Data Governance

With so much data to collect, store, organise and use, most businesses today need a clearly defined program to manage it. It has been demonstrated that companies with strong IT governance can earn at least a 20% higher return on assets than organisations with weaker governance*.

Good for Business

Correct management of data can improve user acceptance, productivity, decision making, compliance and security  – at the same time as reducing total cost of ownership.

More specifically, Data Governance can:

• Increase operational efficiency
• Minimise risk and regulatory non-compliance
• Manage definitions of data for improved data integration
• Reduce re-keying and overhead in managing data
• Set IT standards  to ensure preventative data quality control is inbuilt
• Assigned responsibility of data sets for more efficient issue resolution
• Allow transparency of Data lineage
• Provide secure and auditable Data
• Create Security policies to control access to data
• Audit use of systems to track key changes to data
• Increase data quality to provide right data at right time
• Reduce the number of people/systems handling data
• Improve accuracy of data capture
• Minimise data integration quality issues
• Augment systems where appropriate
• Improve Fiscal Control over IT systems
• Plan Application Architecture to target gaps in data or process
• Set standards to ease system re-usability and development
• Ensure Sustainable growth in systems

The Altis approach follows carefully considered steps that account for overall business priorities and help to identify quick wins that demonstrate the importance of the program. We look at your organisation as whole, making recommendations that cover Strategy, Structure, Policies and Standards, People, Process and Systems.

These steps include:

• Identify Key Business Priorities
• Identify Key Data Sets & Data Quality Issues
• Assess Current State of Data Quality Management
• Recommended Future State of Data Quality Management
• Analyse Gaps and Determine Activities / Remedies
• Assess Current Level of Perception on Data Quality
• Assess Data Quality Risk for Key Data Sets
• Prioritisation of a Phased implementation
• Roadmap and Implementation Plan

Implementing a Data Quality Strategy will provide the guiding framework for all future data quality initiatives, as well as systems related activities such as Data Profiling and Cleansing.

*MIT Sloan School’s Centre for Information