The term “exponential organisation” came to the fore some years ago when author Salim Ismail outlined new paradigms in organisational design and described businesses that were able to fast-track traditional growth and make 10x the impact or grow 10x faster than other companies.
Ismail described several characteristics that made them distinct from traditional organisations, including their hierarchies, cultures and mindsets, appetite for risk and algorithms.
In many cases, exponential organisations were technology-enabled companies that could leverage digital proliferation to overtake bricks-and-mortar firms: Airbnb vs traditional hotel groups, Netflix vs video rental chains or Uber vs traditional taxi services, for example. The organisations with the top Exponential Quotient (ExQ) scores initially were the likes of Google, Amazon and Apple – all highly digitised companies.
But whether or not companies are digital at their core, what truly sets exponential organisations apart from their competitors is the degree to which they are able to make use of data. Having the right data foundations in place – that enable the efficient gathering, sorting and understanding of data – allows for analysis that drives innovative decision making and reduces the risk around business decisions.
LinkedIn CEO Jeff Weiner has said “data really powers everything that we do”. This applies to all the organisations showing exponential growth: Without massive volumes of data, used in ways that add value, the likes of Facebook and Google would not have become so pervasive.
During the pandemic, local organisations tapped into their data and deep understanding of their markets to map out new models that not only helped them survive, but also put them ahead of their competitors. Shoprite, for example, is taking data so seriously that it has a data science academy within its digital business hub, ShopriteX, which has helped power the group’s Sixty60 app and Xtra card, both of which are helping drive more revenue and generating increasingly precise and valuable data for the organisation.
Unfortunately, many South African organisations are lagging in terms of monetising their data. Monetising data does not mean selling a database or collated information: It means turning it into business value that drives results and revenue. What this looks like is different for every organisation: It could mean data-driven decision making to inform strategy, understanding customers better, assessing risks and opportunities more accurately, improving internal efficiencies, or responding to the unexpected.
To catch up with exponential organisations, companies need to start by getting to grips with their data and laying the foundations for analytics that deliver real business value. Putting the foundations in place for a data-driven exponential business should start with aligning data strategy to business strategy, and a road map to getting to the desired state.
Getting value from the organisation’s data is significantly harder when the organisation does not know what data it has or where it resides. Traditional data silos have to be bridged to enable organisations to collate, deduplicate, clean and integrate data for effective analytics. Data systems may need to be modernised and integrated to ensure the free flow of data throughout the enterprise.
The biggest obstacles to becoming a data-driven organisation aren’t necessarily technological
“Gigo” is a term that has been around for as long as computer science. It stands for “garbage in, garbage out”, meaning that the quality of data entering a system determines the quality of analysis resulting from the system. Good decisions can’t be made on bad data. Therefore, data quality and governance have to be on ongoing priority. Choosing metrics carefully to align to business objectives allows for better data consistency, which your modelers and analysts will thank you for.
Once the foundations for analytics are in place, advanced analytics and dashboards need to be implemented that remove the noise from data and ensure that the insights can be easily understood and acted upon.
The biggest obstacles to becoming a data-driven organisation aren’t necessarily technological. For effective data management, governance and ultimately analytics, the people factor cannot be overlooked: Resources must be upskilled to deliver on data-driven business goals, and data-literacy should be prioritised. Don’t forget the influence that data can have internally: Data analysis should be used to benefit employees, not just clients or customers.
Driving a company culture that supports data-driven decision making means that it needs to be embraced at the most senior levels of an organisation. If leaders adopt, and are clear that they expect, a language of evidence-based decision making, this will permeate through an organisation.
Today, while having the right data and analytics foundations in place doesn’t guarantee business success, it is virtually impossible to become an exponential business without effective data and analytics.