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April 20, 2011

Driving Adoption in the New Generation of BI

by Shirish Netke

Jack Welch, the former CEO of General Electric wrote: “When the speed of change outside the organization exceeds the speed of change within … The end is in sight!”

Driving change requires better fact based or informed decisions. Better decisions require insights.  Creating insights requires KPI and scorecards.  Effective KPI and scorecard generation requires multiple sources of data and organization. This causal chain is entirely enabled by modern BI and Analytics.

The use and importance of business intelligence — which usually takes historic data, for example from financial software — and analytics tools, which try to predict what might happen in the future, within organizations of all sizes has increased significantly for several reasons, including:

  • Growth in Structured Data Available for Analysis
  • Exponential Growth of Unstructured Data  (Big Data)
  • Disparate Data Sources
  • Decentralized Decision-Making
  • Better and Faster Fact-based Decisions

Growth in Structured Data Available for Analysis.  Over the last two decades, organizations have made significant investments in automating business processes with software applications (with SAP Business objects, Oracle OBIEE and other products) that generate substantial amounts of data which must be manipulated, analyzed and made accessible to be useful to decision makers. Structured data warehouses are growing exponentially in size. Ten years ago the biggest one was a terabyte and now customers are planning to deploy petabyte-sized data warehouses. That is a 1,000 terabytes. However, this data is often stored in different formats making it challenging to efficiently analyze the data and gain insight from it without using powerful data analytics solutions.

Exponential Growth of Unstructured Data.  Unstructured data in the digital economy is growing at rates that’s beyond belief.  This entire BI and Analytics area now have a category “Big Data. Unstructured data include social media/networks, Internet text and documents; Internet search indexing; call detail records, photo and video archives; and eCommerce catalogs and web logs.  Industry specific unstructured data include RFID; sensor networks, astronomy, atmospheric science, genomics, biogeochemical, biological, and other complex and/or interdisciplinary scientific research; military surveillance; and medical records.  Big Data requires novel technologies to efficiently process large quantities of data rapidly. Big Data technologies include Apache Hadoop Framework,   MapReduce algorithms, massively parallel processing (MPP) databases, data mining grids, distributed databases, cloud computing platforms,  and archival storage systems.

Disparate Data Sources.  To grow topline, companies are expanding operations through geographic diversification, mergers, acquisitions and partnerships. The frequency of these strategic activities can result in a complex web of infrastructure and software systems within a firm. In addition, firms are more closely integrating their systems with those of their customers, partners and suppliers and adopting new software applications to improve business efficiency. As a result, large amounts of data are stored in multiple repositories create big data aggregation challenges. In addition, firms deploy a number of tools, including sophisticated data integration software (like Informatica), purpose-built data warehouses and BI systems, to efficiently aggregate, synchronize and analyze data. Organizing and leveraging the growing sets of repositories and multiple toolsets is a non-stop challenge.

Decentralized Decision-Making.  Many organizations are shifting towards decentralized decision-making in order to respond faster to market conditions and competitive threats. This shift has created the need for data analysis tools that support employees at all levels as they assume more responsibility for making critical business decisions. Additionally, we believe that increases in the power and performance of mobile networks and devices will drive demand for mobile access to business data. The widespread use of simple yet robust personal software applications has driven demand from business users for intuitive analytical tools to make faster and better decisions.

Better and Faster Fact based Decisions –  Traditional BI tools are simply not flexible enough to provide timely and critical insights due to limitations from inflexible data architecture, lack of broad usability and substantial implementation time.  As a result of the limitations of traditional BI tools, many business users have turned to spreadsheets to help them perform data analysis –  pivot tables rule in organizations and are everywhere. Business users have adopted spreadsheets for many applications due to their wide availability; however, these general productivity tools were not specifically designed to facilitate interactivity, aggregation or analysis of data for decision-making.

In seeking to gain an information advantage, many public and private sector organizations have to implement next generation of data management solutions, including BI and data analytics tools.  Managers at different levels will have to examine and re-engineer past assumptions and practices

In closing…”If you always do what you always did — you’ll always get what you always got!”  BI and data analytics as a core competency is about doing things differently.


Definitions of Different BI Categories

  • Corporate performance management software and performance management concepts, such as the balanced scorecard, enable organizations to measure business results and track their progress against business goals in order to improve financial performance.
  • Business intelligence (BI) is a necessary business competency for improving decisions and performance.  the most widely used BI tool is the  spreadsheets. Traditionally, BI has been used for performance reporting from historical data, and as a planning and forecasting tool for a relatively small number of people in an organization. Modeling future scenarios permits examination of new business models, new market opportunities and new products, and creates a culture of opportunity.
  • Data visualization tools, include mashups, executive dashboards, performance scorecards and other data visualization technology, is becoming a major category.
  • Data analytics software and advanced analytics techniques, including predictive analytics, text analytics and text mining, customer analytics and business intelligence – customer, supply chain – data mining,  can help organizations make sense of — and gain a competitive advantage from — all the data that they have in their systems.
  • BI platforms provide a range of capabilities for building analytical applications. Examples are Oracle OBIEE, SAP Business Objects 4.0.  There are many choices and combinations of BI platforms, capabilities and use cases as well as many emerging BI technologies such as in memory analytics, interactive visualization and BI integrated search. The idea of standardizing on one supplier for all of one’s BI capabilities is difficult to do. Increasingly, standardization and more about managing a portfolio of tools used for a set of capabilities and use cases.
  • Data integration tools and architectures in support of BI continue to evolve.  Extract-Transfer-Load (ETL) tools make up a big segment of this category in addition to data mapping tools. Organizations must now support a range of delivery styles, latencies, and formats.
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