Creating a data-first business

13th May 2024
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Using data to help validate and evidence insight is an essential component of any value creation strategy. In our experience, many mid-market businesses are behind the curve, but with the right approach, driving value from analytics can quickly gather momentum. CIL’s Advanced Analytics practice explores how businesses can improve their data maturity, create a competitive advantage and unlock the true potential of analytics to drive growth.


You can’t manage what you don’t measure

Without access to timely, quality data, management teams tend to ‘fly blind’, making it difficult to make insightful, evidence-based decisions. Many businesses that we work with are data constrained, which manifests in various ways: running a business or function based more on ‘gut-feel’ than evidence, difficulty sourcing quality data to solve commercial problems, a data room with subpar datasets, and the list goes on…

Ultimately, if you’re spending time debating opinion without data, thinking about data quality, the reliability of analysis, or why you don’t have the data you need to make informed decisions, then perhaps it’s time to focus on the fundamentals of data maturity.

Why you should be surfacing your data

Being able to extract insight from multiple data sources brings clarity when defining a company’s future strategy and creating value.

This need often surfaces when a business is going through a transaction. During this time, consultants and other advisors are focusing on the business and its data, working through any problems until they find a solution. After the transaction, that focus drops and the expectation to provide data-driven insights falls back into the business itself. Yet, the capability, systems and experience within the business have not changed. Transactions are the peak of business analysis and insight.

For example, CIL’s Advanced Analytics practice was engaged by a large care home provider in the midst of a diligence process wanting to analyse and articulate its average weekly performance. The group’s data was unstructured, and unorganised, with each care home in the group capturing and managing data differently (and independently). The care home provider was unable to fully understand how it was performing on a weekly basis, as conducting analysis over time, at both a group and home level, was challenging.

By bringing together its disparate and inconsistent data sources into a newly created cleaned and structured database, CIL helped the leadership team derive answers to the commercial questions that were important to answer during the transaction process and gave them an approach to using those insights to run the business more effectively after the transaction.

The four key ingredients for data maturity success

Achieving data maturity does not happen overnight. Instead, we think about it as an iterative exercise of continuous improvement. Organisations with a good level of data maturity exhibits competency across the following four areas:

  1. Specific use cases

Many businesses know that they can use their data better. But where to start can feel overwhelming. Identifying and prioritising data use cases that will drive the most value for your organisation and solve the greatest commercial challenges is the first step.

Quality over quantity wins here. Define a concise set of use cases by thinking about the value opportunity first, then the specific data points you need, how you will collect and measure them, who will benefit from the insights gained and how to present that data to aid decision making.

For instance, for professional services firms in particular, detailed reporting on project-level budgets and live tracking of time-on-the-clock helps to manage project margins, defend price points, and drive profitability. Whereas builders’ merchants require timely branch and category-level data on discounts to manage margin performance and prevent margin erosion. In the case of media-focused businesses, tying customer acquisition costs to lifetime value for cohorts and products helps to focus time and investment into the most optimal customer portfolio.

Ask yourself:

  • What are the three areas in my business or function where a clear evidence base or dataset would help me make better decisions?
  • What benefits could I achieve because of this better decision making? E.g. increased revenue, improved margin, better use of resources to deliver the result etc,
  1. Cloud technology stack

Ensuring straightforward and reliable access to data is a critical first hurdle. The majority of mid-market companies work with a mix of data vendors, but without the in-house expertise to efficiently integrate and analyse the data. It may be tempting to immediately integrate third-party systems with PowerBI, but as reporting requirements become more complex, a more unified solution is necessary to avoid conflicting answers to the same question among other issues.

The modern technology stack is designed to reduce the amount of time it takes to get useable data in front of the right teams. Investing in a scalable and efficient technology system pays dividends when it comes to ensuring the consistent delivery of data and analytics to a business.

There are many SaaS-based, off-the-shelf that consolidate business data into a single source of truth (a data warehouse) and can be set-up quickly. These solutions can be focused on specific use cases, and we find these tend to rapidly demonstrate the value of automating data integration and analysis.

Ask yourself:

  • How many systems exist within my organisation, and what valuable insights could be gleaned from effectively combining and analysing data from these systems?
  • How much time and resources are dedicated to manually generating weekly or monthly reports, particularly in the finance department?
  1. The data itself

Data should be readily available and managed in a single source of truth to support defined use cases. The first step to achieving this is to identify and close the most significant data gaps, ensuring complete capture wherever possible. This often means committing and investing in improving data quality and governance and encouraging data use and capture across all aspects of your business.

We find organisations that focus and report on data quality see performance improvements follow suit. For instance, by capturing data on timesheet compliance and reporting it back to teams, timesheet compliance will improve, and the data available to support informed decision making expands.

Ask yourself:

  • In what areas of business operations is there insufficient data to support decision making, aka, where are decisions always made on ‘gut feel’?
  • In which data sets, would an increase in accuracy of coverage deliver a disproportionate impact?
  1. People and culture

Improving data maturity means supporting people and nurturing the right people, with the right skills, in the right part of a business. This includes ensuring accountability for data among the leadership team and throughout the business.

We typically see the responsibility for data strategy and analytics sitting in the wrong place. We encourage clients to think about what people and structures they need in their business now to start creating value from their data, and then plan for what they will need in the future. Creating a data-first culture has to come from the top – this means eventually making space in the leadership team for an analytics professional.

For example, in a £50m revenue, multi-brand SaaS business, the data team lead had two reporting layers between them and the CEO, significantly hindering the team’s influence and ability to drive culture and impact across the group. With some strategic changes to the management team and the organisational structure, the data team found a consistent and credible voice at the C-level resulting in immediate impact.

Ask yourself:

  • Where does accountability for driving the data and analytics agenda sit in your organisation? And does this role cover the commercial and technical aspects of data.
  • How many employees within my organisation are conducting the same analysis on a monthly basis? Could you provide them with professional development and tools to make them more efficient?

Creating long-term value through data maturity

With the right level of prioritisation, every leadership team can drive performance and commercial growth through data-driven, evidence-led insight. There’s no magic solution, but with the right focus, results can be delivered quickly. When data is leveraged as a strategic asset and processes are established to turn it into actionable insights, it becomes a powerful enabler for driving performance and growth.

In particular, we cannot stress enough the importance of using good data well in advance of an M&A process. Insights from a well-formulated analytics strategy drive value during the hold period, but more importantly, enable great articulation of company performance and positioning at exit: a key value driver for PE and management teams.

To discuss how CIL can help you improve your data maturity, get in touch.


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