Data Driven Business

  • Team Collaboration and Data

    Conclusion: The analysis of various and complex data sets could provide a catalyst for team collaboration. One of the challenges organisations will face in combining teams is setting out the conditions in which they will work together. Looking past obvious differences in background, or so-called professional culture, will be necessary to organise roles with the talents available.

    Initially devise pilots to assess teams and roles and the value of the output. The development of data projects should produce quick benefits in terms of output and team cohesion. Understanding of the analytical insights should be shared widely in order for the benefits to reach as many within an organisation and bring change where it is needed.

  • Reframing Business Intelligence as Critical Business Imperatives

    Conclusion: The days of viewing BI as a single solution are over. Organisations should view Business Intelligence as four distinct, but interlocking services that each addresses a different critical business imperative: reporting; self-direct data exploration; operational decision support; and data science. Each of these imperatives addresses different stakeholders and will have its own architect.

  • Lessons from security analytics projects

    Conclusion: Big data and analytics projects can learn important lessons from the domain of information security analytics platforms. Two critical factors to consider when planning deployment of an analytics platform are: the need for a clear business objective and; the depth and duration of organisational commitment required. Without a clear understanding of the objective of the analytics project, or adequate resource commitment, the project will likely fail to deliver on expectations. The worst outcome is that inadequate investment in people could result in an organisation drawing incorrect conclusions from the analytics platform.

  • To improve the Business Intelligence of your Organisation you need to learn the Language of Data

    Conclusion: Machines are becoming increasingly proficient at tasks that, in the past, required human intelligence. Virtually all human domain expertise can be encoded in digital data with the right knowledge engineering tools. The bottleneck in understanding between humans and software is shaped by the ambiguities inherent in human communication, not by the challenge of developing machine intelligence. To benefit from big data, organisations need to articulate knowledge in the language of data, i.e. in a format that is not only understandable by humans but also actionable by machines.

  • Security incident and event management - a primer

    Conclusion: Security incident and event management (SIEM) products can deliver solid insights into the security status of an organisation’s network. However, SIEM requires ongoing support, mature change control processes, and rapid and open communications between diverse teams within the IT department - as well as the rest of the organisation! A successful SIEM deployment must factor-in the resources required for ongoing support. These resources will be in proportion to the complexity of the network.

  • Business Intelligence is automation of operational management

    Conclusion:Pattern-based and repeatable processes, such as gathering operational data, validating data, and assessing data quality, offer potential for automation. The Web and software-as-a-service technologies offer powerful tools that facilitate automation beyond the simple mechanical pumping of data from one system to the next. Operational management tasks that focus on administration and control can and should be automated, so that managers have time to think about the organisation as a system, and can focus on continuous improvement.

  • Key steps in establishing a Business Intelligence Competency Centre

    Conclusion:A competency centre for Business Intelligence (BI) must have an active mandate and involvement from the senior executive to sustain optimised delivery of the organisational BI strategy. This leadership is a key factor in the ability to successfully deliver the initial benefits of the competency centre within a three month development period, establishing long term benefits.

  • Ensuring that IT can lead Business Intelligence across the organisation

    Conclusion:The implementation of Business Intelligence is critical to the optimised operation of even the most basic business functions. When executed well it provides quantifiable competitive advantage for private sector organisations, and improved service delivery outcomes for the public sector.

    IT has a significant opportunity to enhance its business relevance by ensuring that Business Intelligence best practice is active and transparent across the organisation. Organisations without a comprehensive investment and capability in Business Intelligence will struggle to complete and will operate below their potential.

  • What IT security lessons should you draw from the Verizon DBIR?

    Conclusion:The latest Verizon Data Breach Investigation report (2011) continues many of the themes drawn out since its first publication in 2008. However, the DBIR is not a best practice guide on how to secure organisational data; it is an aggregation of cases where organisations failed to secure theirs. Consequently, the DBIR should be viewed as a document which identifies worst practice, and provides instructions on how not to be a follower of worst practice. Some of the breaches that have made headlines this year show that even well-resourced organisations can overlook the basics of IT security.

  • Business Intelligence - from Fata Morgana to Fact: Part 2

    Conclusion: In our experience many Business Intelligence (BI) initiatives end up well short of their original objectives. But all is not lost. Sometimes it helps to learn from the experiences of others. For those:

    • Intendingto embark on a new BI initiative
    • Working to remediate a BI strategy that had lost momentum.

    This research paper examines some case studies and examples which reference breakthrough approaches and reflect the sometimes arduous travails involved in dealing with the many challenges presented by BI projects.

  • Business Intelligence – from Fata Morgana to Fact: Part 1

    Conclusion: To gain insight into C-level executive intentions with information management, Accenture carried out a global survey2 in 2007. Whilst the majority of respondents had well-developed views on the power of Business Intelligence (BI) as a strategic differentiator, the report unearthed an underlying frustration in achieving their vision of an organisation-wide BI capability. This echoes our experiences in the ANZ market in which we observe many CIOs struggling to bring their complete BI visions to reality.

  • Navigating the Bermuda Triangle of Business Intelligence

    Conclusion: In 2008, corporate databases reached unprecedented sizes. Yet despite the abundance and diversity of data, many organisations remain challenged by Business Intelligence (BI) initiatives. They buy on vendor promise, but many have difficulty fulfilling it. Against this backdrop, and in a confusing post-acquisition market, BI vendors continue to release increasingly sophisticated and capable products.

  • Putting Business Intelligence (BI) To Work

    Conclusion:BI technology platforms have been available in various guises for over two decades. Indeed, certain BI terms, such as ‘drill down’ have become embedded into business parlance.

    The technology itself is mature and capable and many organisations have harnessed it to their advantage. However, some of our recent dealings with both IT and business executives reveal an underlying dissatisfaction with their BI implementations. Complaints include costly implementations, poor acceptance of the technology, particularly by middle management, and concerns with data quality and integrity.