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How insight-driven enterprises use data to strategic advantage

Accenture managing directors Nick Millman and Ray Eitel-Porter distil steps for enterprises to turn data into business insight from the firm’s consultancy

Data and the key insights that can be derived from it have emerged as powerful strategic assets for businesses.

Decisions supported by data insights can give a company a strong competitive edge, by enabling it to adapt more effectively to changing market conditions, drive operational efficiency throughout the organisation and seize new opportunities. Companies that are slow to adopt analytics, on the other hand, run the risk of being disrupted and outmanoeuvred by faster and more agile, insight-driven competitors.

To reap the benefits of analytics, a company should look to transform into an insight-driven enterprise. With the internet of things generating large amounts of data, costs of computing plummeting and more intuitive visualisation tools on the rise, now is the time to adopt a data-centric approach to business for a competitive advantage. 

Through our experience of working with leading companies around the world, we’ve identified several key actions taken by organisations that have transformed successfully into insight-driven enterprises:

  • Establishing a "centre of gravity" for analytics. Accenture research has found that two thirds of firms that are analytics leaders in their industries have someone who serves as head of data or insight. This role – often at the "C" level – can be the centre of gravity, or hub, for analytics in a business. To provide the most benefit, this role should not be housed in a single functional area of the business, but instead be situated at the enterprise level, where this person would work alongside the CMO, CFO, CHRO and other members of the C-suite. This structure would help shift the role of analytics from a functional level to the enterprise level – speeding innovation and improving access to analytics talent across the entire business.
  • Initiating a new analytics operating model. A popular way of addressing cultural and organisational challenges involving analytics is through a “hub and spoke” operating model. In this structure, a central hub – responsible for the major analytics management and leadership tasks – steers the spokes, which are embedded in the different business functions or geographies. Typically, the spokes implement tools and procedures specified by the hub, support requests for extracting insights, and pass information – which can include data – back to the hub. This model provides two key benefits: embedding data practices into everyday operations, and democratising information by moving to a team approach within the confines of clear governance standards.
  • Creating internal marketplaces. An effective measure to drive a data-driven mindset is to put resources into creating internal information "marketplaces" where individuals can share and collaborate on areas such as new analytics models or best practice. But doing so first requires overcoming the cultural challenge of deciding who owns the data – the related business function or the IT department? Unresolved, this can block the frictionless flow of information around the organisation and the ability to mine valuable insights from it.
  • Developing a data supply chain. It will be important for businesses to establish a data supply chain built on a hybrid technology environment – that is, a data service platform combined with emerging big data technologies. This environment enables businesses to manage, move and mobilise data across the organisation for those who need it faster than previously possible. An effective way to optimise data supply chains and the collection and storage of data is through the cloud; cloud-based systems can be deployed almost instantly, and they are flexible and cost-effective. They can also enable analytics experimentation without the prospect of tying up valuable resources in a project that could later turn out to be the wrong system.
  • Training talent and building a data-driven culture. Naturally, there’s a talent angle to this transformation. Business users should be offered training to help them better understand and use the latest analytics tools that enable them to extract insights from their data and make more informed decisions. A key element enabling the creation of an insight-driven culture are powerful visualisation tools, which complement traditional business intelligence reporting solutions. These tools present insights in appealing formats and bring them to life, which can help to spur collaboration, data discovery and the desire to incorporate data-driven decision-making throughout an organisation.

Another key element to consider for an effective transition to an insight-driven enterprise is a data scientist – however organisations struggle to recruit them, due to the high demand. One way a company can tackle this shortage is by forming teams of people – such as a systems architect, quantitative analyst, software engineer, visualisation designer and business analyst – who, combined, embody many of the qualities of a data scientist.

The transition towards an insight-driven organisation can be challenging and take several years to accomplish. However, given the growing importance of data in sustaining a competitive advantage, the journey to an insight-driven business is one that all organisations should look to undertake – or risk losing out to those who do.


Nick Millman (left) and Ray Eitel-Porter (right) are managing directors of Accenture Digital

This was last published in February 2016

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As you discussed, enterprises are transforming into data-driven and data-centric organizations to find and drive competitive advantages.

As part of this shift to big data, expensive relational data warehouse are giving way to equally massive “data lakes.” Structured and unstructured data – from customer relationship management, to social media, to machine sensor information – are gathered, combined and stored together in their original formats. They float there together, along with their metadata, waiting to be combined and harnessed at a later date. New software such as Hadoop and NoSQL allow companies to access and analyze data from its original format.

Equinix recently wrote about how enterprises are using these new “data lakes”: http://bit.ly/1SrILoF

Jeff Rutherford
commenting on behalf of IDG and Equinix
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