Crude oil is no doubt the strongest backbone of Nigeria’s economy but I have read a lot of articles and papers about data and how data is related to crude oil and how it will be the new crude oil i.e the next Nigeria’s economy. I was about to fall into the trap of believing that data is the new crude oil and ready to boost the economy of our country.
I think data is underrated and have not been given the right justice by comparing it with crude oil. I think data is not only the new crude oil, it should be regarded with more esteem.
If you follow me through this article, you will find out why I have said that data is more than just crude oil.
Yes! I know what running through your mind, how can I say data is more valuable than crude oil.
I think the best way to get the concept of what I want to let you know is to define and relate both terms and from there, you will be able to know why I said it is an underrated gesture to compare data and crude oil.
The concept behind “data is the new oil” is that just like oil, raw data isn’t valuable in and of itself, but, rather, the value is created when it is gathered completely and accurately, connected to other relevant data, and done so promptly. When properly refined, usable data quickly becomes a decision-making tool – information – allowing companies to react to market forces and be proactive and intentional in their decision-making.
To prove my point, let’s go offshore and learn about data mining a bit.
An article was written by Tech Hunger and he defined data mining:
“Data mining is also known as Knowledge Discovery of Data (KDD); it is the process that extracts implicit, potentially useful, comprehensible, actionable, previously unknown information from a large database and uses it to make a crucial business decision.”
Data mining is a way to dig and analyze an enormous set of data and then extract meaningful information (knowledge) out of it
The steps involved in Data Mining are:
Data Integration: At the very beginning we collect data from a variety of sources (CRM, OLTP, Excel, MS Access, Oracle, SQL Server, CSV, etc) into a single data source called target data/ database using some technology.Data Selection: In this step focus on only those data set which are required to fulfill our hypothesis/ research/ assumption it means only meaningful data is selected to proceed.
Data Cleaning: Data imported from data sources may be in a different format than the target database then we need to clean the data using some data cleaning algorithm.
Data Transformation: Data is then prepossessed and transformed into a standard format.
Data Mining: Then we analyze and identify the type of data mining algorithm which will be suitable for current data and then apply the algorithm(s) to identify the hidden patterns.
Pattern Evaluation: The pattern we identified from the data is then interpreted and evaluated to gain knowledge out of it.
Knowledge Presentation: This is the goal of the data mining technique where knowledge collected from the data mining process is then taken into consideration to make a crucial business decision for the benefit of the organization.
Today, knowledge extraction is a must-have technology for any company, upcoming startups, and even local organizations whether dealing with information or not, and data mining is the central part of knowledge discovery of data and is a very useful technology that identifies the hidden pattern and useful information out of raw data which can be utilized to make a business decision.
However, it can be somewhat abstract for a non-expert, so let’s look through general use cases to understand what data mining can do for business growth.
Financial analysis
Knowledge extraction is the most powerful tool to predict trends and behaviors in the entire financial market and make the right decision regarding monetary investments. By using statistical figures and machine learning tactics, it provides you with an effective and accurate analysis to estimate the business’s stability and profitability. Trends in sales, inventory check, and income analysis through knowledge extraction will help to determine the worth of your business.
Forecasting sales
Sales forecasting using information harvesting is the most accurate prediction method. Through pattern analysis, you may predict your short-term or long-term sales based on customers’ purchase history, industry trends, and comparisons. Sales forecasting will also provide insights into how you should manage your company resources, workforce, and cash flows.
Customer retention
Customer retention is one of the more important challenges in today’s competitive commercial arena, especially in the sales and services industries. Web scraping solutions that integrate pattern analysis techniques help test customers’ lifetime value and market segmentation. Thanks to this form of knowledge extraction in data mining, you can identify when your customers are going to leave you and suggest incentives to persuade them to stay.
