Data Quality refers to the Methodical approach, Policies, and Processes by which an organization manages the accuracy, Validity, timeliness, completeness, uniqueness, and consistency of its data in systems and data flows.
DIP provides an ability to identify the imperfection as well as uncover relationships across different data sources. It profiles the data from multiple sources like data warehouses, cloud applications, spreadsheets etc. A comprehensive set of pre-build business rules as well as customized business rules can be used to accelerate the Data Assets to meet the business requirement standards.
DIP enables users to define and customize the business validation rules against which data sets can be evaluated. These standard data quality rules can be predefined and reused which can save a lot of time.
DIP helps to assess your data proactively by ensuring the data of high quality and fits for all purpose. It helps to measure and track your data trust index score using dashboards and reports where user can define the dimensional weightage according to the business requirement.
DIP helps in cleansing the data within a selected data asset , either by removing or updating the information that is incomplete, inaccurate, improperly formatted, duplicated, or irrelevant through step by step process. Data cleansing usually involves cleaning up data assembled in one area.
Manage the quality of multi – cloud and on- premises data for all use cases and for all workloads. DIP is flexible with any source of data; Can work with any type of Database (MS SQL, MariaDB), Files etc.
Business Terms & Sources should be created and defined based on business goals and requirements. These are the business/technical requirements must comply in order to be considered viable sources.
Discover what the data contains. It helps businesses to develop a starting point in the DQM process and sets the standard for how to improve their information quality.
Design what data should contain. “Quality rules” should be created and defined based on business goals and requirements with which data must comply in order to be considered viable.
Apply injection and execution .Embed the Data Quality services and business rules monitoring into your operational systems& Data Integrity processes.
Publish DQ measurement. Measure and monitor actual versus expected track trends and allocate tasks. Data quality rules when teamed together with analytics, these rules can be key in predicting trends and reporting analytics.
Data remediation is the performance of a “root cause” examination to determine why, where, and how the data defect originated. Update and improve systems and processes.
Having high data quality standards and using validated data, businesses can take fast and correct decisions, increasing their overall outcome.
Having high data quality standards enables organizations to operate more efficiently, leading to higher rate of project completions and less costs.
Reconciling data Pre-defined automated rules help organizations work more effectively with less time and effort.
With the ever-changing regulations, it is important data to be in good quality.
Good data quality ensures trust in data analysis and decision making, which increases confidence in the organization’s analytical decisions.
Data Monitoring helps effective decision making for your sales, customer services, manufacturing etc.
Data monitoring enables you to capture and analyze feedback before it has a chance to harm your business.
Monitoring on all data transfer from the company helps to prevent any unauthorized data transfer by identifying it quickly.
Data Reconciliation Pre-defined automated rules help organizations work more efficiently and effectively Reconcile
If you have queries we are ready to discuss how our Data Insights Platform can help you in improving your organization governance process.