Data analytics strategies for success

05 March 2020

Institutions and organisations are generating more and more data that’s invaluable to their decision making. Everyone is talking about data analytics, but what’s all the fuss about and where should you start with your own data analytics strategy?

‘Data driven decision making’ is the term given to the use of data, analytical facts, and metrics to inform strategic business decisions in support of overall business strategy and goals. This approach can be used by anyone at any level because it is entirely fact based.

There isn’t a right or wrong way to approach data analytics, so it’s important to consider what’s right for your organisation and your staff. With a multitude of tools and technologies available on the market, many of which can be quickly implemented without the need for specialist IT skills, it’s easy to get started but you have to think about your end goals before making any investments.

Start by thinking about your strategic objectives and the problems you’re trying to solve, the level of data literacy across your staff and the specific data analytics capabilities already in place. This will allow you to plan individual data analytics projects as part of a road map to implementing your overall strategy.

What are the benefits of a data analytics strategy?

The large volumes of data that can generated in a short time present challenges as well as opportunities. Some common challenges include:

  • large amounts of unconnected data in different sources;
  • a lack of data governance and poor data quality;
  • a heavy reliance on spreadsheets, resulting in time-consuming and repetitive manual tasks; and
  • teams operating in informational silos, leading to confusion over the right numbers.

It’s therefore important to think about what you’re trying to achieve and to prioritise activities based on the value they’ll add. A clearly defined data analytics strategy provides a structure for delivering data analytics projects with support from the top and buy-in from all staff. It allows you to define expected outcomes and measures for success, and to break deliverables down into realistic tasks that benefit staff and senior stakeholders.

As well as the more traditional analysis of financial statements to monitor income, profitability and EBITDA, data can be used to identify trends and outliers in relation to:

  • student experience;
  • application rates; and
  • degree outcomes.
  • Data can also be used to monitor:
  • staff costs;
  • turnover and absence rates;
  • room usage;
  • maintenance costs; and
  • student accommodation occupancy rates.

This can be particularly valuable for tracking progress towards defined and agreed targets.

A good strategy will include appropriate communications and change management plans, often centring around a data literacy programme to support staff with upskilling in new ways of working with data.

Next steps

Start by identifying a sponsor for your data analytics initiative, and then carry out a data analytics assessment that ensures you:

  • identify your sources of data, including spreadsheets and other data contained in off-line documents;
  • walk through your business processes to understand which data each of them generates, including the quality of that data;
  • understand your existing data analytics capabilities, including data repositories and reporting tools;
  • determine the data literacy level of your staff and identify specialist data analytics resource across your institution; andreview your strategic objectives, identify the data needed to track your progress against those objectives, and determine the availability and quality of that data.

The outputs from the assessment will inform your data analytics strategy and roadmap, allowing you to plan and prioritise your data analytics projects in line with your strategy.