The word strategy, according to an online dictionary (Wikipedia, 2018), is translated from Greek as στρατηγία stratēgia, “art of troop leader; office of general, command, generalship,” and indicates a high level plan to achieve one or more goals under conditions of uncertainty.
So, what’s your strategy for managing company data?
For sure, a company’s material masters could exist in an uncertain environment that requires both planning and maintenance to keep data consistent, clean and reliable.
More and more big companies are investing in big data and reaping the rewards and insights that successful management of it brings. However, there is a hidden threat that can cause multiple issues, including wasteful purchasing spend, bad business decisions that were made based upon bad data, and thousands of wasted hours trying to track down reliable data.
Inadequate, incomplete or inconsistent data on material masters affects not only the Purchasing and Receiving departments, but every other department that relies on dependable data to facilitate the day-to-day running of business from the top on down.
The Not-So-Hidden Costs of Bad Data
Let’s explore the hidden and not-so-hidden costs of bad data and what a company can do to fix it before it mutates into a snake-driven Medusa of Greek mythology—one that will be difficult if not impossible to get under control. Bear in mind that the longer the bad data problem exists, the bigger and more pervasive the challenges become, which inevitably results in a costly nest of snakes up of inaccurate data and wasteful spending.
The fact is that Purchasing, executives and other stakeholders, or perhaps called more fittingly in this situation—snake-holders—might be making business decisions based in part or wholly on bad data, sometimes even unaware that the data they’re viewing is compromised and therefore unreliable.
What’s more, they may not comprehend the far-reaching implications of bad data until unsupported decisions are made and subsequently resonate throughout the company.
For example, if Purchasing and Receiving aren’t consistently using data governance best practices, there is a good chance that the inventory levels on the material masters are distorted, giving a false picture of the material on hand. This has a cascading effect, and the consequences are company-wide for any stakeholders attempting to make sound business decisions based on the corrupted data.
According to a 2016 article by the Harvard Business Review, nearly half of a worker’s day can be consumed by having to evaluate and clean up bad data, which has been shown to result in compromised efficiency, wasted money and time lost. That includes the time it takes to reach out to IT for help—a typically overworked department that may be engaged in other projects and can’t always get to new issues right away.
It’s fairly obvious that a company’s data problems can be far-reaching, even without fully recognizing that there even is a problem. So, what’s a company to do to achieve reliable data quality? Luckily, there are common sense solutions a company can immediately begin to apply to take charge of the situation.
Common Sense Solutions
- Identify the origins of the problem: What is the source of the bad data? That question can best be answered by asking “who has access to the material masters?”To illustrate just how easily bad data can get entered into a system, let’s look at a sample description for a pipe: Joe from Receiving may enter it into the system as UNION ½ MAL 300# THD BLK. When checking stock levels in the database, Tyler from Purchasing may only search for ½” Union Pipe. There could be hundreds of ½” Union pipes in stock—which one is the pipe Purchasing has under consideration?With little other recourse, Tyler will either go out onto the warehouse floor and manually check stock, (assuming he can even find the location in the computer system) or he’ll have to walk back to Receiving and hope that Joe can shed some light on the precise specifications on what has been received.
- Create a data governance model: Those who have the task of entering data into the system will need to sit down and agree upon which definitions and abbreviations to use and how to use them.This is easier said than done because neither Tyler in Purchasing nor Joe in Receiving have any training on how to come up with a data governance model. To complicate matters further, most ERP systems only allow for so many characters of text to be entered into a database. This can be confusing for all concerned if the company has many items that are virtually alike. Making the distinction between two or more similar, yet unique parts can be time-intensive.
- Create a data taskforce: The best way to begin a data-cleansing initiative is to have one or two key employees create a data taskforce. Only these designated individuals should have access to data entry on material masters. This way, a system of checks and balances is created which can prevent well-meaning but untrained employees from unintentionally entering bad data.
- Begin the data cleanse: Unfortunately, just moving the system to a new software system isn’t going to solve anything. It could just make matters worse as bad data migrates to the new system. Your only options are to deal with it internally (and hope that someone knows what they’re doing) or to make the decision to call in our QuadraDot experts to not only cleanse the data but implement processes and procedures which are designed to abide by the data governance created earlier, and would if desired, adhere to the UNSPSC—United Nations Standard Products and Services Code—a system of classification that has universal applications.
Remember the problems encountered earlier by Tyler from Purchasing and Joe from Receiving? The graph below demonstrates non-standardized descriptions that are then properly named and ordered by QuadraDot’s Granite Material Masters™ software. Just as effectively, QuadraDot can bring your company’s materials masters into line…and enact protocols to keep them that way.
Remember…change comes from within.
It’s often not enough to only clean up your bad data. Whether your company internally spearheads the initiative, or the astute decision is made to bring in our QuadraDot professionals, the problem of bad data won’t be stopped entirely until those who have access to the system are trained in best practices regarding data governance. Our QuadraDot consulting team will work with company stakeholders to return your material masters to a spotless condition and then ensure that data stays consistent going forward.
Material masters don’t have to be incomprehensible—with over thirty years combined experience, data governance isn’t Greek to our knowledgeable QuadraDot team of professionals. We’ll help your company develop a strategy to slay those nasty snakes lurking in your material masters and you can get back to your first order of business—running the company.