Big data is one of the important technologies to promote sustainable changes in enterprises, and companies need to understand how big data will improve their business.
When business executives hear the term "big data," they naturally think of an astonishing amount of available data. This data comes from the areas of e-commerce and omni-channel marketing, or from connected devices on the Internet of Things, or from applications that generate more detailed information about transaction activities.
Despite this, big data is not simply characterized by large scale. The data itself is diverse and constantly changing. Therefore, the term "big data" also includes new ways to store, process, manage, and serve information that drives business decisions. It is these new technologies, especially big data analysis technologies, that have brought the benefits of big data that both corporate executives and IT teams hope to obtain.
Here are six ways that big data can improve business:
1. Better customer insight
When modern companies turn to data to understand their customers (whether they are individual customers or corporate customers), there are a wide range of data sources to choose from. Data sources that help understand customer needs include:
The traditional source of customer insights, such as purchasing behavior.
External sources, such as financial transactions and credit status, if these details are available in the company’s terms of service.
Social media activities.
Data from external surveys.
Cookies in the computer
Clickstream analysis of e-commerce activities is very effective in an increasingly digital market. It reveals how customers browse the various web pages and menus of companies to find products and services. Businesses can see what products customers add to their shopping carts, but later delete or abandon no purchases; this provides important clues about what products customers might like to buy, even if they didn't buy them.
is not only an online store, physical stores can also collect customer data, usually by analyzing videos to understand how visitors shop in the physical store, rather than browsing the website.
2. More insightful market intelligence
Just as big data can help people understand customers’ shopping behaviors in more detail, it can also deepen and broaden their understanding of market dynamics.
Social media is a common source of market intelligence for product categories ranging from breakfast to vacation. For almost any business transaction that anyone can imagine, there are people sharing their preferences, experiences, suggestions... and their selfies! This information is invaluable to marketers.
In addition to being used for competitive analysis, big data can also help product development: for example, prioritizing different customer preferences.
In fact, big data is not only helpful for gathering market intelligence. In almost all e-commerce or online markets, almost all market intelligence is driven by constantly changing and diverse data.
3. Agile supply chain management
Whether it is a toilet paper shortage caused by the epidemic, a trade interruption caused by Brexit, or a freighter trapped in the Suez Canal, people now realize that modern supply chains are very fragile.
Surprisingly, in most cases, people will not notice the importance of the supply chain until a major interruption occurs. Big data analysis techniques (including predictive analysis) are usually near real-time, helping to keep the global network of demand, production, and distribution functioning to a large extent.
This is possible because big data analysis can combine customer trends from e-commerce websites and retail applications with supplier data, real-time pricing, and even shipping and weather information, thereby providing an unprecedented level of business intelligence.
It's not just large companies that benefit from these insights. Even small e-commerce companies can use customer intelligence and real-time pricing to optimize business decisions, such as inventory levels and risk reduction, or temporary or seasonal staffing.
4. Smarter recommendation and positioning
As consumers, people are now so familiar with recommendation engines that they may not know how much development and progress have been made in recommendation engines since the emergence of big data. In the past, the predictive analysis of recommendation engines was very simple: you can find those common items in the shopping cart by association rules. One can still expect to find this feature on e-commerce sites.
The new recommendation system is smarter than ever. It is based on complex customer insights, so they are more sensitive to demographic information and customer behavior. These systems are also not limited to e-commerce. Friendly service recommendations are likely to be data-driven—decisions driven by a point-of-sale system that evaluates food inventory levels, popular combinations, high-margin items, and even social media trends. When people share food photos, it will also provide more information to the big data engine.
Streaming media content providers use more sophisticated technology. They may not even ask the customer what content they want to watch next: Even before the end of their watching and listening to a movie, show or song, they will give the next choice, by leveraging the user’s preferences, combining collections from other users and social media A large amount of big data analysis can be recommended to continue watching other streaming media content.
5. Data-driven innovation
Innovation is not just a matter of inspiration. There is still a lot of hard work to be done in identifying the subject areas where the new endeavors and experiments are expected to be implemented.
Big data tools can strengthen research and development, usually new products and new services are developed. Sometimes the data that has been cleaned, prepared, and managed for sharing becomes a product in itself. For example, the London Stock Exchange now earns more from the sale of data and analysis than it earns from securities trading.
Even if the best big data tools are used, the data itself will not generate new insights. Big data analysis still requires the understanding and imagination of data scientists and business intelligence analysts. The breadth and scope of big data can guide enterprise teams to have a new understanding of development trends, especially when stored on a single platform (such as Hadoop or cloud data warehouse), but it is difficult to collect in a less integrated environment.
6. Improve operations
Using big data can improve various business activities, but one of the most interesting and valuable activities is the use of big data analysis to improve business operations.
For example, using big data and data science to inform predictive maintenance plans can reduce costly repairs and downtime of critical systems. You can start by analyzing age, condition, location, warranty and service details. However, some of these systems (such as firefighting and refrigeration in data center facilities) are clearly affected by other business activities (such as staffing and production planning), and these activities may be affected by the sales cycle and therefore also by customer behavior Impact. Well-integrated big data analysis can combine all of these and help companies maintain equipment at the best time.
Big data is now the lifeblood of enterprises
From the six benefits brought by big data, we can see that the potential of using big data is very exciting. In fact, people must become more aware of the importance of the regulatory environment (compliance with privacy, security, and governance regulations). Nonetheless, the advantages and benefits of big data outlined above are worth the effort. Big data is the lifeblood of modern enterprises and one of the most important technologies and resources to promote sustainable change.