Businesses mustn’t assume their pre-pandemic data is still valid. They now have to consider everything they know about their customers, employees and partners through the lens of these new circumstances. We have vast amounts of data, but I’d argue that, without context, we can’t have meaningful, coherent and consistent knowledge.
The pandemic is reinforcing the importance of being able to convert data into a clear, credible, reliable story – and it’s highlighting how we’re largely failing to do this. Despite the collection, analysis and presentation of immense quantities of data, it’s difficult to compare one country’s situation with another’s, because the story is always a bit different. There’s no alignment, no consistency, just partial, disjointed versions of the facts that are all true when viewed in isolation, but don’t connect to make a coherent picture.
The lesson here is that businesses need to look more critically at their data strategy so they can use data to carve out an objective truth.
However, organisations tend to invest time and money in what they know, or think they know. Getting support for a ‘just in case’ infrastructure or capability is much harder, and this often limits data strategies and a company’s ability to flex to discover and model the unexpected.
The starting point for change is recognising where your business and data strategy is now, and where it could go in the future.
In this phase, you’re in emergency mode. There’s no time for any major planning, and definitely no time for refined approaches. There’s just the need to do what has to be done to keep going, to keep serving customers and protecting employees.
It’s ‘the hard way’ to learn how limited your organisation can be when it comes to accessing basic information and how much of a ‘tunnel view’ it has about understanding different aspects of the business. You find KPIs that don’t describe enough, models full of broken assumptions, and data that’s totally unmanaged.
Some organisations cope better than others during this stage. What makes the difference is being able to access key information quickly and exploit it. Security and privacy are the main priorities, and sustainability is lower down the list.
This phase is sometimes confused and hectic, but it’s an opportunity to think out of the box, and to find a way forward through trial and error.
After emergency mode, you need to adapt in a more sustainable way so you can get ahead of the game. To do this, you need a better understanding of the wider context and more structured thinking. This means getting the right information flowing, supported by a more solid and scalable infrastructure, and an operating model that can manage and exploit the data.
Your focus is on identifying valuable data and how to manage it, as well as making pragmatic, but relevant improvements to your data infrastructure. This could range from looking at cloud solutions to creating a more flexible operating model.
The start of your evolution journey can be a difficult phase for a couple of reasons. Firstly, you have to identify solutions that balance the purely pragmatic / tactical with the scalable. And secondly, you may need to decouple the longer transformation of your IT stack from your business requirements, adding concepts like a ‘Data Decoupling Layer’ and ‘API / Microservices wraps / envelopes’.
In this phase, rapid prototyping and beginning to treat data as an evolving product are key. Plus, it’s good practice to define an incubator for products to be rolled out.
Overcoming these challenges means getting your organisation ready to manage sudden changes in the future, as well as understanding how to look at your business from different points of view and feeding this through to your data.
For example, how much do you know about your workforce? You know how your employees are doing from a business perspective, but do you understand their potential and how they could flex to change? Or, taking your models and telemetry as an example, how ready are they to adapt to a change of business context? Many KPIs are rigidly based on certain data inputs, with a defined business meaning. What if that changes? How much effort is needed to re-train models / adapt KPIs?
I recommend focusing on three areas to move your business – and your data – forward. Ask yourself:
The pandemic has forced organisations to look at their data strategy in a more vertical and critical way, from their vision right down to the technology and infrastructure. It’s vital to have inputs from different parts of the business to stop you missing key elements that will help your data strategy drive proper change.
To find out more about how we can help you prepare your data strategy for every eventuality, please get in touch with your account manager. We’re here to help.
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