The age of Startups & Analytics – Competing in a Data-driven World
In recent years the connected world has created a new resource: data, whilst data and analytics capabilities have made a leap forward. The volume of data continues to double every three years causing fresh challenges for businesses – identifying the data available and what insights can be concluded from the past as well as in the future.
To realise this data-driven opportunity requires fresh thinking from startups to transform mid-sized and large enterprises. For a larger business, data – the internal business resource must be harnessed within a digital transformation framework; developing new data-driven business models and integrating them within long-established business models. Whilst the convergence of several technology trends is accelerating progress, capabilities to identify meaningful patterns in large data-sets, from strategy, statistics, AI to advanced market insights, is rare. Turning a world full of data into a data-driven world is an idea that many companies have found difficult to pull off in practice, many startups are disrupting this space. Here we identify key trends:
One of the major challenges larger businesses face is extracting value from data and analytics to take a holistic view of the business – tackling multiple business processes, and how to avoid silo-based intelligence to incorporate data-driven insights into day-to-day business processes. However, introducing new types of data sets can disrupt industries, and the right data integration capabilities can break through organisational and technological silos, enabling new insights and models.
Whilst many businesses are forming internal Data-driven business and technology teams, attracting and retaining the right talent is challenging, including data scientists and data-savvy business experts with functional expertise. Analytical tools are only as good as the strategy and the people behind them. To be effective, Data-driven analytics must be more than just a set of dashboards and reports. It should be a part of a holistic strategy that helps your business units work together towards a common goal, using shared methodologies.
New Business Models
Data and analytics have been a pillar in overtaking traditional competitors. Leading and evolved startups such as Uber use data-driven capabilities to not only improve their core operations but to launch entirely new business models. Uber has been successful by its unparalleled ability to leverage dynamic data. This data-driven approach is a priceless strategic differentiator for the company.
Data is a Corporate Asset
Data comes from a huge array of sources including the web, enterprise systems, phones, and payment systems. The value for businesses as the owners of scarce data is in aggregating data in unique ways, especially to develop valuable analytics taking away the guesswork involved in decision making across the organisation.
Data-driven Decision Making
Data-driven insights and predictive analytics are being leveraged, for example in the consumer-goods industry. These companies now use predictive analytics to refine decision making to determine price, promotions, and stock levels. Tesco systematically integrates analytics and consumer insights from its Clubcard loyalty program data to build sustainable competitive advantage by targeting and segmenting customers.
The relatively slow pace of progress in some domains is due to a lack of realisation of the full value of data and analytics. Some companies have responded to competitive pressure by making large technology investments but have failed to make the organisational changes needed to make the most of them.
Organisations that are able to harness these capabilities effectively will be able to create significant value and differentiate themselves, while others will find themselves increasingly at a disadvantage. It’s important to note that it’s not the data, but the people who interpret and leverage the data who are the real asset. At Seven Startup Advisory, our consultants help startups combine data science credentials, with strategic acumen and an ability to explain analytics in layman’s terms.