Data lies at the crux of Industry 4.0 initiatives. But data analysis, although critical, is not as easy as it sounds. To truly make a business impact, organisations need to acquire, process, and analyse the right data, and present it with actionable insights to business decision-makers at the right time.

In the manufacturing sector, death by data is real, for several reasons:

1. The presence of unlimited data sources: A successful manufacturing business functions using an array of machines, devices, and sensors – each of which produces humongous amounts of data on a daily basis. Although collecting data from all these different sources is important to enabling evidence-based decision-making, not all of the data that is present is useful or relevant. Understanding what data to collect, from which sources, and in what formats is important to feed data algorithms with just the right amount of quality data for effective processing and analysis.
2. Poor data security: Data, although the new fuel powering manufacturing businesses, also acts as a vulnerable spot for numerous data breaches and attacks. In the absence of the right data security and compliance procedures, manufacturers stand a greater chance of security incidents, helmed by attackers who are constantly on the prowl. Poor data collection, control, and access mechanisms make it easier for bad actors to exploit existing vulnerabilities and plan their attacks to misuse IP data, financial information, or customer details.
3. Lack of proper data cleansing: Although Indian manufacturers are constantly driving efforts in collecting data from as many sources as possible and feeding them to their AI and BI models, a lot of this data is unclean – which negatively impacts the outcomes of analytics. Manufacturers that do not spend time in removing, improving, or updating information that is incomplete, incorrect, or just improperly formatted often end up making bad decisions. Feeding analytics algorithms with bad data can have an impact on business decision-making and can also increase the likelihood of attacks and breaches.
4. Reliance on legacy tools: The heavy reliance on legacy tools is another major challenge that makes data collection and analysis a Herculean task. These legacy systems, built on proprietary technology and code, are infamously known for being rigid and inflexible in nature. Offering poor integration capabilities, they typically operate in silos, making it difficult for manufacturers to collect and collate data from across the organisation and attain a single source of truth. This not only limits enterprise-wide visibility and control but also causes decisions to be made on half-truths.
5. Poor data visualisation capabilities: For manufacturers who can successfully collect, process, and analyse data from different sources and formats, poor data visualisation continues to be a dampener. Despite having all necessary data analysed, in the absence of proper visualisations, it becomes extremely difficult to understand manufacturing data or make decisions that are accurate and forward-thinking. Unless the data that has been processed is presented to decision-makers in intuitive and easy-to-understand visual formats, it becomes very difficult to make timely, accurate, and comprehensive, evidence-based decisions.