1. Chapter 7: Business Intelligence and Decision Making
Making smart decisions takes a clear understanding of the information relevant to solving the problem, and knowledge about where that information can be obtained. It also takes insightful analysis, often relying on sophisticated software tools that can do much of the work. This chapter explains what business intelligence is, why you need it, where you can find it, and what decision support tools are available to help you analyze it all. You will also learn how to organize and display the information that is most important to you.
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2. Learning objectives Decision making Business intelligence Data mining
Web analytics
Dashboards and portals
The material in this chapter will enable you to:
Define business intelligence and describe the three levels of decision making that it supports.
Describe the major sources of business intelligence and provide examples of their usefulness.
Explain several approaches to data mining and decision support that help managers analyze patterns, trends and relationships, and make better data-driven decisions.
Explain how web analytics are used as a source of business intelligence, and why they are so valuable for understanding customers.
5. Describe how dashboards, portals, and mashups support decision making, and explain the role that the human element plays in using these tools.
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3. Business intelligence
Information for data-driven decision making
Includes software applications, technologies, and practices
Business intelligence (BI) is an umbrella term that includes the vast quantities of information an organization might use for data-driven decision making, from within its own data repositories and also from external sources.
The term also encompasses the software applications, technologies, and practices that managers apply to the data to draw out the insights that help them make better decisions.
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4. Levels of decision making
Employees working at the operational level make countless decisions as they deal directly with customers and handle routine transactions. Operational-level decision making requires detailed, structured information about actual transactions by customer, so staff can follow up and track each transaction. Many decisions follow predetermined policies and procedures that spell out how to handle different situations. Timely data showing the outcome and effectiveness of decisions can dramatically affect performance.
People at tactical levels draw on business intelligence to make mid-level decisions, the kind that may guide individual business units. Decisions about marketing plans, product development, membership drives, departmental budgets, and other initiatives are generally tactical. At the tactical level, decision makers need more aggregated information summarized in different ways to monitor success and plan next steps.
The organization’s leadership guides longer-term strategy. Decisions made at this level can have widespread effects throughout the organization and beyond to suppliers, customers, and the entire industry. While executives need summary and historical data from the company’s own transactional systems and data warehouses, they also need competitive business intelligence about rivals, broader information about the industry landscape, and a forecast of overall economic and business conditions.
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5. Sources of BI Internal External Transactional databases
Data warehouses
Cloud
External databases
Websites, social networks, and text messages
Throughout the organization and well beyond its borders, managers find valuable sources of information that can improve decision making.
The heart of BI is the transactional systems used for daily operations. Within the organization’s own databases is a treasure trove of data about customers, employees, suppliers, and every financial transaction. To avoid slowing down operational systems, most organizations build data warehouses by extracting part or all of the data from those databases and moving it to another server. This process offers an opportunity to combine the data housed in separate systems, cleanse and transform it, and load it to one database optimized for complex queries.
As individuals put more and more information online, and organizations deploy software as a service, considerably more data that is useful for BI exists in the “cloud”—on servers that could be quite distant from the company’s headquarters. External databases that are either purchased or publicly accessible are also excellent sources of business intelligence. Websites, blogs, wikis, social networks, photo sharing sites, video repositories, discussion forums, and text messages may contain insights, and business intelligence can draw from these sources. Technological advances are giving these sources more structure and making it easier to access and analyze these sources.
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6. Analyzing patterns Online analytical processing
Statistics and modeling techniques
Text mining
Online analytical processing (OLAP) systems allow users to interactively retrieve meaningful information from data, examine it from many different perspectives, and drill down into specific groupings. The software allows users to “slice and dice” massive amounts of data stored in data warehouses to reveal significant patterns and trends. OLAP systems use multidimensional cubes, which are data structures that contain detailed data and aggregated values for dimensions, measures, and hierarchies.
Data mining and decision support systems rely on statistical analysis and models to identify real patterns and differences that probably did not occur by chance. Statistical relationships can be especially useful to spot. For example, a technique called market basket analysis looks for relationships to reveal customer behavior patterns as they purchase multiple items. Results show meaningful patterns that help retailers decide how to position products in physical stores and online.
Dipping into the vast storehouse of unstructured text-based data contained in s, blogs, tweets, online product reviews, and comments yields critical business intelligence. Text mining, a variation of data mining, is a discovery process in which unstructured text information is the source of business intelligence. Text mining software tools rely on key words, semantic structures, linguistic relationships, parts of speech, common phrases, emotion-laden words, and even misspellings to extract meaningful information.
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7. Simulating and forecasting
What-if analysis
Goal seeking and optimization
Forecasting
What-if analysis builds a model that establishes relationships between many variables and then changes some variables to see how other variables are affected. Excel is popular for building relatively simple what-if models. A what-if model can also prepare a sensitivity analysis, which calculates the impact of changes in a single variable.
