Data-Driven Decision Making

With an ever increasing amount of data being generated across the globe every second it is not surprising that data has become incredibly valuable and is being harnessed to shape the future.

Data is rapidly becoming a business’s greatest asset but how can it drive decision making? This challenge needs to be met as your client’s customer experience, competitive advantage and business growth depends on it.

What is Data-Driven Decision Making?
Data-driven decision making (DDDM) is being open minded to explore data insights to make decisions. This is different to data informed decision making where data is used to support a decision that is already made or we want to make. For DDDM a business needs to have:

• Enough data – a large data sample size
• Quality data – which has been tested and validated
• Ability to impartially consider the data to make decisions based on it
• Commitment to tracking the decisions
• Continuing to evaluate future data and have a willingness to change tack if required

What is the Current State of Play?
Advances in the digital world are exciting: data science and using machine learning, deep learning and artificial intelligence (AI), insights from these tools will increasingly filter down to impact our day to day commercial considerations and applications. Currently they are often not the focus for most businesses when it comes to using their data.

Not all businesses have data readily available to them and for some, having data other than financial data available, even on a spreadsheet, would be a beneficial step forward. Using AI in the future is an aspirational step for many and will undoubtedly add value to an organisation. However, it is more valuable to focus on making small incremental gains now and building the infrastructure and culture in an organisation as this is ultimately going to allow them to do the more sophisticated things in the future.

Most businesses, regardless of their size or sector, have problems that can be solved or value added by using simple data such as: How many? and How often? More predictive or prescriptive questions to benefit from are: What might happen if? or What would the best outcome be?

To answer more sophisticated questions, investment in the data infrastructure and advanced analytics is required. The investment is worth it if the outcomes will add value to the organisation.

Data Analytics Adds Value in the Public Sector
The public sector is constantly looking for ways to use data analytics to improve their services in cost effective ways. The three main areas to consider are:
Data Visualisation –presenting data visually e.g. in graphs and charts using tools such as Microsoft’s Power BI or Tableau makes the data much easier to understand and use. Transparency is vital in the public sector and tools such as these allow government departments to share their data and enables intuitive interactions with the general public or stakeholders. Powerful self-serve reporting can be provided where data tables are made available to stakeholders and this allows them to do their own analysis. This removes onerous reporting requirements by making the data available as real time or on a daily refresh basis.
Centralising Data – gathering all data into a database that is located, stored and maintained in a single location. This is a huge opportunity for the public sector to build data links in a cost effective manner. Tools such as a Microsoft’s Azure, Google Cloud Platform or Amazon Web Services all give access to an enterprise scale cloud with little or no Capex and indeed on an ongoing basis swap Capex for Opex.
Targeted Offers and Services – this is an under-utilised area in the public sector. Private sector companies often use sophisticated data to target customers for products and public sector could analyse its data for more targeted communications. For example, data could be used to offer targeted benefits and services to make sure segments of the public are accessing services that are specifically there to help them.

A common perception is that organisations use technology to create cost savings, primarily by reducing the staff time required, thus making people redundant. This is not necessarily true, especially in the public sector who are averse to making redundancies.

Rather, technology solutions are designed to enable people to focus on higher value tasks and activities in the job that they are there to do. By replacing manually laborious and repetitive processes with process automation or self-serve reporting, the team and stakeholders can access the information they require without anyone spending hours producing reports.

The Impact of Covid-19
Over the past year there has been a significant increase in companies starting or accelerating their digital transformation. Work that pre-Covid could have taken years has been carried out in a matter of months. Businesses have reacted and adapted by putting digital channels first, moving to eCommerce and introducing new ways of working with their staff, customers and suppliers. Many of these changes have become part of their new reality and led to a growth in demand for data analytics to assess the impact on the business and the customer journeys.

The Future of Data-Driven Decision Making
New technology and tools that support data capture, centralisation and democratisation means data and insights will continue to become more accessible to a wider range of roles within a business. The availability of data and insights will help the attitude and culture in many organisations to evolve becoming more open to and supportive of digital transformation.

The public sector is going to instinctively want to maximise the impact of these developments but there may be some barriers. The main risks going forward are the levels of regulation that will be applied and the attitudes of customers regarding how their data is used.

Data privacy is a hot topic due to increased awareness of how data is now used and also with the move away from more established and transparent decision making processes to Black Box or algorithmic methods that are inherently less transparent.

Current regulation requires decision making processes to be explained, this may limit how much use can be made of these powerful new tools in the future. However, these issues are likely to be resolved as leading organisations work toward making sophisticated automated techniques more explainable. Organisations that work with other businesses, without using personal private data will continue to mine their business information at pace.

There is a big opportunity for the public sector to use data in a much better and more targeted way. Cost savings and efficiency are not the only driver and increasingly focus will be on delivering better services.

With thanks to Neil Macdonald CFA, Director and Founder of Forecast, a full-service global data consultancy business offering financial modelling, data analytics and data visualisation services.