Data Transformation in large companies – the value it creates and how to get there

Hedley May hosted a Round Table event on Data Transformation in large companies – the value it creates and how to get there.
Our discussion was led by Andy Hill, the Chief Data Officer at Unilever, whose prior career was spent supporting clients at Dunnhumby, the global customer data science organisation. We were joined by eight FTSE CFOs.
Below are the key lessons learned from the Unilever data transformation journey over the past five years:
Lesson 1 – Timing & organisational buy in
  • Data strategy needs to be integrated into the business strategy as an enabling force.
  • Successful Data Transformation will require organisational restructure. In the case of Unilever, this was when
    digitisation came to the forefront of strategy in 2017, with a supporting £2bn organisational restructure and
    associated cultural shift, which reached deep into the employee population.
  • Timing is key to secure company-wide support at every level, from the Board through all the management layers of
    the organisation.
Lesson 2 – Focus where your impact can be seen
  • Start with forming a fertile land where you know data intelligence will have an impact.
  • ‘Hit the big rocks!’ i.e. focus your data transformation activities on areas of the business where you can impact what
    matters to strategy (e.g. Consumer Price & Promotion spend in the case of Unilever).
  • Create an integrated data platform that will serve as the heart of the digital ecosystem, bringing together data
    from across the enterprise onto a platform for all to access. This is a strategic play: it breaks down silos of data
    ownership; reduces complexity by creating a single version of truth; increases the governance and resilience of the
    data ecosystem; and brings down cost while removing barriers to entry for new analytical product innovation.
  • ‘Go local’ i.e. choose to add value in manageable and distinct areas.
Lesson 3 – Create a high level of engagement and measure everything
  • Focus on the employee experience – embrace intensive user design partnering and agile working.
  • Build an environment where every single piece of data (and algorithm) has human oversight and responsibility
  • Reduce risk by building awareness of the importance of context.
  • Create data followership through enhanced and frequent communications on key wins and impact stories (see Lesson 5 below).
  • Create a data quality measure and bring metrics to bear – e.g. KPIs, including Net Promoter Scores, that show business value.
Lesson 4 – Raise the Floor and Raise the Ceiling of the organisation
  • Build data literacy for all and embed it as part of digital upskilling to raise the floor
  • Attract and nurture deep data & analytics functional expertise to raise the ceiling.
  • Build out upskilling models for existing employees to retrain in new technical areas (e.g. Data Science, Data Engineering).
  • Develop cross-functional, collaborative and agile teams in the first line.
  • Foster diverse, inclusive teams to drive growth (#WomeninData).
Lesson 5 – The approach to change management
  • Advocate and communicate across all programmes & delivery
  • Commit and work the politics with willingness to make an example of detractors.
Concluding remarks

Many of our attendee FTSE CFOs highlighted the challenge of getting an organisation, which is performing well
commercially, to embrace data transformation.

Furthermore, for all the benefits that data delivers in terms of time-saving and improved decision-making, these tend to
fade in importance in sectors where the impact of digital disruption is not yet perceivable in commercial performance.

The future holds more automation and less human decisions. The successful companies of the future will be those that
bring together the best of human and machine intelligence to drive growth and improve business operations. It is data
transformation that will allow this to be realised. As many corporations seek to become the equivalent of a ‘driverless
car’, the scope for differentiation from competitors becomes narrower. The optimisation of data should ultimately
enable difference.

As this shift takes hold, there is a significant risk that human input diminishes to the point of imbalance. How do we
ensure that our organisations don’t dumb down? Critical to this, is the retention of human judgement, emotional
thought and interpersonal connectivity, through a shared sense of responsibility and purpose.

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