Data Management

Data Management

• • Achieve high data quality by establishing robust data quality management and integration practices

  • Define a practical value-driven and business-led data governance framework to support data quality, security, modeling, and integration

  • Improve transparency and trust in data and increase data management efficiency with data lineage and metadata management

  • Make data and analytics easily accessible to a wide range of business users and drive user engagement with those data systems

  • Ensure successful delivery of high-priority business-use cases while simultaneously evolving the data architecture and operating model

Services

To achieve these goals provides the following consulting services:

• • Data strategy consulting

  • Data landscape profiling

  • Analysis and prioritization of data and analytics pain points and business scenarios

  • Solution roadmap definition-architecture and operating model evolution, POC funnel, business case delivery

  • Capability assessment, target state envisioning, solution design, technology and tools selection and implementation in:

    • Data governance and quality management

    • Metadata management and data lineage

    • Data modeling and integration

    • Data architecture and storage

    • Data security

    • Data warehousing and reporting

    • Business Intelligence and Advanced Analytics

    • Master and reference data management

    • Graph and semantic data

    • Data distribution and data products

What is Data Governance?

Data governance is a collection of processes, roles, policies, standards, and metrics that ensure the effective and efficient use of information in enabling an organization to achieve its goals

These tools should help you:

• Capture and understand your data through discovery, profiling, and benchmarking tools and capabilities. For example, the right tools can automatically detect a piece of personal data, like a social security number, in a new data set and trigger an alert.

• Improve the quality of your data with validation, data cleansing, and data enrichment.

• Manage your data with metadata-driven ETL and ELT, and data integration applications, so data pipelines can be tracked and traced with end-to end data lineage.

• Control your data with tools that actively review and monitor.

• Document your data so that it can be augmented by metadata to increase its relevance, searchability, accessibility, linkability, and compliance.

• Empower the people that know the data best, to contribute to the data stewardship tasks with self-service tools.

Let's discuss data warehouse concepts related to Master Data Management (MDM), Data Quality Management (DQM), and Data Governance, along with an example.

Master Data Management (MDM):

MDM is a process that focuses on creating and managing a single, authoritative source of master data within an organization. Master data represents the key entities of an organization, such as customers, products, employees, and suppliers. MDM ensures consistency, accuracy, and integrity of master data across different systems and departments.

Example:

Consider a multinational retail company with multiple branches and online stores. To implement MDM, the company establishes a centralized master data repository that holds customer information. This repository integrates customer data from various sources, such as sales systems, CRM systems, and online channels. MDM ensures that customer data is standardized, de-duplicated, and up-to-date across all systems. This helps the company gain a holistic view of its customers, improve marketing campaigns, and deliver personalized experiences.

Data Quality Management (DQM):

DQM focuses on ensuring the accuracy, completeness, and consistency of data within an organization. It involves defining data quality metrics, establishing data quality rules, and implementing processes to monitor and improve data quality over time.

Example:

A healthcare organization wants to improve the quality of its patient data. They implement DQM processes to identify and resolve data quality issues. For instance, they define data quality rules to check for missing values, validate formats, and identify duplicate records. By running regular data quality checks, they can identify and address data inconsistencies, ensuring that accurate and complete patient information is available to healthcare providers and administrators.

Data Governance:

Data Governance encompasses the overall management, policies, and processes surrounding data within an organization. It involves defining data standards, roles, responsibilities, and guidelines to ensure that data is used, managed, and protected appropriately.

Example:

A financial institution implements a data governance framework to ensure regulatory compliance, data privacy, and data security. They establish data governance policies that define who can access sensitive financial data, how it should be handled, and how long it should be retained. They also assign data stewards responsible for overseeing data quality, defining data standards, and resolving data-related issues. Data governance helps the institution maintain data integrity, safeguard customer information, and comply with industry regulations.

In summary, MDM focuses on managing master data, DQM ensures data quality, and Data Governance sets policies and processes for data management. These concepts work together to ensure accurate, consistent, and reliable data within an organization, leading to improved decision-making and operational efficiency.