If you have ever tried to do a data project, business model, KPI project, dashboard, or any other project that involves using data from your company you have had to deal with data flaws.
Studies have shown that roughly 60% of data projects fail.
Why is that?
These failures are caused by data flaws.
Our experience over 25 years, working with hundreds of companies in countless industries, has allowed us to identify four types of Data Flaws that exist in every single company.
The only wildcard is to what degree any company has these four Data Flaws:
- Data Fracturing;
- Localized Perspective
- Multiple Sources of “The Truth”, and
- Data Voids
Data Fracturing is a reality of businesses today. Ready access to a myriad of SaaS (software as a service) products each designed to do something specific leads to critical data being collected, used, and reported on in several different systems.
It is very common for us to find companies with different SaaS products for each of the following areas:
- List Management
- Project Tracking
- Team Collaboration
- Endless others
Many of these individual products can be linked together but this rarely done well, if at all.
This results in critical pieces of information and data across the company being stuck in silo’s and oftentimes not effectively leveraged, communicated, or shared.
The proliferation of various SaaS products makes it easy, quick, and inexpensive for people in different areas of the company to purchase and use their own products. And capture, use, and rely upon their own sets of data.
This provides limited perspective about what other areas of the company are doing. This causes individual areas of the company to make decisions based on their data to maximize the performance of their area.
While this may sound like a good thing, it often isn’t.
The true goal is to maximize the performance of the overall “organism” (the company as a whole).
And this is often not achieved, nor even possible, when individual parts of the company are allowed to maximize their performance from a purely localized perspective.
Multiple Sources of “The Truth”
With Data Fracturing and Localized Perspective comes multiple sources of “the truth”.
With no structure around data consistency, definitions, and formatting people in different parts of the company rely on “their” data as the “truth”.
Sometimes their version is accurate – sometimes it is far from it.
This becomes an extreme challenge to the company when “official” data is at odds with a particular truth version. In this situation, mis-trust, conflict, and doubt become major factors and lead to dysfunctional reactions and actions.
Data Voids are areas in a company where data is non-existent, inaccurate, or non-current.
It is impossible to have perfect data in any company so Data Voids are a natural occurrence. They are even healthy because they represent a resource allocation decision in many cases.
The key is to acknowledge and accept that Data Voids are real and present.
This allows the accuracy and reliability of any data projects to be properly built, accounted for, and an appropriate degree of precision ascribed to them.
Hug The Ugh
Data Flaws exist in your business. They exist in every business.
The key is to understand their existence, acknowledge it, embrace it, and ensure your data projects and degree of reliance is appropriate given your level of Data Flaws.