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  • Writer's pictureShivani Deshmukh

A Fascinating Behind-the-Scenes Look at How Data Is Driving Business?

Updated: Oct 14, 2022

Data is the new oil and data mining is the new drilling.

It's all about data, and it's all about how you use it. Data fuels growth and innovation in every industry.

In today's world, data has become a commodity that can be bought and sold.

Data has become an essential part of every aspect of our lives, from our financial transactions to our social interactions. As such, there has been an increasing demand for data-driven solutions that can help businesses make better decisions and improve their overall efficiency.

The increasing popularity of big data has led to the development of new tools and technologies that can help companies gather and analyze large amounts of information quickly and easily. The explosion of data has created a massive opportunity for businesses to generate insights from it, and build strategies to move forward and evolve. It can be used to create new products, drive revenues, increase customer satisfaction and loyalty, improve marketing campaigns, increase employee management, optimize supply chain management, predict future trends, and enhance internal operations.

Table of Contents

Why is Business Data Important?

Businesses collect huge amounts of data on customers, products, and finances among other things. This data is used for predicting customer behavior.

Predicting customer behavior - By analyzing past purchases or behaviors, companies can predict what people will do next or how likely they are to make a particular purchase. This allows them to tailor their marketing campaigns and products to suit their customers' needs.

For example- If a company knows that 80% of its customers prefer strawberry ice cream over chocolate then it will make sure that there is a large supply of strawberry ice cream available when those customers visit the store or website. This helps ensure that customers get what they want when they want it which will increase customer satisfaction and loyalty which could lead to increased sales over time.

What is a Data-driven Business Model? Or Why do Businesses Use Data?

The world is moving towards a more connected and automated economy, which means that companies need to be able to react quickly to stay competitive.

A data-driven business model uses data to drive decision-making in an organization. It's the process of collecting, analyzing, and interpreting data from various sources to make better business decisions.

Data-driven businesses can adjust their operations quickly, efficiently, and effectively because of having access to real-time information. They're also able to improve customer experience because they can see what customers want based on their behavior and preferences.

1. Data can be used to make strategic decisions such as where to open new stores or how much inventory needs to be ordered. It can also be used at an operational level, such as determining which products sell better than others. By understanding customer preferences through data analysis, companies can develop targeted marketing campaigns that match their customers’ needs and interests better than those of their competitors.

For example- Amazon uses past purchase history to recommend related items that people might like based on their previous purchases or browsing patterns. It also uses customer data to determine what products are most likely to sell in each area of the country based on past sales performance and then automatically adjusts inventory levels accordingly.

2. Increased sales revenue - Businesses can increase sales by using data analysis to identify what products are most popular among their target audience.

For example- if you sell car parts online, you can use Google Analytics to see which types of cars are most popular in your area and then target those categories with ads on Facebook or Instagram.

Another example is - A major insurance company collects data from its customers on how they use their insurance policies. The company uses this information to determine what products its customers need so that they can purchase more expensive plans with more coverage options rather than buying cheaper ones with fewer coverage options. This way, the company makes more money by selling more expensive plans with more coverage options at higher premiums than selling cheap ones with fewer coverage options at low premiums.

3. These types of businesses are also able to improve their processes every time they make an error or encounter a problem.

For example- if a company has inefficient processes that result in wasted materials, it can use data analysis software to identify these issues and correct them before they become too costly or damaging for the business's bottom line.

4. Using data allows businesses to reduce costs by optimizing supply chains, reducing waste, and increasing efficiency across all departments.

For example- Amazon tracks its warehouse workers' productivity so that it knows exactly how much time each worker spends walking between tasks or stopping at their locker during a shift — allowing it to make changes where necessary (such as reducing worker breaks or moving items closer together, so they don't have to walk as far). This helps drive down costs because the company doesn't have to hire more people or expand its warehouse space just because there's more work.

Who are Data Brokers?

Data brokers are companies that collect and sell personal information about consumers. Data brokers collect information from a variety of sources, including property records, public records, social media sites, and commercial transactions. Some data brokers also purchase information from other companies that collect data directly from the public.

Data brokers then sell this information to other companies that use it to target consumers with marketing offers or other products and services.

Data brokers collect information about you from many different sources to build detailed profiles about your life:

  • Your demographics (age, gender)

  • Your interests (sports teams you follow)

  • Your lifestyle choices (what kind of food you eat)

For example- a company may use your personal information to send you an advertisement for a new credit card.

How do data brokers use personal information?

Data brokers use personal information to make money by selling it to other companies. The companies that purchase this information may use it for several purposes:

  • To market products or services based on preferences and interests (targeted advertising).

  • To determine if you're eligible for certain loans or other financial products (creditworthiness).

  • To evaluate your eligibility for employment based on your education, criminal history, and other factors.

For example- one marketing firm may tell a data broker that it wants to reach out to people who live in certain neighborhoods and who have shopped for particular items online or in retail stores. The data broker then uses its vast database of information on individual consumers to find those people so they can be targeted by the marketing firm's sales pitches.

