Data and analytics strategy for business unlock data assets and increase innova

n Chapter 3 we talked about the five waves of transformation. We’ve now laid the groundwork with a business case, a data and analytics strategy and a team. This first wave is called Aspire because we know what we want to achieve, but it’s all ahead of us. Now the real work begins. We need to move forward and deliver some quick wins. The first projects are both a test and an opportunity. They are a test, because they demonstrate that it is possible to find unrealized value in the data your organization holds. But they are a once-only opportunity to establish credibility and prepare the business for a radical transformation. Get this right and we are on the march towards Mature

PRAISE FOR DATA AND ANALYTICS STRATEGY

FOR BUSINESS

In my experience, taking people somewhere they’ve not been beforerequires leadership and trust, whether that’s climbing Everest or

making a success of your business through the use of data and

analytics In Data and Analytics Strategy for Business, Simon

Asplen-Taylor cuts through the jargon and provides a clear route for success

Kenton Cool, 15 successful Everest summits, one of the world’s

greatest high-altitude mountaineers and leaders, expedition leader behind Sir Ranulph Fiennes’s north face of the Eiger ascent and

Everest summit

One of the benefits of going digital is that organizations can collect,review and analyse enormous quantities of data Correctly

interpreting this data provides the intelligence which enables a

business to understand the consumer and marketplace in a completelynew way Successful organizations require a clear data strategy and adisciplined set of operational processes Simon Asplen-Taylor shows inpractical detail how to make this happen in the real world He

demonstrates that data is key but reveals that an effective data officernever loses sight of the commercial application and human element ofthe intelligence created

Kevin Gaskell, serial entrepreneur, and former MD, Porsche GB and BMW GB

Brilliant book! Genuinely the best and most readable book for existingand aspiring CDOs Every CEO should read the first chapter SimonAsplen-Taylor has shared his significant expertise to create the go-to

data guide for business and data leaders Data and Analytics Strategy for

Business uses examples from a wide range of organizations to explain

why data can revolutionize a business A genuinely good read, the

book’s structure superbly guides the reader through all aspects ofdelivering a data and analytics strategy with vital tools and tips

Whether your organization is struggling with trust in its reports orready to launch the bots, this book is for you

Nina Monckton, Head of Data, Just Group plc

Businesses operate in an increasingly complex and fast-moving

environment, where making the right decision at the right time canmean the difference between winning and losing Key to this is thesuccessful management and use of data, underpinned by a robust data

strategy Data and Analytics Strategy for Business provides a structured

approach to show how you can succeed – whether you are just

embarking on your journey, part way through or just fine tuning Thebook is full of practical advice, anecdotes and experiences to help youwin and not lose

Carl Bates, former Senior Partner, Data and Analytics, and leader of the Ventures practice, Deloitte

One of the ways we can encourage women into data leadership roles is

to provide the advice and methods to help remove barriers, while

sponsoring next-generation leaders In Data and Analytics Strategy for

Business, Simon Asplen-Taylor does just that He has shared his

experiences and strategies for success to create a level playing field forall data leaders He also talks specifically about how to build and

leverage the strengths of a diverse team

Roisin McCarthy, Founder, Women in Data

As we strive to gain more value from our data assets, we create morerisk, opportunity and motivation for breaches Simon Asplen-Taylor’s

new book, Data and Analytics Strategy for Business, provides amazing

insight on how you can create more value in your organization’s datawhile also ensuring its security! Highly recommended

Ned Finn, CISO, Currys

A very interesting book in such an important and contemporary area

of knowledge and skills Data analytics is not just an area of knowledgethat you need to learn more about, but it is considered to be a crucialskill that is required for every business Therefore, this book is a greataddition to the intellectual body of knowledge that can help students,especially those studying post experience executive programmes, such

as MBAs and DBAs, to gain clear insights of the key elements of

business analytics and to acquire the required set of skills to compete

in the changing world of practice

Amir Michael, Professor of Accounting and Associate Dean (MBA, DBA), Durham University Business School, UK

