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Why every business needs a data analytics strategy

Nick Bouch Partner - Data and Analytics, PwC United Kingdom

The importance of data cannot be understated. It impacts on nearly everything we do, and nearly every successful business today harnesses the power of data. Organisations today also face a challenging regulatory landscape; the likes of GDPR and MiFID II carry the threat of massive fines, in addition to the danger posed by cyber-attacks with perpetrators wanting to get to the underlying data in many cases. These factors have helped make data – what it is, where it comes from, how it is handled and secured and how it can be harnessed – a boardroom priority.

But getting a handle on data can be a huge challenge for organisations. Not that long ago, for nearly all businesses, data was a byproduct of the business processes. Data management, processing and storage were done by the IT department. But as data has moved out of the shadows, many organisations have been establishing teams and individuals whose responsibility it is to secure, manage, make sense and derive insights from the data. Organisations find themselves caught up in a battle over who is accountable for data; including quality and accuracy, and crucially who to point the finger at when things go wrong.

The data itself is not typically wrong, but rather the data handling processes can often cause quality issues, which can then have implications on how we record and interpret the data downstream. The issue here often comes down to how a business operates and the implications on data is that it is fragmented across business operations. An IT department will typically build and manage the infrastructure responsible for data processing and storage, but business teams will use data thereafter. At this point the problems start to be uncovered…. inaccuracies, incompleteness, misinterpretation of the data meaning to name a few...Correcting data at this point is a complex challenge.

This is a common challenge faced by many established businesses. Many new market entrants don’t have the baggage of legacy architecture, existing processes or data silos. In fact, many were built with data analytics in mind which then forces better data management. In many respects, they’re data-first companies. But in established companies, replacing existing systems and processes and putting a data-driven architecture in place is complex and expensive. Even in the world of big data, there are just as many stories of failed and canned projects as there are of successful implementations.

Data itself is not typically wrong, but rather the data handling processes can often cause quality issues, which can then have implications on how we record and interpret the data downstream.

Questions for businesses:

  • Do you have an appropriate data strategy in place?

  • Are you upskilling and recruiting effectively now to reduce costs in the long term?

How do I start my data transformation journey?

One of the first steps a business needs to do when approaching how it will handle and harness data is to decide what it’s trying to achieve as a business. This might sound obvious, but believing that you’re about to become something completely different as a result of the use of data and analytics is unrealistic. The future strategy needs to examine the markets (new or existing) that you want to go into, what the workforce needs to look like, what clients you’ll target, how to achieve regulatory compliance and what role data plays to help you get there.

A successful data strategy cannot be detached from the business strategy. The former needs to be aligned with the latter, and there will be several considerations. Some of the considerations will be technology-driven; what’s your cloud strategy? How are you planning to use AI? Some will be down to what controls you have in place. Other considerations will be down to the models that get put in place, and who is responsible for the ownership of data.

Businesses can’t just take a data strategy off the shelf and deploy it across the organisation. Every strategy must align with the firm’s culture and how it uses that data and employs the use of analytics. There are some strong threads and commonalities, but what would work for one firm does not have the same chance of success in another.

Another area a lot of organisations fall down on when trying to harmonise their business and data strategy is time, or rather patience. Typically, a business will commit to a three-year data strategy and change programme then about a year in they start asking whether it is the right strategy. If you’ve written a three-year strategy, you need to give it three years - or at least two. That doesn’t mean the strategy needs to be rigid; some changes along the way are unavoidable. The ability to adapt needs to be built in. But within the scope of a three-year strategy, an organisation should try and keep the foundations the same, even if they alter some of the components above.

The reason why a data strategy needs to be given time is that the first year of any new strategy or change of direction can be expensive and hard to get off the ground. And, of course, a lot of things out of your control can happen in a short space of time – new technology and/or the arrival of new regulations are two common external factors that might derail a business or data strategy. As well as the fact that a lot needs to happen before value can be derived so the communication and stakeholder strategy is imperative for a successful data strategy to work.

