Pentaho

Recently I had the opportunity to give a keynote speech during the IIoT World Days Virtual Event on one of my favorite subjects: the economics of data. You can see a recording of this presentation here . Manufacturing organizations are notorious for storing lots and lots of data from the shop floor, quality, suppliers, maintenance and business systems, while only using a small percentage of this to provide real value to the company. 

Most organizations lack a methodology for determining the economic value of their data and analytics. But data and analytics are unique economic assets that not only never wear out, but can actually appreciate, not depreciate, in value the more they are used. During the presentation, I talked about how an organization can embrace a systematic approach to build out their data and analytic economic assets one use case at a time.

Data is an asset that can be used across unlimited use cases at zero marginal cost. So how effective is your organization at leveraging data and analytics to power your business models? Data-driven companies are more successful. In fact, the McKinsey Global Institute indicates that data-driven organizations are 23 times more likely to acquire customers, 6 times as likely to retain customers, and 19 times as likely to be profitable as a result. ¹

But the new economics of big data, i.e. 20x to 50x cheaper to store, manage and analyze data today, enables organizations to think differently about how they apply data and analytics to their key business processes.
 

The Big Data Business Model Maturity Index indicates where your organization sits with its use of data and looks something like this:

·       Level 1 Business Monitoring:  Most organizations are stuck in the Monitoring phase and while it does provide some value as a rearview-mirror look at performance, they struggle to create real-time, predictive capabilities to drive future results. 

·       Level 2 Business Insights: Leverage predictive analytics to uncover customer, product and operational insights (propensities) buried in your growing wealth of granular customer and operational data sets.

·       Level 3 Business Optimization:  Build prescriptive analytics to deliver recommendations to managers and frontline employees. 

·       Level 4: Insights Monetization:  Leverage insights (propensities) uncovered about customer behaviors, supplier reliability and quality, product demand, operational performance and changing market dynamics to create new monetization opportunities.

·       Level 5: Digital Transformation:  Re-invent a continuously-learning and adapting (AI-driven and human-empowered) business model that can derive and drive new sources of customer, product and operational value

The secret is in leveraging artificial intelligence to create new value from the data that you have, which in turn gives you more and more value for your company. It is like Elon Musk said, “If you buy a Tesla today, I believe you're buying an appreciating asset, not a depreciating asset.” This is because the autopilot system is continually learning and getting smarter as you drive it. So, the car that you are driving in a year is providing more and more useful feedback to you versus what it does today, simply because you are using it. Same thing holds true for your manufacturing data, the more context you derive from it, the more that context can be multiplied and associated with even newer data as you operate, which in turn can provide greater insights and new ideas on how to run your business. Once again please do view my presentation online and engage with us at Hitachi to see how you can apply this to your organization.