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Seeking out hidden savings

why it pays to rationalise your BI infrastructure

Your IT budget is razor thin, your staff is ‘maxed out’, and management has ordered another round of cuts. Meanwhile, three departments are clamouring for specialised applications to compete effectively against the market leader that’s eating your lunch. Somehow you’re expected to shave more from the budget while funding new, powerful decision-making applications.

Enterprises are demanding that IT play an increasingly critical role in reducing enterprise costs by changing business processes, workforce practices and information use. This includes data integration and the delivery of business intelligence (BI) tools that have a direct and positive impact on the business performance of the enterprise.

According to Gartner’s 2009 CIO survey, the current economic downturn has placed IT budgets under unprecedented pressure, with 21 percent of CIO’s reporting a cut in their IT budget. Furthermore, IT is being asked to do more with less – 15 percent less is a typical target, according to Gartner. Challenged to cut costs and improve performance, IT leaders need to think strategically and creatively, especially when it comes to BI.

The truth is there’s money and opportunity hiding in pockets across the organisation. Many departments have specialised data analysis applications that only serve specific employees. Replacing or augmenting these with a single platform that delivers flexible, in-context, decision-making applications to multiple departments across various business processes is the route to achieving true ROI, driving a much broader user adoption across the enterprise.

The BI ‘lock-out’ conundrum

In the 1990’s the collection of more data helped propel the widespread adoption of traditional BI software across enterprises. BI vendors promised to empower users to query a repository of integrated data – such as a data warehouse or data mart – and create their own reports using solutions that would enhance and speed their ability to make decisions, create effective plans, and optimise business processes.

However, ‘self-service’ BI has proved overwhelming for all but the most sophisticated power users. The reality is that many users find BI query, reporting and analysis tools just too complex or time consuming to use. This has resulted in a disenfranchisement – or ‘lock out’ – of the every day business user that BI was most supposed to serve. As the recent BI Survey (2009) by the Business Application Research Center (BARC) reveals, just over eight percent of the employees in organisations employing BI are actually using BI tools. Even in industries that have aggressively adopted BI tools – like wholesale, banking and retail – usage barely exceeds 11 percent.

Aside from restricting access to information to a privileged few, traditional BI tools primarily report static information and therefore are rarely suited to supporting the real-time operational aspects of monitoring and controlling business processes. What’s more, they are poorly suited to deliver what business users need today – direct and fast responses to questions on the fly.

Connecting the dots

The booming ‘90s created a costly IT legacy. In the scramble to harness the benefits of BI, many organisations failed to deploy tools and solutions in a systematic or consistent manner. Instead, individual workgroups, departments and divisions – impatient to find a solution that delivered data in relevant context – built their own data warehouses and purchase their own BI tools and specialised applications.

As a result, many organisations are now riddled with ‘analytic silos’ generated by small and disconnected BI deployments, each supporting very different business processes and varied users. Departments now have specialised data analysis applications that only serve specific employees, are often hard to administrate and are impossible to apply to other business processes in different organisational areas.

The scale of the challenge this creates for IT is illustrated by The Data Warehousing Institute’s (TDWI)1 discovery that organisations have on average 2.1 data warehouses, six independent data marts, 4.5 operational data stores, and 28.5 ‘spreadmarts’. Invariably each of these analytic silos employs a different set of BI tools, as borne out by the TDWI finding that organisations average 13 BI tools in total. For many IT leaders today, technology rationalisation is clearly the first step to lowering costs, reducing risk and complexity, and increasing productivity.

The future – enterprise analytics

Technology consolidation and standardisation represents a huge opportunity for IT leaders. Enterprise analytics unlocks the full potential of information to enhance worker productivity by letting users do their own analysis and querying, delivering a common set of application services that can be customised to meet the information and analytical needs of large numbers of individuals and groups, and provides an opportunity to optimise processes to achieve strategic objectives and goals.

Consolidating multiple specialised applications, and creating a single enterprise-wide analytics architecture that delivers the best of BI – stats, data mining, and domain-specific applications – enables IT to give the broadest range of users new insights into their information while reducing the support burden associated with numerous custom and packaged applications.

Alongside reducing operational costs, a customisable enterprise analytics infrastructure improves scalability and enables the enterprise to augment and extend BI – both within the distributed organisation and beyond the firewall to incorporate partners and customers – while providing IT with a centralised means to administer and deploy analytics across the enterprise.

