In daily work, product, operation, R&D and data analysts often find that data processing tasks such as processing, processing and identifying data often take up 80% of the entire workflow. There are roughly three reasons for this predicament:
1) The amount of data is large and confusing, and the data quality is uneven;
2) The overall summary information is lacking, and measurement information such as maximum value, minimum value, average value, summary value, variance, median Dimension information such as the distribution of enumerated values cannot be directly visible to users;
3) Metadata management is not perfect, such as table name remarks, field types, descriptions are inaccurate, caliber is not unified, etc. There are metadata management confusion or Missing case.
The above problems can be effectively solved through data exploration.
01. What is data profiling?
Data profiling is the foundation of data development and a very important step in ensuring data quality. If there is no data exploration, data analysts will frequently perform repetitive work on data management projects, which is low-quality and inefficient behavior for project development and operation and maintenance, and also delays the project cycle.
Data exploration can analyze data content, background, structure, path and other information through automated means, and check whether there are problems in data components, data relationships, and data formats. Through the process of accurately identifying the data conversion mechanism, establishing data validity and accuracy rules, and verifying the dependencies between data, it helps enterprises comprehensively analyze data and determine the availability of these data.
02. What are the common scenarios of data exploration?
Data exploration can help enterprises improve their understanding of data, avoid missing scenarios due to insufficient understanding of data, and take precautions in advance to improve data quality, control data sources, and reduce rework. Common scenarios include: 1) Field label analysis:
in In the absence of field comments, by analyzing the field value, the content described by the field can be identified, the readability and interpretation of the data can be improved, and strong support can be provided for subsequent data analysis and decision-making.
2) Analysis of the relationship between data: discover the primary and foreign key fields, reveal the interrelationships and dependencies between data, analyze how many duplicate values are in the fields, and the number of rows affected by duplicate values, etc. Help us discover hidden patterns, group structures and network connections in data, so as to better understand the complexity and interaction of data, and assist business personnel to make more accurate judgments in decision-making and business optimization.
3) In-depth insight into field values: By analyzing the data distribution of calculated fields such as data types, null values, unique values, average values, standard deviations, and variances, a deeper insight into data can be obtained, data quality can be improved, and data cleaning and pre-processing can be improved. Processing provides guidance to help business personnel improve data cleaning and processing efficiency, so that data analysis can obtain high-quality data out of the box.
03. How to conduct efficient data exploration?
Under the traditional method, operations such as filtering, replacing, and merging in the data exploration process are all independent single models, and there is no fusion between the steps, so the data needs to be processed separately, and each model and method has different usage modes and interfaces, which is difficult In conjunction with. In addition, traditional methods rarely involve text fields, making it difficult to conduct in-depth data analysis without knowing the content of the data description.
Therefore, we need a more comprehensive and flexible data probing method that can process and analyze different types of data at the same time. The data probing function of the Tempo data governance platform can meet this demand. It only takes 3 simple steps to help the data team Understand the characteristics and laws of data, and provide basis and support for subsequent data processing and analysis.
△Logical frame diagram of data probing algorithm
Step 1: Statistical analysis from multiple data sources in three dimensions of tables, fields, and field values, including: total amount, null value, unique value, repeated value, time, increment, etc.; Step
2 : Insight analysis of data content through regular expressions, machine learning algorithms, etc., including identification of attributes such as entities and events; Step 3: Synthesize the attributes
obtained in the first two steps, and use big data mining and artificial intelligence algorithms for business modeling , quickly realize the jump from manual experience to automation and intelligence, and accelerate enterprise data quality verification and management.
04. What is the value of data exploration?
The data exploration function of the Tempo data governance platform has been applied in a coal enterprise. Through data governance and data middle platform projects, mt_csms (coal sales management system), mt_erp (electronic procurement platform system), and mt_hrs (human resources system) three The verification results in a business system are as follows:
Accuracy rate: 81.76%
Recall rate: 100%
The Tempo data governance platform can also conduct data structure detection, data content detection and data relationship detection, which can help the data analysis team go deeper Understand data sets, reveal the inherent characteristics and regularities of data, and provide data-driven decision support.
▶ Data structure exploration: You can understand how data is organized in memory, so as to better design algorithms and optimize data processing processes.
▶ Data Content Exploration: Enables data analysis teams to discover distributions, anomalies, and trends in data, helps identify data quality issues, handles missing values and outliers, and improves data preprocessing.
▶ Data relationship exploration: reveals the relationship and interaction between features, helping companies dig out hidden patterns, find key features, and build more accurate predictive models.
Small T summary
Through data exploration, it can provide enterprises with an intuitive understanding of data, reduce reliance on subjective assumptions, make data analysis and decision-making more reliable and credible, prevent and control risks early, and effectively use data resources to provide business decision-making and product optimization. and provide strong support for innovation.