Big data is one of the important technologies to promote sustainable change of enterprises. Enterprises need to understand how big data will improve their business.
When enterprise executives hear the term “big data”, they naturally think of an amazing amount of available data. This data comes from the field 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.
Nevertheless, 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 technology, that bring the big data benefits that enterprise executives and it teams want to get.
Here are six ways big data can improve enterprise business:
1. Better customer insight
When modern enterprises turn to data to understand their customers (whether individual customers or enterprise customers), there are a wide range of data sources to choose from. Data sources to help understand customer needs include:
Traditional sources of customer insight, such as buying behavior.
External sources, such as financial transactions and credit status, if these details are available in the enterprise’s terms of service.
Social media events.
Data from external surveys.
Cookies on your computer
Click stream analysis of e-commerce activities is very effective in the increasingly digital market. It reveals how customers browse various web pages and menus of enterprises to find products and services. Enterprises can see what products customers add to their shopping cart, but later delete or give up without buying; This provides important clues to what products customers may like to buy, even if they don’t.
Not only online stores, physical stores can also collect customer data. Usually, video analysis is used to understand how visitors shop in physical stores, rather than browsing websites.
2. More insightful market intelligence
Just as big data can help people understand customers’ shopping behavior 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 from breakfast to vacation. For almost anyone who can imagine a business transaction, some people share their preferences, experiences, suggestions… And their selfie! This information is invaluable to marketers.
In addition to being used for competitive analysis, big data can also help product development: for example, giving priority to different customer preferences.
In fact, big data not only helps to collect market intelligence. In almost all e-commerce or online markets, almost all market intelligence is driven by changing and diversified data.
3. Agile supply chain management
Whether it is the shortage of toilet paper caused by the epidemic, the interruption of trade caused by brexit, or the cargo ships trapped in the Suez Canal, people now realize that the modern supply chain is very fragile.
Surprisingly, in most cases, people do not notice the importance of the supply chain until there is a major interruption. Big data analysis technology (including forecast analysis) is usually near real-time, which helps to keep the global network of demand, production and distribution running well 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 to provide an unprecedented level of business intelligence.
It is not just large enterprises that benefit from these insights. Even small-scale e-commerce enterprises can use customer intelligence and real-time pricing to optimize business decisions, such as inventory level and risk reduction, or temporary or seasonal staffing.
4. More intelligent recommendation and positioning
As consumers, people are now so familiar with the recommendation engine that they may not know how much development and progress the recommendation engine has made since the emergence of big data. In the past, the prediction analysis of recommendation engine was very simple: you can find those common items in the shopping cart by association rules. People can still expect to find this function on e-commerce websites.
The new recommendation systems are smarter than ever and are based on complex customer insights, so they are more sensitive to demographic information and customer behavior. These systems are 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-profit items, and even social media trends. When people share food photos, it will also provide more information for the big data engine.
Streaming content providers use more complex technologies. They may not even ask customers what they want to see next: even before they watch and listen to movies, programs or songs, they will give their next choice. By taking advantage of users’ preferences and combined with a large number of big data analysis collected from other users and social media, they can recommend them to continue to watch other streaming media content.
5. Data driven innovation
Innovation is not just a matter of inspiration. A great deal of hard work remains to be done in identifying thematic areas promising to implement new efforts and experiments.
Big data tools can strengthen R & D and usually develop new products and services. Sometimes the data that is cleaned, prepared, and managed for sharing becomes itself a product. For example, the London Stock Exchange now generates more revenue from the sale of data and analysis than from securities trading.
Even with the best big data tools, the data itself will not generate new insights. Big data analysis still needs the understanding and imagination of data scientists and business intelligence analysts. The breadth and scope of big data can guide the enterprise team to have a new understanding of the development trend, especially when stored on a single platform (such as Hadoop or cloud data warehouse), which is difficult to collect in an environment with low integration.
6. Improve operations
Using big data can improve various business activities, but one of the most interesting and valuable activities is using big data analysis to improve business operations.
For example, using big data and data science to inform predictive maintenance plans can reduce expensive maintenance and downtime of critical systems. You can start by analyzing age, condition, location, warranty, and service details. However, some of these systems (such as fire protection and refrigeration in data center facilities) are obviously affected by other business activities (such as staffing and production planning), which may be affected by the sales cycle and therefore by customer behavior. Well integrated big data analysis can combine all these to help enterprises 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 increasingly realize that the regulatory environment (compliance with privacy, security and governance regulations) is crucial. Nevertheless, the advantages and benefits of big data outlined above deserve efforts. Big data is the lifeblood of modern enterprises and one of the most important technologies and resources to promote sustainable change.