It's your turn to host Christmas for your family this year. The tree is set, the fairy lights are glowing, the presents are sitting there tempting you with their promise of answered dreams and excitement. In your plan, you've decided that two days before Christmas is the time you'll be heading to the shops to buy all the fresh stuff you need for the big day (everyone knows that the day before Christmas is a total NIGHTMARE!). And so off you go to the shops armed with a giant shopping list of food, items and last minute panic purchases.
But wait - how did you generate this list? Where did it come from? Was it pure intuition, spur of the moment stuff - or was their a science or method behind it? What considerations did you make?
In today's post I'm going to deconstruct this process and explain how successful data analytics organisations work, (hopefully) drawing out some keys for you to use in your analytics journey. Read on to see if it helps!
#1 It's about people
My first point is the foundational point for successful analytics. People matter. Only in the context of helping, assisting and improving peoples lives is data given context and meaning.
In the example above, the primary driver is people. If you're anything like me, the first consideration would have been the number of people coming along, their likes and dislikes. This could have followed on to traditions within you or your partners family, and probably would have led on to questions about whether there were enough presents, Christmas crackers, drinks and so on. All of these requirements, can be traced back to one thing - the people.
In a similar manner, organisations who are successful in implementing data analytics start with people. Typically, they break this down into three major areas:
#2 It's about supporting decision making
My second point is that data analytics provides decision makers with information around their decision points - it does not take over the decision making process.
When it's done right, data analytics provides tools to build a picture of what is happening. In the above example, once you know the rough numbers of individuals coming, the next question to ask yourself is probably around what to eat. There's a lot of factors to consider in this - some of your guests might have specific allergies; or you may have some traditions which cannot be contravened. Each of these pieces of data will shape and guide what you purchase, but the point is that you remain in control of the process as the key decision maker.
Contrast this to a process where a computer program simply surveyed a mythical information system which had tracked everything all of your guests had eaten for their entire year, generating a list for you to buy. In this case, your system has staged a coup. You have lost control of the decision making process, generating results which lack context, theme or reason. Moreover, you'll probably get it wrong, because it's statistically unlikely that your guests eat a Christmas meal more often than 3-4 times a year.
In a similar way, the goal of data analytics and your data analytics team is provide accurate, relevant data around your decision making. Data analytics and your data analytics team are not there to replace your decision making process - simply to inform and decipher the data resident in your analytics system.
#3 It's about understanding why
The third thing I'd like to point out about successful data analytics is the continual pursuit of understanding WHY things occur.
This may seem like a pretty easy process, but it can get quite complicated very quickly. In the example above, lets take a look at why you made the decisions you did for food. Firstly, you surveyed the numbers attending, and came up with a figure you were fairly confident with - we would call this a confidence level. Then, you started to make decisions about what types of food to buy, and here is where it gets complex pretty quickly. Ask yourself 'why?'
To start with, you might consider your guests preferences, allergies, traditions and arrival times. This cuts away a list of things you definitely cannot cook or use. Then, diving down a bit further, you need to make an assessment of how much time you're going to be able contribute to this process (and the likelihood or otherwise of people assisting you). Going a bit deeper, you might start to consider when different foods are to be served, or what kind of serving capabilities you have on hand. Then, what sort of finances you are able to contribute. And the list goes on.
Your end game might be to serve a delicious meal and have a great time - but imagine this process repeated millions of times across all sorts of locations and figuring out ways to make this as efficient and effective (business words) as possible!!! That is what data analytics can do for you, but it means a singular commitment to the question WHY.
Why is consumption of these items at the level it is? Why do people keep making the same error on webpage when they do? Why does stuff break after this many uses? Why do I need new equipment? Don't be afraid of this process, rather embrace and pursue it and you'll be amazed at the transformation in your organisation!
#4 It's about the data
My final point in todays post comes back to my title - becoming data driven. Any information system, analytics capability and analytics team is only ever as good as the data being fed into it/them.
Successful organisations take time and care to invest in building systems which make it easy, simple and clear to their employees and customers on how to do this and do it well. This isn't just about how to guides and online videos; it's about your visual presentation, your design choices and your engagement with the process and team.
My starting example could be blown right out of the water if you had no idea who was coming, what they liked and what your traditions were. In this case, you would be presenting your assessment of a 'good' result, and while none of your guests would say so, you'd probably end up being 'that guy/girl' for the next few years!!!
Sadly, many companies today do this with their products and services, and as a result introduce risk and waste into what they're doing. Take the time to get it right and think about the people and I am confident that your organisation will prosper.
Hopefully this post has helped, and if so, feel free to drop me a line. I'm always looking to improve on my own knowledge and understanding, so if you feel I'm wrong, speak up! You can check out some of my other posts at http://www.creativeappnologies.com/data-driven-blog and I'm always looking for new challenges! Look forward to seeing you all next time,
As always, I'd love to hear your thoughts and comments. Use the tag #creativeappnologies to let me know.
I love analysing data. I've done it for nearly 10 years now in various shapes and forms, and for me it's an endless world of wonder. There's nothing else I'd rather be doing!