The world of a chief data officer (CDO) requires a full understanding ofhow a business operates, the sector it works within and the peopleinvolved Simon Asplen-Taylor’s book gives a fine insight into the

approaches, decisions and specific actions a CDO can use to bring realvalue to an organization and make it a critical part of business strategy

Helen Crooks, Chief Data Officer, Ofgem

Beyond his remarkable expertise across all the complexities of today’sdata sphere, this book clearly demonstrates Simon Asplen-Taylor’smastery of the art of data storytelling Yet, what makes it even morecompelling is that it is so helpfully structured in waves that align to thevariety of levels across the entire data maturity spectrum – making it

instantly transformative regardless of where an organization

currently finds itself in its data journey This book is an absolute

essential for anyone who wants to successfully leverage the abundantvalue that can be derived from data and analytics

Edosa Odaro, Chief Data and Analytics Officer, Tawuniya, and author

of Making Data Work

Data and Analytics Strategy for

Business

Unlock data assets and increase innovation with a

results-driven data strategy

Simon Asplen-Taylor

Task: Ten questions to ask about your business

Are you worried?

References

The business case for data

Introduction

The cost of doing nothing

The value of data is only as good as the value of your business case for it

Identifying the most pressing data problems

05

06

Defining the CDO role

Task: Setting priorities using the data periodic table Task: Using the data periodic table to design projects Five waves of transformation

Value, build and improve

Task: share the story

References

A team game

Introduction

Cracking the code as a CDO

The résumé problem

Discovering problem-solvers

Task: Recruiting for PQ and AQ

Beware of ready-made data teams

Anatomy of a quick win

Task: Identifying the right project

Make sure it’s a quick win for everyone

Quick wins are not strategic wins

References

Repeat and learn

Introduction

Why build a repeat-and-learn culture?

Listen to what data is telling you

08

09

Task: Define a data process

Task: Develop a business change process

Learning and innovating through experimentation References

PART THREE Wave 2: Mature

Data governance

Introduction

What is data governance?

The importance of accountability

Data stewards, data owners and the data executive Task: Implementing data governance

References

Further reading

Data quality

Introduction

The risks of low-quality data

The upside of high-quality data

The four principles of data quality

A data quality strategy

Task: Setting a baseline and a target

Task: Build a data quality team

Task: Improving data quality in the short term

Task: Improving data quality in the long term

11

12

Other single views

How do you build an SCV?

Shadow data is the enemy of the SCV Ownership of the SCV

References

Reports and dashboards

Introduction

Task: A report audit

From static to dynamic decision support Task: Designing your dashboard

Task: Dashboard implementation

From reporting to insight

Task: Information architecture

PART FOUR Wave 3: Industrialize

Automation, automation, automation

Introduction

What can we automate?

How much can we automate?

Task: The business case for automation Task: Manageable automation projects Will I use a tool?

From quick wins to big wins

Be dull and repetitive

Task: Choosing how and when to scale

Use your resource multipliers

Task: Implementing your hackathon

The dividend from scaling

References

Optimizing

Introduction

Best intentions are not optimal

Task: Plot a path to optimization

Task: Overcoming resistance

The limits of automation

This is a tipping point

References

PART FIVE Wave 4: Realize

The voice of the customer

Introduction

Hearing their voice

Reasons to use other sources of data

Data science, not data magic

How not to do data science

Task: Integrating data science

Task: Sustaining data science

Embrace the potential for failure

Sharing improves markets

Exposing data to customers

Task: Prepare to share

Exposure is inevitable, so do it your way

References

PART SIX Wave 5: Differentiate

From data-driven to AI-driven

Introduction

What does AI do?

A hierarchy of data value

The AI journey

Task: Detection

Task: Process automation

Task: Improved clustering

20

Data bias

Task: Complex analysis and prediction

The limits of AI as a guide or manager

Task: Creating commitment to AI for independent real-time decision-making

From data-driven transformation to AI-driven business

References

Data products

Introduction

What is a data product?

A data and analytics centre of excellence (CoE) Task: Creating a data CoE

Three functions of research

A continuous improvement life cycle

References

Right leadership, right time

Introduction

Leading a sustainable data culture

Which leader are you?