Fraud detection
To detect fraudulent activities, organizations and business entities trust special pattern analysis techniques. For example, pattern analysis is widely used in identifying and fighting cyber credit-card fraud thanks to competent AI techniques that are implemented to detect fraud from anomaly patterns gathered from extracted data.
We’re in a digital economy where data is more valuable than ever. It’s the key to the smooth functioning of everything from the government to local companies. Without it, progress would halt. I have only three points to convince you that data is underrated and comparing it to crude oil is a grave mistake.
Data is a Decision-Maker
The economic reality of a world with COVID-19 is such that the value of oil has decreased drastically. Oil, as a resource, is simply not in demand. For data, it’s a very different story. I would venture to argue that in today’s environment, especially with regards to widespread concerns of the health-related and economic implications of COVID-19, that data is in more demand than ever.
Why? It’s simple economics. COVID-19 related data is being generated quickly. In concert with the unprecedented speed at which the virus has spread, many organizations must now make difficult decisions on how to thrive and survive. The result? The demand for accurate and up-to-date data, and further methods of organizing and utilizing said data to make informed decisions, is through the roof. It’s undeniable that there is great value (e.g. monetary, health, safety, and otherwise) in getting access to new information faster.
But what we have seen with much of the COVID-19 data is that more data does not always equal better information. Government organizations and media outlets have frequently reported on the numbers of COVID-19 related deaths, positive tests results, hospitalizations, and tests administered. From this, there have been ratios reported on the death rate, recovery rate, infection rate, hospitalization rate, etc. However, primarily due to limited testing, not all positive cases are known. In addition, not all organizations (e.g. countries) are classifying or reporting on the data the same way (or perhaps more sinister, hiding information). Therefore, while there may be an abundance of data, and that data may be in demand, without ensuring that it is complete, accurate, timely, and connected to other relevant data, the information may not be valuable in driving key decisions. Worse, it may lead to the wrong decisions. We’ve seen this play out in numerous businesses over the years.
Data Infrastructure Should Become a Profit Center
For many companies, their data infrastructure is still a cost center nowadays and should become a profit center by using the data to improve everything, day by day. Companies must begin treating data as an enterprise-wide corporate asset while also managing the data locally within business units.
This enables sharing of data about products and customers – which provides opportunities to upsell, cross-sell, improve customer service and retention rates. By using internal data in combination with external data, there is a huge opportunity for every company in the world to create new products and services across lines of business.
Good Data Beats Opinion
When your business is growing, more and more people have opinions about which steps need to be taken. It helps to work with a ‘good data beats opinion’ philosophy. Almost everything can be tested, measured, and improved. If you can measure it, you can improve it. Inspire people to come up with new ideas and pick up new opportunities and just test them and see what the impact is. A test that you’re measuring is never worthless. At least you get new insights about if it’s working or not (and why) and in the best case, you get insights and improve your business directly.
Make sure you’ve real-time access to the most important data in your business. Only knowing your total revenue, profit or costs is not enough. Knowing which KPIs influence them and other business goals is much more important because you learn how to improve your business. The value lies in microdata, not in macro data.
To conclude, I’d say data is an asset that never depletes, never wears out, and can be used across unlimited use cases at near-zero marginal cost economies. An increase in spending produces an increase in value greater than the initial amount.
There was a time that oil companies ruled the globe, but "black gold" is no longer the world's most valuable resource — it's been surpassed by data.
The five most valuable companies in the world today — Apple, Amazon, Facebook, Microsoft, and Google's parent company Alphabet — have commodified data and taken over their respective sectors. Data is underrated and should not be compared to crude oil
Data is not the new oil. Data is unlike any other corporate asset It never depletes. Data is the new sun. It never wears out and is used across infinite use cases.
References
Tech Hunger: https://ravisatyadarshi.wordpress.com/
Bill Schmarzo BILL SCHMARZO Top-ranking Blogs
Hitachi Vantara CTO, IoT and Analytics University San Francisco School of Management, Executive FellowHonorary Professor, National University of Ireland-Galway