Myth-busting 2025 2015 Procurement Predictions and Trends! Part 8

Introduction

In our first instalment, we noted that the ambitious started pumping out 2025 prediction and trend articles in late November / early December, wanting to be ahead of the pack, even though there is rarely much value in these articles. First of all, and we say this with 25 years of experience in this space, the more they proclaim things will change … Secondly, the predictions all revolve around the same topics we’ve been talking about for almost two decades. In fact, if you dug up a Procurement predictions article for 2015, there’s a good chance 9 of the top 10 topic areas would be the same. (And see the links in our first article for two “future” series with about 3 dozen trends that are more or less as relevant now as they were then.)

In our last instalment, we continued our review of the 10 core predictions (and variants) that came out of our initial review of 71 “predictions” and “trends” across the first eight articles we found, in an effort to demonstrate that most of these aren’t ground-shattering, new, or, if they actually are, not going to happen because the more they proclaim things will change …

In this instalment, we’re again continuing to work our way up the list from the bottom to the top and continuing with “Data”.

Data

There were 4 predictions across the eight articles which basically revolved around “data-driven decision making” with some sideline focus on the need for “data governance”. As with almost every “prediction” and “trend” in this series, this is yet another prediction that makes headlines every year, no more important this year than the last as no Procurement tech works without good data (although some work even worse with bad data), and unlikely to get more attention now that a certain analyst firm has latched onto a new buzzword to hide the importance of good data. Before we discuss further, as is our custom, we will list the four predictions.

  • Data-Driven Decision Making
  • Data-Driven Decision Making
  • Data-Driven Decision Making Will Become More Critical
  • Data Governance and Data-Driven Decision Making

All strategic decisions should be, and more importantly, should have been, data driven for the last four decades in any organization (given that the first IBM PC hit the market in 1981, making computer-based data analysis affordable for any mid-sized or larger organization. And while it wasn’t possible to give every office worker a computer and internet access until about 25 years ago, limiting “data analysis” decision support to only the most important strategic decisions, once everyone had a computer and internet access, every strategic decision should have been supported by data to some extent).

And with the emergence of web-based data services, it’s never been easier to get data. Moreover, most organizations are swimming in data. In fact, some organizations have so much data that the problem is not the lack of data, but the lack of good, appropriate, data. In most organizations, there are drives bursting with data, where the quality ranges from reasonably good to completely wrong, and if you use that wrong data, you’ll have a wrong analysis and make wrong inferences. Also, not all data is appropriate for all types of analysis, so there’s no guarantee the feeds you have are the right ones. Moreover, most users in most organizations don’t know how to judge the quality of the data, or how to do a proper cleansing and correction if the data quality is poor.

Good decisions only come from a proper analysis on good data, so while there will continue to be pushes for data-driven decision making, because that’s the age we are in, there needs to be a continued push for good data! But that will only occur if an organization has good data governance, which is what the majority of these predictions and trends are missing.

The organization needs to ensure that, before any data is stored, there are processes in place to make sure that any data stored in an organization’s system is correct, complete, in a standardized format, and linked to any associated records using unique ids. That no record is stored unless these requirements are met. And that all records are verified on at least an annual basis to ensure they are still complete and correct. In particular, any time a record is updated, the data should be (automatically) verified again, and any time a record is touched for use, critical data should be verified. A lot of this can be automated if the organization has identified masters for all types of data and trusted external feeds for new data verifications and annual rechecks. And if it’s not, the organization can’t really do data-backed decision making because that relies on good data.

What Should Happen? (But Won’t!)

E-MDMA. The adoption of an Enterprise Master Data Management Administration strategy. Since data is so fundamental to good decisions across the organization, enterprises should not only be proactively managing their data but managing it in a manner that ensures it is actively maintained, highly accurate, and available to use by any system that needs it. This requires identifying, for each piece of data, a (master) system of record, verification rules, (third party) data sources for corroboration and verification, and access rules. All boring stuff … that has to be done enterprise wide … but absolutely necessary for data-based decision making. Especially if you want to use AI.

Now, we know it sounds very boring, but it’s critical. But we also know that no one will want to do it. So don’t call it Enterprise Master Data Management Administration … just call it E-MDMA and tell your employees its going to bring ecstasy to their job. Let them think its a new drug, and maybe they’ll buy in.

Seven down, three to go.