Big data refers to massive, complex datasets traditional tools struggle to handle. It includes structured, unstructured, and semi-structured data that, when analyzed properly, reveals key patterns and insights.
Big data for companies offers a wealth of opportunities. By analyzing it, businesses uncover trends, streamline operations, and make more informed decisions.
Here, we’ll explore how companies across industries are putting big data to work.
Why do companies use big data?
The rise of big data in business is undeniable, and companies are using it to improve everything from decision-making to cost efficiency.
The insights gained from big data are helping businesses transform how they operate, innovate, and serve their customers. Here’s why companies are investing in big data:
- Analyze large volumes of data—57%
- Get new or better data analysis possibilities—55%
- Build predictive models—51%
- Analyze information from different sources—50%
- Get data for analysis faster—46%
- Accelerate decision-making—31%
- Monitor and analyze streaming data—27%
- Gain a better cost-benefit ratio—23%
- Automate decision-making—19%
- No pressing challenges in our company—6%
By processing and analyzing large datasets, businesses make smarter decisions and optimize daily operations. Whether it’s refining marketing strategies, improving customer satisfaction, or finding new ways to cut costs, big data helps companies tap into previously inaccessible insights.
How do companies use big data?
When it comes to utilizing big data, companies adopt various approaches depending on their needs, available resources, and long-term goals. Here are the most common ones:
- In-house
- Outsourcing
- Hybrid
Let’s look at each approach in more detail.
In-house data management
This involves building internal teams and infrastructure to gather, store, and analyze big data. Companies hire data scientists, engineers, and analysts who develop and maintain data pipelines, processing systems, and analytics tools.
This approach is best suited for:
- Companies with highly specific data requirements.
- Businesses that handle sensitive data and want full control over security.
- Organizations that have the financial and human resources to build and sustain a dedicated data team.
It allows businesses to fully customize their data operations but can be costly and time-consuming to maintain.
Outsourcing data management
With outsourcing, you partner with external vendors or big data services providers to handle data collection, processing, and analysis. These third-party companies specialize in big data and offer solutions tailored to a company’s needs.
Outsourcing is ideal for:
- Companies that lack the internal resources to manage big data.
- Businesses looking for faster implementation with expert knowledge.
- Organizations that want to scale their data operations without hiring full-time staff.
This approach reduces overhead and allows for scalability, but companies may have less control over the data processes.
Hybrid data management
A hybrid approach combines in-house data teams with outsourced services. Companies may handle sensitive or critical data internally while outsourcing less crucial tasks.
This approach fits:
- Companies looking for a balance between control and cost-efficiency.
- Businesses that want to tap into specialized expertise without fully relying on external vendors.
This method provides flexibility but requires careful management to ensure smooth integration between in-house and external systems.
Companies that use big data
56% of data leaders admit that they increased the budget for data and analytics in 2023. The companies mainly focus on data governance, quality, data science, machine learning, and DataOps.
But how exactly is big data used by companies? Let’s look at the examples below.
Amazon
Amazon uses big data extensively in multiple areas, but one of the most impactful is its supply chain and recommendation systems.
Amazon uses big data to simplify its vast logistics network. By analyzing customer orders, product demand, and shipping routes, it optimizes inventory management and ensures that products are stocked in the right warehouses. This leads to a more efficient fulfillment process and helps maintain Amazon’s reputation for fast, reliable deliveries.
Another key area where Amazon applies big data is in its recommendation system. Amazon collects user data—browsing history, previous purchases, and geographic location. This data feeds into its machine learning algorithms to offer personalized product recommendations for driving up cross-selling opportunities.
Netflix
Netflix is a prime example of how companies leverage big data to create a personalized, engaging user experience.
Netflix’s recommendation engine is powered by big data. It analyzes viewing habits, search queries, and the time of day users watch content to provide personalized recommendations. Approximately 80% of the content watched on Netflix comes from these recommendations, so it’s a crucial feature for user retention.
The streaming company also uses this information to guide its content creation. Netflix determines what genres or themes are likely to be popular, and then creates original series and films that align with user interests.
Data helps Netflix predict when a user might cancel their subscription by tracking how much content they consume, how often they engage with the platform, and what types of content they favor.
Walmart
Walmart was one of the first companies to integrate big data on a large scale.
Like Amazon, Walmart uses big data to implement dynamic pricing strategies. Walmart analyzes competitors’ prices, product demand, and customer behavior to adjust its pricing in real time.
It also has a supply chain management system in place, just like Amazon. Merchants can track how their products are selling and adjust production and distribution accordingly. Big data also helps Walmart design the layout of its stores. As the system tracks customer movement and shopping patterns, the company then optimizes product placement to maximize sales.
American Express
American Express uses big data to safeguard against fraudulent transactions. By analyzing spending patterns, transaction history, and geographical data, the company detects suspicious activity almost instantly. This allows them to act quickly and prevent fraud before it impacts customers.
Moreover, big data gives American Express a window into customers’ spending habits. As the company knows where and how customers spend, they personalize rewards programs and marketing. For example, frequent travelers might get special travel-related perks, while those who shop locally might see offers from nearby businesses.
American Express also analyzes financial behavior to better assess credit risk. This allows them to make informed decisions about who should receive credit and on what terms.
Wrapping it up
Across different industries, companies that have embraced big data are reaping the rewards—higher profits, greater customer loyalty, and smoother operations. We’ve explored how Walmart, Netflix, Amazon, and American Express are making data work for them.
However, not every business has caught up. In fact, only around 30% have fully integrated big data into their processes, leaving a significant gap in potential growth.
If you’re a business leader, now is the time to take stock. Does your company have the right tools and expertise to make the most of big data? Maybe it’s time to invest in more advanced analytics or bring in external experts to help. The sooner you start leveraging data, the sooner you can gain the competitive advantage your business needs to thrive in the data-driven future.
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