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From Data Modernisation to Data Monetisation: Building the Bridge

The path to establish true data-centric transformation for your business.

Photo by Franki Chamaki on Unsplash
Photo by Franki Chamaki on Unsplash

The last 3 years have brought upon us an unprecedented level of change at a global scale. From the way people live and work, to how companies are organised and operate, the level of transformation being induced disrupted many aspects of our existence, and continues to do so.

If we focus our attention on how Data and Analytics technologies are being used throughout organisations, the level of change has also been massive. Some aspects of this change that I could highlight are:

  • The rise of public cloud as the standard storage model for D&A workloads;
  • The demise of legacy on-premises big data ecosystems and its replacement by cloud native alternatives;
  • Data as a Business driver being leveraged as a core tenet of corporate strategy;
  • Rise of the Chief Data Officer, Chief Analytics Officer and similar roles to empower the democratisation of data in the enterprise;
  • Rise of Artificial Intelligence and data-intensive applications to capitalise on massive data sets being collected;

In such a state of flux and aggressive change many different paths have been charted, leading to some success but as well much failure, in the attempt to build new data ecosystems, and leverage the ecosystems to promote and sustain effective business change. We may call these efforts the "data modernisation" stage of the transformation journey.

With such a great focus and level of investment in the modernisation of Data ecosystems, we could expect very swift progress and little resistance to this new paradigm. Yet some pointers show us different reasons causing delay in moving tech-centric data agendas forward have emerged:

  • An excessive focus on technology, without adequately understanding the business issues and implications at hand;
  • Lack of a well articulated Business Strategy where the role of data is clearly defined and integrated within the wider enterprise ecosystem;
  • Lack of focused leadership for data-centric programs, that fails to represent the complexity of the organisation in its different facets;
  • Lack of change management principles, leading to the "build it and they will come" effect for data platforms and solutions (no they won’t).

Fast forward to 2021 and the stakes have only gone higher. The continued efforts on Digital Transformation has resulted in ever growing amounts of data available to explore and extract valuable business insights from it. Businesses demands from their Data & Analytics teams are only increasing, with increasing pressure to find deliver the "hidden diamonds" in data:

  • Uncovering the elements that lead to great customer experience and retention, and key ingredients of customer loyalty;
  • Understanding product performance in the context of different channels and how to optimise the mix of physical and digital ecosystems;
  • Creating effective and objective marketing strategies without overspending on advertising and leveraging the correct mediums for connecting with the company audiences;
  • Creating and developing a loyal talent base based on objective and quantitative metrics, where employees feel encouraged to stay and develop, becoming themselves brand ambassadors;

The list goes on, and on, and on.

We can then confidently say that companies are starting to accelerate their move from the "data modernisation" phase to a new phase that we will call "data monetisation". In this new phase it is assumed that data assets are mature, curated, have the necessary degrees of quality and are therefore ready to be exploited from an economic standpoint.

In that respect, effective data monetisation requires a sound data platform in order to be successful, as well as well-established data management principles, on top of which data monetisation initiatives will be launched and deployed:

  • Clear ownership of data assets, as well as upstream and downstream data lineage to understand the data assets’ ecosystem;
  • Automated and quantifiable data quality checks, that can quickly be used to triage data assets for meaningful exploration;
  • Clear representation of data freshness attributes, making sure that analyses aren’t being carried over stale data;
  • Automation frameworks that prevent manual tampering of data and code in critical production environments;

Again, many more examples could be used to illustrate the absolute necessity of a professionally-managed data ecosystem, in order to enable accurate exploitation of data assets for critical business objectives.

The question is, are we there yet?

The answer is far from simple and has different angles to it, but if I had to sum it up in one sentence if would be "not quite".

The rapid evolution of the technology ecosystems has created a conflict with the cadences of business and business strategy, which are inherently slower – with good reason – leading to IT being pushed to deploy new tooling and solutions, against which business use cases are not yet clearly specified and lack solid ROI analyses to prove their effectiveness.

Companies are many times in "chasing shiny new object" mode with little consideration for strategic value and integration of new solutions in their production landscapes. This causes the dreaded "PoC graveyard" effect where new solutions go to, after proving a very residual use case but failing to scale beyond that.

There are however cases of success in adoption at scale of new Data & Analytics technologies that can effectively address relevant business use cases at scale, and demonstrate a clear return on investment. It is not "mission impossible" land, far from it. There are just a few things that need to be taken into consideration, to make this promised land a reality (more on that below).


So where do we go from here?

As in all transitions, there will be winners and losers. Those who can modernise at pace will be better positioned to monetise their data assets and utilise them strategically in pursuit of business goals.

Monetisation efforts based in legacy data platforms will deliver limited benefits and results, as legacy platforms will be unable to provide the level of richness and detail of the new customer experiences that are captured and stored in modern data platforms.

What advice then, can be given to organisations charting their path from modernisation to monetisation, or even deciding where to start in each one of them:

  • Start with business outcomes in mind. Fleshing out the data strategy behind the monetisation initiatives will bring about the business use cases and associated ROI, which will later be needed to justify investment in data modernisation projects.
  • Business and IT teams need to work together to make it happen. Business teams will lead the data monetisation initiatives and IT will lead the data modernisation piece, but a shared vision and roadmap needs to be in place to foster alignment, and ultimately success. There is no other way.
  • Use the Data program to promote cross-enterprise collaboration. There are few initiatives that impact so many areas of a company such as data-centric transformation. If your company is used to working in silos this is a great opportunity to team up and create a culture of shared ownership and collaboration.
  • Measure your progress along the way, but be prepared for a slow start. Significant amounts of effort will be spent at the beginning of the program creating alignment between different teams, and putting together a solid play with key milestones and stakeholders. You will experience frustration along the way, but perseverance will be your friend.
  • A solid start will position you for huge benefits down the road. Delivering visible benefits and quick wins for your Data modernisation and monetisation program early on, will create enthusiasm and ramp up further demand for use cases. Plan how to best showcase your successes, as a way to energize the team as well as secure funding down the road.

Data-centric transformation is picking up speed but is still an early concept for many companies. What used to be done in silos is now being accomplished collaboratively between departments, which increases complexity but also creates much bigger potential for companies to go after.

Leveraging the wealth of data that companies have amassed, modernising their data platforms and implementing advanced data monetisation strategies will only accelerate the advantage for those who do it right.


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