Any predictions about the future and particularly given the speed at which technology and business practice is changing, is purely speculative. In this article I attempt to put my own perspective of the future risks and opportunities in this area.

The first major development I expect is that business intelligence as a concept will have greater profile and influence over the average business structure. The entrance of competitors and the need to constantly revise practice in line with new trends means that without good business intelligence, firms will be hampered in the extent to which they can make viable ventures.

It is a very imprudent manager indeed who fails to plan for the expansion of business intelligence. Those firms that fail in this endeavor will continue to lose customers and money. If they are not ultimately driven out of business, they will provide a cautionary tale for those who ignore business advice.

Information technology has democratized communication systems. The growth of social media has enabled end users to participate in the assessment and general direction of the brand. It is only a matter of time before decision makers realize that the customer will have to be involved in running the organizations. A case in point is how airlines have been forced to provide better facilities for people with disabilities due to a concerted publicity campaign by ordinary members of the public. One of the methods used to publicize this cause were social networking sites.

There will also be specific developments within the business intelligence technology such as the creation of bespoke packages to deal with the increase in demand and the diversity of objectives from each of the organizations. Already individual companies are in the process of defining their requirements for the future. It is highly likely that the next step would be to commission these InfoTech services. It might also be that the technology will start specializing by sector or business types and that generic technology will fall out of fashion.

Business intelligence is also likely to foster a new climate of collegiate working whereby management information is no longer just restricted to the senior executives but can also find its way down to the end users who are then able to determine how it can be useful to them in their day to day work. The utopia is that all customers and employees feedback to managers. This would make them stakeholders and would presumably develop brand loyalty.

Of course there is concern about the risk that technology will completely override human discretion and companies will be further de-personalized. This is a risk that every developing organization faces. However given the popularity of new media connectivity, it is likely that the customers will not mind having digital interaction with businesses.

Customers are no longer willing to go into profligate spending based on unsustainable credit. There have been many bankruptcies both personal and institutional. The darkened mood of the consumers makes them prefer saving to spending.

Unemployment has virtually depleted the reserves of disposable income that so defined the age of the boom and burst. This article aims to ask the question as to whether business intelligence should act as a reactive or even defensive mechanism to cope with fluctuating economic situations.

The sources of business intelligence can be both local and industry wide. Even internationally there are mechanisms to disseminate useful data about what has happened and what is likely to happen. Recessions and periods of austerity are not new to us. In the same way that a business can predict that sales are likely to pick up during the Christmas festivities, an economy can predict that where it is supported by vast amounts of dubious credit, the economy will fall at some stage.

Why is it then the people seemed to have been caught by surprise? The banking crisis’s that plagued the globe were talked about in the most awestruck tones as if people had never had of banks failing. Governments reacted with horror at the rising unemployment figures as if they had never had unemployment before.

The biggest victims were individuals and small businesses which generally do not have very sophisticated business intelligence systems but were never too big to fail. They did not have the ability to pressure governments into giving them crisis loans or bail-out money. All they did was to put up and if they went down, the entrepreneurs would wait and give it another go when the climate improved.

Does it have to be this way all the time? One would hope that many business people have learned the lessons of the global downturn and if it next happens (as it surely will), they are better prepared. Sadly history tells us that the same mistakes will be repeated over and over again.

Nevertheless even at this time of a recession firms can look at business intelligence as a means of mitigating some of the more devastating consequences of a difficult economy. For example some of the supermarkets have started offering quite a lot of budget in-house items which tend to cost much less than the branded items.

This was based on market intelligence gleaned from the buying patterns of their customers during the global recession. Credit card companies also scaled back on their lending criteria on the basis that their customer pool had an altered income and spending profile. That is the essence of using market intelligence to cope with market pressures.

One of those tools will have to be Online Analytical Processing (OLAP). The purpose of this article is to provide a generalized framework in which the data skimmed online can be processed and eventually embedded within the day to day infrastructure of a business.

