In my first article in this MYOD [Make Your Organisation Data-Driven] series, I articulated a one-line approach to successfully injecting data into your organisation’s DNA: Using a Dataset -> Skillset -> Mindset framework. This will take your people and processes on a journey to data actualisation.
The first and most critical stage of the framework is the Dataset stage. The term Dataset used here is a synecdoche or figure of speech standing for all aspects and processes for making data available, reliable and credible.
And it is Data Availability Maps (DAMs) that will you help better understand the lay of the data land and resolve information conflicts to supercharge data-driven decisions.
Why Dataset is the first stage
Have you ever been in a meeting where the simplest of questions seem to be the toughest to answer? A good one is, “What’s the number of new customers we have acquired in the last three months?”
If you ask the tech team that managed the shop/point-of-sale database, you’d get one number – say 1000. When you ask the marketing team using a different set of tools, you’d get a slightly different number – say 980. Finally, when you ask the team responsible for customer experience, you’d get 920. Which one is correct and have you spent a lot of time debating these in crucial meetings?
This ‘which-metric-to-trust’ debate is a key component of all big and small meetings alike and is better known as data confusion. This confusion happens in organisations of all sizes, due to many diverse systems capturing the same information in many different ways. It also leads to the famous “guess we can’t ever know the truth and can’t trust the data” resignation, which is the biggest and most dangerous bottleneck to data-driven decision making in an organisation.
A second big hurdle is GIGO (Garbage in, garbage out). This implies if the data is unreliable, then any super-smart insight or algorithm built on top of it will be unreliable by extension. Despite the most expensive artificial intelligence (AI) tools or software on the market doing what they do best, if the internal data landscape isn’t better mapped out and governed, it makes the whole process nullable.
GIGO along with data confusion, makes it extremely important to first understand the data landscape before going any further on the data journey.