Data Governance
Uncovering the Strategic value of data.
If Data is the New Gold
Our Data Governance & Quality Practice
Ensures your Enterprise Data Stays True to Quality
Availability
Making data available to all stokeholders
Applicability
Ensuring applicability to Business needs
Integrity
Assuring Source of truth of Data
Security
Guaranteeing data is not Compromised
SPAR unlocks significant benefits to ensure accountability and lasting business value.
- SPAR unlocks significant benefits to ensure accountability and lasting business value.
- Implement policy through business rules and quality rules; enforce compliance through issue management and data quality reporting
- Deliver meaningful business dashboards that answer key questions about your data’s quality
- Enable data citizens and data stewards to collaboratively resolve data issues
Data Quality Practices to Data Governance Programs
Data profiling
- Profile full volumes of data, not just samples
- Browse and interrogate data and metadata
- Discover unknown data entities or unusual values
Data cleansing
- Detect and eliminate formatting errors
- Build business rules to keep data formatted consistently
- Transform and improve the quality of your consumer data
Data monitoring
- Control your data quality with a platform designed for business users
- Monitor your data quality and receive alerts if quality drops
- Generate visual dashboards and reports
Spar’s Case Studies
Data Governance & Quality Practice
Business Capabilities
-
Data Analysis & Collection
-
Data Lineage Tracking
-
Reporting & Dashboard Management
-
Data Visualization
-
Predictive Modeling
Technology Capabilities
-
TIBCO Jaspersoft
-
Microsoft BI
-
SAS BI
-
IBM Cognos
-
OpenText Analytics
Spar’s Data Governance & Quality Practice easily provide solution to the clients. Our specialists have designed this platform with their committed efforts. One of our clients approached us for a solution to his problem. Spar’s Data Governance & Quality Practice is a 4-phase process. They are: Discover, Define, Apply, Measure & Monitor. Our Business Capabilities include Data Analysis and Collection, Data Lineage Tracking, Reporting & Dashboard Management, Data Visualization and Predictive Modelling. We are proficient in using TIBCO Jaspersoft, Microsoft BI, SAS BI, IBM Cognos and OpenText Analytics.
With our effective and prudent methods, we improved client’s business and revenue share by robust Data Governance and Quality Process and changed their business perspective.
Data Ingestion Components
- This is the first essential part of the Ingestion framework. Rest API framework is utilized for bulk data transfer from various source locations to a common zone.
- Secure HTTPS protocol is utilized for the transfer in order to avoid port openings and permissions configuration for scp or sftp port.
- Authentication process is taken care of in the API
- For stream Datasets Kafka is enabled for ingestion.
- Before the file is ingested into the BIG data platform the trail is maintained in a DB to ensure File audit and Data Quality.
- This the heart of the Ingestion Framework. DD&P provides a GUI to define the metadata, define partitions and the frequency of loads.
- Once the design is committed the DD&P creates the required definition on HIVE stage and Gold or DF’s in Spark thus avoiding any manual work for interacting with the platform.
- It also configures the job to be run based on the frequency
- It provides exit points for the user to define loading & transformation scripts if any.
Data Quality Framework
- Define data ownership for all the Data Models deployed in any system from traditional to Hadoop Platforms
- Create Metadata framework for the models
- Define access control and centralised deployment process for change in views or addition of new data definitions
- Create single window access for change request approvals for data definition
- Data Validations: Ensuring values of data sets are consistent per definition.
- Source and Target audits: Ensuring source and target match across defined metrics.
- Process Audits: Identifying records dropped due to errors or other reasons during data transformation phase.
- Alerting Mechanism: Framework to alert any breach or mismatch in the metric.
Data Quality Framework
-
Define data ownership for all the Data Models deployed in any system from traditional to Hadoop Platforms
-
Create Metadata framework for the models
-
Define access control and centralised deployment process for change in views or addition of new data definitions
-
Create single window access for change request approvals for data definition
-
Data Validations: Ensuring values of data sets are consistent per definition.
-
Source and Target audits: Ensuring source and target match across defined metrics.
-
Process Audits: Identifying records dropped due to errors or other reasons during data transformation phase.
-
Alerting Mechanism: Framework to alert any breach or mismatch in the metric.
Data Governance Portal
Data governance helps businesses to gain competitive advantage. If an organization does not have Data Governance Platform, it will be exposed to many risks.
Through our Data Governance Portal, we help our clients to find ways to improve their business. The most important feature of our portal is its morphology. It is studied in three blocks. In the first stage, all the logins are Role based Logins only. The Metadata Repository, SPI Masking rules, Data Models and Approvals and Audit are in a single block. Subsequently, this entire data is used in the target systems for better outcomes.
As a team, we have the capacity to implement Data Governance across multiple sectors.
