Habit #1 – Be Proactive


Habit #1 – Be Proactive

For the first habit, Covey starts with what may be the most obvious habit of all – that of being proactive. Simply put, you have to be motivated to make things happen in life, rather than sitting back and waiting for them to happen. While it may be the most obvious habit of all, it also may be the toughest one for people to grasp. In my house, my kids are allowed to say nearly anything, but the biggest pet peeve I have is when one of my kids says, “I can’t”. There are all kinds of things in life that I have never succeeded at – but not succeeding doesn’t mean it’s impossible for me to do so. In this section of his book, Covey classifies people as proactive and reactive. He delineates the difference by stating that reactive people wait for things to happen, then blame others when they fail. Proactive people simply keep pushing through failures until they succeed.

For data workers, I think this is a pretty key concept. I have seen so many times where data workers at each step of the process don’t take the initiative to question the quality of the data, or to even learn the business rules. They simply state “it matches the source”. In today’s world, where data may hop through several systems before getting to an end user, this attitude simply isn’t good enough. To be clear, it’s important that the ETL is done right, and that the data transformation and movement quality remains consistent. However, if your source data quality is terrible, it’s imperative that you act proactively and take the initiative to try and improve it. Otherwise, you’re essentially a sewage worker, and not a data worker.

The other key concept Covey attributes to proactive people is that they act within their circle of influence. In a nutshell, this means that you take action in areas where you control the results. Far too often, I see data workers jury-rigging business rules in report queries, views, or stored procedures in order to cover up data irregularities. This is a mistake. Instead of masking the issues with the data, expose it! Show it to your data providers within the supply chain of data in order to get them to determine the root cause of the irregularity. This allows for all downstream processes from their system to be more accurate. In addition, it may be that their data is irregular because of a gap in the business process – which could very well be costing your company millions of dollars annually. An example I like to use is that if I go out to eat and order a steak, and the waiter brings me a fish sandwich, then I’m going to send it back. I’m not going to accept the sandwich just because the waiter who moved the food from the kitchen to my table says that it’s the same sandwich he was given in the kitchen, nor will I accept the kitchen’s excuse that their supplier only sent them fish sandwiches to serve. I’ve made my request, and I expect it to be fulfilled exactly as I asked for it – 100% of the time. By working with the upstream data providers and the downstream consumers of your data to improve the process overall, you will be exerting a positive influence on those within your circle – and by knowing you have their best interests in mind, perhaps you can also gain their trust.

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