Decision support systems have been around since the early days of business computing, helping users extract valuable information from large amounts of data.
Business intelligence is just the latest iteration of these tools, following on from executive information systems, data warehouses and online analytical processing tools. Bringing together elements of all these approaches, business intelligence tools and techniques help knowledge workers explore and analyse business information, with the intent of helping them make effective business decisions.
There's a lot to implementing a business-intelligence solution, from extracting information from multiple data sources to building an analytic framework; from providing desktop analysis tools to delivering results and reports across a business. You need the appropriate tools and skills for each element of the solution, building on tools and techniques that are often already in use inside your IT department.
There's a big difference between raw data and information — and businesses are generating more of the former, with unstructured data-storage requirements increasing by an order of magnitude every two years.
However, it's not that raw data that helps businesses and their staff make decisions; instead, it is data that's been refined into information and transformed into knowledge. Business intelligence is a key part of that process, giving knowledge workers the tools they need to extract information from massive data stores and transform it into actionable information, adding to the store of corporate knowledge.
Business-intelligence tools extract information from seas of data, and give users the analytic tools they need to give the information meaning.
Part of a process of knowledge engineering, business-intelligence tools extract information from seas of data, and give users the analytic tools they need to give the information meaning.
The biggest change in the world of business intelligence has been in how it's delivered. Early iterations of business analytics, like data mining, required specialist knowledge and tools — slowing down information delivery, and preventing ad hoc analysis. With the latest generation of business-intelligence solutions, familiar desktop productivity tools like Excel are used to analyse and explore data.
Excel 2010 adds support for massive data sets — especially when used with the Power Pivot plug-in — gives users access to a palette of analysis tools, including pivot tables and charts, data slicing, and a large selection of different charting tools. You don't even need to have it on your desktop, as large-scale analyses can be delivered as batch processes to Excel Services, running on your servers.
A large part of modern business intelligence is still data mining, extracting information from many different data sets. That means you still need to bring your data sources together, and you still need use a database, like SQL Server, to implement online analytical processing (OLAP) techniques as part of preparing data for analysis. You can then start using the tools in dedicated business-intelligence platforms like Oracle's Hyperion and SAP's Business Objects, or through the analytics features in Excel.
It's important to remember that business intelligence isn't a service that operates in isolation. Results and data sets need to be shared to allow different groups of users to gain their own insights.
Results and data sets need to be shared to allow different groups of users to gain their own insights.
Reports and charts can be bundled into corporate portals and desktop dashboards, giving managers and executives access to at-a-glance views of business performance. Reports don't have to be complex, with a simple red or green dot on a scorecard giving as much information as a pivot chart.
As a result, business-intelligence tools are an important part of the processes that drive modern business methodologies, helping implement key performance indicators and balanced scorecards. They can also be used to feed into benchmarking tools, helping compare your business with its competitors.
It's not just the results you're expecting that have the most business impact, as business-intelligence tools can expose underlying trends that can have a significant effect on business revenues. High-end analytic tools, like those from SAS, power the automated systems that detect fraud or determine credit ratings, mixing machine learning and rules engines with analysis.
They're also providing dynamic call-routing for call centres, prioritising calls based in information about callers held in enterprise resource planning (ERP) and CRM systems. Automating business intelligence is...