Emerging Trends of Business Analytics

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Thanks to the authors and Stanford for the advanced topics and the emerging trends in the area of Business Analytics.

Business Analytics:

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. The data sets created by integrating clickstream

records generated by web sites with demographic and other behavioral data dwarf, in size and

complexity, the largest data warehouses of a few years ago, creating massive databases that

require a mix of automated analysis techniques and human effort in order to provide business

users with critical insight about the activity on the site and the characteristics of the site’s visitors and customers. 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-adoptiontrends in the area of business analytics.

The key consumer of these analytics is the business user, a person whose job is not directly

related to analytics per-se (e.g., a merchandiser, marketer, salesperson), but who typically must use analytical tools to improve the results of a business process along one or more dimensions (e.g., profit, time to market). Fortunately, data mining1, analytic applications, and business intelligence systems are now being better integrated with transactional systems creating a closed loop between operations and analyses that allows data to be analyzed faster and the analysis results to be quickly reflected in business actions. Mined information is being deployed to a broader business audience, which is taking advantage of business analytics in everyday activities. Analytics are now regularly used in multiple areas, including sales, marketing, supply chain optimization, and fraud detection.

The Business Users and their Challenges

Despite these advances in analytic systems, it continues to be the case that the business user,

while an expert in his area, is unlikely also to be an expert in data analysis and statistics. To

make decisions based on the data enterprises collect, the business user must either rely on a data analyst to extract information from the data, or employ analytic applications that blend data analysis technologies with task-specific knowledge. In the first case, the business user must impart domain knowledge to the analyst, then wait while the analyst organizes the data, analyzes it, and communicates back the results. These results typically raise further questions and hence several iterations are necessary before the business user can start acting on the analysis. In the second case, analytic applications must not only incorporate a variety of data mining techniques, but also provide recommendations to the business user of how to best analyze data and present the extracted information. Business users are expected to better utilize the extracted information and improve performance along multiple metrics. Unfortunately, the gap between the relevant analytics and the critical needs of the intended business users still remains significant. The following challenges highlight characteristics of this gap:

1. The time to perform the overall cycle of collecting, analyzing, and acting on enterprise
data must be reduced. While business constraints may impose limits on reducing the
overall cycle time, business users want to be empowered and rely less on other people to
help with these tasks.

2. Within this cycle, the time and analytic expertise necessary to analyze data must be
reduced.

3. Clear business goals and metrics must be defined. In the past, unrealistic expectations
about data mining “magic” led to misguided efforts without clear goals and metrics.

4. Data collection efforts must have clear goals. Once metrics are identified, organizations
must strive to collect the appropriate data and transform it. In many situations, data
analysis is often an afterthought, restricting the possible value of any analysis.

5. Analysis results must be distributed to a wide audience. Most analysis tools are designed
for quantitative analysts, not for the broader base of business users who need the output
to be translated into language and visualizations that are appropriate for the business
needs.

6. Data must be integrated from multiple sources. The extract-transform-load (ETL)
process is typically complex and its cost and difficulty are usually underestimated.