DATA DATA ENGINEERING 101
What’s The Real Value of Data For Business?
Data Engineering Beyond Hype #2
As businesses become more data-driven, the importance of data has increased exponentially.
But what is the real value of data for business?
In this article, we will explore the impact of data on various industries through real-life case studies.
We will also discuss what data professionals can learn from these case studies to better prepare for business rounds in interviews.
Despite the growing importance of data, many data professionals struggle to understand the value of their work in the day-to-day functioning of a business.
Let’s take a closer look at some industries and how data has helped them succeed.
Section 1: E-commerce Industry: Personalized Recommendations
The e-commerce industry heavily relies on personalized recommendations to drive new business and increase customer satisfaction.
By using historical data, e-commerce companies create recommendation engines that suggest new products to customers based on their past purchases and browsing behaviour.
These recommendation engines use algorithms that analyze data from multiple sources, including customer behaviour, transaction history, and product catalogue data.
There are machine learning models that are trained on the data collected by the e-commerce company.
The models analyze patterns in the data to understand customer behaviour and identify products that are likely to be of interest.
By providing personalized recommendations, e-commerce companies can improve the customer experience, increase engagement, and drive new business.
The same recommendation engines are also prevalent across the OTT apps and entertainment industry.
How often have you seen, you think of watching a show or a game and Netflix/Hotstar pops a new notification for the same show? This isn’t black magic. This is the recommendation engine getting the prediction right.
Section 2: Manufacturing Industry
The manufacturing industry is another sector that has seen significant benefits from data-driven decision-making.
One of the most critical applications of data in manufacturing is predictive maintenance. By using sensor data and real-time processing, manufacturers can forecast when equipment is likely to fail and plan maintenance before it happens.
For example, General Electric (GE) / UTC uses data from sensors in its aircraft engines to predict when maintenance is needed. The data is analyzed in real-time, and when a problem is detected, a maintenance team is dispatched to fix the issue before it causes a costly downtime event. By using predictive maintenance, GE has been able to save up to $200 million per year in maintenance costs and improve its equipment uptime by up to 20%.
Other manufacturing companies, such as Bosch and Siemens, also use data-driven predictive maintenance to improve efficiency, reduce costs, and increase revenue.
UTC provides Predictive Maintenance of their engines as a service to Airlines. This is how they use data to generate new avenues of revenue.
Section 3: Healthcare Industry
The healthcare industry is one of the most data-intensive sectors, generating vast amounts of patient data every day.
By leveraging this data, healthcare companies can create personalized treatment plans and prevent medical issues before they happen.
Predictive analytics is one of the most critical applications of data in healthcare, allowing companies to use patient data to predict and prevent medical problems.
For example, IBM’s Watson Health uses machine learning algorithms to analyze patient data and predict which patients are at high risk of developing sepsis, a potentially life-threatening condition.
By using this data to intervene early, hospitals can prevent sepsis from developing and save lives.
We have all seen how massive data gathering helped to isolate and cure covid throughout the world.
Based on epidemiological models, India was able to predict growth areas and restrict the spread of the disease.
Section 4: Financial Industry
The financial industry is heavily reliant on data-driven decision-making to detect fraudulent activity and protect customers. Financial companies use transaction data to identify patterns of fraud and prevent unauthorized access to customer accounts.
The Importance of Fraud Detection
Detailed Use Case: Financial companies use advanced algorithms and machine learning models to analyze transaction data and identify potential cases of fraud.
They do this by analyzing a wide range of factors, including transaction amounts, transaction locations, and customer behaviour. By analyzing this data, they can identify patterns of fraudulent activity and take action to prevent further damage.
For example, suppose a customer usually makes small transactions at local stores but suddenly starts making large purchases at high-end retailers in a different city. In that case, the financial company’s algorithm may flag the transaction as potentially fraudulent and alert the customer to verify the transaction’s authenticity.
This may involve blocking the transaction, freezing the customer’s account, or notifying the customer to verify the transaction’s authenticity.
Section 5: Retail Industry
Retail companies use sales data to predict demand and ensure that they have the right products in stock at the right time. This is crucial for reducing costs and increasing profits.
Detailed Use Case: Retail companies use sales data to predict demand and ensure that they have the right products in stock at the right time. They do this by analyzing historical sales data to identify trends and patterns in customer behaviour. This data can then be used to forecast future sales and determine optimal inventory levels.
For example, suppose a retail company sells a particular product that typically experiences a surge in demand during the holiday season. In that case, the company can use historical sales data to predict future demand and ensure that they have enough inventory to meet customer demand during the holiday season.
Walmart and Amazon have invested heavily in data-driven inventory management, using technology like RFID tags to track inventory levels and optimize restocking.
Marketing:
Customer Segmentation Data can help businesses understand their customers better and tailor their marketing efforts to specific segments.
By analyzing customer data, businesses can identify common characteristics and group them into segments.
For instance, PepsiCo used data to create a new line of snacks targeted at health-conscious customers.
By analyzing customer data, they found that this segment was looking for healthier snack options, which led to the creation of their “Smartfood” line of snacks.
By analyzing customer data, businesses can identify common characteristics and group them into segments.
For instance, PepsiCo used data to create a new line of snacks targeted at health-conscious customers. By analyzing customer data, they found that this segment was looking for healthier snack options, which led to the creation of their “Smartfood” line of snacks.
Personal Story:
I have worked in the lending industry, for more than 8 years. In banks, businesses use data to run campaigns and generate new customers while engaging old customers by sharing specialized offers.
In the lending industry, another key dataset is customer credit history. Many companies maintain the credit data of all customers and share their creditworthiness scores with different lending institutions. The banks use this score and associated income and address data for evaluating risk score for the loan proposal and based on this they decide whether to disburse the loan or not.
The credit risk department of any lender is heavily dependent on data.
I have also worked with Pratt & Whitney to help in their data ingestion for a predictive maintenance model. Pratt & Whitney was able to generate new revenue by providing predictive maintenance as a service to its existing and new customers.
The impact of data on business is clear. By leveraging data, businesses can improve their processes, increase revenue, and enhance customer satisfaction.
How can you understand the impact of your work?
- Observe the business of your customer.
- learn what is their key performance indicator.
- Learn how the business operates and uses data.
- Try to see the data and correlate it with the business process.
- Speak to stakeholders to know the big picture and impact of your work.
Example: If you are working for a Bank and maintaining their data, you can try to learn
- What is their process to generate new applications
- What is the process used to repay the loan?
- What is the process to track EMI paid?
- Understand which table you are working on.
- And also figure out the impacts of these tables. Ex. Customers’ income data tables will contribute to the underwriting/approval process.
- Customer credit-related tables will be used for risk scoring.
- Transaction tables will record all disbursements and reimbursements made.
Data professionals need to be aware of the impact of data on different industries to fully appreciate its value. By understanding the impact of data on business, data professionals can better prepare for business rounds in interviews and make a greater impact in their daily work.
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