How Data Can Generate Revenue for an Organization?

Data is collected about every aspect of our lives. From the websites we visit the apps on our phones, everything we do leaves a trail of data that can be analyzed and used to predict our future behavior.

Data is also an asset that can be monetized by organizations. This can be done by either selling data as a product or service (for example, IoT), or by using data analytics to drive business performance (for example, using machine learning algorithms).

The reason for this increase is simple – data is money! Companies who have access to large amounts of data can sell it to others and make millions. Google generates over $1 billion every month from advertising alone.

Advertisers use this data to target their ads more effectively at potential customers who are more likely to buy their products or services. The advertisers pay Google based on how many people click on their ads, so they want those clicks to be as accurate as possible. They also want to show you ads that are relevant to your interests, so they know what they need to show you if they want you to click on them (and pay them).

Data collection isn’t just limited to online activity though – there is also plenty happening offline too.

For example - stores such as supermarkets collect your shopping habits when you use their loyalty card (or scan their barcode). This information can then be used by other companies who want customers with similar profiles so that they can target them with offers and incentives.

Let's look at how data can be collected and sold by companies.

Data has two main sources: internal and external.

Internal sources include employee information, sales data, customer records, and financial information. External sources include social media posts, search engine results, website traffic, and weather reports.

Companies collect this data and use it to create profiles of customers or employees to make more informed decisions about their products or services. They then sell this information to third parties who want to use it for their purposes — like marketers who want to target ads based on your interests or insurance companies who want to determine whether you're eligible for coverage based on your health records.

Data is collected in a variety of ways: search engine queries, social media posts, online purchases, browser history, face recognition software, and even your location.

The more data a company has about you, the better it can target its ads toward you. Data collection has become so fine-grained that we're now seeing ads for specific products on websites like Amazon — even if we've never searched for them before!

Data is sold as a product or service in the following ways:

  • Data as a product: Data can be sold as an anonymized and aggregated set of information (e.g., credit card transactions) to third parties who want to use it for their purposes, such as marketing campaigns or research. These companies would then pay for this information.

For example- Google sells search data to advertisers who want to know what people are searching for so they can target ads more effectively.

Another example - Data is sold as a product by companies like Palantir and many others who offer products like geo-location intelligence software or social media monitoring software; they then sell their data products to other companies who use them to better understand their customers.

  • Data as a service: Data can also be sold as a service where customers get direct access to specific data sets from companies or third-party providers and then use that information in their applications or services. This is mostly seen when companies allow developers access to their APIs to allow them to build apps on top of their platform (e.g., Twitter's API). This usually requires payment from developers but provides them with valuable insight into how users interact with their products, which helps improve user experience and engagement over time.

For example - Data analytics are sold as services directly by companies like Splunk, which offers an analytics toolkit that customers can use to analyze their own data sets. These types of companies often have professional services teams to help customers integrate their tools into existing systems and processes, provide training, etc.

The Impact of Data on Company Culture

Data is an integral part of the business world today. Data is transforming the way we work, and it's doing so for companies both large and small.

The impact of data on company culture has been significant in recent years. The shift away from gut-feel decision-making and towards more evidence-based decision-making has been driven by the availability of new sources of data. This has led to a change in how companies operate, and how they view their employees.

An increased focus on data is often accompanied by greater accountability among employees. Employees are now expected to use data as part of their everyday decision-making processes, rather than relying on their own opinion or experience alone.

For example-sales teams are expected to use analysis tools such as Google Analytics or HubSpot to track their progress and identify areas for improvement.

Managers must also be equipped with the right skillsets if they are going to manage teams effectively using data-driven approaches. They need to be able to communicate clearly what they expect from their team members when it comes to using data, while also supporting them that they can use this information effectively in future projects.


Since the Industrial Revolution, there has been a steady increase in the complexity of businesses that we have today. Businesses can now take advantage of different market channels and exchanges with the help of information systems. They are also able to take advantage of software development and communications technologies that allow them to reach new markets. That’s why today, more than ever before, we need algorithmic solutions to keep up with the progress and transform our data into knowledge that will support enterprises in their strategic development.

Data is the most important asset you have. It tracks customers; builds products, ad helps understand whether the business is growing or not. Data can come in many different forms like statistics that show you how much money you make off a product and where it's being used. Data also shows you when to step in and offer help to a customer that might be struggling with your product.

Data is sold in the market, and the data of many big companies like Google, etc are used to generate revenue. As more and more organizations are using this data to generate their wealth it shows that we live in an era of business where data is the main driver of many businesses across the globe.

Key Takeaways

Every piece of data has a value that can be sold to help businesses.
Data is driving businesses in the direction of smarter decisions.
Businesses are using data to make better decisions, which helps make operations more efficient, save money, and provide customers with a better experience.
As data proliferates and becomes available at new rates and scales, the ability to organize, protect, support, maintain and leverage it has become more imperative.

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