Last Updated on Tuesday, 18 January, 2022 at 12:36 pm by Andre Camilleri
Marc Rizzo, Data and Analytics Lead, Digital Solutions, KPMG in Malta
Marco Vassallo, Partner, Digital Solutions, KPMG in Malta
Curt Gauci, Director, Digital Solutions, KPMG in Malta
More businesses are recognising the importance that data plays within their organisations. They continue to identify key areas where machine learning, dashboards, and optimisation algorithms can be used to drive efficiencies and improve insights. Although there is general consensus on the need to use such tools in today’s business, many still find it difficult to pin down the benefits that investment in data science, data analytics, and business intelligence will bring to their shareholders/owners.
Analysing the business metrics
Methods for making the link between lower-level objectives (ex: ‘spotting fraud efficiently’, ‘finding new customers’, ‘aiding with the maintenance and servicing of assets’) and the higher-level financial goals of the C-Suite differ based on the scope of the project. However, every data analytics intervention must be undertaken to generate operating cost savings, or to generate revenue.
Cost saving plays are usually much easier to justify financially through Return on Investment considerations. Targeted implementation of such projects is very helpful in building confidence within organisations on the ability of data analytics tools and techniques to impact business outcomes.
However, in order to survive and thrive, the competitive imperative of organisations needs to be the transformation from traditional service or product companies to technology and data enabled vehicles. This requires the implementation of a holistic, long-term digital transformation strategy focused on meeting information needs in the place and at the time it is required. Key to this is designing IT systems to seamlessly interact with employees, customers, and their environment to diagnose operational and product quality issues and initiate corrective actions.
The challenge here is that investment in initiatives aimed at generating revenue through the implementation of digital transformation strategies are not usually characterised by near-term top-line gain. The example of Tesla analysing data of the performance of all their cars and wirelessly pushing software updates to increase the range of their vehicles shows this well. Tesla were able to improve the range of 2-year-old vehicles beyond the original range of a brand new car. A marked improvement in product quality and hence customer satisfaction, the impact of which is not immediately transferred into revenue gains.
The big why of data analytics
The pay-off of the implementation of data analytics techniques for shareholders and owners must be the ability to grow, or in cases of competition form non-traditional entrants, maintain market-share profitably. It is difficult to demonstrate a direct link between investment in digital data infrastructure and market-share.
Therefore building a strong, focused message on why investment is being made is key to maintaining support of any transformation efforts. The ‘why’ will differ from one company to another but must be viewed as an opportunity to create value.
Business leaders need to look outward, then inward. Outward to evaluate what tools are available, and how their competition is using data. Inward to derive competitive advantage by empowering their organisations to make better, faster, customer-focused decisions from data. Finally, they must combine the external and internal considerations to derive a cohesive raison d’être for data analytics in their organisation and communicate this effectively to their shareholders and owners.
What is your organisation’s ‘Big Why’ for Data Analytics?
