With all these new ways to collect and analyse data, it can be difficult to know where to start. So, what are the common pain points of data analytics, and how can we address them?
We all seem to suffer from Data FOMO. We get anxious about not being able to generate and collect data on every piece of our businesses. However, do we need it? Not that long ago, factories had no computers. When humans analyse real world stimulus, we unconsciously scrap what is not important to us. Blindly collecting data with no real way to use it is equivalent to having all the gear with no idea. Looking at this issue from a lean perspective, data management can be approached through the lense of continuous improvement. Start where you’re hurting and improve your management there. This will split up the issue, and before you know it you will have a clean data management system that analyses your business processes without all the background noise. Let go of your data hoarding tendencies and streamline your data requirements.
MERGING DIGITAL WITH REALITY
We seem to have trouble connecting the real world to the digital world. Many factories still run on legacy hardware or lack the infrastructure to bring the information from the factory floor into a database. Now, this doesn’t mean you should throw out your old gear to buy new IoT technology and machinery. It does mean that you should look at integrating technology that can analyse and monitor your systems using sensors and data capturing processes. The marketplace is packed with little gadgets capable of these tasks. However, if none of this seems applicable to you and your business, data can always be collected the old-fashioned way: manually by hand.
Finally, many business tools suffer from digital fragmentation, where multiple models exist for the same system with varying levels of accuracy and complexity. One of the most powerful applications of data is when accurate simulations and representations of real-world processes are generated in the digital realm; this is known as a Digital Twin. With current computational capabilities and available software, gone are the days where we need multiple tools to simulate the same system. An example of this is planning models. Instead of having multiple planning models for different time periods (daily, weekly, monthly, quarterly) that simulate the system with varying levels of complexity and accuracy, a Digital Twin can reflect the complexity of the real-world system for any time period.
So, there you have it – you have all you need to fix where it hurts. Now go forth and wield the power of data analytics to revolutionise your business.