LIST OF FIGURES

FIGURE 2.1 The investment in data and analytics leverages your

other investments

FIGURE 3.1 Data periodic table

FIGURE 3.2 The five waves to data maturity

FIGURE 3.3 Value, Build, Improve (VBI) content of each wave

FIGURE 4.1 Data and analytics capability model

Value, Build, Improve content of this wave

FIGURE 5.1 Quick wins

Value, Build, Improve content of this wave

FIGURE 7.1 Data governance maturity model

FIGURE 8.1 Data quality dimensions

FIGURE 8.2 Customer data quality examples

FIGURE 8.3 Data quality maturity model

FIGURE 9.1 Overview of single customer view

FIGURE 9.2 Single customer view benefits

FIGURE 9.3 Shadow data resource

FIGURE 10.1 Example dashboard – single customer view

FIGURE 10.2 Reports and dashboards maturity model

Value, Build, Improve content of this wave

FIGURE 12.1 Automation maturity model

Value, Build, Improve content of this wave

FIGURE 16.1 The data science and AI method

Value, Build, Improve content of this wave

FIGURE 18.1 Value from data

FIGURE 18.2 Data science and AI maturity model

LIST OF TABLES

TABLE 3.1 Revenue increase

TABLE 3.2 Decreased costs

TABLE 3.3 Reduced risk

TABLE 3.4 Strategy

TABLE 3.5 Compliance

TABLE 3.6 Enabling capabilities

ABOUT THE AUTHOR

Simon Asplen-Taylor is one of the most experienced and

successful data leaders in Europe, having served as chief dataofficer for several FTSE firms and led some of the largest data-led transformations in Europe

He specializes in transforming businesses through the use ofdata, analytics and artificial intelligence whilst delivering

significant upside in revenue, customer satisfaction,

organization efficiency, cost reduction and reduced risk He has

a unique depth and breadth of data experience covering morethan 30 years across many industries, having led the data

capabilities at Lloyd’s of London, Tesco, Rackspace, Regus,

BUPA, UBS and Bank of America Merrill Lynch and been a dataconsulting leader at IBM and Detica

His major achievements include:

For a UK FTSE 100 financial services organization,

delivering a $1 billion per annum contribution to the

bottom line and also generating a significant uplift in

share price

For a FTSE 250 business, delivering a 64 per cent

improvement in the company’s margin through the use ofdata

Troubleshooting and fixing the data capabilities of twoorganizations under direct threat from regulators

Being a leader in the data and analytics consulting

practices of BAE systems and IBM – advising clients across

financial services, telco, retail, entertainment, media andinsurance.

Designing and leading the implementation of the dataecosystem for the UK’s financial regulator, which in turnsupported the first prosecution of insider dealing in theUK

He has an MBA from Durham University, is a Fellow of the

British Computer Society and a Fellow of the Royal StatisticalSociety He has studied artificial intelligence at MIT

His awards include being shortlisted for Data Leader of theYear (2019) and DataIQ100 top 100 influential people in data(2020 and 2021), the only fully curated list of influential people

in data and analytics He is a frequent data blogger and regularspeaker, panel member and chairman at data events He is akeen sailor and an avid rugby supporter

Why Data and Analytics Strategy for Business?

Whether you’re a CEO, CFO, CIO or indeed any C-level role,

whether you’re a businessperson who wants to understandmore about data strategy so you can decide how to manage it inyour organization, or just want oversight then this book willgreatly help you not only to understand the challenges of those

in your data and analytics teams but also your role in

supporting them If you’re a chief data and analytics officer,data scientist, data engineer, MBA or business student wanting

to understand the growing role of data and analytics in

business, or just data curious, then there is much to be found inthis book for you

This book has been written by a practitioner who’s seen itwork and, just as importantly, seen it fail As the author I onlywish this book had been around when I started out, and that’spart of the motivation for writing it

What makes this book different from other data books?

