Three vital steps to analysing big data

Posted on 23 Aug 2018 by Maddy White

Analysing big data involves numerous steps, from gathering and storing, to data mining, mapping, visualisations and monitoring. The result? An enhanced production, transparency in operations and a tidier business model.

To improve manufacturing performance, streamline production processes, and enable strengthened customer relationships, big data must be properly processed; here are the three crucial steps to make that happen for your business.

1) Gathering, storing and tidying data

Gathering data and having the capacity to store it, are the first steps to utilising and enhancing a business.
Gathering data and having the capacity to store it, are the first steps to utilising and enhancing a business.

Collecting data and having the capacity to store it, are the first steps to utilising and enhancing a business through analytics.

Storing data enables manufacturers to maintain and evaluate equipment, production processes and the supply chain.

It is crucial for manufacturers to ensure the quality and integrity of their data for analysis, which is challenging as big data comes from many variable sources.

Tidying up big data can transform it into readable, unified data sets for multiple users, but to do this it needs to be put into consistent formats and systems that are accessible.

Once data is gathered and cleaned into usable data sets, the next step to utilising business’ data can be taken.

2) Data mining, mapping and analysis

Using data mining tools allows manufacturers to quickly identify and access the information they need to in order to make important decisions.

This means manufacturers can better access data, and identify and address potential issues.

By mapping and analysing equipment, production, and supply chain data, manufacturers can drive outcomes through more thoughtful decisions and better leadership.

Using techniques such as data mining, mapping and analysis, enables manufacturers to better identify patterns and measure the impact of those patterns. This then helps them to create actionable insights, and even predict results.

3) Visualising and monitoring data

Not enough is currently being done to create and nurture the talents required to create a pool of data scientists.
Manufacturers can make the most of their machines, production, and supply chain data by utilising big data.

Data visualisations allow results of data analytics to be communicated to manufacturers and consumers visually.

These tools can transform quantitative figures in spreadsheets and databases into graphs, charts and infographics, making it easier to generate insights and make more informative decisions.

Monitoring data allows issues to be made aware of and resolved, tools that track and monitor help to ensure an improved performance of equipment and an efficient production process.

Using these three steps to utilise data in manufacturing businesses could completely transform and restructure decision making and production processes. 

Manufacturers can make the most of their machine, production, and supply chain data and significantly raise their productivity and efficiency, if they are able to effectively integrate big data tools.