Becoming Data Literate

Building a great business, culture and leadership through data and analytics

David Reed

• Publisher: Harriman House
• Year: 2021
• ISBN:
  9780857199287
• Topic: Business
• Subtopic: IT Industry
• Index: Business

Data is a must-have for any business looking to thrive. So is having leadership who 'get' data and use it to support their decision-making.But how do you embed the use of data and analytics across your organisation so they truly support every process end-to-end?Becoming data literate in this way is a journey that involves vision, strategy, value creation, culture and data foundations. With an evidence-based framework to guide you, this book lays out a roadmap to ensure you get where you need to go.

Chapter 1: Laying data foundations

Roadmap – in this chapter:

• Data integration can appear too expensive for individual projects to afford.
• If multiple projects need to draw on the data asset, they can be ‘taxed’ to pay for it.
• Without integrated data, value-creating projects will stall.
• Data quality is another obstacle that can cost 8.8% of annual revenue.
• Data technology is becoming a commodity – more affordable, but providing less competitive advantage.

Technology is not the transformer

Crossing the data bridge

Back in 2018, the chief data officer (CDO) of a telco giant recognised the opportunity that existed from monetising anonymised, aggregated location data. As a tool for developing and supporting services as well as for the targeting of marketing messages based on retail proximity, mobile data has unsurpassed coverage and depth.

But there was a problem. Data silos existed right across the business, which had grown through acquisition as much as organically. Data management had tended to be an afterthought and was under-invested. While the business case for putting location data into the marketplace was compelling, it would require significant upfront investment into data integration with year one costs in excess of expected revenues. This made getting buy-in from the executive a real challenge.

As many data leaders have discovered for themselves, despite the impetus behind data as a transformational asset and the widespread advocacy for adopting data and analytics, it can be a struggle to get their investment case approved. This is because of the point of view that, ‘the first person to cross the river pays for the bridge’. What this means is that the full cost of a data project, such as a major data integration, is often imposed on the first new business project which needs it, be that a digital transformation or a new data product.

So how can the CDO get around this obstacle? The approach taken at that telco was to build up a fund by including an incremental levy or data tax on all business projects in the run-up to and during digital transformations. Just like the tolls paid by traffic to cross a real bridge and thereby pay for the cost of its construction, gaining smaller contributions towards a larger project means that no single business process or department leader is left facing the whole bill. This can also establish the data office as a stand-alone function with cross-functional support from within the business, giving it greater independence and resilience.

Accelerating growth of digital technology and its adoption by organisations, governments and consumers will be the indisputable trend of the 2020s. As part of this, data is moving from being a simple raw material that fuels these technologies to being a form of digital currency – the price of operating in the digital space at any level is the supply of data in some form.

For companies that want to thrive – and more pressingly for those which hope to survive – during the 2020s, rapid adoption and maturity of data and analytics capabilities is therefore fundamental. This was already recognised in the 2010s when data-led transformation was just getting underway under the badge of ‘big data’.

In a landmark report by Nesta, the UK’s innovation foundation, published in 2014 under the title, Inside the Datavores, the authors noted: “We find that a one-standard deviation greater use of online data is associated with an 8% higher level of productivity – firms in the top quartile of online data use are, other things being equal, 13% more productive than those in the bottom quartile. When we distinguish between the different data-related activities that firms undertake, we find that greater data analysis and reporting of data insights have the strongest link with productivity, whereas amassing data has little or no effect on its own.”

Firms have taken notice of this and investment into data foundations is now a differentiator between leaders and laggards across most sectors. As the UK’s National Data Strategy spelled out in 2020: “Poor data foundations can be a real blocker for driving the transformative power of data. For example, when the source data needed to power AI or machine learning is not fit for purpose, it leads to poor or inaccurate results, and to delays in realising the benefits of innovation.”

Growth can be driven by taking the first steps into data and analytics, especially if transforming from a very low, immature base. The economic argument for doing so is virtually irresistible and can often be made by focusing on fixing the data foundations ahead of innovating and value creation.

As an example of this, the digital transformation of the Lloyd’s of London insurance market is expected to remove £800 million in operating costs, equal to 3% of its current total operating costs, with a core data store being built to support digital processing. Its roadmap, Blueprint Two, spells this out clearly: “The transformation envisaged is only possible if complete, accurate and timely data is available to support and connect digital processes. It is the quality of this data that makes the difference between an automated process that happens immediately and a manual process that routinely takes days today.”

Similarly, Lorenzo Bavasso, data, analytics and AI director at BT Global, states: “We have to move towards data foundations that are defined/built for every business function to define their data-driven plans and execute them. Also, the funding approach has to evolve from central/use case-based business cases to a model where the core capability is built as a fundamental need of the business and then exploited (value-driven) across the business, with a degree of autonomy.”

Another common basis for the investment case into data foundations is to fix issues with poor data quality. Unless concerted attempts are made to resolve these, they can have an ongoing and direct impact on turnover by increasing costs (through customer service overheads or logistics failures) or decreasing revenue (through lost customers, sales and opportunities).

As Figure 1.1 shows, this negative impact continues to rise, hitting an average of 8.8% of annual revenue in 2020 compared to an average of 5.6% in 2017. This not only flags that data quality can be an evergreen thorn in the side of the organisation, but it also presents a risk – fines for violations of the General Data Protection Regulation (GDPR) in the EU (or Data Protection Act (DPA) in the UK) can reach 4% of global turnover. A clear link can be made between errors and gaps in data and the ability of an organisation to know whether its data has been breached.

Figure 1.1: Average annual cost of poor-quality data

Pens, pencils and winning the (space) race

Given the need to put data foundations in place, it can be tempting to view data technology as both the fix for existing problems and also the heart of a digital transformation. But technology rarely achieves the second of these goals in its own right, as a story from the early days of the space race helps to illustrate.

NASA discovered it faced a tec...

Table of contents

• Contents
• Foreword
• Introduction: Towards evidence-based decision-making
• Chapter 1: Laying data foundations
• Chapter 2: Organising for data and analytics
• Chapter 3: Becoming data literate
• Chapter 4: Business strategy and data strategy
• Chapter 5: Building a data culture
• Chapter 6: Data leaders
• Chapter 7: Data teams
• Chapter 8: Data individuals
• Chapter 9: Data and economic value
• Chapter 10: Values and ethics
• Chapter 11: Becoming data literate… and beyond
• Data never sleeps, neither does business
• Publishing details