Goal seeking is similar to what-if analysis, but in reverse. Instead of estimating several variables and calculating the result, the user sets a target value for a particular metric, such as profit/loss, and tells the software which variable to change to try to reach the goal. An extension of goal seeking is optimization, in which the user can change many variables to reach some target, as long as changes stay within constraints identified by the user. Optimization is used in many settings in which the best solution must meet many constraints. For example, airlines try to optimize profit per flight, taking into account constraints such as fuel costs, ticket discounts, gate availability, airport congestion, and connections.
Forecasting tools analyze historical and seasonal trends and then take into account existing and predicted business conditions to estimate some variable of interest.
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8. Artificial intelligence
Examples
Challenge
Amazon.com
ALADDIN
Jeopardy!
Scammers
CAPTCHA
Artificial intelligence (AI) describes the capability of some machines to mimic human intelligence and display characteristics such as learning, reasoning, judging, and drawing conclusions from incomplete information.
For example, Amazon’s software continually learns about each customer to make more relevant recommendations for new purchases. An ongoing project called ALADDIN is working out ways for large collections of intelligent agents to draw on incomplete information from sensors, share what they learn, and collaborate on decision-making. This kind of AI is valuable for fast-moving emergency situations when the volume of incoming data could overwhelm human decision makers.
The AI underpinnings for Watson, IBM’s supercomputer that beat human champs in the game show Jeopardy! in 2011, show extraordinary promise. The stunt demonstrated that AI has a very bright future for interpreting human speech and drawing on an immense knowledge base to find answers.
One downside of the growing power of AI is that scammers use the same techniques to gain access to sites, gather information, or overwhelm a service. One such obstacle that thwarts most software bots is the CAPTCHA, a test the visitor must pass before continuing to register or enter the site. One variety presents an image of some letters and numbers, and the user must correctly read and enter them before proceeding.
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9. Artificial intelligence applications
Robotics
Expert systems
Neural nets
Some of the most useful applications of AI for organizations are found in robotics, expert systems, and neural nets. Industrial robots have been in use for decades to assemble cars or clean up floors, but service robots with more intelligence and mobility are appearing in business, government, and other sectors. Service robots that can take on military, security, or rescue operations are particularly in demand as they become more mobile and capable of replacing humans on dangerous assignments.
An expert system mimics the reasoning of a human expert, drawing from a base of knowledge about a particular subject area to come to a decision or recommendation. To build an expert system, developers work with experts in their specialty as they provide answers to questions and explain their reasoning processes. The output is fine-tuned continually as the experts contribute more knowledge to the base, refine the rules, and add additional questions.
Neural networks attempt to mimic the way the human brain works. The neural net learns from training data selected by humans that contains cases defining the paths from input to output. For example, a neural net being trained to predict housing values could absorb millions of cases in which the input includes location, sales price, square footage, and number of bathrooms. Neural nets are widely used where massive data sets are available, such as in the finance industry where detecting fraud is an important application.
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10. Web analytics Website Social media E-commerce
The sheer volume of business intelligence available from an organization’s own website can be overwhelming. For example, clickstream data includes every single click by every visitor, and may cover millions of clicks per day.
Measures of website traffic are important to all organizations. Web metrics come from server logs, and each entry contains detailed information about the date and time, page, source, and clicks on the page. The logs also contain information about each user, including IP address and browser. If the site uses cookies, it can collect more information about each user.
For interactive websites with registered members who contribute their own materials and build friendship networks, even more metrics may be available to analysts. Number of active users, posts per user, photo tags, profile information, purchases, data on friends, group memberships, and product ratings are examples of what these sites can collect.
If a website includes advertising or e-commerce capabilities, many metrics about visitors and content will be relevant, such as the nature of visitors, how visitors found the site, how long they stayed, and the pages they found interesting.
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11. Analyzing traffic Analyzing software Reaching goals Ad effectiveness
Organizations must rely on analytical tools to make sense of all this information, especially given its volume. Products specifically designed to analyze clickstream data are getting more powerful, with easy-to-use interfaces, graphing capabilities, and advanced statistical techniques. Analysts can quickly see important details about their web traffic, their visitors, and the links that bring customers to their site.
Web analytics software spews out thousands of aggregated graphs, tables, and charts. To use it wisely to make decisions, companies should have a clear notion of the major goals for the site so they know what to look for. These goals will guide them toward the appropriate metrics, so they can see whether their decisions bring improvements.
E-commerce metrics that summarize data on advertising campaigns are essential for marketers. Cost per clickthrough, conversion rates, and other measures reveal how well ads are doing and whether advertising dollars are being spent wisely. Over time, historical patterns can also predict how much the company needs to spend on online campaigns to achieve sales goals.
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12. Dashboards Graphical user interface Key performance indicators
Staying on top of this endless stream of data from so many business intelligence sources can be an immense challenge. Business users need strategies to sort it out and pick the most useful data to make good decisions.
Like a plane or car dashboard, the IT dashboard is a graphical user interface that organizes and summarizes information vital to the business user’s role and decisions. The dashboard should summarize key performance indicators (KPI), which are the quantifiable metrics most important to the user’s role and organization’s success.
