Few of the key things that could hamper their decision making is not having a consistent definition of metrics across the company. Finding cheap and effective way to store business information with minimal inconsistencies is essential. Along with this is required a solution that is scalable for data explosion and variety of future business / analytical needs.

There are lot of BI tools in the market from Top Premium tools to low cost open source tools. Finding the right tool and people for maintenance of the tool and even more people for using the tool is essential for every company.

Analytical needs of the company requires data capturing at granular levels. This transactional data needs to be captured for business intelligence at higher levels. There are lot of new technologies that are currently available such as databases on schemaless Mapreduce architecture or Databases on Cloud computing etc..

These new technologies can make the Implementation of BI much more simpler and easier than before. At the client end however, you need tools for analyzing and manipulating data.

Even web analytics tools like Google Analytics provides tools and features to create your customized reports which can be viewed online or scheduled to your email address. The most important thing however is to identify the few key metrics that matter to your business.

Its common that most business at the start want to look at total pageviews, total visitors as metrics in the dashboard. However everyone knows these metrics do not change on day to day basis. The report ends up showing almost the same data every day.

Identifying key metrics can be difficult and needs feedback from business heads and top management. Lets  take an example of Online Marketing, instead of generic report on total acquistions, total cost, pageviews, clicks, CTR, Revenue, ROI.

You could make different dashboard which contains – Profit from Branded Keywords, Profit from Long tail keywords, Profit from Acquisition keywords. Similarly Bounce Rate of landing page for each of the keyword segments. Another metric could be ARPU (average revenue per user) from each of keyword segments.

Since the action points are at keyword level or banner level. The segmentation is required first at keyword / banner level. You have to segment keywords/ banners based on their value delivered.

In case value is not directly visible as in revenue or profit we need to define proxies to identify the value. Some keywords may attract users who are more engaging on the site. This engagement has to be identified.

Few Laws of BI:

First law of BI is anything that matters is detectable. If a metric does not matter, then you dont care if it is detectable. Second law is anything that is detectable is measurable.

Therefore if a user engagement is important and matters to business then it is detectable. Engagement is different for different sites. For blog may be it is adding comments, for a e-commerce site it is browsing more products, for a classifieds site, it may be number of searches. For a social networking site it is number of pokes, scraps, wall posts etc. You have to define for your engagement metric for your business. Now go back and segment your action points with your engagement metric.

Hope this is useful..Please share your thoughts .. send in comments..

This article is originally found on http://www.praveenkodur.com/blog/

Decisions are taken by knowledge workers at all levels of the company. Most of the knowledge workers generally adopt decision making style based on experience, gut feel, instinctive & spontaneous techniques. Such methods soon become predictable & stagnant. In a long run it does not prove useful in rapidly changing economic, technology & business environment. In rapidly changing technological environment, process become increasingly complex,

To stay competitive in a dynamic business environment requires a decision making style which is supported by knowledge driven by information & data. Analytics can provide a strategic value in providing competitive advantage to the companies.

For example, Internet company observes a lot of customer attrition on their website. These users may switch to a competitive website. The company is looking to reduce this churn by offering a discount or a trial. To increase the effectiveness of campaign is to recognize those customers and estimate their probability to discontinue the service. From a given list of customers we can a implement a model to estimate the probabilities and arrive at a smaller subset of users to be targeted for the campaign. This smaller set of users will be the majority of the future discontinued users.

Such tools and methodologies support knowledge workers in making better decisions. Generally several options are available to users before making a decision. This actually helps them choose the best methodology.

Often there is a enormous amount of effort is required to extract knowledge from data. Data is present in most raw format almost incomprehensible. Data needs to be transferred to human readable format and then has to be processed (sometimes tortured) to extract information and then to knowledge to make it useful for knowledge workers.

More articles in this blog http://www.praveenkodur.com/blog will talk in detail about BI systems.

Extracting knowledge from data requires deployment of various systems, process and maintenance. Installing Transaction servers to maintaining data warehousing systems to creating decision support systems. The idea of a Decision support system is to promote a scientific and rational approach to management.

Business intelligence architecture must be designed to function efficiently with cost effective components. There are various components such as logging systems, data warehouse, BI methodologies.