5 best practices for data management

translate October 31, 2017 17:15:39

The original:Top 5 Data Management Practices to Consider
Author:Lakshmi Raman
Translation: Vincent

The power of the data has been ignored all the time. This time let us give the data the same democracy as human, and listen to what it will say. Enterprise institutions also work together to learn about the five kinds of management practices that need to be adopted. The following is the translation.

Agents are trying to distinguish their products by different means, although they have made a lot of investment in the data, but there is little guidance. The following are the five data management practices that should be considered by agents in the near future in order to maintain relevance and to be different from other families.

1. possession and control of its own data

For a long time, we have been relying on point - to - point integration between systems. As long as we connect network analysis and search and enter into a customer relationship management system, we will simply believe that everything is good. Then it became a comprehensive project within the team. After spending a few weeks in extracting data from various platforms, it finally presented an incomplete view in the mess of Excel tables. Having and managing your own brand data will enable you to build a deeper trust between the brand and the brand. And it also helps the data science team to continue to dig out new insights and bring additional value to customers.

2. capture minimum granularity data

Aggregation index, summary report and display dashboard are important, but the value that original data can provide is unthinkable, and many values have not been developed. As long as the number of contact points is more, the more detailed information can be captured by users during each interaction, which is conducive to the following exploratory analysis, such as building custom attribute models, overlapping matrices, analyzing trends, identifying patterns and applications.Machine learning and artificial intelligence.

3. outsourcing integration

It is undeniable that the integration process is very time-consuming and difficult to maintain. Data format and APIs are developing every day, and it takes the entire team engineer to keep up with it. At the same time, many companies specialize in data integration to reduce the complexity of the integration process. It is possible to find a solution that provides a reliable data collection framework and a built-in security solution.

4. establish a sustainable data platform

It's easy for engineers to focus on these short-term needs and BUG, and it's hard to see a long-term plan. Develop a specific application or a dashboard that can solve the short-term needs, so it will go farther. Focus on building an open data platform to keep up with changing needs.

5. priority should be given to security

The security of the client data is needed in the first place. Build security in the data platform you build and the solutions you invest in. Ensure that data is always encrypted in the transmission process, and is encrypted in rest, and the platform has all the necessary compliance to handle different types of data.

The power of the data has been ignored all the time. This time let's listen to what the data will say.

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