Graphical user interface
Key performance indicators
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13. Portals Variety of information on one screen Enterprise portal
Portals are gateways that provide access to a variety of relevant information from many different sources on one screen. Portal users, who include customers and suppliers as well as employees, access the portal with a company-supplied login ID and password. That login determines which applications users are able to access and what level of access they are granted. From within the portal, users can personalize the display. Enterprise portals were inspired by the consumer portals offered by several major web companies that help people aggregate content to their liking.
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14. Mashups Aggregate data from multiple sources Customizable web pages
Users want to easily aggregate an increasing array of content from countless business intelligence sources, merging maps with customer data, combining dashboards, news sources, and Excel spreadsheet data, adding live camera feeds, and blending all kinds of information in imaginative ways that support their work roles. Mashup is a newer approach to aggregate content from multiple internal and external sources on customizable web pages. This approach relies on Web 2.0 technologies and open programming interfaces and standards such as XML to blend content and updated feeds from various sources in innovative ways.
Mashups can easily incorporate a web feed, which is standardized and regularly updated output from a publisher such as CNN or Weather.com. Simple consumer-oriented mashups with maps, feeds, data, and other elements can be created using free online tools such as Yahoo Pipes. For enterprise mashups, organizations use software platforms, such as the IBM Mashup Center, which make it easier for developers to build secure and robust modules that draw on enterprise resources as well as external content.
Aggregate data from multiple sources
Customizable web pages
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15. Human element
Although humans choose intelligence, tools, and interpretation, they have weaknesses.
Must consider human motives in design and implementation of information systems.
While humans are the critical element in decision making, choosing what intelligence to rely on, what tools to use, and how to interpret results, we will still sometimes make poor decisions. For example, research shows that people in positions of power are more likely to seek information that confirms what they already believe and avoid information that could refute it. That means we must consider human motives, weaknesses, and capabilities when designing and implementing effective information systems.
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16. Summary Decision making Business intelligence Data mining
Web analytics
Dashboards and portals
Business intelligence encompasses an array of information sources that contribute to better decision making. Levels of decision making that draw on different types of information sources include operational, tactical, and strategic.
A primary source of business intelligence is the transactional database or data warehouse used by the organization for operations. External online data can also be sources.
Data mining and decision support tools are used to analyze patterns, trends and relationships include online analytical processing (OLAP), statistics and modeling techniques, and text mining software.
The organization’s website is a key source of business intelligence with its own metrics. For e-commerce and advertising, web analysts rely on display ads and search engine ads, with their own metrics and payment schemes.
5. Dashboards provide graphic displays that summarize key performance indicators (KPIs), and their content can be customized to meet the needs of individual users. Enterprise portals control access to the organization’s resources, and mashups also aggregate content.
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17. Insurance fraud case $68 billion lost in fraud each year
Fraud detection systems rely on rules and patterns detected by analytic engines
Analysts work closely with investigators to apply human judgment as they create new rules and follow data leads
About 3 percent of the $2 trillion spent on healthcare in the United States is wasted on fraud every year. Data mining and analytics provide insurer’s special investigative units with information about potentially fraudulent billing patterns and claims buried in millions of legitimate claims, spotting unusual trends that no human being working alone could ever see.
Fraud detection systems rely on rules developed by human beings, and on patterns and trends that analytic engines detect. A key step is to spot fraud before any claim is paid. However, with pressure on payers to reimburse quickly, the time window is short. Fraud detection systems can operate quickly enough to catch suspicious claims before they are paid.
Analysts must work closely with investigators to apply human judgment as they create new rules and follow data leads. Continued training is essential, especially because the fraudsters continue to launch novel and increasingly complex schemes, changing their tactics to stay a step or two ahead. Knowing when, where, and how to drill down into the data to see new patterns and trends is a skill agents must learn.
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18. TV and Twitter Nielsen relies on ‘People Meters’ in homes
Social media metrics evaluate audience engagement by tracking Twitter tweets
Relationship between Twitter and TV ratings confirmed by research
To understand the audience for TV shows, TV ratings giant Nielsen relies on electronic ‘People Meters’ placed in representative sample of homes to track viewing habits. The company also has its viewers fill out paper-and-pencil diaries about their TV viewing habits. These ratings affect not only the show’s survival, but the cost of the ads that appear during the show.
In the age of Twitter, however, TV viewing is becoming a social experience that involves many more people than those in a single home. Nielsen teamed up with SocialGuide, a social media metrics company that measures the engagement of the TV audience by tracking tweets and posting the results on dashboards for the company’s clients. The software tracks over 30,000 programs, so it can generate comprehensive comparison ratings for ‘social TV’ viewers. The use of Twitter feeds to analyze social TV patterns adds a great deal to Nielsen’s capabilities.
Twitter feeds also have disadvantages as an audience rating tool, however. The tweets are not generated from a representative sample, for instance, so their content is biased toward a certain population of viewers. Despite drawbacks, research confirms a relationship between Twitter activity and TV ratings measured by Nielsen’s other tools. Social TV may also be drawing people back to viewing shows live rather than recording them. If Twitter volume is high during the show, it means that people have points to make in real time. Nielsen is leading the way toward new ways to learn about social TV.
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19. Chapter