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These are just an overview of the three types of analytics primarily used in business

“You can look at a series of points on a two -dimensional graph, for example, andnotice a pattern or relationship. Are there outliers in a pattern that requireexplanation? Are some values out of range? “ [1]

“Visual analysis helps us to “stay close to the data” using “exploratory data analysis(EDA),” an approach that the great statistician John Tukey made respectable andEdward Tufte further popularized by helping people create clear visualrepresentations of their data.

You may remember from your college statistics course that “measures of centraltendency”— means, medians, and modes (everybody always forgets what a mode is;it’s simply the category with the highest frequency)— are useful ways to express

what’s going on in data. Sometimes analysis simply means a visual exploration of datain graphic form.

Standard reasons *anyone* working with data should do EDA:(1) Gain intuition about the data(2) Make comparisons between distributions(3) Sanity checking – make sure the data is on the scale you think is, in the format youthought it should be(4) Finding out where data is missing or there are outliers

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(5) Summarizing the data “ ---Davenport, T., Harris, J. and Morison, R (qtd in the bookfrom Analytics at Work: Smarter Decisions, Better results. 2012).

[1] Davenport, T., Harris, J. and Morison, R. (2012) Analytics at Work: SmarterDecisions, Better results.

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“Some areas of analytics, like environmental sustainability, haven’t historically useddata or analysis, so you could become more analytical by creating simple metrics ofkey activities, reporting them on a regular basis, and acting on the patterns thatemerge.

The key is to always be thinking about how to become more analytical and fact basedin your decision making and to use the appropriate level of analysis for the decisionat hand

It’s not our goal in this Module to provide you with a list of all possible analyticaltools, but rather to persuade you that putting analytics to work can help yourmanagers and employees make better decisions, and help your organization performbetter.

—when analyses and decisions are working well, not to rest on your laurels, lest youget stuck in a decision-making rut and be unable to adapt quickly when conditionschange.

This initial step would accomplish a lot —but getting an organization to agree uponmetrics in a new area is no easy task.” ---Davenport, T., Harris, J. and Morison, R (qtdin the book from Analytics at Work: Smarter Decisions, Better results. 2012).


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Image used. http://www-03.ibm.com/press/us/en/photos.wssDisclaimer: Logos Used are here are from Bigger Oil companies and not a property ofIBM

All product and company names are trademarks™ or registered® trademarks of theirrespective holders. Use of them does not imply any affiliation with or endorsementby them

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Descriptive analytics answers the questions what happened and why it happen.Looks at the past data or historical data, understand and find reasons what reallyhappened and why it happened. Almost all management reporting such as sales,marketing, operations and finance, uses this type analytics model

“As what has data shown, it was generated to provide users the outcome of whatreally happened on the campaign. The report generated clearly shows the graphicalrepresentation of each Revenue statistics. Which is higher and which is lower, in thiscase it is easily identifiable which campaign has the lower sales and not, whichcampaign has the higher capacity to maintain its track, and which campaign hadreached and can be used as others for future references.

This clearly shows the fact how descriptive analytics can help the Sales Teamdetermine what really happened in the sales and why it happen.” ---Davenport, T.,

Harris, J. and Morison, R (qtd in the book from Analytics at Work: Smarter Decisions,Better results. 2012).

[1] Davenport, T., Harris, J. and Morison, R. (2012) Analytics at Work: SmarterDecisions, Better results.Image used. http://www-03.ibm.com/press/us/en/photos.wss

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This will be further discussed at the next slide.

Data : We need data to be collected, data repository is needed to access for essential

data.Analyze : We need to analyzed the data we collected, Analyze Data Model to assessand query the data collected in the process.Provides Prediction: In this step prediction are now provided so that Business Userscan actually define and track the next steps on their business analytics before it willhappen.Smart Decisions : We need to decide the ways on how we analyzed the data.Analytics can allow Managers to create a better and smarter decisions.

