Business Analytics

CMBA675 - 9040

AGENDA

Lessons Being Learned from Week 2

Where We Are in CMBA 675

Big Data and Business Analytics

Descriptive analytics

Predictive analytics

Prescriptive analytics

Overview of Week 3 Assignments

“Office Hours” – Your questions or concerns

Lessons Being Learned from Week 2

Operations Management (OPM) offers lots of tools and concepts to improve business efficiency

Use of OPM tools is growing and expanding New ideas and tools becoming popular

Many types of models already exist Quantitative models Decision support systems Simulation Analytics models

Types of Analytics

Descriptive analytics – gathering, organizing, tabulating, describing and depicting data.

Predictive analytics – identify the associations among data and predict the likelihood of the future occurrence of similar events.

Prescriptive analytics – using such techniques as experimental design and optimization to answer the question of “why”.

Source: Davenport, T., & Kim, J. (2013). Keeping up with the quants. Boston: Harvard Business Review Press, page 3.

Business Analytics Descriptive: [discovery phase]

Looks at past data and tries to find important relationships to gain insights as to how to approach the future. Tries to answer the question, “What has happened?” Uses data visualization techniques along with data mining techniques to unearth key relationships. Key terms: dashboards, data visualization, data mining, knowledge discovery

Predictive: [prediction phase] Applies modeling techniques to find a quantitative algorithm that relates inputs

and outputs to provide actionable insights. Tries to answer the question, “What will likely happen?”. Uses machine learning and other modeling approaches to quantify the relationships. Key terms: neural networks, regression, machine learning, classification, decision trees

Prescriptive: [operationalizing phase] Tries to answer the question, “What do I do when the expected or predicted

happens?” Prescriptive analytics focuses on optimization techniques and suggests actions designed to improve business operations. Attempts to leverage what has been learned from descriptive and predictive analytics to develop improved business solutions. A large part of prescriptive analytics includes the use of ‘business rules’ to embed the analytics findings into operations. Key terms: business rules, decision management, optimization models

Source: Davenport, T., & Kim, J. (2013). Keeping up with the quants. Boston: Harvard Business Review Press

How is Analytics used?

Source: Aligned Resource Optimization , retrieved from: http://www.sas.com/resources/whitepaper/wp_4183.pdf

How is Predictive Analytics used?

Associations: e.g., linking purchase of diapers with beer

Sequences: e.g., linking events in order or together, such as

graduating from college and buying a new car

Classifications: e.g., recognizing patterns, such as the signs

of customers who are most likely to leave the company for a competitor; applicants as low, medium, or high risk; nature of insurance claims as normal or suspicious

Forecasting: e.g., predicting buying habits of customers

based on past patterns

Estimation: e.g., estimate the probability of positive response to a direct mail campaign; Estimate customers’ lifetime value to the enterprise.

Prediction: e.g., predict customers who are likely to attrite; predict the number of customers who will accept an introductory zero interest credit card offer and not repay within the time limit of the offer.

What types of decisions can predictive analytics help with?

Source: Wessler, M. (2014). Predictive Analytics for Dummies. : Wiley. Retrieved from: http://media.wiley.com/assets/7225/54/9781118859643_custom.pdf

Some Recent Success Stories:

Source: Goldstein, M. (Webinar, Apr 16, 2015) Beyond the numbers: Using Prediction to Save Lives. Retrieved Apr 20, 2015, from http://www.information-management.com/web_seminars/beyond-the-numbers-using-predictive-to-save-lives-10026665-1.html

Source: Taylor, J. (2015). Analytics Capability Landscape. Decision Management Solutions.

http://www.information-management.com/pdfs/Analytics_Capability_Landscape_Report_Final_1.11.15.pdf?

How does the use of analytics improve decision making?

Helps “frame” the decision

Provides “transparency” of decision process

Enables the handling of (much) more data

Speeds up decisions (?)

Fosters innovation

Enables the use of more complex models

Performs complex calculations

Standardizes approach to decision making

Provides an audit trail for decisions

Improves/changes the business model

Raising the bar with analytics (based on a recent survey of over 2,000 managers)

Source: Kiron, D., Prentice, P., & Ferguson, R. (MIT SLOAN Management Review, Winter 2014). Raising the bar with analytics.

Raising the bar with analytics (based on a recent survey of over 2,000 managers)

Source: Kiron, D., Prentice, P., & Ferguson, R. (MIT SLOAN Management Review, Winter 2014). Raising the bar with analytics.

Raising the bar with analytics (based on a recent survey of over 2,000 managers)

Source: Kiron, D., Prentice, P., & Ferguson, R. (MIT SLOAN Management Review, Winter 2014). Raising the bar with analytics.

Raising the bar with analytics (based on a recent survey of over 2,000 managers)

Source: Kiron, D., Prentice, P., & Ferguson, R. (MIT SLOAN Management Review, Winter 2014). Raising the bar with analytics.