DQ High Level Architecture for Ingestion Framework
Integrated Data Quality Framework
Data Governance & Quality Practice
Business Capabilities
-
Data Analysis & Collection
-
Data Lineage Tracking
-
Reporting & Dashboard Management
-
Data Visualization
-
Predictive Modeling
Technology Capabilities
-
TIBCO Jaspersoft
-
Microsoft BI
-
SAS BI
-
IBM Cognos
-
OpenText Analytics
Spar’s Data Governance & Quality Practice easily provide solution to the clients. Our specialists have designed this platform with their committed efforts. One of our clients approached us for a solution to his problem. Spar’s Data Governance & Quality Practice is a 4-phase process. They are: Discover, Define, Apply, Measure & Monitor. Our Business Capabilities include Data Analysis and Collection, Data Lineage Tracking, Reporting & Dashboard Management, Data Visualization and Predictive Modelling. We are proficient in using TIBCO Jaspersoft, Microsoft BI, SAS BI, IBM Cognos and OpenText Analytics.
With our effective and prudent methods, we improved client’s business and revenue share by robust Data Governance and Quality Process and changed their business perspective.
Data Ingestion Framework
Data Ingestion Components
-
This is the first essential part of the Ingestion framework. Rest API framework is utilized for bulk data transfer from various source locations to a common zone.
-
Secure HTTPS protocol is utilized for the transfer in order to avoid port openings and permissions configuration for scp or sftp port
-
Authentication process is taken care of in the API
-
For stream Datasets Kafka is enabled for ingestion.
-
Before the file is ingested into the BIG data platform the trail is maintained in a DB to ensure File audit and Data Quality.
-
This is the first essential part of the Ingestion framework. Rest API framework is utilized for bulk data transfer from various source locations to a common zone.
-
Secure HTTPS protocol is utilized for the transfer in order to avoid port openings and permissions configuration for scp or sftp port
-
Authentication process is taken care of in the API
-
This is the first essential part of the Ingestion framework. Rest API framework is utilized for bulk data transfer from various source locations to a common zone.
-
Secure HTTPS protocol is utilized for the transfer in order to avoid port openings and permissions configuration for scp or sftp port
-
Authentication process is taken care of in the API
-
This the heart of the Ingestion Framework. DD&P provides a GUI to define the metadata, define partitions and the frequency of loads.
-
Once the design is committed the DD&P creates the required definition on HIVE stage and Gold or DF’s in Spark thus avoiding any manual work for interacting with the platform.
-
It also configures the job to be run based on the frequency
-
It provides exit points for the user to define loading & transformation scripts if any
Data Migration & User-Analytics for a Telecom Giant
Saving Cost & Improving efficiency
Call Detail Records (CDRs) form the prime revenue base for any telco. CDRs or Events, as they are called today, not only provide the revenue analysis but also deep insights into various aspects viz customer calling patterns, Network issues, Call drops, Download rates, QoS etc. These events are voluminous in nature and though they are important to be stored and harnessed substantial investments are needed to get the required insights. Storing such events on certain platforms is expensive. Hence there was a need to move such elements to a cost effective and efficient platform.
Big data is the driving force of most modern businesses, and big data never takes rest. Data integration and data migration are very important steps in the Big Data process. Spar’s method of Data Migration involves three phases: Before Migration, During Migration and After Migration. A competent data migration plan helps businesses to have control over budget and data processes. It also plays a very crucial role in finding whether the data operations are up to the expectations.
Our Data Migration starts with Before Migration measures. We review Data classification & Retention Standards in the first stage. Then we prepare the Integration roadmap for legal, GRC, IT departments. This is followed by the assessment of the process, and then we Inform migration impact to all stakeholders. After this, a cutover date will be fixed, and legacy will no longer receive data.
In the second stage, which is called as During Migration stage, we Track migration progress with vendor / IT department and avoid environment changes by taking regular data backups. During this stage, we avoid tickets clutter on helpdesks and keep the vendor posted of regular maintenance windows, data outage scenarios etc.
In the third stage, which is called as After Migration stage, we review all custody reports, look out for failed items and document the data that has to be migrated for work to be considered as complete. Subsequently, we delete original data or backups of any original data after all the stakeholders have submitted a successful audit report. This process of Data Migration involves the Business Capabilities like Server or storage consolidation, Relocation of the data center, Server or storage equipment maintenance, including workload balancing or other performance-related maintenance. To ensure a successful Data Migration Process, we perform Data Migration in Salesforce, Data Migration in SAP, Data Migration in IBM, Informatica and PL SQL scripts.
Inflow of Quality Data Results in Quality Business Outcomes
Spar’s big data practice is all about turning the inherent potential into profits. As champions of the art of data, we have made our presence felt in every vertical.
Let’s Start Something new
Say Hello!
Our data experts will dive right in.