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Getting the best from data dashboards

The Demos paper explores the potential and limitations of big data when used to transform public services and private sector operations

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Data dashboards have the potential to mislead as well as inform
Data dashboards have the potential to mislead as well as inform

In a new report published today, Demos, Britain’s leading cross-party think tank, has identified risks associated with implementing poor data dashboards and offered recommendations to overcome these issues.

 

Dashboards are visualisation tools that are used to make the vast quantities of data intelligible to non-technically trained personnel as they provide the opportunity to create more efficient and data driven solutions across both public and private sectors.

 

The Demos paper explores the potential and limitations of big data when used to transform public services and private sector operations.

 

In recent years the UK Government Digital Services team has been rolling these dashboards out across departments, with over 800 built so far. These dashboards have the potential to help local and national government operate with greater efficiently and responsively, but according to the report, can frequently be poorly implemented.

 

Some ’off the shelf’ programmes don’t allow dashboards to respond to the keenest challenges that face organisations. They can also be inclined to bias users towards short-term operational issues rather than long term ones. Staff all to often, don’t have the necessary operational skills to effectively operate these dashboards, or fail to ‘buy-in’ to the change in operations.

 

The new Demos report recommends three principles that should be considered when using data dashboards.

 

1. Identify purpose and use: Dashboards are a generic response to collect, analyse and act on large data sets. In and of themselves, they are not necessarily the best way to understand all problems, and must be carefully designed to match real organisational needs. Once the purpose has been identified, it must be communicated clearly to its designers as well as its intended users.

 

2. Understand limitations: Dashboards have the potential to mislead as well as inform. By their nature, dashboards leave out more than they include: usually with the user not knowing how the data presented was created. Users can be blinded by large numbers or have insufficient understanding of the strengths and weaknesses of the data they are using. Dashboards will often prioritise operational issues, rather than longer-term strategic issues, and may marginalise more reflexive approaches to a problem. Like all metrics they can be ‘gamed’, and so users must be encouraged to have a critical eye for the limits of a dashboard.

 

3. Select the right staff and skills: Just because they are designed to be user-friendly, it is dangerous to assume users will intuitively understand how to use them. To maximise the take-up rate among staff, they will need to be provided with training to understand the dashboard’s purpose, where the data is drawn from and the way it is framed. The skills to create and manage dashboards are extremely valuable and sought after in the private and public sectors. A whole new generation of analysts will need to be trained, with a new combination of skill sets, ranging from data analytics, design, social science and public policy.

 

In addition, the Demos recommends that organisations need to understand the danger of ‘off the shelf’ models. Government or private bodies will have to choose between using a commercial provider or designing something bespoke.

 

Bespoke dashboards allow for more flexibility and more tailored design as well as often being cheaper but pre-existing dashboards will often be easier to learn to use, supported with external training courses, and are often better suited for those organisations with high staff turnover.

 

With the public increasingly concerned about the privacy of their personal data, having a clear purpose for any data dashboard should help to protect against ‘mission creep’ and make sure these data sets are used for their intended purpose.

 

 

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