One of the most significant trends currently shaping business is the realisation that data holds the key to making better decisions, responding rapidly to market shifts and, most important of all, anticipating new opportunities.
Based on data, managers and ultimately everybody in the company can gain a truthful picture of the company itself, the market it operates in and what its customers want; it can then develop strategies that will optimise its processes and improve performance by meeting customer needs.
On the face of it, this seems simple to do. After all, companies have an abundance of data with more coming as business continues its headlong march towards digitalisation. It is also possible to purchase data sets that can sometimes complement internal data by providing information about the market, customers and competitors.
With all this raw material available, becoming a data-driven organisation seems like a slam-dunk.
Nothing could be further from the truth. The availability of large amounts of data, while a prerequisite for becoming a data-driven organisation, is only the first step. One problem is the sheer volume of the data, a second is its quality. The old adage, “garbage in, garbage out” remains more relevant than ever.
Haste is perhaps the most fearsome enemy when it comes to turning data into valuable information.
In short, haste is perhaps the most fearsome enemy when it comes to turning data into valuable information. Too many companies buy into the value to be derived from data, but fail to take the time to understand how value is created in the organisation, and how data can be used to enhance the value-creation process.
Strategy is key when it comes to data
A strategy for dealing with the data is required in order to realise value. In a business sense, data is not valuable in and of itself; its value lies solely in the contribution it makes to helping achieve the business's strategic goals.
My view is that the place to start is the business strategy and developing the data strategy in line with it.
I always like to begin the conversation with clients by quantifying the benefits of developing and implementing a data strategy by looking at the gains that can be made − for example, an improvement in net profit margin of 1%. It's a goal that causes eyes to light up.
It's achievable through various scenarios, like incrementally improving sales revenue, lowering costs, and optimising inventory and collections by using data-derived insights.
It should be immediately obvious that to achieve this, the entire value chain should be considered. Only then can the process of value creation be understood, and the opportunities for improvement identified. The data itself flows across the value chain and is generated by it.
A framework for collecting, cleaning, transformation and modelling this data is needed to allow for the data to generate insights and draw useful conclusions that incorporate the entire value chain.
The benefits to be realised include cost reduction, finding patterns and anomalies, predicting events, optimising business processes, finding new prospects, plus focusing on the right areas and allocating resources accordingly.
Creating the data strategy
Once the value-creation process is understood and the business's goals set, it is time to start crafting the data strategy. The first step is to create an inventory of what data assets exist, and then to assess how mature the organisation is in terms of its data and analytics operations. Any gaps should be defined.
The final portion of the initial phase is to understand how the company's strategic goals can be linked to its data and analytics operations.
All this work creates a baseline that outlines what the company's data looks like and where it is, and a data framework to align the data and analytics with the value chain.
The next stage is to identify and prioritise use cases related to data usage, and define what data is required and what the implementation model is.
Each use case should be prioritised based on the value it would deliver and the estimated cost and effort needed to implement it. These elements should be integrated into an implementation roadmap.
Challenges and how to overcome them
Most of the roadblocks can be traced to the lack of a comprehensive approach to the governance of data and analytics. It's important to enable a culture change that positions data as a key corporate asset.
A second, related point is that in order to conceptualise how data integrates with value creation across the whole value chain, existing data siloes (which typically correspond to organisational siloes) must be eliminated.
This introduces an important principle: the need for a centralised core data model and a centralised approach to data operations. Only in this way can the data strategy govern how projects are executed, and the proper management and governance of the data.
At the core of this centralised framework, a formal data and analytics centre of excellence should be established. The centre’s main responsibility is to oversee all activities related to data: identifying, evaluating and prioritising use cases and then developing and implementing delivery.
It would also interact with the various business units and the technology by means of which the data flows and are analysed. Staff, be they existing employees or new recruits, would also be incorporated into the centre of excellence.
In conclusion, it's worth mentioning that the tendency to see data through the prism of technology alone is extremely dangerous.
Technology is obviously critical on multiple levels, but realising value from data is primarily dependent on organisational and cultural change that is driven by business strategy.
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