If we want to build a data-driven business, we need to appreciate the various roles that data and intelligence can play in the business – whether improving a single business service, capability or process, or improving the business as a whole. The examples in this post are mainly from retail, but a similar approach can easily be applied to other sectors.
Sense-Making and Decision Support
The traditional role of analytics and business intelligence is helping the business interpret and respond to what is going on.
Once upon a time, business intelligence always operated with some delay. Data had to be loaded from the operational systems into the data warehouse before they could be processed and analysed. I remember working with systems that generated management infomation based on yesterday’s data, or even last month’s data. Of course, such systems don’t exist any more (!?), because people expect real-time insight, based on streamed data.
Management information systems are supposed to support individual and collective decision-making. People often talk about actionable intelligence, but of course it doesn’t create any value for the business until it is actioned. Creating a fancy report or dashboard isn’t the real goal, it’s just a means to an end.
Analytics can also be used to calculate complicated chains of effects on a what-if basis. For example, if we change the price of this product by this much, what effect is this predicted to have on the demand for other products, what are the possible responses from our competitors, how does the overall change in customer spending affect supply chain logistics, do we need to rearrange the shelf displays, and so on. How sensitive is Y to changes in X, and what is the optimal level of Z?
Analytics can also be used to support large-scale optimization – for example, solving complicated scheduling problems.
Increasingly, we are looking at the direct actioning of intelligence, possibly in real-time. The intelligence drives automated decisions within operational business processes, often without a human-in-the-loop, where human supervision and control may be remote or retrospective. A good example of this is dynamic retail pricing, where an algorithm adjusts the prices of goods and services according to some model of supply and demand. In some cases, optimized plans and schedules can be implemented without a human in the loop.
So the data doesn’t just flow from the operational systems into the data warehouse, but there is a control flow back into the operational systems. We can call this closed loop intelligence.
(If it takes too much time to process the data and generate the action, the action may no longer be appropriate. A few years ago, one of my clients wanted to use transaction data from the data warehouse to generate emails to customers – but with their existing architecture there would have been a 48 hour delay from the transaction to the email, so we needed to find a way to bypass this.)
If you have millions of customers buying hundreds of thousands of products, you need ways of aggregating the data in order to manage the business effectively. Customers can be grouped into segments, products can be grouped into categories, and many organizations use these groupings as a basis for dividing responsibilities between individuals and teams. However, these groupings are typically inflexible and sometimes seem perverse.
For example, in a large supermarket, after failing to find maple syrup next to the honey as I expected, I was told I should find it next to the custard. There may well be a logical reason for this grouping, but this logic was not apparent to me as a customer.
But the fact that maple syrup is in the same product category as custard doesn’t just affect the shelf layout, it may also mean that it is automatically included in decisions affecting the custard category and excluded from decisions affecting the honey category. For example, pricing and promotion decisions.
A data-driven business is able to group things dynamically, based on affinity or association, and then allows simple and powerful decisions to be made for this dynamic group, at the right level of aggregation.
Automation can then be used to cascade the action to all affected products, making the necessary price, logistical and other adjustments for each product. This means that a broad plan can be quickly and consistently implemented across thousands of products.
Experimentation and Learning
In a data-driven business, every activity is designed for learning as well as doing. Feedback is used in the cybernetic sense – collecting and interpreting data to control and refine business rules and algorithms.
In a dynamic world, it is necessary to experiment constantly. A supermarket or online business is a permanent laboratory for testing the behaviour of its customers. For example, A/B testing where alternatives are presented to different customers on different occasions to test which one gets the best response. As I mentioned in an earlier post, Netflix declares themselves “addicted” to the methodology of A/B testing.
In a simple controlled experiment, you change one variable and leave everything else the same. But in a complex business world, everything is changing. So you need advanced statistics and machine learning, not only to interpret the data, but also to design experiments that will produce useful data.
A traditional command-and-control organization likes to keep the intelligence and insight in the head office, close to top management. An intelligent organization on the other hand likes to mobilize the intelligence and insight of all its people, and encourage (some) local flexibility (while maintaining global consistency). With advanced data and intelligence tools, power can be driven to the edge of the organization, allowing for different models of delegation and collaboration. For example, retail management may feel able to give greater autonomy to store managers, but only if the systems provide faster feedback and more effective support.
Related to the previous point, data and intelligence can provide clarity and governance to the business, and to a range of other stakeholders. This has ethical as well as regulatory implications.
Among other things, transparent data and intelligence reveal their provenance and derivation. (This isn’t the same thing as explanation, but it probably helps.)
Obviously most organizations already have many of the pieces of this, but there are typically major challenges with legacy systems and data – especially master data management. Moving onto the cloud, and adopting advanced integration and robotic automation tools may help with some of these challenges, but it is clearly not the whole story.
Some organizations may be lopsided or disconnected in their use of data and intelligence. They may have very sophisticated analytic systems in some areas, while other areas are comparatively neglected. There can be a tendency to over-value the data and insight you’ve already got, instead of thinking about the data and insight that you ought to have.
Making an organization more data-driven doesn’t always entail a large transformation programme, but it does require a clarity of vision and pragmatic joined-up thinking.
Updated 13 September 2019