The application of big data is just like the use of credit cards. The better you use it, the greater the income. On the contrary, can enterprises bear the cost of mistakes in big data? This article describes 6 major mistakes and solutions.

Nowadays, a large amount of data is ubiquitous in the modern business environment, and it has become critical for enterprise operations. In this era, even AI technology needs to be supported by big data. The secret lies in the ability to collect and collate data from different sources, which will improve the insight of enterprises and provide support for data based decisions to enhance business support, including extending from marketing and internal workflow to enterprise sales.

How does big data enter the enterprise business field? This is analyzed below.

What is the relationship between big data and business?

With the progress and development of technology, various organizations need to adopt fine-grained and rich data based on their operations and customers. The main obstacle in this regard is to process massive data, which is difficult to maintain and manage. Although there are some tools, processing these data is still a tedious and trivial activity.

Errors may occur frequently in the process of processing big data. However, big data provides enterprises with multiple supports. These include:

(1) Increase revenue.

(2) Ensure better revenue decisions.

(3) Enhance the customer experience.

(4) Help develop and produce more intelligent services and goods.

(5) Provide better business operation.

Therefore, big data has become the decisive factor for innovative enterprises to gain competitive advantage. By 2020, the global expenditure on data analysis projects will reach 274.3 billion US dollars, and now each person produces about 1.7 megabytes of information per second on average.

Can enterprises bear the cost of mistakes in big data? Therefore, enterprises need to avoid some big data mistakes in order to use their potential and gain the advantages it brings.

Mistakes in big data application

The application of big data is often accompanied by some errors, including:

(1) Inefficient operation

(2) Security vulnerabilities.

(3) Wrong conclusion.

The application of big data is just like the use of credit cards. The better you use it, the greater the income; If it is not used well, it will increase the cost. Here are some mistakes that enterprises should avoid when dealing with big data:

Error 1: Analysis paralysis

• Problem: Analysis paralysis refers to the inability to make decisions due to too much analysis. It seems that the practice of "think before you act" is still unknown to many enterprises, because they invest in big data plans through a large amount of data collection. Project stagnation and analysis paralysis must be the consequence of big data analysis problems.
• Solution: gradually enter the world of big data at a "small pace" (that is, a small amount of data). Let the data collected by enterprises contradict or support their assumptions. If the data is ambiguous, it needs to be matched.

Error 2: Influence data security in the name of innovation

• Question: Security is the first aspect to be sacrificed when dealing with big data. How to mitigate security risks?
• Solution: multiple methods are required to protect big data. This should include understanding the data owned by the enterprise, auditing the operation of data, and controlling privileged users. Ensure a complete and unified process and control system to cover big data security.

Error 3: Lack of supervision over data

• Problem: Complaints about data accuracy and quality are common. However, many enterprises do not see the root of the problem fundamentally. The lack of core supervision of data collection will lead to data duplication, wrong use of columns, wrong input, etc.
• Solution: Determine the management team responsible for data cleaning, and ensure that the big data management team is forced to collate data and train users.

Error 4: Make big data problems "flash"

• Problem: Big data is a huge jigsaw puzzle. If you are eager to solve it, you will face chaos. Few enterprises can solve such a huge problem.
• Solution: Process the puzzle region by region or block by block, which will enable enterprises to meet these challenges. This will reduce the workload.

Mistake 5: Thinking about data in isolated islands

• Problem: It may be beneficial to collect and store Bitcoin, but this is not the way out for data. Therefore, if enterprises only collect data rather than extract its essence and gain insight, it will be useless to think about data in an isolated island. It enhances operations or solves problems, and informs the organization of the product roadmap.
• Solution: Use and extract its essence in a timely manner, and do not let it go to sleep.

Mistake 6: Integrate complex tools

• Problem: enterprises organized by data sets tend to adopt big data solutions. This rapid growth means that a large amount of investment is required to purchase complex tools, which will bring budget pressure to enterprises.
• Solution: The organization should implement data analysis to make wise decisions when dealing with big data. However, not all problems need to use heavyweight tools, but traditional analysis methods of big data can be used.

In addition to these six major mistakes, there are also problems such as the lack of workflow management tools, lower return on investment, and data not being used for evolution. 

Avoiding mistakes is a task

Regardless of the type, big data technology will be widely used in the organization's business. For experts and developers, this is both an opportunity and a challenge. With the increase of data volume, they will continue to migrate to the cloud, and it is predicted that the global data volume will soon reach 175ZB by 2025. With the application and popularization of machine learning, the demand for chief data officers (CDOs) and data scientists is also growing. Big data technology can quickly process and analyze data, so the prosperity of big data will provide more benefits for enterprises.