Amurta

Data Quality

Discover, understand and trust your data that matters in your Enterprise growth

Data Quality

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.

FEATURES

Data Profiling

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.

Data Validation

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.

Data Monitoring

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.

Data Cleansing

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.

On-Premise or Cloud Deployment

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.

APPROACH

01

Define

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.

02

Discover

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.

03

Design

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. 

04

Execute

Apply injection and execution .Embed the Data Quality services and business rules monitoring into your operational systems& Data Integrity processes. 

05

Evaluate

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. 

06

Control

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. 

BENEFITS

Improved
ROI 

Having high data quality standards and using validated data, businesses can take fast and correct decisions, increasing their overall outcome. 

Reduced
Cost 

Having high data quality standards enables organizations to operate more efficiently, leading to higher rate of project completions and less costs. 

Less Time
Spent

 Reconciling data Pre-defined automated rules help organizations work more effectively with less time and effort. 

Regulatory
Consideration

 With the ever-changing regulations, it is important data to be in good quality. 

Increased
Trust

Good data quality ensures trust in data analysis and decision making, which increases confidence in the organization’s analytical decisions. 

Optimize
Decision-Making

 Data Monitoring helps effective decision making for your sales, customer services, manufacturing etc. 

Data
 Analysis

Data monitoring enables you to capture and analyze feedback before it has a chance to harm your business. 

Identify
Unauthorized Data 

Monitoring on all data transfer from the company helps to prevent any unauthorized data transfer by identifying it quickly. 

Data
Reconciliation

Data Reconciliation Pre-defined automated rules help organizations work more efficiently and effectively Reconcile
data.

CASE STUDY

Customer

  • A leading Indonesian Banking Customer.

Use Case

  • Identifying the Unique Customer Reference from multiple sources for Regulatory and MIS purposes.

Business Challenge

  • Automate multiple source data processing using single platform.
  • Ability to process the data conversion accurately during 2011 migration.
  • Managing the future data processing in a robust and faster way.

Project Requirements

  • Create process to validate existing KTP or NPWP.
  • Robust validation on information i.e., Province, City, DOB, Gender etc.
  • Assessing the quality of data captured in individual attributes  i.e., Name, address, gender, DOB, POB, marital status, NPWP, Driver's license and Passport.
  • Knowing the trust index score for the data conversion during 2011 migration.

Solution Highlights

  • Created an automated process to integrate for the data collected from multiple sources.
  • Managing the Data Quality requirements for the selected attributes in Data Asset through the standard validation rules.
  • Build audit and logging functionalities.
  • Assessing the quality of data by generating the trust index score and the reports.
  • Architecture to support feeding the single CIF back into the source system, so that further transactions can be initiated without any confusion.
  • Secondary checks with other Government Database like PEFINDO.
  • Periodic review of KYC for customers based on Risk categorization of the customer (High , Medium, Low) – 6 months, 12 months and 24 months once, through internet banking/mobile banking, mailers.

Customer

  • A leading Indonesian Banking Customer.

Use Case

  • Identifying the Unique Customer Reference from multiple sources for Regulatory and MIS purposes.

Business Challenge

  • Automate multiple source data processing using single platform.
  • Ability to process the data conversion accurately during 2011 migration.
  • Managing the future data processing in a robust and faster way.

AMURTA Value

  • Supported in Identifying the Unique and robust Customer Reference from multiple sources.
  • Reduced the time taken to view the complete customer view with a compilation of multiple source data.

Results

  • Key metric information was provided in near real time to business executives.
  • Related Dashboard and reports updated in Governance Metrics.
  • Over 90% improvement in Data Quality.
  • Improved business resource optimization.

SPEAK TO OUR EXPERTS TODAY

If you have queries  we are ready to discuss how our Data Insights Platform can help you in improving your organization governance process.

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