3 Ways Data Creates Value for Your Business

Csanád Bánhegyi
Csanád Bánhegyi
07 Dec 2023 · 5 min read

Our world runs on data, whether we like it or not. From manufacturing through insurance to the energy sector (and a whole lot more), there’s hardly any sector that creates value without data.

This article helps you explore how data can generate value and drive success for your business.

Data strategy: drawing a straight line from data to value

Before jumping to the three ways data helps create value, there’s one pressing question we need to clarify: how do you get started? 

You should approach data with strategic intent and ask yourself the following questions:

  • What kind of data do I need?
  • How am I going to collect data?
  • How am I going to use data?
  • What’s unique about my data?

Once answered all the above, we believe three branches can help you create a competitive advantage:

  • Data-for-automation
  • Data-for-product
  • Data-for-decision

If you start thinking about your data along these branches, you can group and prioritize available data use cases, helping you solve practical challenges. 

In the following section, we’ll focus on these three aspects: you’ll see what each trifecta consists of with a handful of real-life use cases.

Data-for-Automation

Data for Automation

If you spend a lot of time cleaning and transforming your data to get actionable insights that you can hand over to your employees, your organization will greatly benefit from Data-from-Automation (DfA). And no, it won’t take away any jobs – instead, it will serve as a productivity booster for your employees. Here are two industry examples of how DfA can prove beneficial:

  • Manufacturing: Say you’re trying to optimize the setup and maintain heavy machinery. Through advanced sensors and IoT devices, data collection becomes robust, offering real-time insights into machine performance and operational efficiency that give you input for use cases like predictive maintenance. These data streams help you with decision-making, allowing you to address issues, reduce downtime, and optimize machine utilization. DfA unlocks more precision in production processes, which leads to consistency and accuracy in manufacturing heavy machinery components.
  • Fraud prevention: Fraudsters are tech-savvy, so businesses must keep up with them. Automated data systems help you find unusual patterns and activities, flagging or downright banning them to prevent fraudulent activities. This minimizes financial losses while also ensuring compliance and generating reports for building better fraud prevention strategies.

Data-for-Product

Data for Product

You’re sure your product feature set has to be more comprehensive. You also know plenty of ways you can improve the product’s functionality. But how will you use data to deliver more value via your product? This is where Data-for-Product (DfA) leaps into the fray. Here are three use cases:

  • Broker company’s technical analysis: Understanding market trends, market sentiment, stock prices, and knowing where and when to invest is an art form in itself. But it doesn’t have to be mystical: by providing data, data-boosted analysis, and different tools powered by market data, your end users can make better-informed decisions about when to buy, sell, or hold onto their investment products.
  • AI-based pharma research: The discovery process has historically been a pass–fail process with attrition at every step, making it highly inefficient. AI can help identify the most promising compounds and targets at every stage of the value chain so that fewer, more successful experiments are conducted in the lab to achieve the same number of leads. It can also help predict potential side effects, helping the drug development process, eventually leading to better, more efficient, and possibly more customized medicine.
  • Recommendation engine: Consider an e-commerce platform with thousands of products available for purchase. How do you ensure your customers find what they are looking for? A simple search bar won’t do. Recommendation engines understand user behavior (like previously viewed items and categories, purchase history, etc.), and suggest products based on that. 
  • Parcel tracking: For industrial shipments, logistics companies add IoT devices to track location, temperature, light, and shipment levels. Here at DATAPAO, we helped one of these companies so that they can share this data directly with the companies that send these shipments, allowing them to track these in absolute real-time.

Data-for-Decision

Data for Decision

Any decision-making process that lacks data is hard to be taken seriously. This is probably an obvious statement, but what data should you use, and how it should be deployed? Here are a few instances where data boosts decision-making:

  • Insurance companies: How do you evaluate insurance claims? How can you measure your client’s financial responsibility? How do you set insurance premiums? With the power of data (and a few handy models), assessing clients and claims becomes more accurate and less risky for your business.
  • Telecommunication: Is your company looking to deploy a new cell site? Are you unsure about the tower’s location? There’s a plethora of factors you need to consider before starting construction, including weather, geography, vegetation, urban planning, and many more. You can make data-supported decisions before deploying new towers by utilizing geospatial data, various data science techniques, and deep learning models.
  • Energy sector: Maintaining an energy grid is a massive undertaking. An aging infrastructure, policy changes, and renewable energy integration are just a few challenges providers can face. Data collected from various sensors installed across the grid provides real-time information about energy flow, potential faults, and system performance. This constant stream of data enables you to monitor the grid’s health, predict issues, schedule maintenance, reduce downtime, grid planning and extensions, enhance reliability, and eventually make the right calls.
  • Energy trading: One of our clients’ ML projects focuses on assisting traders in the energy market. We’ve implemented a Deep Learning multi-time-series model to predict electricity price changes at two-hour intervals. This model provides insights by forecasting the direction of price changes along with a confidence score. This gives traders additional information to aid their decision-making process within the energy trading landscape.

Data creating value: summing things up

Navigating a data-dominated world is not a choice anymore. That’s why it’s vital to understand that data’s real power doesn’t (only) lie within its collection but in its strategic utilization. 

Whether it’s helping you build better products, speeding up processes with automation, or assisting you in your decision-making process, data’s transformative nature shines through every aspect of businesses. By harnessing any of the three approaches, your company will find more efficient and lucrative ways to navigate an increasingly data-driven world and eventually use data to create value.