Three Key Steps to Creating a Data-centric Culture
Data has become a company’s most valuable asset, providing a strong foundation for customer experience initiatives. So what can you do to build a data-centric culture and trigger insight-driven actions that enhance your customer experience?
Data has become a company’s most valuable asset, providing a strong foundation for customer experience initiatives. But unlocking its true potential is more than just empowering business leaders to make better decisions using insights extracted from data; it’s about embedding data science into the core layers of the business and recalibrating the company around data. Ultimately, it’s what differentiates a data-centric organisation from a data-driven one.
Data centricity, as defined by global strategic consulting firm Winterberry Group, is “the extent to which an organisation is culturally and operationally prepared to apply audience data as a source of actionable insight in support of advertising, marketing and audience engagement”.
Despite the prevalence of data and the substantial impact it can have on a company’s success, it remains an untapped resource for many businesses. Winterberry Group research showed that while nine in ten marketers and publishers claim they are ‘intensely focused on achieving data centricity across their organisations’, only 1% are ‘extremely confident’ their organisations have the right expertise, experience and skills to get the most value out of their data.
So what can you do to build a data-centric culture and trigger insight-driven actions that enhance your customer experience?
Ensuring that data is readily available throughout the organisation is the mainstay of a data-centric culture. The goal of data democratisation is to give every employee access to the data they need to perform their duties more effectively and bolster customer intelligence activities.
According to Cynthia Stoddard, Adobe’s Senior VP and CIO, “the goal is to enable the business and product teams to capitalise on data assets and drive insights and predictive analytics. With this [data-driven] model, businesses can integrate disparate big data at scale and align stakeholders across the company around a common language, consistent measurement, data governance and actionable insights.”
Data is notoriously fragmented, making democratisation a challenging task. The good news is that easy-to-use analytics tools, dashboards and AI-powered solutions can eliminate bottlenecks and break down silos. They help accelerate the process of democratisation by unifying different types of information and delivering the right insights to the right people in the format that works best for them.
Get the right talent in place
While use of best-in-class technology helps increase the availability of insights throughout the organisation, human expertise must remain at the heart of a data-centric approach. Investment in core data skills is required to get maximum value out of data and technology, and to ensure that the right processes are in place to translate insight into commercial gains.
A customer analytics study published by Adobe in partnership with London Research revealed that top performers are 86% more likely to invest in the skills they need to get the most from their digital technology investment. Competition for skilled talent is stiff though – in 2018, demand for data scientists increased by 56% in the US alone, as reported by LinkedIn.
The role of data scientists isn’t limited to organising, crunching and interpreting the data; they can frame complex business problems through different lenses and drive CX strategies. Or, as Anil Kamath, Adobe’s VP of Technology, aptly puts it, “you need to think of data scientists as not just experts in using algorithms or providing insights, but in terms of how can they be change-makers in their organisation”.
Assess the potential of AI
In the 2019 Gartner CIO Agenda Survey, artificial intelligence (AI) was ranked as the most disruptive technology, ahead of data and analytics in second place. According to the Consumer Technology Association, the benefits are evident: in most sectors, organisations adopting AI at scale or in a core part of their business report profit margins that are 3 to 15 percentage points higher than the industry average. In the next three years, these top performers expect their margins to increase by up to five percentage points more than the industry average.
AI can assist with the collection, refinement and interpretation of data, eliminating repetitive tasks and enabling a more creative approach to problem-solving. This doesn’t mean that manual intervention is no longer needed, but that data scientists and analysts can focus their strengths on work that drives more impact for the business.
Use of AI and machine learning will become increasingly prevalent in the context of data management and analysis, so keeping an eye on potential applications and experimenting with cognitive technologies will enable companies to stay ahead of the curve.
In conclusion, an unrelenting focus on data democratisation and using the right technology and skills will help you concentrate on the initiatives that really matter and make the data-centric vision a reality.
For more tips on how you can build a data-centric culture, download the ‘How Smart Businesses Use Data to Empower the Entire Organisation’ white paper, published by London Research in association with Adobe.