Data Quality – The most important data dimension?

data qualityIn 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?

Informatica defines data quality in the following manner:

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

Emphasis mine.

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.

Data Ownership within an Organization

data ownership in an organizationRecently, I was having a conversation with an IT VP about data management, data access, reporting and analytical approaches to the data stored within the organization.  The conversation was a long and frustrating one.

I started working with this company to help them open up access to all their data repositories to allow people within the company to access and analyze the data without having to go to a data analyst to ask for a report or data set.   Everything that I’ve suggested this organization needs is being stonewalled by this particular individual with responses like “that isn’t secure,” “they can’t see that data” and “that isn’t their data.”

After many meetings without any real movement, I decided it was time to bring in the CIO into the discussion to help with either breaking down the walls this person was putting up or to tell me that those walls were staying put regardless of my suggestions.

The CIO, VP and I sat down and began discussing the issues and concerns that the VP had regarding my suggestions.  After we walked through all my suggestions and all the reasons the VP declared that they couldn’t be implemented, the CIO asked a few questions for clarification. After a few minutes of back and forth, the CIO declared to the VP that “we don’t own this data, the business owns this data…find a way to make this happen.”

I thought that was an excellent response from the CIO – “we don’t own this data” is a far cry from most IT professional’s feelings about dat. Many view data as their ‘property’ because they are the ones tasked with storing and protecting it.   That’s not a bad thing…but it has made many within IT feel like they have to say ‘no’ more than they say ‘yes’ to requests.

Through much negotiation and teeth gnashing, the VP and I were able to work through all of my suggestions and develop a plan to implement the necessary systems to reach their objective.  Included in this plan are proper data management and data governance systems and processes along with the right type of analytics engine to allow just about anyone within the organization  to take a look at data that interests them.

The key point of this is to highlight the fact that IT professionals don’t “own” the data. We don’t get to say ‘yes’ or ‘no’ to who gets access and what can be done with that data. We are just the keepers of the data and need to think about systems and processes that allow the organization to use that data in whatever way makes the most sense for the business at any given time.

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