This morning my page-a-day calendar asked me to draw something that makes me happy. It was cold and snowy outside. I had a few minutes. I was thinking of warmer days. My inner creative self took on the challenge.
I reached into my overstuffed and overflowing pen cup for a black fine line Sharpie only to pull out a green felt tip marker. My second dip into the pool yielded a yellow highlighter. I was close on my third attempt, a red fine line Sharpie. I was ready to spill the contents onto my desk, but I pulled out the fine line Sharpie enabling me to draw.
I am pen hoarder. I pick them up at conferences, vendor presentations and just about any chance where one is offered free for the taking. My logic is that you can’t have too many and when you have a bunch, you will always have one handy when you need it.
Pen and pencil hoarding is similar to many ill-fated data management strategies. I will start my 12-step pen hoarding detox by sharing 4 hoarding sins that applies to pens, data and life.
- Indiscriminately picking up and saving everything
Who could not use a pen? Who could not use 10 pens? Using that logic, it seems natural that the more pens the better. This is the mantra of many data folk today. Their measure of success is that we save every piece of data we have. It’s bound to be valuable someday.Many big data vendors tout the ease of socking away data in lakes and reservoirs. The architecture and technology of big data is capable.However, a save-it-all scenario is not necessarily the right answer. Good old fashioned data analysis and data management practices need to be employed to assure that stored data is reliable, clean and applicable to the business. Big data is not an excuse to discontinue the data management practices that protect and add value to the enterprise data asset.
- Saving it in an inappropriate or undefined place.
I cram all of my pens in a souvenir mug. When there is no more room, I store them on the desk or in a drawer. As Staples says, “That was easy!” This morning’s artistic endeavor would have been easier had I organized them so that I could pull out the appropriate pen.Finding data often encounters the same issue. Data may be stored on a platform without understanding how the data will be retrieved. A component of data architecture, particularly in an environment where diverse DBMS solutions are available, is matching data to not only the DBMS functionality but to the accessibility and integration with other technology assets and audiences.
- Not knowing what it is and how to find it.
That black pen was in there somewhere. Unfortunately with most pens, you don’t know the color and type of ink until you expose the tip. It would be nice if all databases were easily understandable. Accurate data models and documentation seldom exist. Profiling data and writing queries are common means by which the purpose and content of data is uncovered.We will not build silos of data. I heard that countless times over my data career. That did not happen. If you live in the typical corporate IT world, you face a wall of data silos. The source of data is not obvious. The integrity of data is even more suspect. Good data management strategies address what to do with existing silos and how to mitigate the propagation of future data silos.
- Not knowing when to throw it away.
I often pull out a dry pen. Pens do not have an atomic half-life. They have a shelf life. The same applies to data. Technology allows the storage of a vast history of data. It has enabled analytics that contribute significantly to an enterprise’s bottom line and efficiency in the workforce.Knowing when to throw away data makes the data resource more valuable and manageable. Data strategy, by its name, is strategic in nature. That means that every piece of data needs to be evaluated for how it fits in the near term and future strategic directions of the enterprise. That takes some skill in defining a data management strategy that has a clear vision on where the data asset fits into the corporate vision.