Strategy

An actionable guide for small and medium-sized businesses on how to create a data & analytics strategy which delivers maximum business value.

Introduction

In the 21st century, data is an increasingly coveted resource. Businesses around the globe are in the process of re-orientating to a data-driven approach.

Recent research shows that 82% of CEOs are taking steps to transition toward digital operations (a 20% increase from two years ago) and 77% are planning to increase their investments in digital capabilities. 

Tie Your Data and Analytics Initiatives to Stakeholders and Their Business Goals. Gartner, 2020

The ready availability of powerful data collection and analysis tools, means that businesses of all sizes can now access valuable, information-driven insights, once reserved for the biggest and most well-resourced corporations. Small and medium-sized businesses (SMBs) are using these advancements to drastically improve or even transform the way they do business. 

Tools and technology are only part of the picture – digital natives already view data and analytics as both an asset and intrinsically linked to overall business strategy. However, too many organisations still take an inconsistent, half-hearted, or ad-hoc approach and, in doing so, fail to maximise possible benefits.

Truly harnessing the potential of data and analytics requires a fresh perspective on its importance as a resource, a fundamental re-evaluation of its role in executive-level decision making, and a clearly defined strategy.  

We’ve created our data and analytics maturity framework to provide SMBs with insights on the core competencies needed to succeed as a digital business, in an increasingly digitalised economy.

Our framework is constructed using six categories:

  • Strategy
  • Process
  • Data
  • Platforms
  • Analysis
  • Culture and Skills
How Data-Driven is your organisation

This guide provides an overview of how a data and analytics strategy can lead to measurable benefits for your organisation.

It examines four essential dimensions:

Whether you’re consolidating existing data and analytics operations or are taking the very first steps towards accelerating your data and analytics maturity, designing a strategy can seem daunting at first glance.

An effective data and analytics strategy aligns closely with your core goals, it has a clear outline with concrete milestones and is periodically evaluated against key metrics to ensure that the desired business outcomes are achieved.

Before any of that can even begin, there also needs to be direction and momentum coming from ‘digitally literate’ decision makers at the top of your organisation. Without ambitious sponsorship, even the most robust data and analytics strategy will likely run into cultural resistance and structural bottlenecks.

So, let’s unpack these issues and look at them in detail.

1. Sponsorship

Sponsorship is a key indicator of the degree to which data and analytics is seen as a critical driver to business success within an organisation.

A sponsor is the person, or team, that drives the adoption of data and analytics practices throughout an organisation. While this may sound simple, it is in fact crucial. For many non-digital native companies, sponsorship has often stemmed from the IT department and this has led to data and analytics being used in supporting roles or as a reactive service to solve problems on a case-by-case basis.

Such an approach has a limited scope and can frequently lead to misaligned priorities, poorly allocated resources or siloed pockets of excellence that do not scaffold outward across the business; thus, missing their true potential.

Data and analytics maturity requires leadership, and there is an increasing recognition that an organisation’s data and analytics strategy needs guidance and sponsorship from executive-level decision makers. Gartner analyst Andrew White sums up this change in corporate thinking: “Organizations have realized the need to dedicate a specific leadership position to this field.”1

Sponsorship that stems from senior leadership brings organisational focus to the overall process and sidesteps a number of roadblocks. It ensures that your data and analytics operations are fully aligned with your long-term corporate goals, that there are cohesive and consistent practices across teams, and that resources are allocated where they really need to be.

It also impacts upon two crucial areas: work culture and data literacy. Some of the most prevalent hurdles that SMBs encounter while developing their data and analytics operations have less to do with budgetary or infrastructure constraints and more to do with cultural resistance to change and poor data literacy.

A recent survey of Chief Data Officers (CDO) revealed that for the last three years running, cultural resistance and poor data literacy have been listed among the top three obstacles to success for their data and analytics teams.2 Simultaneously, Gartner predicts that by 2023, data literacy will become an explicit and necessary driver of business value” and will be formally included in “over 80% of data and analytics strategies and change management programs.3

These two factors together draw a line under the need for senior level sponsorship of data and analytics. Change comes from the top, and only leadership can create a culture of data-based decision making that cascades downwards from the boardroom throughout the organisation. Data and analytics maturity requires a prioritisation of data literacy as a core competency, not just through access to training but also through a work atmosphere that places value on this skillset.

2. Alignment to Business Strategy

It’s not uncommon for organisations to focus initially on the challenges of data acquisition and management, without stopping to consider what data should be collected in the first place and how it will eventually be used. Only 23% of respondents to a recent Gartner survey had a well-defined data and analytics strategy that measured stakeholder outcomes.2 This worryingly low percentage illustrates that many companies are still not treating data and analytics as the critical resource it is.

We all know that data is valuable, some business leaders have compared it to gold, oil and plastics.4 Yet a raw material by itself holds only a fraction of the value that can be extracted through strategic refinement and manufacturing. Unfortunately, this is a lesson that far too many businesses seem to forget when it comes to data and analytics.

