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 executives hear the term "big data", they naturally think of an amazing amount of available data. This data comes from e-commerce and omni channel marketing, or from connected devices on the Internet of Things, or from applications that generate more detailed information about trading activities.
However, 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 methods of storing, processing, managing and serving information that drives business decisions. It is these new technologies, especially big data analysis technologies, that bring the big data benefits that both enterprise executives and IT teams hope to obtain.
Here are six ways that big data can improve enterprise business:
- Better customer insight
When modern enterprises turn to data to understand their customers, whether personal or corporate, there are a wide range of sources of data to choose from. Data sources that help understand customer needs include:
Traditional sources of customer insight, such as purchasing 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 PC
Clickstream 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 as to what the customer may like to buy, even if they do not buy.
Not only online stores, but also physical stores can collect customer data, usually by analyzing videos to understand how visitors shop in physical stores, rather than browsing websites.
- 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 business transactions that almost anyone can imagine, some people share their preferences, experiences, suggestions... and their self portraits! 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 is not only helpful to collect market intelligence. In almost all e-commerce or online markets, almost all market intelligence is driven by changing and diversified data.
- Agile Supply Chain Management
Whether it is the shortage of toilet paper caused by the epidemic, the trade interruption caused by the Brexit of the United Kingdom, or the cargo ships trapped in the Suez Canal, people are now aware that the modern supply chain is very fragile.
Surprisingly, in most cases, people do not notice the importance of the supply chain until a major disruption occurs. Big data analysis technology (including forecast analysis) is usually nearly 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 unprecedented levels of business intelligence.
It is not just large enterprises that benefit from these insights. Even small 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.
- More intelligent recommendation and positioning
As consumers, people are so familiar with recommendation engines 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 engines was very simple: association rules could find those common items in the shopping cart. People can still expect to find this function on e-commerce websites.
The new recommendation systems are more intelligent than ever, 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 point of sale systems that assess food inventory levels, popular combinations, high margin items, and even social media trends. When people share food photos, they will also provide more information for the big data engine.
Streaming content providers use more sophisticated technologies. They may not even ask customers what they want to see next: even before they finish watching and listening to movies, programs or songs, they will give them the next choice. By taking advantage of users' preferences, and combining the analysis of a large amount of big data collected from other users and social media, they can recommend them to continue to watch other streaming media content.
- Data driven innovation
Innovation is not just about inspiration. A great deal of hard work remains to be done in identifying thematic areas where new efforts and experiments are likely to be implemented.
Big data tools can enhance research and development, usually developing new products and services. Sometimes, 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 selling 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 enterprise teams to have a new understanding of the development trend, especially when it is stored on a single platform (such as Hadoop or cloud data warehouse), but it is difficult to collect it in a low integration environment.
- Improve operation
Using big data can improve various business activities, but one of the most interesting and valuable activities is to use big data analysis to improve business operations.
For example, using big data and data science to inform predictive maintenance plans can reduce costly repair and downtime for critical systems. You can start by analyzing age, condition, location, warranty, and service details. However, some of these systems (such as fire protection and cooling 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 of these to help enterprises maintain equipment at the best time.
Big data is now the lifeblood of enterprises
From the six benefits of 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 critical. Nevertheless, the advantages and benefits of big data outlined above are worth making efforts. Big data is the lifeblood of modern enterprises and one of the most important technologies and resources to promote sustainable change.
