Part 1: What do I mean by a ‘Really Offensive’ Data & Analytics Strategy?
Video Transcript
Hi there everyone, my name is Neil Mason, Founder and Principal Consultant at Applied Insights. In this series of short videos, I'd like to take you through how to build what I call a really offensive data and analytics strategy. But I guess that I should probably start by explaining what I actually mean by that.
I first came across the notion of an offensive data strategy reading an article in 2017 in the Harvard Business Review by Leandro DalleMule and Tom Davenport. In this article, they outlined the differences between what they called a defensive data strategy and an offensive data strategy taking the terminology from American Football notion of the defence team being there to stop things going wrong versus the offence who are the ones trying to score points. In the article they outline the components that make up each type data strategy.
Essentially defensive data strategies are about minimising downside risk and so tend to focus on areas such as data management, governance and control whereas offensive data strategies focus on driving and supporting business objectives and driving business value such as increasing revenue, driving customer loyalty and so on.
Their view was that depending on the type of organisation you were and where you were in your overall maturity in data and analytics would potentially dictate which strategy was more applicable, a data defensive data strategy or an offensive data strategy. Let’s dig into that a little bit more and take a look at this in the context of the business outcomes value chain.
In this diagram here we can see the various states of data as it flows through the value chain from raw data being collected and then being turned into consumable information. Information is then used to generating insights. Insights are then consumed and hopefully lead to some sort of action which then in turn eventually leads to beneficial outcomes. Hopefully.
Driving the value chain are the processes involved in the transition from one state to another. For example, the whole process of data management and data governance, collecting data from various sources, curating it into usable datasets, defining what the data means and managing the quality of it. In other words, creating consumable information that can be used across the organisation.
Then we have the process of analytics which takes that information and generates insights that can be used to support, augment, or even automate decisioning. Decisions lead to actions and the execution of those actions generates some type of outcome.
So, in terms of defensive data strategies these are very much concerned about those processes that the beginning of the value chain around data and information management, ensuring that the data processes are correct and that the correct governance is in place. This type of strategy is particularly appropriate to highly regulated of environments like the financial services or healthcare sectors where there are significant concerns around issues such as the quality, security and privacy of data.
In terms of value creation, a defensive strategy is necessary but not sufficient. An offensive data strategy is more focused on taking information turning it into insights through analytics and applying those insights within the business to achieve the strategic business objectives. This is the traditional preserve of a business intelligence and/or analytics department who supply those insights to the business, whether that be through regular dashboards or ad-hoc projects and investigations.
What I mean by a really offensive data strategy is one that you take that to the next stage and incorporates the complete value chain. In other words, the outcomes from data. To do this requires that you focus on what I call the Last Mile. The Last Mile in this instance is the process of informed decisioning and execution. This means deciding how to take the insights and intelligence, whether that be human or artificial, through a decisioning process that informs actions which will lead to valuable outcomes. Also, and importantly, organisations need to have mechanisms in place to track and measure those outcomes. This Last Mile is an area that I believe that many organisations still struggle with and that’s why I believe organisations need adopt a really offensive data and analytics strategy that takes and end to end view to maximise the opportunities to realise or unlock value in their businesses.
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So, what are the key components of a really offensive data strategy?
I think there are three main ones.
First of all is that focus on the Last Mile and that's really about making the shift to what's called Decision Intelligence. I'm going to be talking about that in the next video.
The second component is the development of value-based analytics products and again we'll get into this in a bit more detail but essentially what we're talking about here is creating artefacts, deliverables and software products that are designed in a user-centric way to deliver distinct and discernible value to their users.
Then the third component is creating robust value management delivery and tracking processes around those products to ensure that the right products are being developed and delivered at the right time in line with the strategic needs of the business. So over the course of the next few videos, I'll be taking a more detailed look at each of these areas in turn.
I look forward to seeing you then!
