We have been saying data is the new oil or information is the new oil, I don’t agree with this statement. As it is much more than the oil as rightly advised Distinguished analyst from Gartner.
CIOs need to go beyond thinking and talking about information as the new oil. Information has unique economic characteristics that render it potentially much more valuable to their business than any fossil fuel.Douglas Laney | Vice President and Distinguished Analyst,
Analysis vs Analytics
Traditionally we have been Data Analysis using Traditional Business Intelligence Tools for decades now, but talking about Advanced Analytics, which is an autonomous or semi-autonomous examination of data or content, which is beyond the Business Intelligence System.
Analytics is mainly about making predictions and recommendations by discovering deeper insights into data or content. And this became possible because of the Industry 4.0 related technology.
Big Data and Advanced Analytics
I have mentioned many times that Big data is the driving force for enabling many technologies, where the major focus changed from App Driven data to Data-Driven Apps which are based on advanced data analytics.
There is no doubt that Big data can benefit every industry and every organization.
Not just the traditional transactional data, but organizations are able to access more data today than ever before. But it’s of no value unless you know how to put your big data to work.
Advanced Analytics is about machine learning, pattern matching, forecasting, complex event processing, neural network, and so on. Analytics has emerged, what is advanced today may not be advanced tomorrow.
Companies are taking advantage of data insights to improve decision-making, enter new markets, and deliver better customer experiences.
Industry 4.0 and Advanced Analytics
As I mentioned analysis of data captured from Transactional Data using ERP is not Advanced Analytics. It is beyond transactional data, analytics of data to do predictions from the data generated for various resources, a
Think about Plant and Machinery, Smart Factories, Robots, or the software systems that are being used by the companies, all are generating the data.
If I talk about the Major Industries Like Manufacturing, Retail, Healthcare, Banking & Finance, Telecom, and Oil & Gas. Each of the industries has benefitted from Advanced Analytics.
How Advanced Analytics Helps?
Now as we know that Advanced analytics is a data analysis methodology that uses predictive modeling, machine learning algorithms, deep learning, business process automation, and other statistical methods to analyze business information from a variety of data sources.
I can also say that Advanced Analytics is the combination of prescriptive analytics and predictive analytics.
Advanced analytics helps to solve complex business problems that traditional BI reporting cannot.
BI can answer how many products we have sold over a period of time and we can see that in various dimensions, be it time, location, gender, age group, product category, customer type, season, and so on.
All this comes from the Historical data, but advanced analytics combines consumption models with historical data and artificial intelligence (AI), to help an organization determine precise answers to the questions like ;
- When is a customer likely to exhaust their supply of an item?
- What time of the day or week are they most receptive to marketing advertisements?
- What level of profitability is achievable when marketing at that time?
- What price point are they most likely to purchase at?
Big Data and Advanced Analytics Use Cases
There are thousands of use cases that can tell us how advanced predictive analytics can help organizations to improve. I will mention three use cases of Big Data and Predictive Analytics related to the
Predictive maintenance of Plant or Machine Components;
Though we have been capturing the structured data of Machines, like equipment year, make, model, cost, vendor details, and so on. But the machine is also generated the data using sensors, like log entries, error messages, temperature, running time, and so on.
With this data, manufacturers can maximize parts and equipment uptime and deploy maintenance more cost-effectively. This data can be used to predict more than just equipment failure. For many manufacturing processes, it’s also important to predict the remaining optimal life of systems and components to ensure that they perform within specifications. Falling out of tolerance—even if nothing is broken—can be as bad as failure.
How to achieve predictive maintenance of machines?
Companies must integrate data coming from different formats and identify the signals that will lead to optimizing maintenance.
Operational efficiency is one of the areas in which big data and analytics can have the most impact on profitability. Analytics assess production processes, proactively respond to customer feedback, and anticipate future demands.
How to achieve operational efficiency?
Data teams must balance the data volume with the growing number of sources, users, and applications.
Optimizing production lines can decrease costs and increase revenue. Big data can help manufacturers understand the flow of items through their production lines and see which areas can benefit. Data analysis will reveal which steps lead to increased production time and which areas are causing delays.
How to achiveve Product Optimization?
Optimizing production requires manufacturers to analyze their production equipment data, material use, and other factors. Combining the different kinds of data can pose a challenge.
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