I’ve thought carefully about the content and at times I feel I’vegiven away the ‘crown jewels’ So rest assured I’ve not held

back from giving the best advice I can

Key features include:

Artefacts that will bring significant value to you I drawyour attention to the data periodic table in Chapter 3 Itlooks simple but it is the culmination of years of

experience and something I use almost daily

Real case studies that bring the strategy to life

Ideas underpinned by solid business theory – with

references to relevant research If you’re a businesspersonyou’ll want to know that it’s based on proper research Ifyou’re a student you’ll want to follow up on the research.Key concepts highlighted throughout the book making iteasier to remember and use, and assuming no prior

knowledge in the subject

I’ve followed a maturity model with five waves

throughout the book, to give you a practical approach tofollow and to help you measure your progress

The book is written from the perspective of data being akey business enabler, not a technology capability – theway it should be done

It’s written by someone who’s made mistakes and is

willing to share the learnings so you don’t make the sameones, and who’s seen all elements of data and analytics –

consulted, delivered, owned the capability and been

responsible to the board

I hope you won’t just read this book I hope you’ll use it!

Follow up

If you have feedback on this book please do share it withme: feedback@datatick.co.uk

If you’re interested in having me consult to your

organization or provide coaching and mentoring servicesplease do get in touch There is more information

available at www.datatick.co.uk

Along the way I’ve had the privilege to work for some greatfirms They’ve had blood, sweat, tears and I hope a lot of valuefrom me I’ve had some smart colleagues and we’ve had mostlyfun times with a few, inevitable, bumpy patches, but we’ve alllearned a lot along the way

I’d specifically like to acknowledge the help and support of:Kogan Page for believing in me, enabling this to becomereal, and for all the support you’ve provided

Tim Philips for his support, knowledge and experience,and Catherine Walker for making my illustrations work.Durham University for giving me the best education Icould have had, and for giving me the desire to keep

learning

My friends and family – you know who you are

My three boys William, Alexander and Matthew, who arethe main motivation in my life, without whom this wouldnot have happened I dedicate this book to you

PART ONE

How data and analytics can help you grow your business

01

How can this book help you?

The data your business holds, and acquires through doing

operational and trading activities, is the key to its future You may be surprised to discover how much you can use that data to drive increases in revenue, reduce costs, improve operational efficiency, reduce risk, and improve customer and employee

satisfaction.

KEY CONCEPTS

Chief data officer (CDO)

Digital transition, digital transformation

Data and analytics strategy

Single customer view (SCV)

Single source of truth

Shadow data resource

Data leakage

Introduction

‘The world’s most valuable resource is no longer oil, but data’(Economist, 2017) The article pointed out that Alphabet

(Google’s parent company), Amazon, Apple, Facebook and

Microsoft were (and are) the world’s five most valuable listedfirms

The assumption that data-equals-oil is broadly true, but notnew It is casually tossed about in meetings or announced withgreat portent at the beginning of hundreds of conference

keynotes

Before we discuss the value of data, let’s start by definingwhat data is for our purposes You have a business, and thatmeans customers have transacted with it Every transactioncreates data: contracts, orders, invoices, and perhaps refundsand credits Storing this information means that you have

created a database of your customers, containing their names,addresses, mailing preferences, and much more You have

employees too, and so that means another database containingtheir remuneration, the hours they have worked, where theylive and the office they work in, their skills and experience Atsome point you purchased goods and services for your

business: raw materials for manufacturing, stationery, energyand water; you spent time on the telephone and rented an

office Records of what you did all become your data You

launched your own products and services that were

researched, developed and released to the market You wrotepress releases, published your accounts, and updated your

websites That has generated even more data Your competitorsdid the same, and you have stored data about that too Yourcustomers and journalists reviewed your products and

commented on them on social media, which created even moredata that you can use

This, and hundreds more items of information that you

capture every day, is what we mean by data It matters how wemanage and store that data, and also how we use analytics,

artificial intelligence (AI), and machine learning to extract thevalue from it.

