Data Literacy: What It Means and Why It’s Essential for Supply Chain Success

Big data presents a phenomenal opportunity to predict demand fluctuations, implement timely actions, and reduce risk – if you have the means of reading and analysing it.

Supply chains are getting smarter. Emerging technologies such as artificial intelligence, robotics, and the Internet of Things (IoT) are capable of gathering and sharing data across the length of the supply chain, offering companies around the world unprecedented opportunities to close the gap between logistics challenges and effective remedial action.

But data literacy is not keeping pace with it. In fact, four in five of the 45 data-intensive businesses interviewed by Nesta are struggling to find the skills they need to meet growing demand Without individuals capable of interpreting large data sets, the information gathered is unintelligible, putting businesses at a distinct disadvantage against competitors with the ability to access, mine, and draw accurate supply chain insights from increasingly large data sets.

Data skills key to minimising the cost of poor quality data

Data must be reliable, timely, validated, and accurate before it can be depended on to provide true insights. A 2017 Deloitte study found that 49% of CPOs believe that the quality of data is a major barrier to implementing systems, and the lack of data integration was the number two barrier (42%).

Poor data integrity can stem from a number of causes, including a lack of individuals with the data skills necessary to govern and maintain it. According to IBM (writing in the Harvard Business Review), poor-quality data cost the US alone $3.1 trillion in 2016, yet companies continue to invest millions in technology and systems without coherent data strategies and the skills that underpin them.

Predictive models improve supply chain performance

‘The leading retailers will be able to effectively analyse and interpret big data to stand out from their peers by winning customer experience’, said Erich Gampenrieder, Global Head of Operations Advisory, KPMG Germany.

In particular, demand forecasting enables businesses to predict gaps in complex supply chain operations. These insights can then be used to optimise processes using dedicated tools; for example, data analyst intelligence and geoanalytics can be used to improve the accuracy, speed, and quality of supplier networks.

Improved efficiency and order-to-cycle delivery times increase customer satisfaction, enabling businesses to deliver supply chains that exceed end-customer expectations.

Improved forecasting accuracy slashes inventory costs

In addition to improving performance, data analysis can significantly reduce inventory costs. Using predictive modelling, organisations can determine with more precision how much of a product range should be held in stock, in what volumes stock should be held, and when to expect peaks and troughs in demand, optimising their storage and inventory overheads.

Key to this is the organisation’s abilities to a) interpret the data necessary for developing increasingly sophisticated predictive models and b) implement them in their network.

Ensuring specificity in your data requirements

Effective data analysis involves a deep understanding of how data is measured, sorted, categorised, and presented. Data requirements should therefore be as thoroughly defined as possible.

This requires clear definitions relating to the data’s context, quality requirements, how the data will be used, what counts as evidence-based decision making, and data sharing expectations.

For example, analysing transportation data could be an effective way to inform shipping and distribution strategies. In contrast, operational warehouse data could be used to to identify inventory management inefficiencies — and ways to optimise them.

Companies must commit now to providing clear definitions of what the data represents from a business perspective.

Recognising the importance of data skills for intelligent supply chains

The use of big data clearly has the potential to offer users a competitive advantage. But — and this is a substantial caveat — unless businesses have the necessary analytical skills to interpret and make use of it, big data could end up being an underutilised resource.

It is therefore crucial that companies focus on obtaining data-driven value by attracting individuals with expert forecasting knowledge and a deep analytical ability.

Gartner predicts that, by 2020, 80% of organisations will initiate deliberate competency development in the field of data literacy. By developing data literacy in-house, or working with trusted third parties, businesses will be better equipped to read what their supply chains are telling them, driving future growth and supply chain success.

In part two of this article, we’ll identify a number of key skills required for utilising data more effectively in supply chains.