Strategy – arguably, the most misunderstood concept in business and one that companies new and established have struggled with since it first emerged as a modern business discipline in the early 1960s.
Data strategy – the same confusing concept applied to your organisation’s data (as opposed to the company’s reason for being), with equally effective results.
And yet, we persist. Partially because making strategic decisions and taking action makes us feel like we’re in control, and of course, the only other option is pure anarchy.
I’m a big fan of the “Playing to Win” approach to strategy, published by A.G. Lafley and Roger L. Martin in 2013. It provides a very simple, 5-step framework for making strategic decisions, distinct from just creating a disconnected plan of actions and activities.
So how, then, can we apply this framework to the question of an organisation’s data strategy? Some might argue otherwise, but I believe that data can be considered an asset in the same way as a production plant or string of retail stores can, so let’s begin:
Winning Aspiration
This is the big one as far as I am concerned and the question most often neglected by organisations. What are you asking your data to do for you? Is it a question of operational efficiency? Of customer intimacy? Of informed R&D and product development? Or simply a case of regulatory compliance and records retention?
The answer to this question is a critical input to all other strategic considerations and must define how your organisation can use data for competitive advantage in the marketplace. For example, do you believe that you can use (previously unavailable) data from your IoT network to leapfrog competitors in the transport and logistics industry?
Where to Play
From a data perspective, this question is a little easier for organisations to answer. The most obvious playing fields are internal (think operational data like manufacturing output or product sales/inventory) and external (typically customer or environmental data). A challenge for many organisations occurs when they want their data assets to support entry to new markets outside their core business.
For example, in recent years, many enterprises have considered “monetising” their data by selling it or using it to expand into adjacent industries – consider grocery retailers now selling financial services. Without the clarity of a winning aspiration, it is very easy to misunderstand where you should be playing.
How to Win
What seems like the hardest strategic decision can sometimes be the (relatively) easiest. Strategy is a cascading series of questions so once you have figured out why and where your data has value, you can focus much more clearly on the specifics of how.
At this point in your strategy, data needs to be explicitly linked to winning, whether that is becoming the lowest-cost provider, innovating new products/services, providing a distinctive customer experience, or being the first to market.
Making the tough decisions early allows the organisation to prioritise resources and personnel effectively – it’s unlikely that an organisation (no matter how good its data) can win in every aspect of the business. For example, companies providing commoditised products, such as electricity, typically focus on using their data to provide superior customer experience as a means of competitive advantage.
What Capabilities
This is where most organisations start with their data strategy – an abstract view of general-purpose capabilities that can be used for any and all data-driven scenarios. For the reasons stated above, this is also where most organisations fail with their data strategy.
Once you have determined the why, where, and how (to win), it becomes much easier to figure out the capabilities required. Do you need real-time data? Point-of-sale data? Manufacturing data? Shipping data? Customer data? The list is endless without any focus or priority. Likewise, the thrill of focusing on data storage, transformation, integration, and processing (visualisation, reporting, etc.) is something that technologists find hard to resist.
A good data strategy is based on the capabilities needed for business success – anything else is a waste of time. Unfortunately, many organisations source and manage massive amounts of data “just in case they need it” or develop capabilities that “might come in handy at some point”. As the saying goes – hope is not a strategy.
What Management Systems
At this point in our data strategy, the decisions become a little simpler. Not because they are easy but because most of them are obligations rather than choices. Data practitioners well appreciate the need for management systems (aka governance) and are used to dealing with concerns such as security, data quality, data lineage, data management, data storage, as well as regulatory controls like privacy and data retention.
From a strategy perspective, layering on the requisite controls is relatively straightforward once you know what data you need and how it will be used. That’s not to say this step is somehow less important, though –management systems are typically risk mitigation systems whose failure can massively impact an organisation (fatally, in some circumstances) and are an essential element of any data strategy.
And there you have it. Data strategy is both deceptively simple and maddeningly complicated – asking fundamental business questions of your organisation allows you to frame and prioritise your data initiatives, ensuring relevance and value, not to mention operational and cost efficiencies. Companies that ignore the why, where, and how their data can be used (which is most companies, sadly) are setting themselves up for failure.
The good news is that these questions are simple and can be answered relatively quickly – they require thoughtful consideration, though, and an actual understanding of your business.
Domo is a low-code, self-service business intelligence and data analytics tool that streamlines all of a business’s data into one mobile-friendly platform. For more information, visit www.domo.com.
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