In this book I’m not going to make the case for data in itself as

an asset – because at this point, I’m assuming that you get it Atthis stage, it’s like making the case for railways, or the internet.But despite the fact that every leader in every business

implicitly knows that the company is sitting on a potentiallygame-changing asset, not many of us really know how to turnthat data into value

Seventy-three per cent of data kept by companies is unusedfor analytics (Gualtieri, 2016) because most of its potential liesdormant; it is either locked up somewhere it can’t be easily

accessed, or it isn’t business-ready

We will discuss both of those problems at length in this bookand outline how to solve them But think about this for a

second: three-quarters of data is kept, and never used

If data is oil, your chief data officer (CDO) will locate the

reserve, estimate its value, and extract and refine it Not onlythat, but if you stick to your strategy to create value from data,your CDO will make sure you get better at it Unlike oil reserves,which are ultimately exhausted, you will never run out of data

or, in my experience, ways to use it to help your business Infact, the volume and variety of data available to a business aregrowing rapidly In 2021 it was calculated that the volume ofdata created, captured, copied and consumed worldwide would

be 64 zettabytes (Zb) (Holst, 2021) That’s 64 billion terabytes, or

64 trillion gigabytes – 10 terabytes for every person on the

planet By 2025, this number is expected to triple

This book is a guide to how your business, channelled

through a data executive led by the CDO, can leverage its data

assets successfully But, be warned: however tough you thinkit’s going to be… it’s harder.

There’s a reason why so few businesses do this well It’s easy

to get distracted, misallocate scarce time, resources and

investment, or get stuck in the weeds This book will also give astructure, five waves of innovation, that can be a template forusing data in your business

CHIEF DATA OFFICER (CDO)

The person responsible for enterprise-wide governance and use of information as an asset This involves data collection, governance, processing, analysis, business

intelligence, data science and key elements of data-driven AI and other techniques A CDO usually reports directly to the chief executive officer (CEO).

The chances are that your business is not handling any of itsdata processes as well as it could Right now, there’s no shame

in that But it’s costing you lost earnings today, and potentiallyyour future Your teams today are wasting time finding,

correcting and managing bad data; they have missed

opportunities because they didn’t know they existed; they havemade bad decisions because they didn’t know the facts or didn’ttrust the data; they are formulating bad strategies or driftingbecause they have a limited ability to predict what will happennext; they are even risking censure or fines from regulatorsbecause of poor data management

You know your business can do better This is a great

opportunity It is inspiring and fascinating, but it is also

stressful If data and analytics can make a difference, the

window in which to do that is rapidly closing

The title of this book is Data and Analytics Strategy for

Business because you have one of the greatest challenges that

any business executive can face: the once-in-a-generation shift

to a new way of doing business The data and analytics-drivendigital transformation, not least due to the impact of the Covid-

19 pandemic, is happening much faster than we expected, and

it requires a strategy to Unlock Data Assets and Increase

Innovation.

DIGITAL TRANSITION, DIGITAL TRANSFORMATION

The process of taking analogue information and translating it to a digital environment The accompanying transformation involves changing business processes underpinned

by data and analytics to take advantage of the opportunities.

There is no holding pattern This is both thrilling and extremelystressful As I will mention many times, this book is not a guide

to writing code, or a shopping list for technology It is intended

to be a guide to the projects that your business can launch andcomplete Like any executive with a ‘C’ in their job title, a CDOcan’t do everything at once or keep everyone happy This

transformation will need to demonstrate results at every pointand change the culture of the business, and that will sometimescause conflict

It is daunting Part of the reason I wrote this book is so thatyou can learn from my mistakes as a CDO – and hopefully avoidthem – while sharing in my successes

If you cannot use your data optimally, firms that have

mastered the ideas in this book and go on to complete the fivestages in our journey will challenge your business, and withdata to help them, they are likely to win Data and analytics willhelp them build better products and services, take your bestcustomers and make them happier, run processes at a lowercost, defend themselves against new competitors, and innovatebetter, quicker and cheaper, with happier employees

Your smartest competitors will get on with solving their

underlying business problems using data and analytics Theymay also be able to invent entirely new ways to do business.Already, the world’s biggest provider of transportation does notown a car The biggest provider of accommodation does notown a hotel Supermarkets provide banking services Our

economy is changing, driven by the success of data-driven

innovators

CDOs hold the future of their organizations in their hands

The strategies to get through this are not secret There is a

community that I have found universally inspiring and

supportive who are eager to share their insight: other

experienced CDOs We’ve been there, and we have the scars toshow it This book would not have been possible without theconversations I have shared with them

When first-time or would-be CDOs ask me for advice, perhapsthe most useful thing I can tell them is not to get lost in the

weeds, and that applies to everyone who gets involved with adata and analytics strategy Step back, plan, think about howyou are going to prioritize, and communicate what you are

doing and why And so before we get into what you could do,how you can do it and what you stand to gain, let’s confront theproblems you face head-on.

