Decision-making in both old and new business intelligence environments is collaborative. However, the decision latency that prevails in old BI environments was unacceptable in environments where decisions may affect business operations on the day they are made. That means that the decision-making process supported by new business intelligence has to keep moving at a fast, steady pace. To do that, new BI systems have to assemble a huge volume of data, analyze it and present it in accessible ways to many users.
Not only do the types of data presentation and analysis used in business intelligence have to be geared toward large numbers of people; few of those users have the advanced technical and analytical skills of those using old BI systems. And even if the skill levels are comparable, those users do not have the luxury of time because they need to make operational decisions immediately. They need quickly to access, absorb and act on information and analytical results.
The lack of timely access to information – latency – is one of the most difficult challenges to using business intelligence systems successfully. In one business intelligence study, for example, nearly three-quarters of respondents said that reducing the time it takes to update their data was somewhat important or very important. In addition, nearly one-quarter of the respondents said that adequate business intelligence analysis requires real-time data, and more than one-third said they require daily or more frequent updates.
Collaborative decision-making in a BI environment depends on shared data, but data access often is confined to those who use it regularly. It is critical to share information to be able to improve the quality of decisions. But many organizations do not enable collaboration or understand it to be important; this business intelligence study found only a small percentage of users who rated collaboration as a high priority.
Compounding the collaboration problem are data silos, which effectively hide data from many users. Departmental transaction data that users require for business intelligence frequently resides inaccessibly in other functional or departmental areas of the enterprise. Even when it is available, automated transfer mechanisms are not in place, so users must extract it manually, which can be difficult and time-consuming to do. It can be even more difficult to acquire data for the business intelligence platform from external sources, such as trading partners and industry associations. And some of the data may be unstructured and therefore inaccessible to older reporting tools, further compounding the problem.
Integrating data from all the disparate sources into the business intelligence environment requires intra-company and inter-partner collaboration. To enlist the cooperation of these separate units requires executive support and diligent bridge-building. What’s more, an operational extraction, transformation and loading (ETL) environment of some sort has to be present at the front of the system to automate transport of the data. This extra step can add overhead to the process, of course; as well, it sometimes can create disparity in versions of the data. An effective BI system can provide dynamic access to data across data silos, making it possible to provide real-time information to operational workers.