Making Industry 4.0 work: 8 vital elements for right framework

Industry 4.0 relies on a large volume of devices and masses of data to be successful. Businesses must process data into timely and valuable information if they are to integrate it into production processes. For example, there is little use in having sensors that can track real-time maintenance issues if the infrastructure is not available to suitably analyse and act on those.

Unlike typical business data, IoT devices and sensors produce non-standardized and unstructured data. This is information that does not have a pre-defined model, usually very text-heavy but could contain numbers and dates as well. Big Data Analytics platforms are required to manage complex algorithms and programming models. It is one reason why we haven’t seen huge adoption to this point as industries don’t have the expertise in data science required to progress.

An Industry 4.0 architecture will vary depending on your business but in the main, the features below will be the key considerations for IoT and Data.

Gateway – sensors need to connect seamlessly throughout the framework. Having equipment that talks to each other is imperative for communicating data at the right time. This will usually require ethernet cables or wireless mechanisms, both of which have reducing costs making it feasible for factories to install

Edge computing – these are router services that can make fast, low latency decisions allowing for real-time data analytics. Edge services tend to sit nearer to sensors and machines to do faster communications, they do not connect to a wider Data Lake (see below)

Ingesting data – multiple data sources need to be transformed into standard formats so they can be used in decision making processes. Data professionals with experience in such transformations will be able to apply the appropriate architecture for the best speed and consistency

Data Lake – if you have all this data, it needs to be stored somewhere. A Data Lake is a cloud-based platform like Amazon Web Services (AWS) or Microsoft Azure or Google Cloud Platform (GCP) that is entirely scalable to the business needs and can be accessed from anywhere. Various scripting languages and libraries can be added to Data Lakes depending on the data ingestion strategy.

Analytics and ML - understanding patterns and developing accurate models will require good quality data at scale and will lead to significant gains in overall productivity.

Data Visualization – data from the Data Lake needs to be presented in such a way that the business can use it. Many businesses will do this using API’s that connect to commercial platforms like Tableau, Qlik, PowerBI or MS Dynamics to name a few.

Data Security – appropriate software must be added to devices to ensure they are secure with the vast amounts of data they are producing and transforming.

Automation of Data Flows - Robotics process automation (RPA) platforms such as UiPath or Automation Anywhere or Blue prism would provide BOTS framework and API’s to delegate system workflow autonomously without any human intervention.

Each of the elements above will have more detailed specifications but each of them are vital in having the right framework for Industry 4.0.

By Ram Narasimhan, Global Executive Director - AI and Bigdata, Xebia