Below are 10 questions that I ask the board, directors andsenior managers when I start a data transformation process for

a company The answer to each is either ‘Yes’, ‘No’, or ‘I don’tknow’ Keep track of the answers, and then I’ll explain why I’masking the questions

Task: Ten questions to ask about your business

1 Do you invest more than 1 per cent of your revenue indata (excluding the cost of IT infrastructure)?

2 Can you describe a product or project driven specifically

by data that had a measurable positive impact on the

8 Is there a revenue-generating opportunity that your

colleagues could tell you about that isn’t captured in data?

9 Is the collection of data functionally isolated from the

process of creating value with it?

10 Can you explain to your customers everything that you dowith their data, and would they approve if you did?

Do you invest more than 1 per cent of your revenue

in data (excluding the cost of IT infrastructure)?

The exact amount of your investment doesn’t matter as much

as whether you know what you are investing in I suspect thatmany managers answer ‘I don’t know’ to this One obvious

reason is that the cost of data is often wrapped into the cost of

IT in general Historically, this made sense: in the early years ofthe information technology boom you could imagine that

somewhere there was a fixed lump of data, an inventory of

every piece of knowledge your organization could assemble,that just had to be interrogated The constraint was the

investment in systems that could process it

This is not a useful way to think about the world we now

inhabit On one hand, with the emergence of cloud computing,computing power and storage are services that are as elastic asthe need you have for them Digitization has also created thepotential to acquire almost infinite amounts of data to analyse.The data and analytics strategy determines the investment in IT,not the other way round In fact there is an absolute need todecouple the costs of data and technology

DATA AND ANALYTICS STRATEGY

The choices and priorities that create a course of action to achieve the high-level goals

of the organization Through the use of all aspects of data – including data generation, data storage, governance, quality, analysis, business intelligence and data science –

these goals will create returns or competitive advantage for the business and support its wider goals.

And, of course, high spend does not mean a high return,

although some of the numbers invested by data-driven

companies are extraordinary (Krauth, 2018) Google, for

example, invests $3.9 billion in data science every year UPSspends $1 billion Amazon spends $871 million

If you don’t know how much you are investing in data, wouldyou know how to find it out? Who would you ask, and do youthink you’ll get an informative answer if you do? Often this is auseful exercise Many businesses have many pockets of dataactivity that are ineffective because they are buried in silos.Adding up the cost of all these activities may lead you to think:could we be investing this money more effectively?

Which brings us to the next question

Can you describe a product or project driven

specifically by data that had a measurable positive impact on the business?

Note the emphasis on measurable results that are attributable

to data, rather than technology or improvements in general Ifyou answered ‘no’, why do you think that is? It might be that

there weren’t any projects of this nature Or it might be that youdon’t know about them yet.

We often implicitly know that data will be, or has been,

valuable to delivering results – think about a project to sell, or to increase customer satisfaction that increases yourshare of that customer’s wallet But often we don’t structurethese projects as data-driven, even when they are We tend toattribute the success to people, products, or an improved

cross-process And, often, that’s part of the story Being able to tell astory of how data was the catalyst to create value, and beingable to quantify that value, has three benefits:

1 It is a way to work out whether you are successfully usingdata Can you isolate the role of the data in the businesssuccess, and can you express the return on that

investment?

2 It is a way to think about how we can innovate Often ittakes a long time, or is not practical, to change a product,process or service So instead, reframe the question: can

we apply better data to existing decision-making, or can

we make the data more relevant, to improve a process?

