I was thinking about what my third post was going to be about when I noticed what I had done on my earlier posts. I had neglected and rushed my metadata! Feeling a sense of shame, I then proceeded to clean up the elements I had rushed and scratch my head over the ones I had left blank. If you are wondering why I decided to obsess over this, it was because I broke one of my professional cardinal rules around data. I treated data as an afterthought.
What is the big deal then? Over the years I have been reading and researching around why data related projects fail, as well as being involved in a few myself. Depending on the source, somewhere between 60 to 85% of data-driven projects ended in failure. When I first came across this, my first question was how could this have even happened, especially on projects that were specifically about data.
I then proceeded to try and find a common answer across the web and in journals and came across many things. Some of the most common things I saw included:
- Poor executive knowledge of data and lack of strategy: Here I basically saw that they underestimated the task in front of them and failed to treat data as an asset. Also, they had no strategy on how data would work within their organisation. Lack of a centralised data function also played a factor.
- Lack of appropriate tools: It was deeply disheartening to see companies use Microsoft Excel as the backbone of their Data Quality/ Management strategy instead of the appropriate industry tools.
- No data team: Leave it the Developers and the business experts. As talented as they may be, they were not data specialists and the people that were assigned to these projects had to be the jack of all trades from a data perspective. Meet the Data Architect Governor Engineer Steward Analyst.
- Not enough business buy-in: This one may sound a little familiar, but it often sounded like there was no attempt to build a Data Community or grow the idea of Citizenship
There were a lot of other reasons why these projects failed, but in the end, it all came down to the same reason. Data was treated as an afterthought every single time. The emphasis on having a solid strategy, an educated executive, the right team and tools was not a priority and for many companies, they ended up paying it back in lost time and productivity. Going back to my earlier conundrum, I ended paying in time because I had forgotten what some of the values should have been as they were not in my mind anymore. While my problem was quite simplistic compared to the real world, these issues can cause companies serious costs when data is not considered properly.
This is why my professional rule is to never treat data as an afterthought. Over the years, I have been involved in projects in and outside of my work life and in the initial conversations, it is amazing to see how quickly data is overlooked. When brought up, it becomes one of those things that is dismissed as “we’ll get to that later” or “not the focus right now” only to end up in a series of crisis meetings and lose on the most precious commodity – time. In my career, I have worked out a ratio of 1:3. For every one hour spent in pre-planning, three hours of crisis or emergency remediation work is avoided. When getting people to understand the importance of why we need to think about data upfront, I use this ratio to sell the value of getting this done right at the start.
Some people will even ask me “why bring it up?”. Data can be something of a beautiful beast – majestic when it is accurate and providing you with the insight you need to grow, but it can be ugly and painful when ignored. Similar to Beauty And The Beast, data can often incorrectly viewed as a beast and as something that is too complex to understand. Over the years, I have learnt that people that ask that question view data purely as a beast, becoming overwhelmed by the complexity and not wanting to talk about it. The irony is that it ends up turning into the beast they were trying to avoid. To get around this problem, I sell the beauty of data alongside the time and resource savings that I mentioned earlier. It is easier to sell beauty as this is what drives value in your organisation and gets your team to treat data as an asset.
If you have gotten this far and you have wondered about the relevance of the picture above, think about this. Would you jump off a cliff without thinking about it prior? It is great to say “oh, I should have had a parachute” once you are descending at 100km/hr, but you are already heading for a crisis and failure. The lesson that I’m taking away from today’s post is to never treat data as an afterthought. This does not need to be hard or take a lot of effort. The next time you sit in a meeting and no one is bringing up the data requirements, stand up like the Data Citizen you are and bring it to everyone’s attention.
Meanwhile, I’m going to clean up the rest of my metadata.
