Today’s corporate end-users are far more tech-savvy than their productivity with IT tools indicates. After all, screen-deep in IMs, widgets, and elaborate consumer Web apps, they’re proving themselves well-versed in the production and distribution of content as facilitated by the consumer Web 2.0 craze. ... Business analytics @ Infoworld
are defined as the extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling, and fact-based decision-making. Analytics may be used as input for human decisions; however, in business there are also examples of fully automated decisions that require minimal human intervention. In businesses, analytics (alongside data access and reporting) represents a subset of business intelligence (BI).
The field of business analytics has improved significantly over the last few years, providing business users with better insights, particularly from operational data stored in transactional systems. As an illustrative example, analysis of e-commerce data has recently come to be considered a killer-app for data mining ....With many millions of clickstream records being generated on a daily basis and aggregated to records with hundreds of attributes, there is a clear need for automated techniques to find patterns in the data. In this paper we discuss the technology and enterprise-adoption trends in the area of business analytics..
Business Analytics focuses on effective use of data and
information to drive
positive business actions. The body of knowledge for this area includes both
business and technical topics, including concepts of performance management,
definition and delivery of business metrics, data visualization, and deployment
and use of technology solutions such as OLAP,
dashboards, scorecards, analytic applications, and
data mining ...more
Definition: Business Analytics refers to the methodology employed by an organization to enhance its business and make optimized decisions by the use of statistical techniques i.e. collecting data, assembling and analyzing it to better their products, services, marketing etc.
History of Business Analytics
The use of analytics in business has been around for over two centuries now. Back in the late 1800's, the science of data analysis found use mainly in the development of industrial machinery. It wasn't until the 1960's that organizations realized the potential of analytics and made their first attempts at automating the data analysis process. These efforts included heavy usage of mainframe computers which were assigned the more tedious tasks associated with data storage and processing but these were known as the very first "decision support systems”.
With time, however, business intelligence has evolved tremendously. As early as the 1970's, software was readily available that could handle huge amounts of statistical information and process the data very quickly for the busy executive. Though these software packages were largely overlooked, they have now developed to the point that highly sophisticated infrastructure including hundreds of terabytes in storage and the latest technology in processors and security applications have been solely dedicated to them. For example, Wal-Mart, the "discount store giant” was known to singularly possess over 460 terabytes of analytical data in 2004 stored in mainframes (source) which is rumored to have grown to nearly 600 TB by now.
Applications of Business Analytics
Business analytics comprises a great many techniques that deal with statistics. The most commonly used of these are product tracking systems and surveys. When these techniques are applied accurately, they open up numerous avenues of opportunity for organizations to optimize and augment their business model. A few such possibilities are explored below:
* Critical product analysis
Data retrieved from various markets can indicate how well a product is received by the target audience and also what crucial changes must be made to it to maximize revenue. Furthermore, the organization can gain insight as to how a particular territory or market reacts to certain features of the product. This not only makes it possible to make minor alterations to the item for specific locations but also helps in studying trends associated with those regions.
* Improved customer service
Organizations have learned through experience to keep track of their recurring customer queries and support issues. This tremendously helps them in enhancing performance by not repeating past mistakes while also enabling them to provide faster customer service and satisfaction.
* Up-selling opportunities
Tracking trends in customer behavior can be very helpful in identifying the most prominent needs of a company's customer base, hence allowing it to up-sell or cross-promote products that cater best to customers' needs.
* Simplified inventory management
This is an often ignored yet very efficient application of business analytics. With the help of trend-tracking, business managers can minimize write-offs to a great extent, in turn, minimizing losses due to outdated inventory items. For example, analytic data gathered beforehand can help predict which products are on the verge of going obsolete in the near future. With this information, those specific products can be gotten rid of before they end up as write-offs.
* Competitive pricing insights
This is yet another important highlight of business analytics that is ironically used by very few players in the game. Companies are known track trends in purchasing behavior among customers to figure out price ranges that the target market is most comfortable in. Thus, businesses can effectively make their prices competitive without having to cut down too much on profits.
Disadvantages and limitations
Companies that are new to the analytics game often start fresh and without any data to begin with. In this case, they will have to focus on gathering or purchasing data first. Data gathering can take quite some time as census studies and surveys are generally the most trustworthy methods of doing this. Alternatively, companies could purchase data already collected bt third-party sources but this can come with many risks including obsolete records or inaccuracies. Thus, without high-quality statistics, business analytics can be time-consuming and/or expensive for new organizations.
Another huge turn-off for most executives and business managers is that researchers estimate discrepancies in analytical data very high. In some databases, this was up to 1/4th of all the existing records. The main problem here is duplicate and misleading records caused by flawed entries.