Did you know from 2010 to 2020, the amount of data created, captured, copied, and consumed in the world increased from 1.2 trillion gigabytes to 59 trillion gigabytes, an almost 5,000% growth. (Source Forbes).

Wow, and that’s just the beginning.

Never has it been more important for CEOs and Boards to understand the value of data to transform an organisation. This goes beyond our own internal data, but deeply understanding the strategic risk and value of connecting to many data sources. IDC quotes that 75% of enterprises will use in 2021 new, external data sources to enhance their cross-functional decision-making capabilities in ways that increase value compared with using internal data alone.

In this blog, I have the pleasure to co-write with Dr Ratneesh Suri, one of New Zealand’s leading data and AI experts as we explore how to create value from data, governing a data strategy, building data platforms, and the capability shift in an organisation to both create data assets, and to use them.

Creating business value from data

As we know, the volume of data being captured is immense, but the creation of data does not necessary create profit.  More important than the volume itself is how it is used for creating digital business growth.

We see the value of data loosely fitting into these five categories where we can use data-driven decisioning to:

1. CREATE GROWTH

  • Differentiate your products & services
  • Improve your products and customer journeys
  • Digital marketing for personalised recommendations of products based on customer needs
  • Measure your product sales as the start of new revenue stream

 2. SAVE TIME

  • Proactively fix something when it’s convenient, not when it breaks
  • Less time in traffic, e.g. google maps
  • Automate processes
  • Move to prescriptive maintenance for products and services
  • Connect data sources in one place for easy access and wider organisational adoption

     3. SAVE COSTS

    • Reduce costly shutdowns and maximize uptime
    • Reduce energy consumption
    • Reduce insurance costs
    • Optimise operations, people, and assets

      4. HEALTH & SAFETY

      • Home and business security
      • Health and Safety of workers
      • Realtime health responses and medicine transformation
      • Food safety and security

        5. ENVIRONMENT

        • Manage carbon use
        • Optimise air quality
        • Sustainability

        If we look at these buckets of value, they can offer gains internally to the organisation, and externally to customers.

        Creating internal data value

        The challenge in creating internal value is that in most organisations data exists in silos or is not trusted.  Often too,  data initiatives are not linked to value-driven outcomes meaning there is little clarity on how the data assets and data products will be operationalised and used.  Furthermore, many times we see a lack of alignment in the business around what are the key metrics to measure business performance, and how are they constructed.

        To start, we must understand what are the metrics that matter?

        As a foundation, every organisation should have a common understanding of the metrics that drive the organisation, and these should be highly visible. The metrics should be able to drive business performance at all levels of the organisation, from the c-suite strategic outcomes to individual business unit KPI’s through to frontline operational drivers.

        These metrics should be at three levels, driven from the same data sources:

        1. Enterprise Metrics that Matter, balance scorecards or C-suite KPI’s
        2. Business Units KPI’s, dashboards and interactive reports
        3. Operational Measures, near real time information

        Once these foundations are in place, we can start to apply more advanced analytics, leverage insights for AI, and start moving towards greater data maturity.

        Creating external data value

        For customers, we seek to use data to augment products and services or create entirely new ones.  This can be achieved by asking relevant questions linked to the five value buckets outlined above, here are three examples from HBR:

        • What type of information will help my customer reduce their costs or risks? Multi-billion-dollar businesses such as Yelp, Zagat, TripAdvisor, Uber, eBay, Netflix, and Amazon crunch quantities of data including ratings of service providers and sellers in order to reduce customers’ risk.
        • What type of information is currently widely dispersed, but would yield new insights if aggregated? Is there any incidentally produced data (such as keystrokes, or location data) that could be valuable when assembled?
        • Is there diversity and variance among my customers such that they will benefit from aggregating others’ data with theirs? For example, a company selling farm inputs (seeds, fertilizer and pesticides) can collect data from farmers with dispersed plots of land to determine which combinations of inputs are optimal under different conditions. Aggregating data from many farms operating under diverse soil, climatic, and environmental conditions can yield much better information about the optimal inputs for each individual farm than any single farmer could obtain from their own farm alone, regardless of how long they had been farming that parcel.

        This comes back to Episode 1 of UpSw!ng when we talk about understanding your customers valuechain, and the role you will play in the ecosystem.

