Do managers underestimate the value of data analysis?

People make decisions under constraints. These might be knowledge constraints or constraints imposed by analytical ability. Decision-making is therefore based on heuristics: experience-based techniques for problem-solving, or ‘knowing by trying’. The ‘recognition heuristic’ is when people make a decision based on only one piece of information, recognition – the knowledge that many others have chosen the same option.

Analytical input can be considered too slow, misguided or irrelevant to the problem at hand. The divide between analyst and senior manager can be further widened by:

  • The challenge of choosing the right analytical approach to fit the managerial problem. This is partly an issue of whether analysts have effective communication skills. It is also a question of whether analytical teams have the skills to unpick problems and questions from senior managers in ways that match the analysis they can do. This is an area in which external management consultants often excel.
  • Expectations of what constitutes good (enough) analysis. If senior managers have little experience in data analysis, they may not be able to recognise the value that data analysis can bring and the difference that it can make.

One way to bridge the divide is to invest in people who span professional boundaries. In our previous report, Understanding analytical capability in health care: Do we have more data than insight?, many interviewees described the importance of analytical leadership, in particular people who understand the possibilities that good analysis can engender (and how analytical teams work), yet who can also engage with managers at the highest levels to influence and shape demand for analysis. Organisations with a well-developed analytical workforce also tend to have strong leaders who are influential within the organisation, whether these are chief analysts or in Chief Information Officer roles, and in some cases they may be strong clinical professionals. The implication is that enhancing the profile of good-quality analytics within an organisation must involve recognising the current generation of leaders as well as investing in the next generation. A key skill is to spot opportunities for analysis that other senior staff don’t see, and manage expectations around requests for analysis.

The right presence at the organisation’s decision-making level can help shift the relationship between decision-makers and their in-house analytical teams. The direction of change is from a relationship where the analysts unquestioningly provide whatever they have been asked to provide, to one where the requests received will address the problems and begin to be anticipated, and to an extent shaped, by the analytical team (Box 13).

Box 13: What does a health service with a strong analytical component look like?

  • Both the provider and the commissioner of care have some understanding of the outcomes, costs and quality of care they offer. More important still is that they constantly test how changes in the organisation of service are affecting patients and populations.
  • Existing health service data are widely used. The data are easily and securely accessible, actively curated and renowned for accuracy and utility. Individual organisations have ways to link new data streams to existing patient records to expand the overall understanding of quality and effectiveness throughout patient journeys over time.
  • Clinicians and managers rely on a range of analytical tools to understand local performance and quality. They can access expert commentary and advice on interpreting such data and initiate new analyses.
  • A thriving analytical community in which new developments and methods are actively shared between organisations. There is a role and career structure that is attractive to new graduates and that retains the best people, developing them into senior analytical roles.
  • Where analytical teams, with the provider and the commissioner, can access expertise from academia and the industry to help them solve problems and implement new methods of working. Opportunities exist for changing career paths from a specialist data scientist to an analyst working in the service (and vice versa).
  • The boards of major organisations exploit the right analytical methods to support their deliberations. For example, analysis of change over time replaces static Red Amber Green ratings, performance is assessed using valid comparators, statistical uncertainty is recognised and data are interpreted in context. Board-level reports are succinct and focused on the most important issues, yet capable of supporting an understanding of quality of care.
  • Major changes in service, delivery and innovations in care are designed with input from analytical teams from the start, and are accompanied by evaluation programmes to help with further course correction.
  • The publication and dissemination of information about health-service performance does some justice to the complexity of health care delivery. The media reports focus on substantive issues, not coincidences in data.
  • The public can access and understand a range of comparative information about health care delivery, which helps them play a greater part in their own care and in shaping services more generally.
  • Information about the wider determinants of population health is routinely used to shape decisions about investment.
  • Senior managers and clinicians have developed a full understanding of where better analytics are needed in their organisation and address these in their local workforce plans.
  • The public and patients are engaged in conversations on how data are used. There is broad public support for how the NHS uses data and individuals can opt out of data-sharing.
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