We are currently in the Digital era where Data is being considered as the most useful and valuable resource. We all are so much connected with the digital world that everyone generates tremendous amount of data and we don’t even realize it.
At the micro-level (Individual level) we might feel that this data is of no use. But at the macro-level (Group Level) this same data could give more insight into the behavior, pattern, relationship, etc.
Data is just like a crude oil.
Data could be compared with a crude oil. It is valuable resource but unless it is refined to derive insight and analysis; it is of no use. Back in late 1800 – John D. Rockefeller
Created a huge empire by supplying refined oil (unlike others selling crude oil) and in today’s 2000 era – Google, Facebook, and Twitter have created huge empire by selling the refined data i.e. Information.
How Data is converted into Information?
Traditionally, every organization has data center and the quantum of the data stored is proportional to the business workflows, operation cycle, and various application interfaces. Thus, the storage of data is widely considered as a cost center and huge amount is spend on the infrastructure, server, database maintenance/administration, data storage/backup, etc.
Additionally, the external sources – Website traffic, social media analysis, competitor’s analysis, and business engagements/customer feedbacks data are also very variable data which very few of them track and analyze. It.
With the evolution of the Artificial Intelligence and Machine Learning Algorithms; the data (which is just residing in the database or any medium) is being use for predictive analysis, forecasting, pattern identification and study behaviors. The same piece of data could be converted into source valuable Information and potential assets.
A simple example – say stores like Walmart or local Brick & Mortar could perform study and data analysis on their customer purchase – say soft drinks,
- Which types of soft drinks are preferred by the customer – as per the weather or holiday or festival? – Time series / Forecasting
- Which are the other products customer buy along with soft drinks (like chips, cups, straws, water bottles, etc.)? – Association Rule Mining
- What other brand soft drink, juice purchased?
- What is the average size of the bottle purchase preferred – as per the weather or holiday or festival?
- During what hour of the day sales of Soft-drink is more? — Data Query
- On which day of the month, week or timing does most or least sales happen — Data Query
Above are some of the information which could be easily derived using the Machine Learning Algorithm and predictive analysis. These businesses could utilize this information for their inventory, placement of the products, etc. Additionally, this same information could be sold to soft-drink supplier and their competitors. The above simple example illustrates how the data (internal and external) could be utilize to predict the like hood of future outcomes.
The same piece of data now has become asset for the organization which is helping in sales and extra revenue by sharing with others.
The invaluable Data now has now termed as valuable Knowledge.
How Information could be utilized and valuable Knowledge?
Many organizations are now understanding this fact and have started harnessing the data to have a competitive edge against their competitors. Following are some of the use cases:
- Price Indicator and Optimization
- Fraud Management
- Improvising Operation and Functional process
- Understanding customer expectation and leads generation
- Marketing Campaign
Who are implementing this?
Currently the primarily implementer for Predictive Analysis and Machine Learning by following organizations.
Retail – B2B and B2C
- Utilizing to determine inventory, product placement, promotional events, and price indication
Bank and Financial Services
- Fraud Detection, Credit worthiness, Customer Engagement
- Loan approval
- Reason for FinTech to grow is primarily because of this J
Insurance
- Underwriting
- Pricing and Rating against the risk characteristics
- Fraud detection against Claims
Manufacturing
Healthcare
As a matter of fact, if we one needs to be competitive in this digital era, one needs to think ahead of the competition and visualize how they could utilize this Digital asset to solidify their thought process and decision making with this valuable analyzed data (sorry Knowledge) instead of gut feeling J.
Lately with the evolution of the Data Analytics
In economics Macros and Micros econmics