The importance placed upon designing strategy is directly linked to the amount of value it can potentially generate. Research has shown that a CDO’s involvement in strategic development “increases the chances of business value production by a factor of 2.6 times and the odds of demonstrating verifiable business value by a factor of 2.1 times”.5

Data and analytics strategies should aim not only to be clearly linked to the goals and aims of the organisation, but to be symbiotic to the organisation itself. The most data mature organisations utilise their strategy to drive operational excellence, customer intimacy, product innovation and risk management. It is this alignment that in turn drives business value.

When it comes to designing a strategy that aligns with your business, there are no shortcuts, no one-size-fits-all solution. The possible applications for data and analytics within SMBs are as varied as the businesses themselves.

Data and analytics leaders must not be afraid to examine the organisation clearly and ask the big questions: What is the heart of the business? What is the vision? Where are we and where do we want to go? What new doors can data open for us? Answering these questions is the basis of forming a long-term data and analytics strategy that delivers measurable value for your business.

Want to assess the data and analytics maturity of your organisation?

One of our data experts will be in touch to run through our detailed assessment, and help you on your journey to becoming more data-driven

Lets assess!

3. Data and Analytics Roadmap

It’s commonly said that the devil is in the details. Well, when it comes to a data and analytics roadmap, the details are what will get you to your destination. A roadmap document translates all the objectives of your strategy into a practical, actionable plan; it is the comprehensive overview of what you’re going to do, why you’re doing it, who’s going to work on it and when they’re going to do so.

Advanced planning is crucial to any successful strategy and should not be rushed. Data and analytics strategies consist of a complex web of interdependencies: hardware acquisition, training and hiring, developing new competencies, data ecosystems, architectures and delivery models are a bare handful of the factors that may come into play.

A mature data and analytics plan not only outlines key goals, metrics, and milestones, it thoroughly investigates how any investments (governance, data management, data visualisation etc.) will be sequenced over the projects’ duration. It articulates and communicates this vital information to stakeholders across the organisation.

Another key purpose of the roadmap is coordination. A data and analytics strategy will, by its nature, require synergy between your IT department, your analytics team, and the business. These three elements should be thought of as a tripod – IT provides robust, enterprise class software infrastructure, the data and analytics team provides the data insights and best practice processes and policies, and the business leaders have the birds eye view to track the overall progress against stated goals. Needless to say, removing any one leg of the tripod leaves the whole thing unstable and unworkable.

Without a roadmap, even a relatively simple data and analytics strategy can all too easily devolve into wasteful inefficiency. Ad-hoc solutions can lead to misallocated resources, breakdowns in communication between teams and, ultimately, unrealised goals.

Beyond safeguarding against waste and bottlenecks, roadmaps help you to identify key metrics and milestones that can be used to evaluate the efficacy of the strategy during its implementation. Tracking this progress and making real-time adjustments if necessary is a frequently overlooked but hugely important aspect of a data and analytics roadmap.

Check-out our article on ‘How to build a data & analytics roadmap which delivers business value’.

4. Benefits Tracking

Even successful data and analytics strategies will leave room for improvements. Identifying what worked, what could work better and what did not work at all is an essential exercise for a business of any size.

Benefits tracking provides valuable insights into the intended outcomes of a strategy by measuring the financial and non-financial value that was delivered. Yet, this crucial step is perhaps the one most frequently neglected on an organisational level.

According to Gartner, “only 15% of data & analytics strategies feature defined metrics with which to measure success. By contrast, the remaining 85% either outline no metrics or mention vague, intangible aims.”

Measure the Business Impact of Data and Analytics. Gartner, 2020

One reason for this is that many organisations fail to prospectively identify which benefits to track and measure when building their roadmap. A further possible explanation is that the positive benefits of a strategy may not result in purely monetary value but may extend into areas that are hard to measure with traditional means.

Take for example an investment in automating repetitive data entry tasks. The outcome of such a strategy offers up clear quantitative data that can be easily evaluated in terms of time and resources saved. However, for many data and analytics strategies, this kind of data is much harder to come by – instead, the more qualitative impacts (such as changes in decision making culture) must also be acknowledged and evaluated as and where possible.

A mature data and analytics-driven organisation clearly defines both financial and non-financial measures of success while also giving consideration to possible impacts upon adjacent functions. Any identified metrics are evaluated in line with the sequencing and milestones outlined in the strategy roadmap. The practical result of this process is an objective, fact-based evaluation of the strategy’s outcomes.

Gaining a retrospective overview of the project allows you to link any future re-investments to fully realised benefits, reduce potential waste and maximise the efficient use of your resources. Furthermore, it enables buy-in from business units as the value of the analytics is demonstrated.

Summary

As an increasing number of SMBs look to the potential of data and analytics to open new areas of revenue or improve existing ones, robust data strategies are the foundation upon which ambitions can be built.

Developing your data and analytics maturity in the four essential areas outlined within this guide will provide you with a starting point for further expansion into the digital business space.

If you want to learn more about how to transform your business with data and analytics, check out the framework or contact us so we can have a chat about your requirements.

The Data-Driven SMB

An actionable guide for small and medium-sized businesses, on how to become more data-driven in an increasingly digitalised economy.

Download eBook