How to Fall Back in Love with Your Data

Is your organization’s data a lot like a bad relationship? You started off aligned, complete and everything seems to click with immense accuracy. But over time, maybe just a few years in, things that once seemed so easy, get more complex. The time you want to spend “enjoying” your relationship data is overshadowed by the large amounts of time you spend troubleshooting, problem-solving and trying to figure out why it seems like your data wants to leave you broke and unhappy.

It is time to face it, data quality and governance is a key imperative in any digital, data-driven organization, so before you decide to settle and think this is the best you’ll ever get… let’s look at how data quality and governance can be the “data therapy” you need to get back on track.

What Went Wrong?

The health of your business data can be gauged in terms of completeness, consistency, conformity and compliance to your business rules. If you don’t pay attention to your data and give it the TLC any loving relationship deserves, you can run into a host of issues like inaccurate reporting, inconsistent customer information and imprecise inventory. Poor data begets poor decision-making which ultimately can take a toll on your bottom line and keep you up at night wondering what else is out there.

Now the first rule of any good couples data therapy is not to point fingers and place blame – you and your data are in this together and it’s important to remember that good things are worth working at. There are three contributing factors of data quality management – People, Process and Technology.

People come into play because often your data and systems are owned or managed by multiple sources. Who is responsible for how data is entered, maintained and kept consistent? Is there a centralized check-point for the hard decisions on business data rules? The more people involved in your relationship, the more complex it can get.

Your data should be managed by a realistic, implementable process. What are the series of steps for entering in materials, records or items in your management system? Is the process unwieldly, unused or maybe even completely circumvented by the aforementioned people factor? If the process is easily manipulated and exceptions are regularly made, you are doing your data a disservice. Incomplete data prevents you from consistently accessing accurate information, and in turn, reaching your desired result. If your organization makes exceptions in their data management process, you are welcoming poor quality data.

Many data therapists companies out there will try and offer you a one size fits all solution. One self-help book won’t solve your relationship woes, just like a quick-fix software solution won’t fix your data quality and keep it that way. Technology plays a role in the data management as a tool to enable and facilitate data quality when paired with the right people and process.

How to Reignite the Spark with Your Data

You need to lay all your cards on the table. What went wrong with your relationship data in the first place? Was it a process that wasn’t enforced? Was there a lack of consistency between data sets that were never resolved? And what steps are you going to take to make sure the same mistakes aren’t made again? If you don’t work first at transforming your data and then keeping it clean, you’ll be right back where you started… crying into an empty heart-shaped box of chocolates.

Data transformation is done in four stages – which can be referred to as Utopia’s 4Cs:

  • Cleanse – Parse data to its appropriate attributes, normalize and standardize
  • Create – Enrich data attributes or create new ones where needed
  • Consolidate – De-duplicate data within the database or among multiple databases to create a single-view of customers, inventory, materials, etc.
  • Classify – (If needed) – Standardizing the data to meet industry/market standards ex) UNSPSC, eCl@ss, eOTD

Clean data should never be a one and done activity – think of it as the long-term relationship that’s worth putting in a little extra effort. Your next move will reveal a lot about the health and future of the data. Key stakeholders have to be ready to assess and make the tough decisions around the organization’s data standards.

  • What are going to be the rules moving forward to improve consistency?
  • What is going to be implemented to improve accuracy and necessary consolidation?
  • What can be done to match unstructured and structured data to provide better information?

Relationship Data utopia is obtainable, even if you think it’s too late to salvage. First, you need to remember that you’re not alone. Governing data at the point of creation and throughout the data lifecycle is one of the most challenging data management exercises for many organizations. Some of them may choose to deny there’s trouble in paradise, but for anyone who is ready to fall back in love with their data, Utopia can be your data therapist by:

  • Creating a best practices-driven organizational governance model including roles and responsibilities before implementing a new IT solution
  • Guiding your organization to create data in a unified and consistent manner that will keep long-term data standards in place
  • Creating data cleansing routines based on data quality standards and the organization’s business rules
  • Developing an operational business process model to manage data and the prescriptive guidance to implementing it effectively in a new IT solution
  • Establishing a recommended IT landscape to support corporate goals in a cost-effective manner
  • Providing data governance advice and frameworks in the areas of Materials, Assets and Retail
Don’t give up just yet, let Utopia help you re-ignite the spark and fall back in love with your data.

Topics: Data Governance, Data Quality, 4Cs