Catapult BI

Data Governance

“Those that establish a formal Data Governance program, exercise authority and control with great intentionality, are better able to increase the value they get from their data assets.”- DAMA DMBOK-2. Data governance strategy is a practice of exercising authority, control and shared decision making, including planning, monitoring and enforcement, over the management of data assets.

At Catapult BI, we offer tailored Data Governance and Data Management services to our clients, guided by the DAMA International – Data Management Body of Knowledge (DAMA – DMBOK).

Get in touch with our experts to learn how you can benefit from implementing it.

Enterprise Data Governance and Strategy​

It is important to frame a strategy in accordance to Data Governance
framework, to enable the entire enterprise function as one single unit, for
more cohesive and optimum benefit. Catapult BI’s core practises to implement this system are:

  • Manage data as corporate asset.
  • Best practises to be incensed across organisation
  • Enterprise data strategy aligned with overall
    business strategy
  • Data management processes should be continuously

Master Data Management

The process of managing shared data to meet organisational goals, reduce risks associated with data redundancy, ensure higher quality and reduce the costs of data integration, is referred to as MDM. Catapult BI’s vision to achieve the outcomes is to:

  • Identify Drivers
  • Identify requirements
  • Evaluate and assess data sources
  • Model Data
  • Establish Governance policies, and
  • Implement Data sharing/integration services. 

Data Classification, Lineage and Traceability

All a part of Information Lifecycle Management, it is essential to follow these 3 steps to ensure that right data is put to right use in the right form. At Catapult BI, our experts define each of the terms as:

  • Classification- Categorising data to enable organisations to effectively associate data to respective   concepts. It can be done using 3 major techniques- Paper based, automated classification and user-driven.
  • Lineage- documenting the lifecycle of the data including its origin, what happens to it, where it resides, etcetera, all is established under data lineage.
  • Traceability- When data is available to be evaluated throughout its lineage, it is called data traceability.  

Data Quality Analysis and Monitoring

The planning, implementation and control of activities that apply quality management techniques to data in order to assure it is fit for consumption and meets the needs of data consumers is referred to as Data Quality analysis. As important as it sounds, Catapult BI believes that monitoring this changing quality of data is equally beneficial to improve the business systems. Our experts analyse DQ on the basis of:

  • Accuracy
  • Completeness
  • Consistency
  • Integrity
  • Reasonability
  • Timeliness
  • Uniqueness/ Deduplication
  • Validity

Data Ownership and Stewardship Programs

Data stewardship involves the exercise of authority, control and shared decision making over the management of data assets. Catapult BI’s Data Stewardship practises include:

  • Enable an organisation to manage its data as an asset.
  • Define, approve, communicate and implement principles, policies, procedures, metrics, tools and responsibilities for data management.
  • Monitor and guide policy compliance, data usage and management activities. 

Enterprise Metadata Management

It involves planning, implementation and control activities to enable access to high quality, integrated metadata. Catapult BI believes that it is important to-

  • Have an understanding of the terms used in an organization and covey it
  • Have a standardized way for all to access metadata to understand data better
  • All the metadata in a heterogeneous environment is merged in one as a whole for cohesive access and use
  • Ensure that DQ and security is not compromised.

Contact us to understand how we do it!