by Nicolas Ponset, Chief Executive at Aleron

The quantity of data being generated each year is increasing exponentially. The opportunity to use data to transform how organisations operate, manage talent, and create value is growing at a similar rate. But, data on its own doesn’t provide executives and other decision makers with valuable insights. In fact, when a coherent approach to data collection and analysis is lacking, working with data can fast become burdensome, expensive and distracting.

For data to deliver any meaningful value for an organisation, the information must be directly relevant to their strategic objectives. Furthermore, there must be a logical approach to the way in which data is gathered, analysed and then acted on. This article explores the fundamental principles that will help to ensure that your organisation can harness the power of data, and not get lost in a labyrinth of unhelpful numbers.

Data Analysis – Key Principles:

Advanced data analytics is above all a business matter
Data analytics is often thought to be too complex or granular to warrant discussion at the leadership level. It is left to technical experts. When this happens, organisations lack the focus and collective direction required for data to become a strategic tool in performance improvement. Data analytics is only a means to an end. CEOs and senior management must therefore be able to clearly articulate the purpose of data and then translate into action, in each division of an organisation, how insights should be generated and used. If insight is to translate into action, leaders must be responsible for setting the organisational direction for data.

The question should lead the data, not the other way around
There are too many examples of organisations who look at what they already have, rather than at what they really need. This approach often sees significant resources wasted on analysing vast amounts of available data (e.g., customer data, supply chain data, performance metrics) without delivering tangible benefits to the organisation. By asking themselves what they really need to know, before they begin collecting data, organisations will be far more likely to achieve impact, and far more likely to achieve it quickly. The precise questions your organisation should ask depend on your strategic priorities. We often use strategy maps to determine key objectives and then map out the data that can help to achieve them. This process also presents a great opportunity to ensure senior management share a common vision for data analytics. The more specific the questions are, the better. It enables you to swiftly identify the key drivers of performance and impact, and pay close attention to these data points. For example, questions could include: “How can we improve the quality of outcomes for patients?”, “How can we make our small contracts financially sustainable?”, “How do we improve the win rate of our business development team?”.

Start with the best data, not the perfect data
Once the questions are agreed, the next step is to define the data required to answer each question. This is a tricky and iterative step as the perfect data is not always the most accessible, at least not in the short term. As with any new initiative, achieving quick wins is a powerful way to create momentum within the organisation.
Be creative and practical. Creativity is required to identify the best proxy possible. You may have read in the press about a company that uses drones to collect data about the movement of cars in car parks to estimate the sales of US supermarkets. Practicality is needed to ensure that the solution is commensurate with the resources available to an organisation. Deploying a drone enabled solution may not be either affordable or suitable for many organisations. One of our clients wanted to improve the overall outcomes of their contracts, but did not even know the exact number of contracts they were delivering. They started by developing a template that enabled each service team to list all the contracts they were managing and provide a self-assessment of the outcomes. Although it was not perfect, it was the first time that this information had been made available across the organisation. Then, every quarter, they distributed data requests and incorporated additional information from a range of internal and external systems. Within twelve months they had a clear view which contracts were underperforming, as well as the root causes of this underperformance. They are now using predictive analytics and pro-actively identifying any contracts that are at risk of delivering outcomes below expectations.

Improve data quality over time and build a feedback loop
Insights are only useful if they influence decision-making and are part of a continuous process of improvement. High performing organisations adopt this approach in their planning and business cycle: the classic ‘Plan, Do, Review’ approach. Analytics plays a vital role in this process as it informs the planning (e.g., forecasting and predictive models), supports the doing (e.g., data to inform daily decision making) and provides the insights for the Review phase (e.g., evaluations, benchmarks, assessments). This loop back is fundamental to improving both data quality and insight. To solve the quality question, it is critical to engage staff in identifying issues and then have a process to correct them at source.

An international development organisation suffered for years from the poor quality of their data and the constant resistance from staff to do anything with it. Most people were blamed internal systems but an external review pointed to major issues in the way they handled data. There was no consistency in their data structure and errors were never usefully reported and corrected. They embarked on a project to redesign their data architecture and set up a data governance board to enforce the new rules as well as facilitate the central collection and resolution of issues. After 9 months, data quality had significantly improved and each department felt accountable for preserving the quality of their own data. Performance reports and other analytics tools were subsequently developed and are now a foundation of their strategic planning and project prioritisation processes.

Communicate data insight in a way that encourages reaction and action
Insights from analytics are only useful when they are understood by senior management and staff. Although data scientists can produce the smartest algorithms, they are not always the best qualified to communicate the outputs. Nicolas Boileau-Despréaux – a French author from the end of the 17th century famously said: “whatever is well conceived is clearly said… and the words to say it flow with ease”. There is no doubt that scientists can explain what they have done, but the results must be articulated in a way that resonates with the target audience. Researchers have found that:

  • Almost 50% of our brain is involved in visual processing
  • 70% of all your sensory receptors are in your eyes
  • We can get the sense of a visual scene in less than 1/10 of a second
  • Colour visuals increase the willingness to read by 80%

It is not surprising, therefore, that the use of infographics and data visualisation tools has boomed in recent years. These techniques are not just the preserve of magazines, but should be leveraged by all organisations, regardless of their size.
Let’s consider the example of Unilever. In 2010, the company set itself the target of doubling revenues in a decade or less – without doubling its costs. Information management played a crucial role in achieving this goal. To help its employees make better decisions, the company looked to embed data into its business processes and make effective use of data visualisation and analytics. With data analytics and visualisation, employees became more analytically minded without needing expert statistical skills. It also enabled global managers to drill down into the granular information required to make well-informed, effective decisions. Through analytics, Unilever bridged the gap between their global and local perspectives. It helped them to connect small, regionally specific patterns to a global view of momentum-defining trends.

Focus on user experience and adoption
This brings us to the need to focus on user experience and adoption to ensure that analytics are supporting the achievement of strategic priorities. The data in itself is insufficient. Culture makes adoption possible and as we have all experienced, changing culture takes time and effort. Organisations must embed analytics in their processes and decision making. Again, this requires significant engagement from the leadership team in asking the right questions and ensuring that messages are clear and actionable at all levels of the organisation, from the C suite to the front line.

The management of a large UK based charity was convinced of the potential for data analytics to improve the impact they deliver to children and young people, but was worried about the willingness of their staff to embrace it. They have seen that Excel dashboards were ineffective with many workers, due to a lack of technical knowledge about how to use the tool. To better understand the needs of their workforce, they ran focus groups to discuss the key questions people were trying to answer and how data analytics could help. In parallel, a data visualisation platform was set up to test the reaction of staff to a new format. The feedback was overwhelmingly positive. The new approach provided an engaging, sensorial experience of the information. Employees could see the important messages and touch the screen to drill down to the underlying information. By focusing on the user experience, the organisation successfully managed to embed data into organisational culture.

In this example like in other successful analytics initiatives, senior management engagement has made the difference. To take control of your analytics journey and harness the power of data, leaders have to help identify key strategic questions and create an environment that supports the embedding of data across each part of the organisation.