In a recent article I wrote over on CIO.com titled Want to Speed Up Your Digital Transformation Initiatives? Take a Look at Your Data, I discuss the importance of data quality and data management in an organization’s digital transformation efforts. That article can be summarized with the closing paragraph (but feel free to go read the full version):
To speed up your transformation projects and initiatives, you need to take a long, hard look at your data. Good data management and governance practices will put you a step ahead of companies that don’t yet view their data as a strategic asset.
I wanted to highlight this, because it continues to be the biggest issue I find when working with clients today. Many organizations have people that are interested in data and they are finding the budget to get their team’s up to speed on data analytics and data science…but they are still missing the boat on the basics of good data management and data quality.
What is data quality?
Data quality refers to the overall utility of a dataset(s) as a function of its ability to be easily processed and analyzed for other uses, usually by a database, data warehouse, or data analytics system. … To be of high quality, data must be consistent and unambiguous. Data quality issues are often the result of database merges or systems/cloud integration processes in which data fields that should be compatible are not due to schema or format inconsistencies
Not a bad definition. My definition of data quality is:
Data quality is both simultaneously a measurement and a state of your data. It describes the consistency, availability, reliability, usability, relevancy, security and audibility of your data.
Now, some may argue that this definition covers data management and data governance more than data quality…and they may be correct…but I’ve found that most people that aren’t ‘data people’ get really confused (and bored) when you start throwing lots of different terms out there at them so I try to cover as much of the master data management world under data quality. I’ve found its more relatable to most folks when you talk about ‘data quality’ vs ‘data governance’, etc.
Data quality in the real world
Last month, I spoke to the CEO and CIO of a medium sized company about a new data initiative they are planning. The project is a great idea for them and should lead to some real growth in both revenue and data sophistication. While I won’t go into the specifics, they are looking to spend a little over $5 million in the next two years to bring data to the forefront of all of their decision making process.
While listening to their pitch (yes…they were pitching me…I’m not used to that) I asked one my ‘go-to’ questions related to data quality. I asked: “Can you tell me about your data quality processes/systems?” They asked me to explain what I meant by data quality. I provided my definition and spent a few minutes discussing the need for data quality. We spoke for an hour about data management, data quality and data governance. We discussed how each of these would ‘fit’ into their data initiative(s) and what additional steps they need to take before they go full-speed into the data world.
Early today I had a follow up conversation with the CEO. She told me that they are moving forward with their data initiative with a fairly large change – the first step is implementing proper data management / quality processes and systems. Thankfully for this organization both the CEO and CIO are smart enough to realize how important data quality is and how important having quality data to feed into their analysis process/systems is for trusting that analysis that comes from their data.
As I said in the CIO.com article: ‘Good data management and governance practices will put you a step ahead of companies that don’t yet view their data as a strategic asset.’ This CEO / CIO pair definitly see data as a strategic asset and are willing to do what it takes to make quality, governance and data management a part of their organization.