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 The major gap area for enterprises remains in their ability to scale Business Intelligence (BI) operations across more users, says Arun Ramachandran, Director, Regional Area Technical, Sybase. He spoke to Ashwani Mishra on the evolution, factors and challenges that impact a BI deployment.
How has been the evolution of Business Intelligence (BI) over the years? Has its usage become easier; implementation become less complex and have the implementation costs reduced?
To begin with companies used to perform analysis by running queries on online transaction processing OLTP systems due to which performance used to get affected and the results obtained were not conclusive. Even routine decision making was done by the top management personnel.
But today, the number of decision makers in the organizations has grown. The need for decisions to be taken by respective department heads has become imperative. It is here that BI and analytics has become the need of the hour.
BI already has percolated below top management and is actively being used by middle and regional management in most sites today. The prime driver of this percolation (apart from improving decision making) is the gain realized by users from having a single coherent view of data across the organization.
Also, with the nature of tools available today, most BI implementations find that users can easily perform their own analysis and essentially break actions down to simple decisions. Also, as interactions between organizations and their customers have evolved, having a common view to historical data has become a must.
The definition of BI has changed in the way that now to fully leverage the value of data, companies are turning from decisional BI to operational BI. It is evolving from basic reporting to what if analysis.
The definition of BI has changed. Companies are moving from decisional BI to operational BI so as to leverage the value of data. It is evolving from basic reporting to what if analysis.
With the increase of skills and resources available in the market the solutions have become more user-friendly. Training programs are being introduced by vendors to educate customers and the increase in technology partners coming with tools available for easier implementation usage has definitely become simpler.
In terms of costs with the resources and skill sets of respective vendors available the costs have definitely come down.
What are the factors that drive a successful BI implementation?
The major factor to consider when planning a BI project will always be Return on Investment and Total Cost of Ownership.
While true for all IT investments, these are even more important with BI, as successful BI implementations are primarily dependent on their adoption into standard operating procedures within any organization. For example if a successful BI project delivered a churn rating for a customer, but that input was not used by the concerned department within the organization, the value saved/made by the correct delivery of that information will never be realized, thus delivering a negative return.
Best practices for successful BI implementations have now evolved into significant detail, and most implementers/vendors provide large amounts of detail into these best practices. All recommend a whole host of activities from Information Requirements Survey, Data readiness analysis, Information flow analysis to more implementation specific details like data extract and transform studies etc.
It is essential to have an optimized analytics server dedicated to handle the most challenging data warehousing requirements with ease. A primary reason why companies go wrong is that they begin to perform analytics on the traditional RDBMS whereas it is of utmost importance to have a dedicated transactional warehouse for faster and optimized results.
What according to you are the issues/challenges in BI deployment?
Enterprises are making good use of the information assets available to them. So, large organizations that have invested in BI are making profitable use of the investment in areas like customer management, financial management and internal process.
However, the major gap area remains in their ability to scale their BI operations across more users. Another big gap area is in latency of information available for BI based analytics. A large number of BI implementations still load their data once a week or once a month, therefore making immediate decision making in those areas impossible. Also, it is imperative to have a dedicated analytics server to handle data warehousing requirements with ease for faster and optimized query performance.
How will Service Oriented Architecture (SOA) impact BI deployment?
There is a big difference being brought in by SOA and related to it is the ability to improve data flow between different applications running on different infrastructure. The big boost for BI in this is the ease and flexibility it provides in building an information flow between operational sources and the EDW and downstream BI applications. This will make customers improve their abilities to act on events that happen, in a more "real time" basis.
How do you determine the cost of BI system?
The cost of implementing a BI system depends on the kind of applications you plan to run on them, the analysis that you need, the extent of data mining and reporting required. Also, what needs to be considered is the extract, transform and load (ETL) tool being used, the existing database and applications to be run on it, the hardware and storage requirement and the mining/reporting to be done. One needs to consider how much data growth is going to take place to determine the cost per terabyte.
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