Getting Data in the Enterprise

Introduction
So many organisations today struggle with their data. The world of big data, analytics and predictive modelling is so attractive, yet, the perceived complexity of achieving a data nirvana is so great that data projects either spiral out of control or never start. So how can you get a handle on your current data, whip it into shape and start using it?

Understand Current Reality
Having a clear and honest understanding of the current reality is essential to understand what it takes to make a change. So what is your current reality?

Here are some questions to consider that would help you to better understand your current reality:

  1. Do we have a map of all of the sources of data in the organisation? How much data do we actually generate across the organisation today? Do we have any way to baseline this quantity?
  2. What channels for ethical data enrichment are available to us today? Are we using any of them?
  3. Do we even have a definition of ‘ethical data use’ for the organisation?
  4. Who owns or is responsible for each of the existing pockets of data?
  5. Are those pockets of data compliant with all privacy (and potentially GDPR) legislation? If a consumer came to us today and asked for all of their data to be provided to them would this be possible? What would it take to guarantee this was the case in today’s current reality?
  6. Are there major gaps in our ability to obtain data? Have we identified those gaps and who or what is responsible?
  7. Are each of our pockets of data clean? Could we identify the ‘source of truth’ for any dimension of data today?
  8. Do we have security practices in place to protect our data?
  9. Do we have any form of governance of how data is currently able to be used throughout the organisation?
  10. Do we know who the consumers of data in the organisation are today?

Next You Need a Vision
Corporate vision statements are lofty, yet vague. How can you craft a vision about how data is used in the organisation that gets clarity? Using a technique adapted from the military it is possible to craft an ‘actionable vision’. The Commander’s intent is an intent describing military focused operations and it is a publicly stated description of the end-state as it relates to people, the purpose of the operation, and key tasks to accomplish. Commander’s intent acts as a basis for everyone to develop their own plans and orders to transform thought into action, while maintaining the overall intention of their commander. An ‘actionable vision’ should do the same thing as a Commander’s intent – describing the end state with a degree of fidelity that the reader understands what the future state should look like without explicitly prescribing tactics unless absolutely necessary. Anyone reading the vision should have a clear understanding of priorities in achieving success in that future state.

Some things to consider to help craft your data science vision:

  • What could your vision mean for clients? How could this impact their lives?
  • What kinds decisions could an employee make after reading the vision? Do they know whats important?
  • Does the vision define its ‘end state’?

An example of a ‘vision statement for data’ for a fictional financial services organisation would be something like this:

“Given our organisation’s mission is to empower our clients to realise their financial goals, the most critical aspect of this mission is to provide our client’s visibility of their financial data in a way that is meaningful to them. Our vision for data is to provide our clients with a convenient ‘financial situational awareness’ at all times through the data we collect and process so they can make better financial decisions every minute. This situational awareness is key to helping them realise their financial goals and therefore to us achieving our purpose as a company.

Just as we seek to provide financial insights in real-time to our clients in a way that is meaningful, our vision includes using data ethically within our organisation to achieve our mission. Data is the lifeblood of our organisation and the key to delivering value to our clients and growth for our business. Our vision is an efficient, cost effective way of delivering powerful predictions and meaningful insights for decision-makers through an organisation-wide view of data, while at the same time satisfying our compliance obligations and our commitment to ethical data acquisition and usage.”

The activity of making a vision statement for data takes a short form vision and builds a narrative in people’s minds that they can relate to, buy into and come together around. Some questions to consider to embellish the vision into a clearly defined future state:

  1. Could you be truly data-driven as an organisation?
    • Would you be able to shift your mindset to hypothesis-driven using predictive models to design future products and activities?
    • Could you architect business units to leverage data-driven feedback?
    • What does your data science capability look like as an organisation?
  2. How would employees exploit data? What framework could they use to ensure that:
    • their activities were always ethical (intentionally or unintentionally)
    • they were focused on creating value
    • the data that they need is available to them and they know where to find it
    • clear procedures exist for introducing new sources of data
    • new sources of data are described and distributed across the organisation
    • data being used can be trusted as the source of truth
    • any data or combination of would not put the organisation, employees, or customers at risk
    • data remained secure and the privacy of employees and customers was protected at all times
  3. What would the future landscape of data use look like?
    • What would prediction using data look like?
    • How is data being used? Is it syndicated? Is it available on websites or on mobile devices?
    • Are you using data in real time? What actions are you thinking of taking in ‘real time’?
    • How would you want to route streams of data globally to have a unified view of data in the organisation?
    • How would you navigate multiple conflicting data-sovereignty legislation?
    • How would you engage with trusted partners or suppliers with regards to the use of data? How would you manage the risk while reaping the benefit?
    • What data would be available to trusted third parties?
    • How would those third parties be allowed to use that data and for what purpose?

Finally, Build a Bridge
If we honestly understand our current reality and have an inspirational vision, we can build a bridge that defines the sequence of tactics we will use to go from the current state to the future state. In the tension created by the gap between where we are and where we want to be, tactics for execution emerge and a plan takes shape.

Just as we cross any bridge one step at a time, the bridge we build between current reality and the vision may take many steps. What would the first step look like? What is most urgent? What is a required foundation to achieve our vision? The more time we spend being honest about our current situation and fleshing out the vision, the simpler (and more obvious) the design of the bridge. The vision should set the ‘guard rails’ for our prioritising of actions and to know what is important. Based on the example vision earlier, it is clear that the number one priority is getting meaningful insights into the hands of customers. How? As long as we are ethical in how we obtain and manage the data – it’s up to the team executing on the vision. If they are starting from a fragmented base, they may choose to start small, gather insights from a couple of disparate systems and deliver a mobile app that has a big impact for clients with reasonably low effort. From this, they have a foundation to start consolidating more and more data, while at the same time, ensuring enough of a governance framework exists.

Conclusion
It makes sense to spend time to better understand the current reality and map the detail of the vision: getting stuck on implementation strategies makes no sense if you don’t have clarity on your map of data, where it comes from, how much is being generated, how it is currently being used, and the risks associated with that usage. If you can’t answer the question of data ethics, security frameworks, third party engagement strategies, data governance or how you seek to build internal expertise in data science, then you are not setting yourself up for success.

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