By Francis Han
Business analytics is a hot topic, and several news reports indicate how leading organizations are able to compete better through analytics.
Extracting meaningful, actionable insight from corporate business systems is no longer just an option — it is a requirement in order to compete effectively. Organizations that adopt a data-driven decision-making culture are those best-enabled to compete.
Today, companies realize that the information locked in enterprise and legacy systems is incredibly valuable. Being able to analyze this information, combined with other corporate data sources is essential to drive the business forward based on facts, rather than intuition. Although most organizations have been investing on reporting, query tools and data warehouses for a long time, fewer than 30 percent use BI systems they have.?
The question is – why so low?
Firstly, it?s hard to build analytic applications, to integrate necessary technologies, to extract data from ERP and other enterprise system, to conform and model the data, to define best practice business metrics and Key Performance Indicators (KPIs), and to make all of it compelling and easily usable by a a range of business users.
Secondly, users are bringing whole new sets of expectations analytics around relevance, ease of use and responsiveness.
With business users having higher expectations than before, as they experience the ?Google effect? of searches being returned instantaneously, and consumer devices like the iPhone, which are easy to use and optimized for particular functions. Based on the experience of thousands of analytics implementations, new demands and user expectations emerge which include:
Integrate diverse corporate sources of information into an enterprise view. The reality for almost all organizations is relevant data is stored in fragmented sources. Analytics on a particular silo of information is useful, but creating a consolidated enterprise-wide view is much more beneficial. With the consistency of an enterprise data model, business can perform analysis across subject areas, enabling, for instance, a company to detect customer satisfaction problems are tied to delayed shipments, which are in turn caused by slow accounts payable to critical vendors.
Trusted metrics, following industry best practices. Monitoring metrics and Key Performance Indicators (KPIs) is the lifeblood of performance management. A palette of pre-defined industry best practices metrics, built off curated, trustworthy data sources, and presented in understandable, easy to navigate dashboards is a base requirement. And, businesses need to be able to configure these as necessary for their specific organizations.
Clean, clear dashboards and reports, designed for instant understandability. A single metric or KPI is no use without context. How does the number compare with this period last year? How has market share shifted over time? What is the trend in on-time shipments? These typical business questions can require complex calculations, well-designed dashboard layout, context, and guided analysis paths. This level of design is typically beyond the skills of most users and, even, most IT professionals.
Rich data visualization including location-based, geospatial views. In order to gain the best understanding of the data being analyzed, and for people to engage their right brains, appropriate visualizations are needed.
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 may want also to specialize an existing report or dashboard, or to create their own analysis to reuse and share.
Model outcomes and run what-if scenarios. Businesses want to model what actions to take through scenario modeling, 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. As sophistication rises, they want to employ advanced predictive analytics to see what?s likely to happen. Advanced analytics like data mining and statistical techniques can project what is likely to happen in the future.
Mobile, anytime, anywhere. Today?s mobile workforce demands access to the information they need, wherever they are, securely with no additional development or compromise in functionality 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, external social media feeds like Twitter and Facebook, or to access unstructured information stored in Hadoop.
Integrated technology and analysis tools. Organizations? analytic needs mature and broaden over time. Therefore, a range of analytic capabilities are needed, but in many instances, the customer?s IT department is left to integrate all the necessary technologies. Pre-integrated BI technology with a broad range of functionality for today?s and tomorrow?s requirements is preferable.
This is a challenging list that requires a variety of skills and technologies. Thus, it is hard and expensive for organizations to tackle these items on their own, or just by hiring IT consultants.
Investment in analytics is one of the leading priorities for a growing number of organizations. To gain fact-based insights from data locked in CRM, ERP, HCM and SCM systems, a data warehouse and effective analytics are required. However, custom-built approach is risky, slow, and costly.
Pre-built analytic applications, delivered on a complete and integrated BI technology foundation, and running on optimized engineered systems are the way to go. This combination assures the easiest and risk-free implementation that is proven to deliver strong business value, speed-of-thought performance and massive scale, while remaining open to work with each customer?s individual IT environment.
With the proliferation of new digital information sources, organizations require expanding the boundaries of traditional BI systems to unlock insights from a wealth of customer, employee, and product knowledge that remain untapped. Self-service BI is a common information model that today many organizations seek to adopt in order to provide their users with a logical view of metrics, hierarchies, and calculations which can be easily understood. It also offers business users with flexible access to information filtered and personalized for their identity, function, or role based on predefined security rules.
Concertedly, organizations needs the right analytics tools to capture a wide variety of data types and analyze them within the context of business under a single, standardized and centralized model. With Oracle business intelligence applications on Oracle engineered systems, organizations can gather and analyze the structured and unstructured data to provide their users with complete visibility into the business processes, creating new insights and enabling better business decisions.
The author is the general manager for business analytics at Oracle Asean
?James Richardson, Gartner, ?The Consumerization of BI Drives Greater Adoption,? ID Number: G00212776, June 3, 2011.