3 Finally, storytelling is a way to share stories of data thatwill help others buy into what you want to do So, if youranswer to the question was ‘I don’t know’, this will be themost valuable first step These do not have to be complexstories – often, the simpler the better, to inspire our

pension provider 30,000 And so, by using customer data frombank customers to identify those who would potentially wantinvestment and pension products as well, our team was able toidentify and convert 30,000 new customers for the investmentand pension provider, doubling revenues accordingly Data-driven cross-selling doubled the size of that business.

In meetings, do participants use expressions like ‘in

my experience…’ or ‘what I feel is…’ as a way to

make decisions?

You’re not alone if they do: Forrester Research reports that only

12 per cent of companies use data-driven intelligence to guidekey business functions or corporate strategy (Little, 2016)

Listen to two psychologists describe the limitations of whatpsychologists call heuristic decision-making, but what we know

as ‘gut feel’ Gary Klein, a senior scientist at MacroCognitionwho has analysed the way people make decisions in high-

pressure jobs like firefighting, describes it like this: ‘You need totake your gut feeling as an important data point, but then youhave to consciously and deliberately evaluate it, to see if it

simple and coherent story, they will feel confident regardless ofhow well grounded it is in reality’ (Kahneman and Klein, 2010)

Kahneman concedes there are two contexts in which gut feelhas some validity The first is a predictable situation that is

familiar from experience We all do this every day without

noticing: we don’t have time to research every tiny choice wemake, so our brain fills in the gaps Most of the time our instinctmakes a good decision for us and if we get it wrong (for

example, picking a film to watch on Netflix and finding it’s

rubbish) it doesn’t matter But this is not the case for most

business decisions

Kahneman agrees with Klein that gut feel is a reasonable

starting point in a situation in which the statement is invitingfeedback and qualification What is important is that you testthat intuition with data Think about the opinion as a

hypothesis, one that the CDO can support or dispel by usingdata Gut feel is based on past experience, but high-quality datacan show that it was based on assumptions that no longer apply(a different market, different customers, different

macroeconomic conditions), or one that adds more detail towhat we all assume (showing that the business actually is

underperforming in a region, but that this is driven by one

office, for example)

Remember, in your data and analytics strategy no making meeting is held without relevant data You should nottrust narratives that overconfident minds have constructed thatwill lead to bad choices

decision-Whether you call it heuristics, intuition or gut feel, these

instincts are always with us, and shouldn’t be ignored They justshouldn’t be used as a way to decide – although our data

limitations mean that’s often what we do Which brings us toour next question

Do your colleagues argue about whose version of the data, or report, is correct?

In a recent job, I was called in to adjudicate an argument MyCEO had sat in a meeting in which two of his managers foughtover some figures for their business unit The best we can say isthat neither of them was trusting his gut: they both brandishedreports, with numbers, relating to the same part of the business.But the bottom lines were completely different He set me thegoal of discovering who was telling the truth

It took four weeks to find the answer that nobody wanted tohear: neither of them When we dug into it, both had missed outsome important costs and made some incorrect assumptions,and so both, in their different ways, had given a too-optimisticpicture of the real state of the business, slanted in ways thatmade them look good They hadn’t set out to deceive and

weren’t aware that that was what they were doing Neither

report was an effective basis for decision-making but by lookinginto it we identified some flaws in the business processes and

by fixing them, we generated real business value

This often happens when systems are not integrated, or whenthere are too many manual processes involved in creating theoutput Perhaps the people who create the reports don’t knowwhat the numbers they are adding up really mean Or people indifferent parts of the business interpret numbers differently oruse the output of different IT systems

But that doesn’t excuse the practice Doing this for internalmeetings is bad, but having no authoritative source of truth forfinancial reporting can literally be criminal The most

destructive example of this must be the bankruptcy of

WorldCom By June 2002, the United States’ second largest distance telecommunications company confirmed it had

long-overstated its earnings, mainly by classifying as capital

expenditures those payments it was making for using the

communications networks of other companies It reclassifiedmoney it held in reserve as revenues, making it appear it hadprofits of $1.38 billion As the SEC reported, the improper

accounting entries were easily accomplished because ‘it wasapparently considered acceptable for the General Accountinggroup to make entries of hundreds of millions of dollars withlittle or no documentation beyond a verbal or an email

directive from senior personnel’ In total, WorldCom made

more than $9 billion in erroneous accounting entries to achievethe impression it was making profits (International Banker,2021)

