, let’s recognise that Data is the bedrock foundation of a scalable business. So, the “I” sits on “D”. If the D is not well managed, then the I is suspect, and the enterprise is on shaky ground.

Managing data is a rich topic for further discussion.

Beyond some general principles, the devil is in the detail there. For now, let us just consider the opportunity cost of not managing data: if you take your data for granted, what can you expect and what are the consequences?

Simple case: data generated within the business, for the business. What could possibly go wrong? Well, timeliness for example. Some stuff goes out of date or does not get updated. Validity, another example: people using the wrong codes (product or accounting or other ways of cataloguing data). Life gets more exciting when there is more than one line of business or operating unit and the enterprise needs to bring the satellite data together for an overall view. Then you find incompatibilities, inconsistencies, missing elements… Then there is the matter of “silo” working: different areas of the business working off copies of data or generating their own versions (their own contacts records, for example). In simple terms, there is no concept of “single source of truth” or common “reference data set” (what does “Europe” mean, where does the US$-GBP conversion rate come from, who uses GICS vs NAICS classifications…)

More complex: data provided or processed by third parties (accountants, fundraising advisory, M&A teams…) Are they collecting and sharing data in line with your standards. Do they share your naming conventions for investment vehicles or investors or jurisdictions? What are their data quality controls? The more external sources of data, the more complex data management becomes.

Consequences, consequences…

Poor data is the root cause of operational breakdowns; misreporting, missed reporting, delays/bottlenecks in time-sensitive processes, non-compliance (internal standards, external / regulatory requirements)…

The consequences up the chain are at the very least, inefficiencies (tracking and remediation) but can be poor decision making and reputational damage with investors, third parties, and regulators.

Worse, poor data practices do scale, but they simply scale the problem! You will find out your data weaknesses more quickly and more profoundly when you stretch bad practice.

It is widely recognised that the smart end of the business analytics team “wastes” a substantial part of their working week patching up data sourcing, cleansing and validating. A surprisingly small proportion of their time is available to their data science smarts or to their business operations outputs.

Be Data-Centric!!!

Let’s go back to the POPIT model and review the triangle in the middle. Let’s break that down into Technology and Information and further break the “Information” element down into Raw Data, Curated Data, and Analytics.

Technology plays a supporting role along the way – throughout the Data Processing if you like:

“Undifferentiating Heavy Lifting”

Do your clients, suppliers, collaborators, staff… recognise you and respect you for your output?

You would hope so. Therefore, the value-add from your best people is in what they can do with clean, curated data. Therefore, liberate them from the clean-up and remediation work they are doing today by letting others, specialists in data cleansing and curation, populate the systems and applications which run your business.

Your SMEs are your differentiator, not data cleansers and curators. The latter represent a commodity skill which, over time will apply technology and processes to scale that end of the data supply chain. Leave that end to us. Focus on making the most of the refined, curated data when we have done our job.