In technology, a company’s cloud strategy might have a huge impact. How some businesses store their data in the cloud means they will withdraw from certain countries because there isn’t a cloud infrastructure. For instance, some countries might be a great market to expand into, but legally you can’t store your data in one country and then serve customers in the restricted one. Some companies will opt to not serve markets because of data storage challenges. Just five years a significant business decision to withdraw from a country based on the data storage restriction would rarely have happened.

Think about your people and culture

Talent is a key issue

Put simply, we’ve heard there’s just not enough talent out there, and the talent within most organisations needs to be nurtured and upskilled on what data is and how it is used. Many firms are looking at ways in which they can raise the data literacy of their employees; they won’t become data specialists, but they will have a better understanding and appreciation of why it needs to be better managed. Your employees can and should understand how data can be used to drive business value, the ethical implications of how data is exploited and how to secure it. By upskilling staff, companies won’t need to recruit such huge amounts of expensive analytics resources.

Data literacy in most organisations is too low

Even at board level we are seeing much more appetite for the Execs and NEDs to understand more about data and risks - and the implications of bad data management, security and poor analytical capabilities. While a lot of people on boards today are from a generation who never grew up with technology, over the next 10 years the composition of those boards will change, and we’ll start to see people who grew up with technology and these people will make up a bigger proposition of those on the boards. Right now, we’re on the cusp where senior people know it’s important, but they don’t fully understand it. Most of the things that CEOs will have said about data won’t be from first-hand experience.

The challenge of transforming cultures

Getting everybody to understand the value of making decisions using data-driven insight rather than gut feel is something most organisations seem to struggle with. They buy the data, they buy the systems, they tell people what to do, but organisational change can be a many year process. Shifting culture is not a short journey, it needs senior sponsorship and can cost a lot of money. When people have been used to doing something in a certain way for a long time, it’s a tough journey that potentially entails a lot of change.

The role of the CDO

Whilst some companies have had a Chief Data Officer (CDO) in role for a few years, we’re now starting to see this role become more widespread and the expectations maturing. These executives are perfectly placed to help deliver a programme of change. The CDO would historically have reported to the CIO, though the majority now fall under the COO with some even on the board.

Previously, the CDO was a switched-on data expert who knew everything about data quality and governance. The current generation of CDOs, which has emerged in the last couple of years, is much more business-led; individuals who understand how the business works, can communicate effectively, build a strong team around them overseeing the more niche subsets like governance and quality, and drive the business forward around change. As the role continues to evolve, we are likely to see more chief analytics officers looking after the exploitation of data through insight, while CDOs look after the data itself.

What is the real value of data?

A final part of the data challenge is related to the fact that it is hard to place a value on data. While every other commodity can be valued on a balance sheet, we don’t see data valued in the same way. When developing a business strategy, a CEO can work out the impact of moving location or reducing their workforce, but it is challenging to know what return they’ll get for a data and analytics programme.

Finding a way to communicate and raise awareness the value of data is critical. This is a difficult challenge but not impossible, after all data may be the most critical asset to run your business. We know, given GDPR and data leaks, that the reputational damage to a company can be hugely damaging if they were to suffer a personal data breach. This implies that personal data of clients and employees is high value. The same can be said for other data sources - transactions, products, M&A etc etc. We also need to consider other attributes associated with data to understand the true value for example, its usage, its accuracy, its recency, and frequency of use, As firms start to think more about ‘monetising their data’ starting to model the value of data is the first step to becoming a true data driven business.

Nick Bouch Nick Bouch

Nick Bouch

Head of Assurance Data & Analytics


Leigh Bates Anand Rao

Leigh Bates

UK Banking Data & Analytics Partner


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Kevin Ellis

Kevin Ellis

Chairman and Senior Partner, PwC United Kingdom

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