As CIO.com reports, BI and BA are evolving rapidly and meshing to meet business challenges and create new opportunities. Although nearly all global 5000 organizations already use these tools, 35 percent of them fail to make insightful decisions about significant changes in business and market conditions, according to IT consulting firm Gartner. What’s more, the task isn’t getting any easier as data streams become more intertwined and other Web 2.0 environments pull data from multiple sources at single instance.
I believe business intelligence and business analytics are on the cusp of a major change. There is a shift toward providing deeper insight into business information. And there is a growing emphasis on better tools and putting more powerful and better software in the hands of business decision makers today.
Business Intelligence & Business Analysts are quite disconnected in real world, at least that's what have seen in last many years of experience. BI is evolved as platform or bunch of tools, with architecture, albeit an enterprise-wide approach, lacks deep analytical and predictive capabilities. Traditionally, this is where the work of IT ends and business analytics starts, with statistical, quantitative and predictive work conducted outside of the framework.
This unfortunate reality has contributed to the myth that BA is something totally different from BI. The vision of BI always includes analytics, and BA is merely a subset of BI focused on analytical parts of business intelligence. Because the traditional BI architecture doesn't lend itself to advanced analytics capabilities, such as statistical modeling and data mining, it's not surprising business users collect data and reports from BI systems and then use their own analytics in spreadsheets they control. This approach is not a viable solution however, because uncontrollable processes and questionable data will seriously hamper a BA effort. Research studies estimate that roughly 94 percent of spreadsheets deployed in the field contain errors, and 5+ percent of cells in unaudited spreadsheets contain errors.
What we need is an analytics-oriented BI architecture that incorporates advance analytics and analytic modeling capabilities into the current BI framework. Traditional BI vendors need to build more advanced analytical functionalities within their BI offerings. Many major BI tools don't support advanced statistical and quantitative modeling. Some support limited analytics and require highly technical skills (such as SQL) for use, which most business users don't possess. BI vendors need to provide more user-friendly analytics tools with much broader capabilities for statisticians and business analysts to use without lots of IT support. These new capabilities should include predictive analytics, data mining, text analytics, simulation, decision analysis and advanced modeling.
Second, traditional analytics software vendors need to embed powerful analytical capabilities into the BI platform and make integration much easier for customers. Most BI applications and BA applications operate on very different platforms. Every company needs to reckon with integration and ROI before investment. BI and BA vendors should work together to make the integration much less painful and help customers unleash the best of both worlds.
An integrated solution combines advanced analytics with powerful data visualization and advanced reporting capabilities to support fact-based and data-driven decision-making. Under this new architecture, advanced analytics will be an integral part of BI. Analytics process and technology could be managed under one unified BI framework and strategy that ultimately should align with a company's business strategy. Initiatives such as data management and governance could benefit both BI and BA programs.
Companies that have high quality information that is well-defined and understood across the enterprise already have a solid foundation for BA. In terms of implementation, there could be different deployment approaches based on the conceptual architecture. For instance, analytic models might be built into a database or data warehouse to leverage its processing power.
In-database analytics has lots of advantages - analyzing data where it resides to avoid data movement and duplication. However, in-database analytics can be costly when analytics processes, which are volatile and adaptive in nature (as old models need to be updated or rebuilt with latest data input), are hindering other mission-critical OLTP or OLAP operations. It may lead to a separate environment for development and deployment of an analytic model. Meanwhile, advanced analytics capabilities are better built within existing BI tools for better compatibility and integration with existing BI features. Analytics could also be built into operational systems when less data integration is needed - analyzing data while capturing it. Organizations should choose the best deployment model to fit their business analytical needs.
Lastly, BA needs to be integrated and embedded in business process to be effective & efficient. One such example is to create a closed-loop style repeatable process in the normal workflow of business operations to feed the results back into the operational system where the data for analytics is sourced. This kind of decision automation is used in cases where decisions tend to be high volume. For instance, an online retailer can use an analytical model that predicts high probability of a customer buying a certain new product to attempt cross-selling by dynamically displaying ad banners when the customer visits the online store. An online bank can approve or reject loan applications automatically based on the criteria defined by the application processing rules engine using predictive analytics. Only the exceptions (rejected applications) will be sent to loan officers for review and follow-up. The model significantly reduces the cost and decision time for the bank and customers, a win/win for both.
According to my observations he key characteristics of the analytics-oriented BI architecture are:
- Integrated (data, reporting, analytics)
- Robust and flexible (for rapid changes)
- Evolving and adaptive
- Consistent (standards in process and data)
- Controlled
- Transparent (versus black-box approach) and
- Embedded (analytics as part of business process)
With the burgeoning demand in advanced analytics and emerging analytical technologies, we will see the convergence of BI and BA in the marketplace. BI megavendors will likely acquire smaller BA players and integrate advanced analytical tools and capabilities into their BI portfolios. At the same time, traditional analytics software vendors will likely push more into the BI platform
territory.The reciprocal penetration will accelerate the consolidation, standardization and adoption of analytics while moving toward an analytics-oriented BI architecture.
Historically, this market has been served by vendors such as Business Objects and Cognos. But the competitive landscape is changing. Microsoft has now shrewdly entered the market by driving the placement of SQL servers into the space in order to broadly deploy and deliver its BI suite and reporting services in volume. Oracle has seen the effect of companies moving data out of the database to stage it for analysis. The resulting data warehouses have provided a degree of utility in housing, manipulating and delivering “strategic” information across the organization.
Also every top level boss wants an effective dashboard. To the extent that all of us are CIO/CTO/CEO’s of our own business discipline, we want a simple measurement display of how we are doing and an alert mechanism of when something goes wrong. Additionally, dashboards address the growing urgency around Sarbanes Oxley. Monitoring planning assumptions and key performance metrics has now become mission critical from a regulatory and compliance standpoint. As we all know BI reporting ends with the dashboard, which is sufficient only for some business planning, and BA picks up the rest for the Go-To Guys. Simply, this group must interact with data in a much different way from what traditional BI allows. The requirement of the BI system has been to monitor the data based on pre-configured questions requiring only a thin client environment to inform the user. In the operating world, users need to engage with the information requiring a richer client to support interactivity and the ability to ask and answer their own question without having to go back to IT. Let us make one thing clear, we don’t get business analytics when you buy business intelligence. The requirements are different and the benefits are different. The return on information and expertise achieved by arming your resources, operating managers with analytics will supercharge your existing BI investment.
Do let me know your views suggestions, These thoughts I have collected from CIO.COM, Linked discussions & various discussions with Co-workers & PMI Mumbai members. Thanks to all for sharing inputs in time with free heart. I am available at ravindrapande at gmail.com
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