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The evolution and future of Data Analytics
Clemens Baader, Chief Data Officer, International Personal Finance plc
There has never been a better time to achieve impact with data. Data and analytics capabilities have evolved rapidly in recent years. More sophisticated algorithms have been developed, computational power and storage have steadily improved while, at the same time, become much cheaper to use, and the volume of available data has grown exponentially.
The convergence of these trends is fuelling rapid advances in data analytics and artificial intelligence. Data and analytics are also changing the nature of competition. Leading companies are using their data and analytics capabilities not only to improve core operations but to launch entirely new business models. Data is now a critical corporate asset.
The persistent challenge
While talking about data as the world’s most valuable resource is widespread these days, several technologies- and business-related difficulties make understanding data’s importance and realising its potential a persistent challenge.
Organisations that are relatively new to data and analytics have overhyped expectations of their potential value and return on investment (ROI). These inflated expectations can often lead to misdirected use cases, misallocation of capital, and increased chances of failures. Furthermore, vendors and consultants are only too happy trying to monetise these new trends and buzzwords abound.
Data and analytics implementations are typically focused on storing, governing, and managing data – with an emphasis on project-based and operational measures, for example, delivery on time, on budget, and to scope. However, creating value from this data, in terms of measurable benefits and ROI, is regularly neglected as an afterthought. All too often, a culture of “let’s build it first, think about the value later” approach is depressingly common. Incorrect architectural and infrastructure choices frequently result in projects that do not deliver business results in terms of meeting the promised and expected ROI.
Within data and analytics, we should only focus our resources (i.e. people, capital expenditure, opportunity costs etc.) on initiatives that support any of these two needs, and where the estimated impact significantly exceeds the anticipated costs
So, how can we approach this in a more effective way? There are some key principles we have adopted at IPF to ensure the return on investments is real.
Beyond all the “sophisticated buzz” any commercial enterprise has exactly two fundamental needs: to increase revenues and decrease costs. Hence, everything we do with data must ultimately serve to achieve these goals.
Within data and analytics, we should only focus our resources (i.e. people, capital expenditure, opportunity costs etc.) on initiatives that support any of these two needs, and where the estimated impact significantly exceeds the anticipated costs.
This highlights just how vital it is that establish clear goals for your data and analytics initiatives are established before you start. Chasing the hype by simply moving data from one platform to another is unlikely to provide tangible value. Simply pouring data into a new data store and ‘hoping for the best’ isn't a strategy.
To ensure we are not just hoping for the best, our team at IPF clearly sizes the opportunity so we can prioritise, pilot opportunities that clearly allow us to develop our capabilities and ensure we design and put in place target operating models.
Let's unpack each of those elements.
1. First of all the importance of estimating the expected value of each suggested data and analytics opportunity. This can be a ‘guesstimate’ more than a precise estimate – but it is important to think about the valuable contribution each initiative is expected to make beforehand.
2. Prioritise the most promising analytics opportunities and use cases, based on the expected value of each opportunity, and prioritise the roadmap (the ’what to do when’), of opportunities based on expected ROI impact and feasibility.
3. Next, it is important to pilot use cases to develop data and analytics capabilities. At IPF, we first pilot use cases to create a proof of concept (‘trial and error’, to avoid downward financial J-curves). We then ensure we proceed with further use cases if and when the expected value from data has been established.
4. Finally, the importance of designing and putting in place a target operating model is key. For example, the organisation, people, processes and governance, architecture, and talent. We then build alignment around this, and put in place the designed operating model.
So, while it is true that data is now a critical corporate asset, without careful discipline it can be just hyperbole. Companies that use data and analytics effectively to improve their business and genuinely look for new ways to generate income have the power to unleash their tremendous impact and get ahead of the competition. Those that pay lip service to the latest fashion run the risk of losing out on the opportunities to change a business for the better.