Please Think Responsibly

A maturity model is used to describe where an organization is in terms of their growth and development in relation to a set of best practices.  The higher the maturity or the organization, the more effective the organization is at innovating or improving a specific capability.  In this case, the maturity model shows the risk you take in not setting the right foundations in using data the right way.

There are 4 key phases in data maturity: 1) Belief and Experience, 2) Inertia and Intuition, 3) Data Supported Thinking and 4) Data as a Genie

1. Belief and Experience – Most businesses start this way.  They follow the hunch of someone with experience, who understands the industry or has a “crazy idea”.  Usually there isn’t data to support the idea and it represents an innovation.  The company must progress to demonstrate that there is a need for the product, service or innovation.
2. Inertia and Intuition – Some businesses don’t get data savvy.  They incorporate data driven decision making where it is needed but don’t use it strategically.  Many small businesses work this way – if your bakery is selling cupcakes and you understand and serve your customers, you don’t likely need a robust data infrastructure.  Creative fields are like this too – Stephen King probably doesn’t worry too much about data when writing his novels and I’m pretty sure that Ernest Hemingway never did.
3. Data Supported Thinking –  This is the inflection point and a critical part of the life cycle.  It is the first time that data is used to support decision making.  This is exciting (look at us, look at these new insights) but also risky.  This stage is the most critical time to think responsibly since the decisions you make here will forever impact the culture and processes of your organization.  This when the data genie comes out of the bottle and starts to enable your organization to make decisions at scale.  Your company will be dependent on the path that you take, so it is worth taking the right one.
4. Data as a Genie – A Genie, according to some is the most powerful being in the universe, and data is arguably one of the most powerful tools in the universe.  That said, it is also enslaved.  The question is, who is servant and who is the master?
   1. You own the data –   Data serves you, your customers and the population at large.  It is used to validate business goals, provide evidence to support your strategy and vision and to measure the health of your business.  Amazon does this successful with a data driven culture that is augmented by the “working backwards” process.
   2. Data owns you – You don’t bother thinking for yourself and let the data do the talking.  Running another test, proposing another experiment.  You don’t have ideas, you just have projects and data.  You don’t add anything, but you don’t lose anything after all, everyone is doing the same thing as you!

Data is a tool, not THE tool and in order to use it responsibly and build a successful organization on it consider the following:

1. Choose the right metrics – This is absolutely critical in stage 3 (although broadly applicable across all stages). If you chase the wrong metrics you be working against yourself without even knowing it.  Choose metrics underlie the business typically they center around either the customer, revenue or cost.  If you get too far away from either of those core business needs then you are getting into vanity metric territory (post coming soon!) and you risk diverting vast amounts of resources towards the wrong thing.
2. Always keep a baseline – Metrics change, situations change. This is why we have stories and narratives. Data doesn’t tell what to do, it doesn’t even tell us what happen, it just gives us a reading on a specific thing.  A baseline is always important in order to compare the same to same, even if it causes some strange looking graphs.  If you change a business strategy, or compensation or a product sector you should create new metrics and measurements but keeping a baseline always allows you to say “3 years ago we were doing x, now we are doing y and here is why”.  Without a baseline you can’t see change over time, you just see how change is measured against itself.
3. Don’t be afraid to question the data – People love to talk about human errors but speak of the objectivity of data. One company I worked with as a consultant was uncomfortable with people self reporting activity completion when following up on house calls with patients because “people want to game the system” but was perfectly comfortable with a 10% error rate on a “data driven” solution to measure the same. If you don’t dig into a measurement that seems wrong but people task as an article of faith you run the risk of becoming a 4b type organization where obsession over data doesn’t drive the business outcomes you need.