Data Driven Decisions – Why Businesses Fail to use Data

When we were kids, we were told not to touch the hot boiling kettle. For some of us, we heeded those warnings from our parents, and for some of us… we went ahead and touched the hot kettle. We all learn and decide on our actions in very different ways. However, why is it that despite having these data; or warnings of the hot kettle,… we still proceed in touching the HBT (Hot Boiling Kettle)?

1. Don’t know what data is need. Often time when it comes to a business environment, a lot of things seems far more complex compared to just touching a HBT. Identifying what data holds ground in your decisions may fall into 3 categories –  Goal, Change, Growth.

Goal – Focus on learning data on driving your business closer to it’s goals.
Change – Define gaps and areas of business improvements.
Growth – Actionable and predictive data to build reach for Goals.
*More on this in a future post.

The key is to identify data needed to reach each of your business Goals; what gaps can be seen to build that Change and what action and predictive data can push you towards the Goals.

2. Data is not at hand when needed. When we walk into that meeting room, often times we don’t go in prepared with the data we need, and most likely, need for data might suddenly appeared during the meeting and we might not have it well prepared. It is very important to have these data at hand to get quick actionable data.

Years ago when I was in Product Marketing for a Japanese Consumer Product, we had yearly discussions about what product we would like to bring in for each country. We would prepare our past sales data to help us decide on our future product, however during each meeting there is bound to be something new – A new colour, a new feature. This lead to a lot of speculation on our side as we did not have data at hand when it was needed.

3. Data is not fresh.  The HBT won’t stay hot forever (unless you leave it on the stove). Data has it’s own validity period and often data slides on a lot of factors like period of time, events and individuals. Getting Data is just like getting a Hot Lead, it’s good when it’s fresh or relevant.

Trends can change in a blink of an eye. Towards the end of last year, Perodua released a new Myvi which had features worth drooling over for it’s price range. This release, shook up the whole lower price segment cars and also the imported B-segment cars which were in a far higher price range. The Proton took this as an opportunity, and started marketing their similar price range model directly to Myvi clients, pulling part of the Perodua’s attention back to Proton.

Another example for this is when looking at Car Buyers who are interested in buying a new car. One would like to assume that during festive seasons in Malaysia like Hari Raya, Chinese New Year and Christmas would bring in high number of sales. However, this is true partially as depending on the year and the economic situation, Hari Raya tends to has far more car buyers compared to Chinese New Year.

4. Assuming numbers are just numbers. The worse case to be in is having the numbers, but not knowing what to do with it. One of the biggest challenge to implementing Data Driven Decisions is the bring meaning to data. Sometimes it could be a simple as organising the data into bite sized chunks while other times it could be mixing and matching several data sets together.

Regardless of which, I believe it should always start with the Goal in mind. By placing the goal forward, it not only gives us direction of how the Data could be sorted but also gives us visibility to the gaps we need to fill.

 

Labelling that the kettle is always hot might not be the right way to go around it, nor should one person completely avoid touching the kettle at all because it is hot. But it’s about having data at hand to tell you how long it would take to cool down, where is the right place to hold the kettle and what can you make with the kettle.

We collect data all the time throughout our day. Making use of that Data might seem daunting at first, but it’s a good start to have.  At the start of the day, we all want our cup of coffee, and to get that coffee we need to pick up that kettle and boil some Data.

For more info for Automotive Data in Asia, do check out http://icardata.icarasia.com