Business intelligence in the real time economy

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1. Business Intelligence in The Real-Time Economy Trends shaping the future of business intelligence Johan Blomme Circulation Manager, AMP october 2010…
  • 1. Business Intelligence in The Real-Time Economy Trends shaping the future of business intelligence Johan Blomme Circulation Manager, AMP october 2010
  • 2. 1 Business Intelligence in The Real-Time Economy The changing role of business intelligence Today’s economic environment is marked by increased customer power and complex supply networks. In order to succeed, companies must innovate faster and collaborate with strategic partners. To do so, they need to bring information to a wider audience, do it in real-time and make BI part of ongoing business processes. The role of BI has changed beyond its original purpose of supporting ad hoc queries and analysis on static historical information. Rather than reporting on the business, BI moves into the context of business processes. The classic batch- oriented approach is modified with event-driven processing models that deliver real-time information to users. By embedding BI-functionality into operational processes, BI transforms from a reactive to a proactive decision-making tool. Trends shaping the future of business intelligence
  • 3. 2 Business Intelligence in The Real-Time Economy Introduction Business environments are changing rapidly and face enormous challenges from an increasingly turbulent and highly competitive context. Globalization, mergers and acquisitions, the convergence of digital technologies and the search for economies of scale and scope open new avenues for expansion. However, lowered barriers to entry in nearly every industry also reshaped the business landscape. Therefore, a key challenge is to act proactively and to anticipate the needs of the marketplace before one’s competitors. As a result of the pressures transforming the business landscape, companies have to share control with global production networks, powerful retailers and - most importantly - empowered customers. Social networks enable customers to collaborate and share information, which reflects the knowledge transfer over the Internet1. Increasingly, companies embrace social networks to boost sales and brand awareness. Businesses must be able to sense and respond to customers’ needs and desires. Customer expectations for higher service levels cause businesses to rapidly adopt business models and shorten time to market. This requires a more effective use of information for business decision-making. Time and information drive the transition towards the Information Age. To stay competitive in what Peter Drucker calls “the age of discontinuity”2, organizations transform themselves into agile enterprises acting on information in real-time. The real-time economy is a knowledge economy and the key assets of the organization are intangible intellectual assets. To become proactive and information agile, organizations need BI-tools that transform large data repositories into actionable intelligence. Trends shaping the future of business intelligence
  • 4. 3 Business Intelligence in The Real-Time Economy What follows provides a review of the emerging trends that shape the future of BI. The drivers for change are considered from the viewpoint of the changing economic environment and the increased competition through globalization that both result in the need for actionable intelligence and the pervasive use of BI to reach this objective. Next-generation BI is marked by two areas of innovation. The first trend is about making BI more business-centric by focusing on decision-making as opposed to information delivery and reporting of traditional (IT-centric) BI. The goal of BI-tools is to support operational activities by creating a seamless link between the latter and analytics. Business activity monitoring and predictive analytics are enablers in turning data into insights to make real-time decisions and to predict relevant events. The second trend is about broadening the penetration of BI within and beyond the enterprise. If analytics are drivers for operational execution, then analytics-derived insights must be disseminated to the right people at the right time. The future direction of BI will thereby be shaped by the new age of computing. In both their personal and professional lives, Web-savvy users have adopted the principles of interactive computing and have come to demand customizable BI-tools with high responsiveness. Business intelligence, and the insights it delivers, evolves towards an enterprise service with business users producing own reports and performing analytics on-demand. Furthermore, Web 2.0 and social networks function as catalysts for highly intuitive user interfaces and the collaborative features of social computing allow users to share insights, which transforms BI from a solitary to a collaborative activity. Meanwhile the delivery model of software-as-a-service (SaaS) provides a hosted platform for the deployment of BI in the cloud. As the Web becomes the dominant user-interface, cloud computing and SaaS gain prominence. Not only the economics of BI are changed, but open standards allow businesses to rapidly deploy BI-solutions that broaden the reach of BI, both within and beyond the corporate firewall. Trends shaping the future of business intelligence
  • 5. 4 Business Intelligence in The Real-Time Economy 1. Business intelligence for real-time data More than ever, organizations are realizing that empowering employees at all levels with timely information and insights to make business decisions brings competitive advantage through improved performance, whilst the return on investment of BI-tools increases. However, the path to pervasive BI is not self-evident. The history of BI shows we have moved through the stages of being able to organize data (data warehousing), to extracting useful information from it (reporting) and performing advanced analytics (data mining), to using real-time data to respond quickly to events as they happen (operational BI). Traditional BI concentrates on information access and the use of sophisticated tools that analyze trends and patterns in large volumes of historical data to improve the effectiveness of strategic and tactical decisions. Historically, BI has been used by power users and management teams as a tool to view static performance metrics delivered from a data warehouse that was built for this purpose. Data warehouses are multidimensional structures for so called on line analytical processing (OLAP) to analyze information about past performance on an aggregate level. Traditional business applications help businesses understand what happened in the past. A problem inherent to reactive BI systems is that these tools are disconnected from the underlying business processes, while the management of these processes is essential in a real-time environment. In the traditional model, transaction data from source applications (e.g. CRM, ERP, SCM) is captured in an operational data store (ODS) through a batch process. Data from the ODS is transferred (also in batch) to the data warehouse through an extract, transform and load process (ETL). Analytical applications capture the consolidated data from the data warehouse or data marts for reporting and analysis. This process is time-driven and data-centered. Trends shaping the future of business intelligence
