Companies today need to be able to collect information on a number of indicators to gauge their performance against their own and external targets. These KPIs can vary from number of units produced in a selected time period, to number of orders received through certain channels, to the availability of skilled personnel and particular plant.
This is on top of, and addition to, the usual financial analytics. Clearly the role of business intelligence is changing. As Andrew Stevens, Sage’s enterprise development manager, explains, “We are seeing the requirements for Business Intelligence to become part of the natural workflow of business processes, rather than an adjunct to ERP applications.” So what does this mean for manufacturing organisations in today’s economic climate? What should they be expecting from Business Intelligence software?
Data your way
While broadly defined as the skills, processes, technologies, applications and practices used to support decision making, a better definition is that Business Intelligence represents the tools and systems that play a key role in the strategic planning process of the corporation. These systems allow a company to gather, store, access and analyse corporate data to aid in decision-making.
Generally, these systems will illustrate business intelligence in the areas of customer profiling, customer support, market research, market segmentation, product profitability, statistical analysis, and inventory and distribution analysis, among others.
So we collect data and turn it into intelligence, but how is this to be done? For the US Department of Defence the process consists of six interrelated intelligence operations: planning and direction; collection; processing and exploitation; analysis and production; dissemination and integration; and evaluation and feedback. Our software, therefore, must support these capabilities.
BI technologies provide historical, current and predictive views of business operations. Common functions of Business Intelligence technologies include reporting, online analytical processing, analytics, data mining and business performance.
Phil Howard, research director, data management at Bloor Research, states, “You can use any database as a data warehouse or to support business intelligence management, benchmarking, text mining and predictive analytics. However, if there is any substantial re¬quirement for analytics then general purpose databases without specialised facilities will fail to give adequate performance.” Similarly, Randy Flam, President of IQMS, says, “Business Intelligence is becoming more and more important. It used to be that all you needed was a static report writer to extract and report on data, but now you have to be able to provide the ability to dashboard and drive down to more detailed information — as well as deliver on multiple user platforms, from PCs to mobile phones.” Marge Breya, executive vice president and general manager, Intelligence Platform Group and SAP NetWeaver Solution Management, supports this view. “Customers want to work with their data their way; whether it’s behind a firewall, on the Web, or on their local computer in spreadsheets,” she says.
“With access to data at their fingertips, customers can make more confident decisions, share their insights with others and react quickly to any changes in their business.” Business Intelligence is more than simply being able to extract data and report on it. It is also more than financial analytics or even key performance indicators.
And it requires more than just standard reports, but alerts and graphs and traffic lights. A question we need to ask ourselves, therefore, is in what context was the data collected?
The problems at Toyota that have led the company to recall a significant number of vehicles have been widely reported. Teradata have been working with a number of automotive vendors looking at the problems with warranty. Says Duncan Ross, director of advanced analytics at Teradata, “Warranty data comes from two different sources. One is from the production line, and is well defined and consistent in nature. The other comes from the dealers’ workshops, and is of variable quality. This can lead to the same problem being reported a multitude of different ways — meaning big problems can get hidden.” Ross goes on to describe how Teradata, working with SAS Institute, has developed a solution using statistical methods to allow the dealer’s warranty information to be grouped and analysed more effectively. This results in the detection to rectification cycle being shortened, with Business Intelligence being used as part of a warranty system — a service management component — to analyse the data received.
Ross sees this as just one business intelligence application in the automotive sector. “A car is now a computer on wheels,” he says. “Data is being collected continually that the driver is unaware off. As warranty schemes increase in length, we could reach a time when the OEM actually owns the car for its whole life, and we the driver lease it. Data collected by the computer in the car can stream this data back to a central point, which allow problems to be identified and reported before the driver is even aware of them.” Real-time BI disseminates information about a business in a range from milliseconds to a few seconds after the business event. While traditional Business Intelligence gives users only historical information, real time business intelligence provides a comparison of present business events with historical events — which helps in identifying a range of issues, thereby allowing them to resolve it on time. The primary aim of real-time BI is to enable corrective actions to be initiated and business rules to be attuned to optimise business processes Rick Whitting states that real-time information is no longer a competitive differentiator that produces more timely and relevant business decisions. Decision-makers in SMEs can now communicate and collaborate over broadband networks as if they were in the same office. He sees that it is the ability to forecast where events are heading, and then make informed decisions, based on that assessment. Termed ‘predictive analytics’, it involves running historical data through mathematical algorithms such as neural networks, decision trees and Bayesian networks to identify trends and patterns and predict future outcomes.
An organisation’s ability to make educated guesses to questions such as “will product demand surge?” or “will a customer take his business elsewhere?” is key to improving service, cutting costs and exploiting new market opportunities.
But, ultimately, do I want to run BI in house? Could I use the cloud? The answer to these questions is yes. This February saw SAP announced the SAP BusinessObjects BI OnDemand solution, targeted at casual BI users to deliver a complete BI toolset which requires no prior experience or training. The most interesting aspect of this announcement was that a user would be able to integrate data not held in SAP with a specific interface to saleforce.com included.
Business Intelligence tools have empowered businesses — regardless of sector — to make better decisions on their own, without relying on IT or power analysts to prepare and interpret results for them. Moreover, BI applications have become as commonplace as spreadsheet applications within large organisations — and this will extend to all within a few years.
“We have seen a noticeable shift in the motives for firms investing in BI,” says Stevens. “Historically, BI provided information at a management level only, but businesses are now placing emphasis on utilising BI as a means of transforming data into actionable insights across their organisation. It enables companies to unlock the intrinsic value of data held within their business systems. Indeed, we believe that BI should never be seen as an additional IT layer or standalone platform, but as an integral business tool that can ensure everyone is pulling in the right direction.”
A 2009 Gartner paper predicted the following developments in the business intelligence market:
• Because of lack of information, processes and tools, through 2012 more than 35% of the top 5,000 global companies will regularly fail to make insightful decisions about significant changes in their business and markets.
• By 2012, business units will control at least 40% of the total budget for business intelligence.
• By 2010, 20% of organisations will have an industry specific analytic application delivered via software as a service as a standard component of their business intelligence portfolio.
• In 2009, collaborative decision making will emerge as a new product category that combines social software with business intelligence platform capabilities.
• By 2012, one-third of analytic applications applied to business processes will be delivered through coarse-grained application mash-ups.
To my mind the future in BI will be heavily automatic collection-based. Manufacturing decision makers don’t have the time to spend on collecting and formatting data simply so that they can report on it. As we receive an increasing amount of data to analyse — coming not only from within our own organisations boundaries, but also our customers and supplier, not to mention other external sources — how do we sort the wheat from the chaff? For a business user’s viewpoint, the software has to allow easier definitions of what needs to be collected and provide interaction during the build process. This will include the ability to define different views and collections for different user interfaces. Then, and only then, will we have the agility that we need.