Interactive analytics combine statistical analysis with data mining, ad-hoc querying and visualisation techniques to enable true data exploration and the development of predictive models. The use of flexible in-memory analysis results in fast queries and calculations that allow new ways to visualise and manipulate data. Furthermore, an enterprise analytics platform meets the needs of any type of user – from a chemist to an operations manager, or marketer, or production controller – without dependency on an IT development cycle. Process and decision-driven, a single, flexible platform enables everyone working in the value network to participate – not just power users.

With enterprise analytics, users can integrate data from multiple sources into one analytics environment and obtain real-time answers on-demand. Intuitive and easy to use, sophisticated analytics enable users to review and analyse large volumes of data and to understand visually the hidden relationships between data.

Using a highly responsive drag-and-drop interface, users can get answers to new questions in just minutes from existing enterprise or local data sources without the intervention of IT. Users can view data from any perspective in order to conduct ad-hoc analysis, generate interactive reporting and dashboards, or create domain-specific applications complete with workflows which can be embedded into corporate portals – all without the need for programming, scripting or code development.

Moving BI into the enterprise

In the current economic downturn BI is fast becoming a critical corporate asset. As organisations attempt to rationalise and merge a messy range of overlapping tools, they also need to expand the number of users who leverage BI tools to make decisions. Enterprise analytics delivers the flexibility, ease of use and widest spectrum of functionality that helps drive much broader adoption of BI tools, opening the way to empowering knowledge workers with relevant and timely information to make quality decisions and improve performance.

Reference

1 The Data Warehousing Institute (2004) Moving BI to the Enterprise

The author

John Callan, TIBCO Software Inc.

(ITadviser, Issue 60, Winter 2009)

Organon deploys enterprise analytics across its drug discovery organisation

Organon, a global biopharmaceutical company, found a critical gap existed between the data available to its chemists, biologists, clinicians and statisticians and their ability to analyse it easily. Enormous volumes of data from multiple sources and perspectives made it difficult to give scientists more targeted data with the goal of more quickly identifying new targets. Research teams were forced to make key decisions based on limited analysis and cumbersome Microsoft Excel spreadsheets that reflected discrete islands of information. While the organisation had extensive data, limited insight into the data hindered fast, informed decisions.

The organisation needed a fast, flexible solution that would allow scientists to integrate disparate types of data, ask the right questions of the data, and then catalyse the results. With this approach, Organon hoped to accelerate the progression of high quality compounds from the drug discovery phase through clinical development.

Global exchange and analysis of data

Organon deployed the Enterprise Analytics Platform across its global discovery research organisation to connect its development and research teams. Using the platform, Organon can now link data from different sources – for example, combining data stored in clinical trial management systems with data observed in the lead finding – without having complex IT structures in place that would take years to build.

The platform replaced several BI tools that were in use by limited groups of people and which didn’t integrate with other systems. The switch to a single platform now offers unprecedented integration with massive volumes of data from across multiple systems so that a scientist in toxicogenomics testing a new compound for possible side effects, for example, can easily perform 60,000 experiments in microarrays and quickly identify which genes are de-activated by which compounds in collaboration with colleagues at different stages of the discovery process.

Organon’s scientists can now gather and integrate information from multiple sources using advanced database queries, and then analyse and visualise the expression data. The flexibility of the software allows Organon to leverage analysis guides, or workflows, created in other industries. These help the company better analyse heterogeneous data and form best practices from day one. With these guides in place, scientists can focus on the biological questions they want to answer instead of having to develop the underlying IT structure and basic analysis process.

Widespread adoption

The new enterprise analytics platform has gone from being the domain of expert users to residing on the desktops of most scientists at Organon, many of whom had been using Excel.

Enterprise analytics bridged the analysis gap between expert analysts within Organon and domain experts. The platform enables everyone to tap into the power of analytics and ask critical questions, explore data – no matter where it resides – and get the decision support they need. It has also helped promote new ways of working and collaboration across the entire organisation – no matter where users are located.

Enabling users to turn data into information and understanding it is making it possible for Organon to bring new drugs to market faster and more efficiently. The company is now extending enterprise analytics to enable R&D to work more closely with the marketing organisation, to achieve enhanced portfolio management and get better drugs on the market much earlier than was previously possible.

 

 

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