Of course, the level of detail, investment and sophistication will vary between a small firm and the multinational organization. However the basic concepts still remain the same.

The key difference between OLAP and other data collation mechanism is that it undertakes an approach that is simultaneous navigational, hierarchical and relational. This is a far more comprehensive method than merely examining particular strands and informational patterns. If used correctly, it can enable large-scale interpretation of vast amounts of discrete and varied pieces of business intelligence into a technically relevant set of causes and effects.

In other wards the decision maker is enabled to gather seemingly unrelated pieces of information and thereby create a story that will ultimately translate into profits for the business. Often the challenge for a business decision making process is not that there is not enough information or that it is not even accurate. No, the real challenge does know what the information means for you and your people within the organization. Online Analytical Processing is just one way of making things way simpler.

It goes without saying that there will inevitably raise certain intrinsic problems where the business is using complex systems to resolve even more complex questions. One of them is accuracy: by its very nature the use of often seemingly remote data involves compromise by way of aggregates and assumptions. The thinking behind these assumptions and the criteria for selective reporting in making predictive analysis has to undergo very stringent scrutiny otherwise one single misjudgment could end up producing increasingly inaccurate data. Decisions based on bad information are almost always bad decisions.

The second problem is concerned with the manageability of the database system once it has evolved. The effective use of business intelligence requires that managers ought to be able to access the analytical process tool data on a fairly regular basis. This is not a big problem if the firm is fairly straightforward. However for the more complex business organizations, there will be practical concerns about access and maintenance.

In conclusion it would be arguable that online analytical processing vastly improves the effectiveness of business intelligence by extending the variety of sources of information and collating them in such a way as to make sense to the frontline and the key decision makers. It also addresses some of the more bureaucratic bottlenecks commonly associated with the processing of complex data. It would therefore be very shortsighted to fail to include OLAP within the mix of systems and tools that are used to collect and analysis in business intelligence modules.

If you have a particularly small business which employs less than 10 people and is primarily based locally, then chances are that you know a lot about your business or if not, just enough to make sure that it doesn’t go under. However if you are running a larger show, you may never be able to understand what goes on in your company and surely that is a recipe for disaster.

It is surprising what can be discovered about your business when you explore the rich world of business process discovery. A tool of the business intelligence model, this process enables you to map the activities of your business and you may even be able to make some improvements. In this article we will highlight some of the basic processes and outcomes involved in the business discovery process (BPD). It is possible to automate the whole BPD process so that seasonal or process variations can quite easily be identified and responsive action implemented with the minimum of fuss.

BPD more often than not is primarily based on the examination of existing data/information gleaned through structured interviews with frontline staff followed by evidence-based verification via management information systems. The latter aspect can be automated depending on the resources available. At this point it is crucial to account for human subjectivity when responding to structured or even unstructured interviews as well as the frailties of memory and bias. Verification via an independent information system will provide an estimate of the reliability of the information that is being gathered.

One school of thought is that the ultimate aim of BPD is to standardize all processes, thereby eliminating the inconsistencies of human influence. Those that believe in this approach argue that computerized processed are less likely to be subject to inaccuracy and what some call noise. Whereas standardization is generally a peripatetic business objective not least because it enables proper performance management, it is also worth noting that without human influence in operations most businesses lose customer service.

The two business resources are complementary particularly when the ultimate goal is good business intelligence. It also adds to other information resources by providing data that is well sourced and subject to referrals as well as revisits of the original data sources.

What happens then when you effectively know about your company or organization? The logical step is to move on to embedding the reports from the BPD into the business intelligence model. A fringe benefit of this is that wastage and duplication is quite easily picked up and management action would presumably follow. It also facilitates the construction of business plans and bids for funding or contracts given the fact that it is actually a detailed description of what the organization actually does. So, challenge yourself and understand your business. You never know you might like it.