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Guest opinion: It’s time to transform your analytics strategy

Francis Han | June 2, 2014
Organisations today have realised that they need to adopt a data-driven decision-making culture to extract insights from their business systems, said Oracle ASEAN's Francis Han.

Francis Han - Oracle Asean 

Photo - Francis Han is General Manager - Business Analytics for ASEAN Oracle.


Business analytics is one of the hottest topics and today there are already comprehensive examples of organizations who lead the competition by leveraging analytics. Organisations today have realised that they need to adopt a data-driven decision-making culture to extract insights from their business systems In order to get fact-based insights to compete effectively.

Yet, fewer than 30 percent of organisations today use the Business Intelligence (BI) systems although most of them have been investing on reporting, query tools and data warehouses for a long time. This poses a question - Why is the use of BI systems so limited still?

Firstly, it's hard to build BI systems as it requires specialized skills. Building analytics applications requires organizations to integrate their solutions, systems and technologies, extract data from their Enterprise Resource Planning (ERP) and other enterprise systems, to conform a data modeling structure that incorporates best practice business metrics and Key Performance Indicators (KPIs). And, all of this has to output analytics information that is compelling enough yet user-friendly to a wide range of business users.

Secondly, business users have a whole new set of expectations to BI systems around relevance, ease of use, and responsiveness. They expect a "Google effect" experience of their analytics information searches that is instantaneously and optimized for particular functions. With much greater higher expectations on BI systems than ever before, business users demands analytical insights to be instantly accessible, visually compelling and delivered on mobile devices with responsive performance. This "Consumerisation of BI" is setting the new standards for BI systems.

Thirdly, traditional Build-Your-Own BI Systems have certain constraints. With each business division processes their data independently, it is becoming more difficult for the users to voice their requirements as they do not know what can possible be done (by the BI system) or what their peer businesses are doing.

New demands for analytic applications

Based on the experience of thousands of analytics implementations, the following are requirements on analytics applications have emerged as generic expectations of business users:-

  • Integrate diverse corporate sources of information into an enterprise view. Reality is that data is stored in fragmented sources in the organization. However, with a consistent enterprise data model, organisations can create a consolidated enterprise-wide view on the business and perform analysis across subject areas. For instance, detecting customer satisfaction problems and tying the problems back to delayed shipments, which in turn are caused by slow accounts payable to critical vendors.
  • Build metrics with industry best practices. Monitoring metrics and KPIs is the lifeblood of performance management. A palette of pre-defined industry best practices metrics is needed for organisations to configure the BI systems as necessary for their specific organizations.
  • Clear dashboards and reports designed for instant understandability. To understand 'How does the number compare with this period last year?', 'How has market share shifted over time?', organisations require complex calculations, well-designed dashboard layout, context, and guided analysis paths.
  • Interactive self-service exploration. Once a problem is detected, business users want to "drill down" and perform additional analysis to uncover root cause problems. They demand for capabilities that allow them to specialise existing reports or to create their own analysis to reuse and share.
  • Model outcomes and run what-if scenarios. Organizations want to model what actions to take through the scenario modeling in order to answer questions like "What if I reorganized sales territories and comp plans? Which choices would lead to the best outcomes?".
  • See what's happening now and what is likely to happen in the future. As companies mature in their analytic capabilities, they desire to move beyond analyzing history and react in near-real time. They want to employ advanced predictive analytics to see what's likely to happen.
  • Mobile, anytime, anywhere. Today's mobile workforce demand access to the information they need and wherever they are in a secure manner, with no additional development or compromise in function or form factor.
  • Combine structured with unstructured data and Big Data. While analytics predominantly are used for structured, tabular data, there can be great business advantage to involve unstructured data that might include verbatim text in ERP or CRM systems and external social media feeds like Twitter and Facebook.


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