        Governing data

        It is important that a business and its Board understand what is required when we are governing data, and why it is important.  Data governance is everything you do to ensure data is secure, private, accurate, available, usable, and is used ethically. It includes the actions people must take, the processes they must follow, and the technology that supports them throughout the data life cycle.

        This includes three common roles [google.com]:

        • Data Stewardship – ensuring accountability and responsibility for the data and the processes for proper use.
        • Data Quality – ensuring the quality of data and therefore the way technology is designed. Quality is generally judged on six dimensions accuracy, completeness, consistency, timeliness, validity, and uniqueness.
        • Data Management – This is a broad concept encompassing all aspects of managing data as an enterprise asset, from collection and storage to usage and oversight, making sure it’s being leveraged securely, efficiently, and cost-effectively before it’s disposed of securely.

        It is important that an organisation has a data governance committee or similar, and a simple score card is created for reporting to the board for the assurance that the data is secure, compliant, and private.

        CEOs you should be building data leadership and data capability

        The CEO and the board of any future fit organisation should be investing in data leadership, and prioritising data and analytics outcomes.   Executive teams should also recognise that what they once understood about data and how it can be used by an organisation may be outdated and should take the opportunity to learn from the experts in this field.

        During early stages, the data leadership needs to focus on building a strategy and a capability to deliver the strategic outcomes. The capability should cover people, process, and technology as well as an operating model that is fit for the execution of the strategy.

        Whether the operating model uses a centralised structure or a federated hub and spoke model is dependent upon the maturity of the organisation. Many organisations choose to start with a Data Centre of Excellence to build the right governance before democratising access to data & technology via a decentralised structure.

        Data leadership carries the responsibility of uplifting the data literacy of the organisation, helping the enterprise thrive in the algorithm economy. When we are guided by data and AI expertise it ensures we are mission oriented and using the latest in technology to ensure the scale, security and a future fit design.

        Investing in data led outcomes

        There is no doubt that moving into the data space will feel like an initial cost, however the cost is arguably far less than the cost of doing nothing. Notwithstanding, your customers will be demanding it, and your competitors will be doing it.

        Your finance teams should be upskilling on the different accounting treatments as we move into agile team funding, consumption-based charging and creating intangible assets.

        Once the cost layer has been established to build your teams, establish your foundations and integrations layers,  you should swiftly move to value-based outcomes to enable:

        • Internal value, such as operational value, workforce value, marketing value, stakeholder value.
        • External value, such as increase profit and customer satisfaction by aligning to customers’ needs and create competitive advantage.

        Establishing the value and benefits

        Determining the value and benefits is the responsibility of the business, as the technology and data itself will not create value.  Data leadership in your business should be collaborating closely with the executive sponsors for each initiative to identify the value of the opportunity, the processes and the technology required to create this benefit, and then the training and education to activate it. The Data Strategy should be linked with the Business Strategy, and it is the collective responsibility of every leader to deliver these outcomes as well as understand their individual contribution.

        Managing the benefits

        We recommend an organisation co-designs a way to manage the benefits realisation early, this includes understanding the internal dependencies (like process change, training, technology build), milestones for measuring benefits, and external dependencies.

        CEO’s and boards should be looking for a value management strategy, with regular updates via a value management plan, something the whole executive would be expected to be across and drive collectively.

        Becoming a data led enterprise

        Finally, to bring this all together, it is the onus of the leadership team to embed the ‘data strategy’ as a key pillar of the business transformation, and it will only be successful with CEO endorsed change management.

        Leadership needs to ensure they communicate how their data driven initiatives relate to the strategy and performance of the company.  To achieve data driven transformation, there is a cultural shift underpinning a sustained change, and leaders must embrace the fact this is often a long process requiring them to always model transformational and data driven leadership.

        Whilst the data assets can be built, they will only create value when leadership drives the change management, embedding data driven ways of working into everyday business models as well as building innovative business models for creating new value .

        Are you ready for being a data driven organisation?

        About UpSw!ng

        The content in this series is my perspective, open to discussion and healthy challenge, and welcomes ideas and collaboration should you wish to get involved.

        It is my objective to represent diverse thinking and inspirational leadership towards a fundamental shift in the way technology and innovation can grow profitable NZ organisations, whilst making positive impacts on society.

        What may feel dramatic now, will be considered visionary in the future!

        Ready to take action?

        I would be delighted to help you with your digital growth, get in touch at to start taking action, not just promoting a vision!

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