  • 6. 5 Business Intelligence in The Real-Time Economy “If you really want to put analytics to work in an enterprise, you need to make tem an integral part of everyday business decisions and business processes – the methods by which work gets done and value gets created. … When embedded in processes and workflow, analytics shift from being an ancillary activity to being a consistent, routine, and natural part of doing business. Embedding analytics into processes improves the ability of the organization to implement new insights. It eliminates gaps between insights, decisions, and actions”. Davenport, Harris & Morison, Analytics at work, 2010, pp.121 To address the increased demand for operational efficiency as well as effectiveness, a shift is taking place towards the convergence of BI and business processes. The goal is the deployment of analytics within operational business processes to make decisions in real-time. The challenge to use up-to-the-minute information is twofold : information in disparate systems must be integrated and enterprise software should be able to access and analyze information and deliver it in an actionable form. To support real-time analysis and action, the batch approach (ETL) is replaced with processes that continuously monitor source systems and capture changes as they occur. The technology enabling the event-driven integration of real-time data between applications is enterprise application integration (EAI). EAI is the process of integrating data across company applications, thereby enabling client applications to operate on a unified view of data. With EAI, transaction data is directly and automatically captured. This process is event-driven and process-centered. Trends shaping the future of business intelligence
  • 7. 6 Business Intelligence in The Real-Time Economy Accordingly, there is a trend to expand the field of BI beyond data warehousing. Traditional BI-applications predominantly use historic data from large repositories. These applications are constrained by the limited service-level capabilities of static data. The latency inherent to static data implies that there is no real-time connection between the data and its operational sources. The goal of the transition to real-time analytics is to reduce latency. BI 2.0 extends the definition of BI beyond the traditional data warehouse and query tools to include event processing. The challenge is to turn real-time data into actionable information to enhance operational decision-making. As BI is becoming a part of operational decision-making, BI also embraces closed-loop performance management. New applications emerge by embedding analytics in business processes. To streamline their operational efficiency, companies need to monitor business operations. What is needed is real-time visibility of performance (“what is happening now ? “). Business metrics enable operational business intelligence. A zero-latency environment is created that offers closed-loop processing by rules engines that feedback the outputs of BI-tools into front-line operational processes, either on-demand or event-driven. BI 2.0 incorporates real-time data from event streams and automated decisions are built into processes. Complex event processing (CEP, also in combination with predictive analytics, cfr. infra) is in line with the revival of decision-centric business intelligence. BI 2.0 is meant to provide information to a wider audience of business users. Real-time reporting and alerts are a means for business activity monitoring. For example, performance metrics empower employees to track business metrics against organizational goals. This enables corrective actions and faster reaction to business events, and thus the optimization of a variety of business activities (e.g. call center operations, supply chain tracing and tracking, fraud detection, claims processing, store inventory management). Real-time information is displayed through scorecards and dashboards and BI is deployed as a service towards its customer base. Trends shaping the future of business intelligence
  • 8. 7 Business Intelligence in The Real-Time Economy 2. Self-service BI As a result of the increased use of embedded analytics to manage performance, there is a push to share information throughout and beyond the enterprise. This contrasts with the view that BI-tools are often confined to a small audience while the users that are most able to take action do not have access to the data. Besides accessing data and developing reports, competencies of knowledge workers are increasingly defined in terms of making sense out of seemingly disparate pieces of data and the conversion of insights derived from it into actions. However, many of today’s BI-platforms rely on systems and tools that store and aggregate operational data and deliver scheduled reports at specifically defined intervals. The traditional approach of cleansing data, integration in a data warehouse and finally having IT create historical views of transactional data is much too slow for enterprises to keep pace with the business demands of today. What is needed in the current business environment is insight drawn from the analysis of granular data that delivers faster time-to-value. Information workers need access to up-to-date information and analytics that enable them to make adjustments immediately in frontline operations. Information self-service The new paradigm of a user-centric BI development is enhanced by technologies that sustain a model of self-service BI. What is often referred to as the consumerization of IT means that users of all levels expect to have access to business information in a similar way as they use the Internet and search the Web (sometimes called “the googlization of BI”). This places increasingly pressure on vendors and IT departments to respond more quickly to a growing demand from users for solutions that are easy to use and Web-based. Employees have strong expectations for a consumerized experience with Trends shaping the future of business intelligence
