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Tuesday, 17 July 2012

Information as a Service, Part 2: what do I do about it?

In an earlier post, I defined the concept of “Information as a Service”, and covered off why successful Information Management requires a change of mindset that emphasises the wider context of why information is required, and for whom. I’d like to now like to turn attention to the impact that Information as a Service can have and some hints-and-tips on how to achieve better outcomes.

IT departments have traditionally struggled to engage with an information-aligned business agenda. The rigours and constraints of IT delivery are focussed on managing the technology infrastructure and applications that store and distribute data (containers and connectors), rather than having linkage to the relevance of the contents used in context. This focus typically requires an approach that is oriented towards policing of controls, process compliance and gatekeeping of expenditure.


In contrast to a technology-centric mindset, an information-oriented approach requires a different and complementary set of skills. Most importantly, it requires a fundamental change of mindset for the information team (whether for data warehousing, business intelligence, analytics or Documents & Records Management). To be able to encapsulate the information content within its business context, the team needs to articulate the relevance and impact that the organisation’s information has on business performance and process effectiveness. This in turn creates a clear line-of-sight from business value, through analytic information services to the underlying data warehousing of information assets focuses the people, processes, and technology towards the optimal management of information.

It becomes clear that without a critical mass of these competencies, organisations will struggle to balance investment in information with the information requirements of the organisation. Having a data warehouse and analytic environment that is fully aligned to the business view of the organisation ensures that management of the Information competency is fully congruent with the required business capability.

Data Warehousing and Analytics programs can deliver value to organisations at any strategic pain-point.  However, each initiative must be matched to the performance management strategies appropriate to your organisation’s business model. This process will drive the right investment in the data warehouse and associated analytic outputs.

A “top 10” of actions that help to develop better engagement and alignment with the “Information As a Service” mindset might include:

  1. Managing business performance through visible measurement: this requires explicit investments in executive decision-making via executive dashboards which report directly from operational and management information systems. A strong link is created between reporting processes and actual business performance, setting the foundation for analytics-driven performance optimisation. 
  2. Using the information that is currently available to drive new information capture: focusing data warehousing initiatives on making decisions with the best existing information focuses attention on understanding what additional information is required information but is currently not available. This supports proactive management of decision risks through highlighting the gaps to drive prioritisation of new data sources for data warehousing initiatives.
  3. Driving commonality of analytic solutions and services: optimising the information delivery process by consolidating and standardising the inventory of common management and operational reports. A coherent approach that co-ordinates across all system enhancement, performance improvement, and transformation initiatives means building an integrated suite of reports that delivers repeatable elements common to overall business performance. Resources can then be re-focussed towards specific value-adding tasks, rather than maintaining multiple, duplicated copies of basic reporting services. 
  4. Completeness of data integration: managing a continuous, prioritised pipeline of data integration for all organisational data to ensure the data warehouse suite supports both current and future demands. Data integration requirements should be driven from strategic drivers, proactively acquiring data for future analytic initiatives to enhance responsiveness.
  5. Explicit management of ad hoc reporting and analysis: Implementing a Centre of Excellence / Competency centre that is formally accountable for managing a tiered community of users from information consumers, information integrators, statistical analysts, to decision makers
  6. Metadata Driven Governance and Delivery: driving scope management, governance and delivery through a co-ordinated approach to metadata management. This ensures the requirements quickly find their way into working solutions and are validated by the business early in the project lifecycle. Using a Business Glossary and metadata-centric Agile delivery methods shifts the focus to the organisation information needs instead of the technology used to deliver the information.
  7. “Steel thread” to manage data warehouse delivery risk: When developing business analytic solutions, the focus of planning and management effort should be on business requirements and objectives.  However, there is also a hidden risk in all projects - the unbounded effort in the development work stream caused by detailed technical unknowns. The concept of a “steel thread” maps the end-to-end linkages and dependencies from deploying the underlying warehouse technical platform, through data and analytics application development and on to the final business outcome, based on a tightly bounded sub-set of the overall business requirements. Potential issues in the technical environment, in the use of new technologies, or in the understanding of business logic are identified, tested and mitigated early in the delivery cycle, so that the overall solution implementation is de-risked.
  8. Proactive Data Quality Management: assessing overall Data Quality for data sources and implementing appropriate controls and remedial actions to ensure Data Quality is managed in a proactive manner, both during programme execution and beyond into business-as-usual operations. Measurement and profiling, root-cause diagnosis, remedial action and continuous improvement are all necessary elements of a proactive approach to Data Quality. (The linkages between data and its usage in context are explicitly identified, as data quality is only ever quantified as a function of its usage).
  9. Formal Data Governance: The people and functions who produce and use information are the people who know its value, understand what they need to save and should know how long a given set of data is going to be useful.  And while business people may know these things, it is often difficult – or even impossible – to get them to articulate their own information needs. Additionally, many departments only give consideration to their own information needs and opportunities for re-use, combination and added value are often missed. A robust and sustainable Data Governance regime provides a common foundation of “Rules of Engagement” and identifies explicit decision rights and accountability for the analytic business mandate. 
  10. Delivering analytic solutions that are fun to use: people are more likely to make use of tools and services that are not only easy to use but are also attractive and engaging. Application of user experience (UX) disciplines and data visualisation techniques ensures that information is presented pleasingly and effectively for the intended decision making process.

Aligning data warehouse and analytic services with business outcomes requires a conscious focus and effort, either as an initiative in its own right or as part a holistic approach to implementing Information Governance within an information-enabled business transformation.  Once the overall scope and context of the implementation has been established (e.g. as part of defining the organisation’s Information Management Strategy and Roadmap), the aim is to stand up the new analytic competency as quickly as possible, so that your organisation can begin to realise the benefits of the transformed data warehouse capability.

2 comments:

  1. Hello, the post was really good I, come to knew more useful information about the Data warehousing Thanks a lot

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  2. Thanks Benslin. My experience is that when data warehouse projects fail, it's because they don't give enough consideration to the organisational end-state. At the end of the day, data warehouses are about people!

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