Just a recap, we learnt in my last update the components, success criteria and impact of the new age machines that are learning and thus by utilizing AI/ML mould the world we are going to experience. In this excerpt we are going to quickly look through the making of Data that makes an AI system successful and then dwell upon the design and delivery of Digital business models and solutions thereof in part b of this third post.
As we learnt earlier, each industrial revolution has been catalyzed by a new raw material: Coal, Steel, oil or Electricity. This time around, data is the primary raw Material. Today organizations are able to know precise information on a varied amount of genres – right from how their engine is performing during a particular journey to a specific student’s performance on one of the lessons in a class. All this is possible because of the Data they have access to.
The Amount of data that each organization does collect is humongous and more often than not organizations don’t know what to do with such data. Just reiterate that this is not new for any of the industrial revolutions, during the time when industries were based on Coal, industry was just about seeing the presence of Oil. However in the initial stages Oil was more or less looked upon as an obstruction to Coal mining and industries didn’t know what to do with Oil (Sticky, black, mucky liquid). This happened only till the time when James Young (Scientist) applied a distillation process to efficiently covert the mucky oil into more useful stuff – refined Oil. Today many of these business decision makers are struggling with similar re-conceptualization of their Data. Hence, what will differentiate organizations in the future are the ones who become masters in consistently turning this abundant data into actionable, and proprietary, insight.
So by contrast, the new decision making leaders of today are more relying on ‘data-first’ as their data is not sticky or mucky but the lifeblood of the new machine, the fuel that moves it forward. In sticking with our oil comparison again, data is superior in most senses to Oil. While data lies at the core of our new machine, maintaining a healthy supply chain of this raw material is important to any industrial revolution. Let us look at the 3 key activities that apply here Harvesting of Oil, Refining of Oil and Distribution of Oil. Let us apply the same to the Data world.
Thus this three pronged approach is a useful way of thinking about organizing your technology, staffing and approach to building your own new machine. Let us now also spend a little time on how to give Meaning to the Data which is such an important core of the AI Framework
Going through time, its imperative that the 3 steps above will become a universal practice. Harvesting will more or less stabilize and distribution will have nothing to do with improvement of the data. Thus the middle step of refining or turning data meaningful will be the key competitive battleground. This is where organizations will need to convert the data into insight and apply that insight via a new commercial models, and this is where Business Analytics comes into the frame.
Business Analytics can be defined as the tools, techniques, goals, analytics, processes and business strategies used to transform data into actionable insights for business problem solving and competitive advantage. All organizations that could harness values from data better than others can enjoy an average cost decrease of about 8,1% and an average revenue increase of about 8.4%.
So what do you want your analytics engine to analyze? And there comes the Code and stuff for all of us to look at. We need to turn everything – really everything with instrumentation- with sensors you begin the process of harvesting all the data in your organization but you will also greatly increase the intrinsic value of the very objects you are instrumenting. For instance, the value of a sensor fit car seat which can adjust its height and inclination based on the passenger who travels in it, greatly increase when compared to the static car seat. The sensors being referred to here can just take any shape and size in that context of the car seat like providing temperature data and Lighting conditions etc. If you take this mundane example of something ‘Static’ becoming ‘Dynamic sensor fit’ and extrapolate it into the ‘universe of things’, the impact of instrumentation becomes more and more profound.
This is why The LGs and Samsungs of the world are making the so called Dynamic/Smart Refrigerators and Televisions. It’s also why there an explosion of activity around the idea of instrumenting people. Let’s face it, at times we are pretty ‘static’ too. Instrumenting us will help us get smarter about a lot of important things, like our health as well. We need to therefore now become data analysis savvy so as to prepare for the next wave of AI based systems that can help / support our lives and make earth a better place to live in.
Stay tuned…. Part IIIb of this foray, we will dwell upon the Digital Business Models and Solutions: – “Robots” that outline the design and delivery of the AI platform.
Authored by Venugopala Krishna Kotipalli