  • 9. 8 Business Intelligence in The Real-Time Economy technology that gives them access to dynamic content with a user-friendly interface. Organizations need to incorporate these user requirements. Self-service BI refers to a personalized and interactive BI interface that enables users to access and analyze data to help make better decisions faster (and reduce dependence on IT in building applications). “BI 2.O marks BI becoming more interactive (not only consuming reports, queries and dashboards, but being able to find and analyze the right information), more collaborative (it offers tools to not only work with data, but work on it with others, share analysis, comment on performance indicators, and link indicators together to create causal effects between different business functions), and more personalized (BI tailored on the way how different people consume and work with information”. J. Kopcke, Oracle Senior VP, BI and Enterprise Performance Management (quoted in Gandhi, 2008). A number of technological innovations and features contribute to self-service BI. One way to design a user-friendly BI environment is the “MAD”-framework that consists of three layers to meet the analytic needs of most users3. The monitoring layer lets employees view information about key performance indicators (KPI’s). The root cause of problems can be explored from multiple dimensions or filters through the analysis layer and finally the drill-down layer lets identify customers or products to take action. User-centered application development benefits from the build-in drag-and-drop styles, the possibility to make calculations on the fly and the interactive visualization of multidimensional data. Trends shaping the future of business intelligence
  • 10. 9 Business Intelligence in The Real-Time Economy Speed of analysis is powered by in-memory analytics that query data directly in RAM and results in a much faster performance than conventional disk-based approaches. In-memory solutions also eliminate OLAP-requirements around building multidimensional cubes and aggregate measures, resulting in analytics with far less complexity which allows users to slice and dice data in an intuitive way with fast response times. Businesses collect and store massive amounts of data generated by customers, partners, suppliers and internal processes. Making this data available for timely and informed decisions is a critical step in any environment. The retail sector, for example, is a business where applications are created to support demand-driven decisions. The number of transactions occurring in retail through the use of RFID-devices, point of sales data and customer loyalty programs is huge. The type of data and the granularity of it vary by the role of the stakeholders in the organization. Retail BI should empower the stakeholders with actionable information to improve the performance of stores and categories, optimize promotions, improve inventory levels and reduce out-of-stock occurrences. Faced with these challenges, a retail BI solution must provide end-to-end visibility across the supply chain, let information be exchanged between parties in real-time and integrate BI with business operations. In-memory analysis helps processing large amounts of data and allows insights to be available in real-time. Transactional data can be analyzed at a very granular level and provides a competitive advantage especially in retail where reducing latency between data/insights and actions is key to improve business performance (e.g. respond quickly to changes in inventory levels). BI solutions can be deployed that capture a set of KPI’s that serve as business context information related to areas of responsibility. Members of the retail chain should monitor these metrics and take appropriate action. Other industries (e.g. logistics/supply chain, telecommunications, finance/insurance) benefit from reporting and analytics in a similar way4. Several products address the real-time monitoring needs of business users. For example, IBM Cognos Now! ( delivers self-service, interactive dashboards to an organization’s frontline. Time-sensitive KPI’s are visualized and provide proactive alerting to take actions that have impact on business operations. For instance, by Trends shaping the future of business intelligence
  • 11. 10 Business Intelligence in The Real-Time Economy providing real-time transaction data from point of sale systems, FMCG-companies can help retailers avoid out of stock situations. Inventory tracking can invoke backorders through the replenishment system to improve revenue streams. Pivotlink ( is an on-demand application based on a software-as-a-service (SaaS) delivery model that offers role-based dashboards for its customers. Apart from monitoring operational processes, users can perform advanced analytics to discover underlying trends and anomalies in the data and to obtain answers to specific questions (this can range from drill-down in dashboards to sophisticated calculations and forecasts). Furthermore, Saas BI offers the possibility of collaborative information sharing. Since information is centralized, operations such as product and price updates, tracking of sales performance and automatic replenishment can be achieved in real-time. On-demand BI expands collaboration to the ecosystem of the organization. Organizing knowledge around business processes that the enterprise shares with suppliers, business partners and customers, increases the ability of the organization to meet market demands. Trends shaping the future of business intelligence
  • 12. 11 Business Intelligence in The Real-Time Economy Figure 1 : Interactive dashboards illustrate the trend towards the consumption of analytical insights. These applications make use of in-memory processing and are characterized by their strong visualization capabilities and the possibility to share insights in a collaborative way. Increasingly, these applications are cloud-based. Trends shaping the future of business intelligence
  • 13. 12 Business Intelligence in The Real-Time Economy Enterprise 2.0 There is no doubt that BI embraces Web 2.0-features that focus on the user experience. BI has been redefined along the lines of Web 2.0. The customer-centric focus of Web 2.0 has created a demand for applications that move from a traditional transaction platform to a model that is more accessible and personal for the user. Enterprise 2.0 is a term used to describe th
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