Evidence gathering in the Digital Age


James Hulonce

Digitisation is making data more ubiquitous. Even in ‘data-poor’ industries, there are increasing opportunities to develop robust evidence bases for market monitoring and executive decision making. Organisations often hold more data than they realise or can develop data assets much more easily than they could five years ago.

Data Science expertise, aligned to management and strategic thinking, can help organisations to better measure, analyse, respond, and anticipate future needs.

Through our work with public and private sector clients we have identified several areas where organisations commonly struggle to gather better evidence for decision making. Below we discuss some of these areas and provide examples of where we have assisted clients to overcome these challenges.

Understanding data types

Organisations should view data as more than just numbers. Beyond numerical data, data that are often overlooked include text, visual, speech, spatial, and even metadata. Each of these data types can be valuable in gathering evidence and understanding processes or systems that are important to decision making.

Non-numerical data can generate insights in areas where pure numbers are not suited or not available. Common examples include identification of market segments, and themes from natural language (text) information such as product reviews. While each of these techniques ultimately involves some form of counting, the source data are generally more qualitative.

Non-numerical data can also support traditional analyses. For example, we often collate publicly available information on markets, and apply spatial analysis to understand and visualise market dynamics. This generates more granular considerations than traditionally available through aggregate market measures.

Improving data assets

Once organisations recognise the range of data they hold, there is a need to address the quality of that data. Structuring and organising these data can make them significantly more useful to decision making. For example, we have previously compiled and linked data across a series of word-based reports to provide a client with a searchable time series database of key measures.

Improving data assets requires the organisation to understand current and potential drivers of value. Data improvement efforts should focus on meeting the functional and strategic needs of stakeholders, while also maximising the flexibility of data for other purposes. We support clients to achieve this by assisting them to benchmark their own data use against other organisations, and to identify ways to develop competitive advantages through their data.

Building data assets

So, what if your organisation doesn’t have the right data? The right choice depends on the circumstances of the organisation. Two approaches are to:

Monitoring data assets

Once evidence is collected it is important to regularly review and assess what is captured, and whether it aligns with the strategic needs of the organisation. Our standard review process combines:

ACIL Allen’s Data Science Group works with organisations to solve complex and challenging data problems that address organisational needs. For more information on our capability see our Data Science page or contact James Hulonce on (03) 8650 6000.