Business Intelligence

Business intelligence (BI) is the set of techniques and tools for the acquisition and transformation of raw data into meaningful and useful information for business analysis purposes. The term “data surfacing” is also more often associated with BI functionality. BI technologies are capable of handling large amounts of unstructured data to help identify, develop and otherwise create new strategic business opportunities. The goal of BI is to allow for the easy interpretation of these large volumes of data. Identifying new opportunities and implementing an effective strategy based on insights can provide businesses with a competitive market advantage and long-term stability.

BI can be used to support a wide range of business decisions ranging from operational to strategic. Basic operating decisions include product positioning or pricing. Strategic business decisions include priorities, goals and directions at the broadest level. In all cases, BI is most effective when it combines data derived from the market in which a company operates (external data) with data from company sources internal to the business such as financial and operations data (internal data). When combined, external and internal data can provide a more complete picture which, in effect, creates an “intelligence” that cannot be derived by any singular set of data. Amongst myriad uses, BI tools empower organizations to gain insight into new markets, assess demand and suitability of products and services for different market segments and gauge the impact of marketing efforts.

The BI is related with the business topics:

• Support decision making
• Business performance management
• Benchmarking
• Customer clusterization
• Group consolidation, budgeting and rolling forecasts
• Key performance indicators optimization

The BI is related with the data science topics:

• Data mining
• Process mining
• Complex event processing
• Predictive analysis
• Text mining: to transform unstructured data to easy treatable data
• Multidimensional aggregation and allocation
• Version control and process management
• Statistical inference and probabilistic simulation
• Realtime reporting with analytical alert
• Denormalization, tagging and standardization
• A method of interfacing with unstructured data sources

We can do a simplistic pragmatic ad-hoc definition of BI through the path it follows the used data:

• Data collection:
  • From other company or db: we only have to connect both databases and get the data. Usually is structured data.
  • From unstructured sources
• Data cleaning: adapt the data to our data structures needed by the methods we are going to use.
  • Data Standardization: make data comparable (same unit, same pattern…)
  • Master Data Management: unique referential.
  • Data Cleansing: detect & correct inaccurate data.
  • Data Profiling: check inappropriate value, null/empty.
• Data warehouse and data managing: keep usable this data. Technical support for using data methods.
  • Completeness: check that all expected data are loaded
  • Referential integrity: unique and existing referential over all sources
  • Consistency between sources: check consolidated data vs sources
• Analytics: use statistical or machine learning techniques to get insights of the data or usable conclusions.
  • Apply general models to get some specific insights.
  • Build statistical models to fit the data.
• Measurement: program that creates a hierarchy of performance metrics and benchmarking that informs business leaders about progress towards business goals (business process management).
  • Create descriptive simple and understandable measures to help in the business decision making.
• Reporting: make the conclusions easy to understand. Some times it relies in external and extended data ways to
  • For the clients: which involves mainly visualization techniques, and key indicators
  • For the executives managers: which involves visualization and key indicators, but also methods and tools used and needed to keep the system going.
  • For the technical managers: which involves visualization, methods information and accurate technical descriptions and indicators, as computational time or memory used, tools used, technical problems faced…

It appeared the concept of Business Intelligence 2.0 as an update of new methods in data science and tools as cloud computing and other support systems.

See also

Material

• Raden, Neil (2005). “Start Making Sense: Get From Data To Semantic Integration”
• Nelson, Greg (2010). “Business Intelligence 2.0: Are we there yet?”. SAS Global Forum 2010.
• Berners-Lee, Tim (2006). “Linked Data”. w3c.org.
• “The Key Role Hadoop Plays in Business Intelligence and Data Warehousing” - St. Joseph’s University

Papers

• Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS quarterly, 36(4), 1165-1188.
• Watson, H. J., & Wixom, B. H. (2007). The current state of business intelligence. Computer, 40(9), 96-99.
• Golfarelli, M., Rizzi, S., & Cella, I. (2004, November). Beyond data warehousing: what’s next in business intelligence?. In Proceedings of the 7th ACM international workshop on Data warehousing and OLAP (pp. 1-6). ACM.

Books

• Turban, E., Sharda, R., & Delen, D. (2011). Decision support and business intelligence systems. Pearson Education India.
• Scheps, S. (2011). Business intelligence for dummies. John Wiley & Sons.
• Turban, E., Sharda, R., Aronson, J. E., & King, D. (2008). Business intelligence: A managerial approach. Upper Saddle River, NJ: Pearson Prentice Hall.
• Rausch, Peter; Sheta, Alaa; Ayesh, Aladdin (2013). Business Intelligence and Performance Management: Theory, Systems, and Industrial Applications, Springer Verlag U.K.
• Moraschi, D. (2013). Business Intelligence with MicroStrategy Cookbook. Packt Publishing Ltd.