Organisations with a data warehouse or business intelligence investment are realising that there is more than just technology involved in getting maximum value out of their analytics platform. And organisations that are considering investing in analytics are taking notice of the lessons learned by others.

To get the greatest business benefit from analytics, a strategic approach is required which encompasses:

  • Your information and analytics requirements and how to most effectively meet them as they evolve over time

  • The processes and methodologies to underpin a sustainable analytics ecosystem

  • The team structure and skillsets to support these processes and the accompanying technology ecosystem

The challenges facing an established data warehouse [ / analytics initiative] can range from a depleted vision and adoption by the business; to more technical challenges such as the need to scale, data science and predictive analytics, real-time streaming data processing, and semi- and unstructured data sources; to ever shifting business requirements and a diminished sense of return on the investment.

We can help, starting with independent input to help reinvigorate your organisation’s vision of information/ analytics as a strategic asset — providing a clear sense of direction, helping you redefine your data warehouse as a trusted source of information to empower decision makers.

BIIT Analytics has many years of experience in providing clients with a strategic assessments of the potential — and challenges — of their data warehouse investment and strategy. We review and provide recommendations across any or all of the following areas:

  1. Current state, desired state, and transition state architectures across Information Management; Technology and Infrastructure; Processes, Methodology and Governance; and Organisational Structures and Skillsets

  2. Support processes and structures, and how well these are meeting the needs of information workers

  3. Recommendations and steps to establish and effectively operate a Business Intelligence Competency Centre

  4. Business information requirements analysis, and how effectively these requirements are being met

  5. Technology reviews, recommendations and tool selection as required

  6. The use of report inventories, data dictionaries and metadata management

  7. Management of hierarchical and reference data (Master Data Management)

  8. Logical and physical data model design, effectiveness and best practice across all the layers of the data warehouse

  9. The ETL framework and standards

  10. Consistency of application of standards and conventions

  11. The BI application portal, user interface design and effectiveness

  12. Management of development, test and production environments, migration and version control

Each of these areas is evaluated against known industry best practice – and BIIT Analytics’ real world experience – to provide balanced, realistic and business-driven recommendations that can be implemented over a realistic timeframe.