Transformation: Why Platforms and Data Literacy is Not Enough?

If a new platform materialised overnight, will your organisation realise the competitive advantage analytics has to offer? “Transformation” seems to be irrevocably linked with “culture change” – change in habits that have developed over years, if not decades.

We believe that change is not just about establishing the ability to do analytics, but also about encouragement and learning from doing. A part of the answer may offer a different perspective on the role of enterprise architecture.

Platforms and data literacy creates an ability to, which is distinct from the inspiration to work with data, in the same way that music lessons teach the ability but does not necessarily inspire every child to become a great musician.

Leading with material outcomes (e.g. ability, solution or platforms) fatally assumes that individuals are as passionate about data and technology as the people leading the transformation – that the only thing stopping change is the lack of ability and tools. The reality is that unless the organisation is in the domain of technology, then technology is unlikely to be front of mind for everyone. The task must therefore be more than enablement; there must also be a focus on encouragement, which starts with process change, and also include on how success is defined and recognised.

Imagine that your whole organisation is sent on a “data & dashboarding” course, and while away, PowerBI is rolled out and a data lake made available. If the processes and ways of recognising success remains unchanged, will there really be meaningful and persistent change?

What if instead, the organisation set up “pathfinder teams” who are:

• Trusted and empowered to break existing processes, expected to do new things outside of existing processes;
• Recognised as pathfinders, celebrated as pioneers who will openly share failings as well as successes;
• Invested in by the organisation, so value of exploration is formally recognised rather than something relegated to evenings and weekend.

We believe when it comes to change, visible success is a stronger motivation than corporate mandates – the most effective change comes from positive desire of the inspired individual.

What is data literacy?

Data literacy is the ability to read, write and communicate data as information – or as the Data Literacy Project[1] alludes, “[the ability to] confidently understand, analyze and argue with data”. The focus is on a change in people – not technology, extending beyond the workplace as a general competence akin to the ability to read. The importance of this concept is illustrated throughout 2020, with COVID-19 and political situations often articulated through numbers, and affecting many of us on a personal level. Data literacy goes beyond numerical or statistics skills – it also implies an understanding of potential deficiencies in the raw data, and a general awareness of biases that may exist in how findings are presented.

From a digital transformation perspective, data literacy reinforces that it is insufficient to just “upgrade the technology” – you have also upgrade people’s “skillsets” as well.

Why change is painful

When talking about “transformation”, the focus tends to be on the vision (“what good looks like”) and securing buy-in of the vision. Having a clear destination is arguably necessary [2, 3, 4], but in our experience problematically insufficient. Change is hard, not because of the implementation – after all, technology is designed to be easy – instead stems from the fear of the unknown.

We have found “what works” often through painful trial and error. To get to where we are, in the past we have, in some cases, repeatedly, gotten lost to find ourselves in sticky situations. When facing the unknown again, we are suddenly reminded of the effort needed and costs of failing. Fear of the unknown is rarely explicitly addressed – our Clients tend to lead with “things aren’t good at the moment – what can good be?” without acknowledging “it was very painful getting here”. Unless we understand the true nature of reluctance to change, we risk solving the symptom rather than the cause.

To motivate change, we need to make a compelling argument for why this time around it will be less painful. This is different from “why what we are proposing is great” (vision) or “why we must change now” (urgency).

Reducing the pain

We should accept that change necessitates exploration, and exploration will occasionally lead us down the wrong path. No amount of architecting will completely eliminate the need to try new things; technology evolves – this is the nature of our domain.

Consider the key steps in a traditional approach for “digital/data transformations”.

Based on steps 1 and 2, we try to lessen the pain of taking the taking the wrong turn by anticipating the unknown – building in modularity at the cost of increasing levels of abstraction; potentially increasing costs and alienating those not interested in architecture.

Consider an alternative approach, to accept that there will be wrong turns.

In the alternative model, we make the tools available early on and then free the organisation to go forth and explore, for a controlled time, e.g. three months. At the end of the three months, we evaluate all of the experiments to understand what has worked, and what has not, and only then iterating the architecture. In such a model, we establish the following:

1. Don’t try to prescribe the future, you’ll get it wrong, instead balance design with exploration;
2. Accept that when exploring, “wrong turns” will happen, the trick here is to not attempt to prevent, but instead have effective mitigations;
3. Don’t neglect learning and sharing, signposting successes and failures is the key to familiarising strange things.

In practise, we need to do both. The traditional model is actually one of exploitation where value can be consistently generated if we stand a good chance of controlling the unknowns; whereas exploration is geared for innovation however the ability to realise value is less consistent[7].

On the surfacing the above may look like “waterfall” and “agile”; however, there is a key distinction between implementation methodology, and transformational methodology. Implementation methodology – how we go about the delivery of software is – about understanding and evolving requirements. Transformational methodology discussed here is ultimately about delivering changes in perspectives.

Change can be less painful if we accept failure is part of exploration; instead of trying to avoid the unavoidable, we should instead design for it.

[1] The Data Literacy Project, https://thedataliteracyproject.org/about

[2] Prahalad and Hamel writes about the importance of a “strategic intent” in change – an idea that will motivate, align and challenge the organisation to take risks. Hamel, Gary, and C. K. Prahalad. 2005. ‘Strategic Intent’. Harvard Business Review 2005 (July-August). https://hbr.org/2005/07/strategic-intent.

[3] Kotter argues the importance of clarifying and relentlessly articulating “urgency”, emphasising why it must be now, and not later. Kotter, John P. 2012. Leading Change. 2nd ed. Boston, MA, USA: Harvard Business Review Press.

[4] Kotter later explains for the importance of articulating “The Big Opportunity” to break through the status quo. Kotter, John P. 2014. Accelerate. 1st ed. Boston, MA, USA: Harvard Business Review Press.

[5] Tian Zhang, Facilitating Incremental Change v1-0, Insights & Data, Capgemini UK

[6] Tian Zhang, Facilitating Incremental Change v1-0, Insights & Data, Capgemini UK

[7] Martin elaborates on the relationship between “explore” and “exploit”. p.20 Martin, Roger L. 2009. The Design of Business: Why Design Thinking Is the Next Competitive Advantage. 1st ed. Boston, MA, USA: Harvard Business Review Press.

Tian Zhang

Tian has a background in Systems Engineering and Enterprise Architecture, and over the last decade has formed the opinion that primary causes of failure in ambitious programmes are not poor solutioning or implementation; but that rigorous engineering is necessary but ultimately insufficient to drive change. He now advises organisations on the intersection of corporate strategy (which markets do we compete?), business strategy (how do we achieve and sustain an advantage?) and functional strategy (how can analytics help?).

Sharon Woodward

Sharon specialises in information management and data integration solutions on global strategic transformation programmes. She has over 20 years of industry and consulting experience across Telco, Government, Consumer and Banking sectors for large blue-chip clients.
Sharon combines data integration expertise with a passion for technical delivery and specialist knowledge of data architecture, business intelligence, data warehousing and data management solutions.