BI and Analytics rank consistently in the top 2 priorities for CIO and business leaders in every survey. See this top 10 CIO Business and Technology Priorities in 2012 (Source: Gartner)
|Top 10 Business Priorities||Ranking||Top 10 CIO Technology Priorities|
|Increasing enterprise growth||1||Analytics and business intelligence|
|Attracting and retaining new customers||2||Mobile technologies|
|Reducing enterprise costs||3||Cloud computing (SaaS, IaaS, PaaS)|
|Creating new products and services (innovation)||4||Collaboration technologies (workflow)|
|Delivering operational results||5||Virtualization|
|Improving efficiency||6||Legacy Modernization|
|Improving profitability (margins)||7||IT Management and Cost Takeout|
|Attracting and retaining the workforce||8||CRM|
|Improving marketing and sales effectiveness||9||ERP Applications|
|Expanding into new markets and geographies||10||Security|
- Spot trends and anomalies in business data
- Conduct deep trend analyses using statistical and financial performance management software
- Perform “what if” analysis and predictive modeling to predict potential threats and opportunities
- Facilitate accurate, timely financial and regulatory reporting for proactive planning and budgeting
- Allow executives greater visibility into operational, financial and market risk
What is Business Intelligence (BI)?
- focus is on retrieval and delivery of data
- monitoring and identifying exceptions
- limited variability, ambiguity, uncertainty
- reporting, dashboards, scorecards, OLAP for bounded exploration and analysis
Business intelligence software allows companies to tap into their many databases and deliver easy‑to-comprehend insight to employees, management, and business partners. The focus is on answering “how am I doing”, “why”, and “what should I be doing?”
BI software – Query, reporting, analysis, scorecards and dashboards – is already being used by thousands of companies to find new revenue opportunities, reduce costs, reallocate resources, and improve operational efficiency.
What does BI at Apple look like? Apple’s Information Services and Technology department operates a Teradata enterprise data warehouse, along with Oracle databases. Apple uses “extract transform load”(ETL) and data integration tools from Informatica and other providers deliver access to multiple terabytes of data from SAP enterprise resource planning (ERP) software and other data sources. These provide reporting solutions for the company’s cross-functional business units, including marketing, sales, operations, support and finance.
What is Business Analytics?
- focus is on generation of new data, insight/foresight
- exploring data, finding insights
- expect uncertainty and probability and pattern rather than specific data
- computational and probabilistic techniques
Business analytics is about “anticipate and act” to drive Better Outcomes, Smarter Decisions, and Actionable Insights. Analytics has been defined as “the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions” (Davenport and Harris, Competing on Analytics, 2007). Analytics is an umbrella term that encapsulates data collection, statistics, data mining, predictive modeling, and decision sciences.
There are three types of data analysis:
- Predictive (forecasting),
- Descriptive (business intelligence and data mining) and
- Prescriptive (optimization and simulation)
See Predictive Analytics 101 for a quick overview.
Analytics is growing exponentially in competitive segments like consumer marketing. For example, NetFlix mines its video rental history database to recommend rentals to individual customers. American Express can suggest products to its cardholders based on analysis of their monthly expenditures.
A widely quoted example of predictive analytics insight is the Diapers and Beer sales corelation. A grocery chain used the BI capacity of Oracle software to analyze local buying patterns. They discovered that when men bought diapers on Thursdays and Saturdays, they also tended to buy beer. Further analysis showed that these shoppers typically did their weekly grocery shopping on Saturdays. On Thursdays, however, they only bought a few items. The retailer concluded that they purchased the beer to have it available for the upcoming weekend. The grocery chain could use this newly discovered information in various ways to increase revenue. For example, they could move the beer display closer to the diaper display. And, they could make sure beer and diapers were sold at full price on Thursdays.
What is Big Data?
We’ve entered a new phase in the “industrial revolution of data.” The consumer generates incredible amount of data as they shop, browse, click, comment, game on the Web. Credit card transactions, UPC barcode reads, RFID scans and GPS location data all add even more data. Piles of data is being created by every second sensors from traffic, heating, ventilation and air control (HVAC) and industrial plant monitoring to automotive sensors . For this streaming or event processing data, individual packets may be quite modest in size but start to become “big data” when aggregated and analyzed over many days, months and years.
The problem often isn’t finding data (search), it’s figuring out what to do with it and how to turn it into “relevant information”.
BI or Analytics – Where should you focus?
The line between BI and Analytics is rapidly getting blurred. However, some authors view analytics as a subset of business intelligence (BI): “a set of technologies and processes that use data to understand and analyze business performance ” and “includes both data access and reporting, and analytics” (Davenport and Harris, Competing on Analytics, 2007).
WalMart, for instance, does both. Retail Link is Walmart’s online warehouse for sharing up-to-date point-of-sale information with suppliers. WalMart captures point-of-sale transactions from over 3,900 stores and continuously transmits this data to its massive 10+ petabyte Teradata data warehouse. WalMart allows suppliers, to access data on their products and perform data analyses. These suppliers use this data to identify customer buying patterns at the store display level. They use this information to manage local store inventory and identify new merchandising opportunities. Back in 2007, Retail Link tracked some 800 million transactions per day, with detail down to the store and item level.
Definitions of Different BI Categories
- Corporate/enterprise performance management software and performance management concepts, such as the balanced scorecard, enable organizations to measure business results and track their progress against business goals in order to improve financial performance.
- Business intelligence (BI) is a necessary business competency for improving decisions and performance. the most widely used BI tool is the spreadsheets. Traditionally, BI has been used for performance reporting from historical data, and as a planning and forecasting tool for a relatively small number of people in an organization. Modeling future scenarios permits examination of new business models, new market opportunities and new products, and creates a culture of opportunity.
- Data visualization tools, include mashups, executive dashboards, performance scorecards and other data visualization technology, is becoming a major category.
- Data analytics software and advanced analytics techniques, including predictive analytics, text analytics and text mining, customer analytics and business intelligence – customer, supply chain – data mining, can help organizations make sense of — and gain a competitive advantage from — all the data that they have in their systems.
- BI platforms provide a range of capabilities for building analytical applications. Examples are Oracle OBIEE, SAP Business Objects 4.0. There are many choices and combinations of BI platforms, capabilities and use cases as well as many emerging BI technologies such as in memory analytics, interactive visualization and BI integrated search. The idea of standardizing on one supplier for all of one’s BI capabilities is difficult to do. Increasingly, standardization and more about managing a portfolio of tools used for a set of capabilities and use cases.
- Data integration tools and architectures in support of BI continue to evolve. Extract-Transfer-Load (ETL) tools make up a big segment of this category in addition to data mapping tools. Organizations must now support a range of delivery styles, latencies, and formats.
- Data Cleansing. The first step of any data analysis project is organizing and cleaning the data… basically “data conditioning,” or getting data into a state where it’s usable.
BI and Analytics Stack
- Predictive Analytics 101 (quick overview)
- IBM CIO Study: BI and Analytics are #1 Priority for 2012 (practicalanalytics.wordpress.com)