At a more mundane level, Gartner Group calculates that quality data drains $15 billion annually from businesses whichmiss opportunities to sell, or waste cash without realizing it(Moore, 2018)

poor-As a CDO, you might have gone to work at a business that

already has good-looking dashboards and elegant

presentations But does anyone know for sure they can trust thedata in them to make decisions, or is it what Sir Alan Sugar, amarket trader in London’s Petticoat Lane before he foundedAmstrad, used to call the ‘mug’s eyeful’ of technology: good-looking but ultimately irrelevant decoration?

Or, worse, do people create a single truth by averaging

several different reports, in the vain hope that they are all

equally wrong? Remember, an inaccurate graph will look just

as good, and appear just as precise, as an accurate one Without

a single source of truth, you can’t act with confidence on whatone piece of data is telling you.

Have you checked that your customers’ experience

of your organization matches your assumptions?

Pointillist, a firm that specializes in customer experience,

surveys businesses every year to discover how well they aredoing In 2020 its survey of 1,050 analytics and customer-careprofessionals found that 48 per cent thought that lacking asingle view of the customer was their number one challenge(Pointillist, 2020)

SINGLE CUSTOMER VIEW (SCV)

The process of collecting data from disparate data sources, then matching and merging

it to form a single, accurate, up-to-date record for each customer It is also known as a

‘Golden ID’ or ‘360-degree customer view’.

A single view of all the transactions and contacts and

communications that you have with a customer is possible ifyou have a single source of truth about your business, but it isnot easy One of the problems is that what might look like asingle, consolidated and complete view of the customer might

be missing some vital pieces of data You cannot discover this

by looking only at the data you have So systematically

surveying your business units can help you find out whetheryour data represents the whole of the customer’s experience ofyour business You might be optimizing the customer

experience that your data captures but failing to achieve yourwider goals for the business.

SINGLE SOURCE OF TRUTH

Every data element is stored and edited in only one place, with no duplicates Updates

to the data propagate to the entire system.

This isn’t just about customers: there are two equally importantdimensions of business relationships that you can treat the

same way Do you have a single employee view? A single

supplier view? You might think you do, but often you are

relying on what your systems tell you, without examining theunderlying experiences

The employment contract is ‘incomplete’, in that it doesn’t tell

us much about what makes an employee work hard or be loyalbecause few of us work in an environment in which our

employer can see everything we do Therefore, a view of theemployee based only on what the contract captures is going to

be incomplete As we move towards a culture in which moreemployees work from home or wish to work flexibly, being able

to structure their contracts and incentives efficiently dependsnot just on what they can do for their employer but on the fullpicture of their circumstances, needs, motivations and abilities

If you have a single view of each supplier, you can identifywhen that supplier is servicing many parts of your business orwhere you have multiple suppliers that could be consolidatedwith a bigger volume discount Consolidation means you have ahuge opportunity to save costs

One of the achievements of a successful data and analyticsstrategy is a no-surprises culture No customer, employee orsupplier relationship is perfect, but you can aim for an

environment in which none of your colleagues can respond to aproblem by saying ‘I had no idea that was a problem’

Recall the last time there was a crisis in your

business If you had been given an hour to pull some numbers together for an emergency meeting, would you have been able to find and access the right

data?

We all know by now what it is to nurse a business through acrisis Everyone whose employer was affected by Covid-19 canrecall the scrambling and off-the-cuff decision-making Everyday involved rapid replanning, most often around protectingcashflow And so suddenly people are asking for different data,because there are different decisions to be made They want itnow, because tomorrow is too late

So, imagine that you need to analyse one of these ideas, and

to do that you need data about the current performance of thebusiness How much detective work do you need to do to findthe numbers you need? Will you need to ask someone else tolocate it? How many manual processes will you go through toextract and consolidate those statistics?

Often, it is quite a lot of work to locate this data This happensbecause we too often get used to the tedium of assembling andcurating data Each quarter, season or month there are routine

or repeated tasks that you and your colleagues complete thatrequire the same types of data, just with different dates or