Five hidden costs using replacement data


Choose the cost of the supplier.

61%of the respondents said that according to a investigation by LowenStein Law Firm, the cost of choosing suppliers is the greatest concern of replacing data users. In order to ensure that the data they provide meet the quality requirements, these costs are due to the time -consuming process of reviewing data providers. When these data will become the core element of business processes and it is not easy to replace, this is particularly important. In this case, data buyers are very important to ensure that suppliers can continue to provide these data in the foreseeable future.

A method to reduce these risks is to find industry alliances to find reliable data sources. Other companies operating in the same industry may have similar needs, and may share some ideas and best practices.

Looking for some skilled employees.

According to a survey on the QUANTHUB platform, there is a gap between 2500,000 data scientists in 2020. As of late April 2022, only a British recruitment website listed the vacant positions of 2,700 data scientists. The shortage of professionals with appropriate skills is forcing wages to rise, which makes it more difficult to retain existing employees. In business, data scientists are not the only person to replace data integration. Forresterresearch recommends that enterprises use data hunter services. In order to verify the accuracy and integrity of these data sources, these people's duties are to find feasible alternative data. Munich Reinsurance Corporation is a European reinsurance provider and hired a team composed of 20 data hunters.

The potential solutions for this skill shortage include training existing employees. Compared with new employees, their understanding of business and demand is more advantageous. Establishing contact with colleges and universities providing data science courses to explore the possibility of student internship and graduation training programs is another way to establish skill channels.

Clarify data ownership.

Compared with the data provided by mature and credible suppliers, the nature and non -traditional sources of the replacement data may make the process of verifying data ownership more difficult. Especially when multiple data sources are combined and tracking its source before buying, it may be very complicated. The Intellectual Property Law and Data Protection Law may face difficulties.

By selecting the supplier to relieve the problem, it can provide customers with a certain transparency and reliable data search method. Of course, using internal data as much as possible is another way to reduce risk lte iot.

By processing the alternative data upgrade model.

In order to ensure its stability and processing errors, maintaining data models is the main cost of underestimation of many companies. Idera predicts that maintenance usually accounts for 50-80%of the development budget. Adding a new data source to the model also increases a lot of cost to the intense budget.

This process can be carefully modeling at the beginning and adding a certain flexibility to the model design.

Use appropriate tools to store replacement data.

25%of respondents said a survey of Wenstein Law Firm stated that lacking tools and technology to store alternative data is a serious problem. Some problems are due to the update frequency and inconsistency between the different data sources between the API and the data format. Cleaning data may generate huge costs to ensure the smooth operation of the model and produce consistent and reliable results. Continuous storage solutions (local systems, clouds and hybrid solutions) and ensure that these solutions operate efficiently to meet the requirements of data model intake, which increases another layer of complexity and cost.

As the data continues to provide competitive advantages for companies that can use their business potential, replacement of data will become increasingly important. It is important to understand that although many replacement data sources may be very low or no cost, other (sometimes huge) cost may make these data sources achieve the expected purpose and integrate it to the established settlement to the established settlement In the workflow.

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