Little micro financial services companies are exposed to some of the key issues that big data can’t get around!

Big data is the hottest technical vocabulary of the moment, and it does have a good reason to prove its popularity. Its ability to handle huge amounts of data was previously unimaginable, and now, all industries can take advantage of this big data processing capability and do not require hardware conditions like supercomputers. I also want to know if big data can really help our business form an effective competitive advantage. But what about the big data investment and return ratio? After all, big data technology is not cheap. Therefore, small microfinance service companies like ours have to face some key points in order to access big data.

1. If big data helps me improve my business?

For CIOs, big data is a fast, scalable data analysis from many data sources to support the achievement of business goals. For me, big data is an insight into the integrated analysis of the data in a company’s smooth running. For example, customer orders, inventory levels, legal information, and traditional data such as company websites, negative information, and social relationships. Big data technology can find the direction of the company’s business and judge the quality of customers from the huge amount of data composed of these different information.

I don’t care where the information comes from, nor what technology I use. But CEOs need to know if big data can give me predictions about future trends or make our business and product development grow more effectively. idea.

2. How much does Big Data cost?

Big data applications are not easy to develop and manage, require specific expertise to develop and manage storage systems and analytics platforms, and professional analysts who can perform raw data analysis, commonly referred to as data experts.

A typical big data department consists of a high-performance processing center and an analysis system, data cleansing tools, and visualization tools for building analytical reports; of course, servers, storage centers, and office space. For large enterprises, building an integrated big data analytics department, including employees in content, can easily cost millions of dollars. For example, the US government has just spent $13.4 million to build a data mining center for the National Science Foundation.

The establishment of a big data-related system depends on the complexity and budget of the requirements. To implement the big data project, there are three options:

  • The best combination: Companies can choose the best companies to acquire (purchase) technology from all categories of big data technology, and then develop big data applications themselves, which is an expensive but flexible enough solution (for providing big data technology services) Classification of companies and technology can refer to the article “Company, products and technologyBig Data Enterprise Ecology Map).
  • Packaged solutions: Buying integrated big data solutions from vendors is an excellent option for low cost, especially if your business is concentrated in only one area, such as online customer behavior analysis.
  • Software as a Service (SaaS): SaaS providers are responsible for storing data and applications. They provide online services that meet the business needs of most enterprises, which saves professional systems for office space, data analysis and management, and avoids a lot of hardware, software and The cost of professional staff.

To collect online and offline double customer data information, offline data collection is very important, but entry is a lot of work, including customer’s life data and business data, especially many negative information is not easy to obtain online, but Offline can be easily obtained, and tracking updates in real time. In addition, data security is also the focus of my consideration. I don’t want the data I surveyed to be easily obtained by any software company. After all, the future Internet finance competition is core data security and update speed. ,accuracy.

3. What are the risks of big data?

Finding the right person to execute a big data project can be the best way to reduce risk. If you are not used to drawing conclusions from large and unstructured data scientists and analysts, your company’s big data projects will deviate from your goals. These data experts are not cheap at all, and the number of experts is not much.

Over time, the cost of big data projects will continue and grow. Because of the growth in stored data and the multiplication of business points required for big data analytics, the system becomes difficult to maintain. Enterprises plan for the investment of continuous basic technology; CIOs directly responsible for this business need to carry out effective IT system expansion planning in the most sensible way from the beginning of the project.

Finally, applications and services can also consider the more fashionable approach – cloud computing. Reduce the investment and maintenance of hardware devices through cloud computing service providers such as Amazon or Google, effectively improve the stability of big data projects and reduce the risk of downtime.

4. How to evaluate the return of big data projects?

I want to track the final standards of business improvement and improvement, such as a reduction in application disruption or an increase in online sales. In the financial services arena, big data can be used to analyze the best target customers of a new product in order to achieve better target transformation. In other words, you need to tell me what kind of customer is my potential target customer, and what kind of customer is the most likely to default.

But I also understand that big data can provide a powerful information reference for market decisions, but it does not directly affect business results and sales performance. The smartest way to use big data is to make decisions and improve processes or products by analyzing results and information. The assessment of the effectiveness of big data should also include the impact on the final outcome when decisions and actions are made. After all, no matter how good the data analysis is, it is also statistical analysis, screening test, to allow data errors within a certain range, through this form of input, is the future long-term labor cost and material cost savings. If you must be particularly accurate, you must add a human resources configuration line.

5. How long can I see the results?

Based on media reports and touts, I think that not only many of my business leaders will have great expectations for big data. Importantly, we need to connect more closely with relevant business units and even the industry to understand the potential of big data.

SaaS service providers are able to return business analysis results in a matter of minutes because they have a mature big data technology platform and infrastructure, and the analysis process is pre-configured. But if it’s a company project to build a big data project, it may take longer to see the results. Because a company begins to plan to build big data projects, it needs to touch new technologies, processes and infrastructure; it is wise to plan and start small.

Big data is still in its infancy, and there are still many potential problems and places that are not understood by the public. We need to know more about big data-related knowledge so that angry board members don’t see the results of big data projects. At least, we can explain the above five points to them.