How to improve data quality? Data FAQs and Root Cause Analysis

foreword

In the process of building the data platform, we often encounter various problems such as difficult demand control and low data accuracy. Today, we will summarize the data problems we encountered in the process of building the middle platform and how to solve them.

Summary of Data Issues

data development level

1. There is no visual development platform for data development, resulting in data synchronization process
2. Data tasks are not well managed, task scheduling, resource allocation, audit and alarm mechanism
3. The data model is seldom rationalized for modeling design, and more is to design the table structure to meet the requirements, and the utilization rate of data stored in each layer of the table is not high
4. Low data development efficiency and slow delivery of data reports

Data Governance Level

1. The data assets are not clear, and what data cannot be clearly seen or queried at present
2. Data standards are not defined, and data classification and classification are not clear
3. Locating data problems is slow, and problems are not handled in a timely manner
4. Data is tracked across the entire link, and upstream and downstream business modifications cannot detect the changes and their impact on data in a timely manner.
5. There is no specific quality standard for the data, and the accuracy of the data is difficult to guarantee
6. There is no clear management and definition of data indicators, resulting in inconsistencies between the data and the business demand side
7. Data costs are not controlled
8. Unable to perceive which users are using data reports, and whether the usage rate is high. Data resources and computing resources can be released in time for low-level reports or invalid reports.

data security level

1. Policy without data backup
2. Control without data development authority
3. There is no corresponding data desensitization strategy

Data Requirements Layer

1. There are many scattered needs, and the development progress cannot keep up with the growing demand for products.
2. There is no reasonable system construction of data indicators. For example, some basic indicator data of users may not be complete at present.

Data platform capacity building

Formulation of data development process

Solve the problem of irregular data development process, and at the same time formulate data burying point specification, data governance specification, sensitive data security level, etc. Gradually improve the rules and regulations for data management and development.

Data indicators and work order system

Solve problems such as chaotic data demand management, inconsistent data indicators, and unquantifiable data development workload.

data map

Solve the problem that data assets cannot be queried or managed

Data diagnosis

Data table diagnosis solves problems such as cold data offline and data cost management

Data development process visualization

Solve development efficiency problems

Data monitoring platform

Guarantee data accuracy and tasks

data lineage

Guaranteed full link detection of data to help troubleshoot

Real-time data stream detection

Prevent Data Synchronization Interruption