The Keys to Success in Data Analytics

Earlier this year, I gave a presentation to a group of data analysts in a large organisation. My remit was to discuss how data analytics can be used to enhance performance. They were particularly interested in the insights I had gained from my own experience both in business (my career started as an analyst in the Unilever’s Economics Department in the mid-80s) and in elite team sports. I started off with my basic philosophy that “data analytics is a very useful servant but a poor leader” and then summarised the lessons I had learnt as seven keys to success in data analytics. Here are those seven keys to success.

1.A culture of evidence-based practice

Data analytics can only be effective in organisations committed to evidence-based practice. Using evidence to inform management decisions to enhance performance must be part of the corporate culture, the organisation’s way of doing things. The culture must be a process culture by which I mean a deep commitment to doing things the right way. In a world of uncertainty we can never be sure that what we do will lead to the future outcomes we want and expect. We can never fully control future outcomes. Getting the process right in the sense of using data analytics to make the effective use of all the available evidence will maximise the likelihood of an organisation achieving better performance outcomes.

2. Leadership buy-in

A culture of evidence-based practice can only thrive when supported and encouraged by the organisation’s leadership. A “don’t do as I do, do as I say” approach seldom works. Leaders must lead by example and continually demonstrate and extol the virtues of evidence-based practice. If a leader adopts the attitude that “I don’t need to know the numbers to know what the right thing is to do” then this scepticism about the usefulness of data analytics will spread throughout the organisation and fatally undermine the analytics function.

3. Decision-driven analysis

Data analytics is data analysis for practical purpose. The purpose of management one way or another is to improve performance. Every data analytics project must start with the basic question “what managerial decision will be impacted by the data analysis?”. The answer to the question gives the analytics project its direction and ensures its relevance. The analyst’s function is not to find out things that they think would be interesting to know but rather things that the manager needs to know to improve performance.

4. Recognition of analytics as a source of marginal gains

The marginal gains philosophy, which emerged in elite cycling, is the idea that making a large improvement in performance is often achieved as the cumulative effect of lots of small changes. The overall performance of an organisation involves a myriad of decisions and actions. Data analytics can provide a structured approach to analysing organisational performance, decomposing it into its constituent micro components, benchmarking these micro performances against past performance levels and the performance levels of other similar entities, and identifying the performance drivers. Continually searching for marginal gains fosters a culture of wanting to do better and prevents organisational complacency.

5. Realisation that analytics is more that reporting outcomes

In some organisations data analytics is considered mainly as a monitoring process, tasked with tracking key performance indicators (KPIs) and reporting outcomes often visually with performance dashboards. This is an important function in any organisation but data analytics is much more than just monitoring performance. Data analytics should be diagnostic, investigating fluctuations in performance and providing actionable insights on possible managerial interventions to improve performance.

6. Soft skills are crucial

Effective analysts must have the “hard” skills of being good statisticians, able to apply appropriate analytical techniques correctly. But crucially effective analysts must also have the “soft” skills of being able to engage with managers and speak their language. Analysts must understand the managerial decisions that they are expected to inform, and they must be able to tap into the detailed knowledge of managers. Analysts must avoid being seen as the “Masters of the Universe”. They must respect the managers, work for them and work with them. Analysts should be humble. They must know what they bring to the table (i.e. the ability to forensically explore data) and what they don’t (i.e. experience and expertise of the specific decision context). Effective analytics is always a team effort.

7. Integration of data silos

Last but not least, once data analytics has progressed in an organisation beyond a few individuals working in isolation and storing the data they need in their own spreadsheets, there needs to be a centralised data warehouse managed by experts in data management. Integrating data silos opens up new possibilities for insights. This is a crucial part of an organisation developing the capabilities of an “analytical competitor” which I will explore in my next Methods post.

Read Other Related Posts

• What is Data Analytics? 11^th Sept 2023
• The Six Stages of the Analytics Process, 20^th Sept 2023