The Analytics Mandate (based on a recent survey of over 2,000 managers)

Kiron, D., Prentice, P., & Ferguson, R. (2014, May 12). The Analytics Mandate. . Retrieved May 16, 2014, from http://sloanreview.mit.edu/projects/analytics-mandate/

Limitations with using Analytics

Source: Lavastorm Analytics Survey, “Analytical Skills, Tools and Attitudes 2013”, Retrieved from: http://www.lavastorm.com/assets/Lavastorm-Analytics-Survey-Skills-Tools-and-Attitudes-October-2013.pdf

Source: Lavastorm Analytics Survey, “Analytical Skills, Tools and Attitudes 2013”, Retrieved from: http://www.lavastorm.com/assets/Lavastorm-Analytics-Survey-Skills-Tools-and-Attitudes-October-2013.pdf

Limitations with using Analytics

Source: Lavastorm Analytics Survey, “Analytical Skills, Tools and Attitudes 2013”, Retrieved from: http://www.lavastorm.com/assets/Lavastorm-Analytics-Survey-Skills-Tools-and-Attitudes-October-2013.pdf

Limitations with using Analytics

Source: Lavastorm Analytics Survey, “Analytical Skills, Tools and Attitudes 2013”, Retrieved from: http://www.lavastorm.com/assets/Lavastorm-Analytics-Survey-Skills-Tools-and-Attitudes-October-2013.pdf

Limitations with using Analytics

What to watch out for:

Learning things that are not true Patterns may not represent the real underlying rule

The model set may not reflect the real population

Data may be at the wrong level of detail

Learning things that are true, but not useful Learning things that are already known (or should be

known)

Learning things that cannot be used (time and resources wasted)

What’s Changing?

Analytics “in the cloud”

Mobile analytics

The use of realtime data Internet of Things

http://www.wired.com/wiredscience/2013/05/sensors-listen-to-world/?cid=co8356744

Embedded intelligence/actions in results (OPM)

Fitting analytics with agile SCM, CRM, and ERP

Big Data – How Big?

Some numbers From 2005 to 2020, the digital universe will grow by a factor of 300, from

130 exabytes to 40,000 exabytes, or 40 trillion gigabytes (more than 5,200 gigabytes for every man, woman, and child in 2020). From now until 2020, the digital universe will about double every two years.

Five exabytes (10^18 gigabytes) of data would contain all words ever spoken by human beings on earth.

In 2011, 1.8 zetabytes of information were created globally, and that amount is expected to double every year. This volume of data is the equivalent of 200 billion, 2-hour HD movies, which one person could watch for 47 million years straight.

Only a tiny fraction of the digital universe has been explored for analytic value. IDC estimates that by 2020, as much as 33% of the digital universe will contain information that might be valuable if analyzed.

The Internet of Things (IoT) will connect 6 billion objects to the internet by 2015

Communicate with each other, with people, within the body

“smart objects” will be able to carry out operations/decisions

Source: Fisher, L. (19 May 2013). The internet of Things: In action. Retrieved from: http://thenextweb.com/insider/2013/05/19/the-internet-of-things-in-action/

Source: Tech America foundation report, “Demystifying Big Data: A practical guide to transforming the business of government”, (Oct 2012). Retrieved from: http://www.techamerica.org/Docs/fileManager.cfm?f=techamerica-bigdatareport-final.pdf

Source: Reddy, B. (2011). Introduction to Predictive Analytics. Retrieved from: http://www.youtube.com/watch?v=VE-Os0Nujk8

Unstructured Data

What is your analytics maturity?

Understanding the Analytics Quotient Maturity Model

https://www14.software.ibm.com/webapp/iwm/web/signup.do?source=swg-BA_WebOrganic&S_PKG=ov4398

Quiz to determine your organization’s analytics quotient maturity

http://www-01.ibm.com/software/au/analytics/aq/index.html?mc=web_aq_microsite_au

Predixion – Developing predictive analytics

models

http://www.predixionsoftware.com/

Classification Model Walkthrough. Password: classify2

BigML – Decision Trees and Clustering https://bigml.com/

BigML walkthrough. Password: BigML1

Palisade – multiple analytical tools

http://www.palisade.com/

Neural Tools walkthrough Password: neural1

Predictive Analytics – end user software

Overview of Week 3 Assignment

Individual Assignment: Complete the business analytics exercise specified in the weekly guidance, using the Neural Tools software package referenced. For the exercise, write a brief (one to two double-spaced pages) summary of the results and insights gained. Specify how you see the methods/techniques exemplified in this exercise applicable to a situation, challenge, company or industry that you are associated with, know about, or have interest in. Also address what advantages or benefits the analysis realizes, and why.

Start with the Week 3 Guidance which lists several links to get you started with Neural Tools

See Week 3 Guidance for details

“Office Hours” - Your Questions or Concerns

Ask now if there is confusion at this point on the assignment or

materials for this week.

Ask anytime during the week (use “Let me have any questions”

discussion), especially if:

Clarification would benefit the entire class

Clarification would be useful throughout the course

Ask via personal phone call if:

Question/clarification applies only to you or your team

Emergency situation where information is needed asap