“Many approaches to analysis are fair game, from the latest optimization techniquesto tried-and-true versions of root-cause analysis. Perhaps the most common isstatistical analysis, in which data are used to make inferences about a populationfrom a sample. Variations of statistical analysis can be used for a huge variety ofdecisions —from knowing whether something that happened in the past was a resultof your intervention, to predicting what may happen in the future. Statistical analysiscan be powerful, but it’s often complex, and sometimes employs untenableassumptions about the data and the business environment.” ---Davenport, T., Harris,J. and Morison, R (qtd in the book from Analytics at Work: Smarter Decisions, Better

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results. 2012).

[1] Davenport, T., Harris, J. and Morison, R. (2012) Analytics at Work: SmarterDecisions, Better results.Image used. http://www-03.ibm.com/press/us/en/photos.wss

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Predictive analytics answers the question what will happen . This model is combinedwith historical data with rules, algorithms and other external data to predict theoutcome or a situation.

“Model repository: A place where models and the specification of the tasks requiredto produce them can be stored, revised, and managed. Not all predictive analyticworkbenches have such a repository, and some still store models as script files.Data management tools: Building predictive analytic models requires access tomultiple data sources of various formats; a predictive analytics workbench must beable to connect to and use this data.Design tools for a modeler: Modelers need to be able to define how data will beintegrated, cleaned, and enhanced, as well as the way in which it will be fed throughmodeling algorithms and the results analyzed and used.

Modeling algorithms: Predictive analytic workbenches have a wide array of modelingalgorithms that can be applied to data to produce models.Data visualization and analysis tools: Modelers must be able to understand the dataavailable, analyzing distribution and other characteristics. They must also be able toanalyze the results of a set of models in terms of their predictive power and validity.Deployment tools: Models are not valuable unless they can be deployed in someway, and predictive analytic workbenches need to be able to deploy models as code,as SQL, as business rules, or to a database using an in- database analytics” ---Davenport, T., Harris, J. and Morison, R (qtd in the book from Analytics at Work:

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Smarter Decisions, Better results. 2012).


Image used. http://www-03.ibm.com/press/us/en/photos.wss

[4] : Taylor, J. (2012) Decision Management Systems: A Practical uide to UsingBusiness Rules and Predictive Analytics

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This will be further discussed at the next slide.

Data : We need data to be collected, data repository is needed to access for essential

data.Analyze : We need to analyzed the data we collected, Analyze Data Model to assessand query the data collected in the process.Provides Prediction using Algorithms: In this step prediction are now provided sothat Business Users can actually define and track the next steps on their businessanalytics before it will happen with the use of mathematical algorithms and equationprocess.Smart Decisions : We need to decide the ways on how we analyzed the data.Analytics can allow Managers to create a better and smarter decisions.

“The difference between Predictive and Prescriptive is that Predictive didn’t generatereports, they only provide prediction to business users indicating what will happen,on the other hand Prescriptive has the prediction and the generated reportsindicating why it happened. Many business users actually tends to use this approachfor more in depth analysis of the business. The only downfall for this is, it is verytedious work and needed more time to implement and build/create this model.” ---Davenport, T., Harris, J. and Morison, R (qtd in the book from Analytics at Work:Smarter Decisions, Better results. 2012).

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[1] Davenport, T., Harris, J. and Morison, R. (2012) Analytics at Work: SmarterDecisions, Better results.Image used. http://www-03.ibm.com/press/us/en/photos.wss,http://static.seekingalpha.com/wp-content/seekingalpha/images/SPSS.gif

http://static.seekingalpha.com/wp-content/seekingalpha/images/SPSS.gif

http://static.seekingalpha.com/wp-content/seekingalpha/images/SPSS.gif

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Prescriptive analytics not only anticipates what will happen and when it willhappen, but also why it will happen . Prescriptive model suggest decision for futureopportunity and shows the implication of each decision options. It continually andautomatically take in new data that improve prediction accuracy that provides betterdecisions and understanding. It predicts what lies ahead and then prescribe how totake advantage without compromising other priorities.

In this sample the graph defines the activity under the earth ,impacting the drilland extraction of natural gas and oil used by Big Oil Companies.

“Many types of captured data are used to create models and images of the Earth’sstructure and layers 5,000-35,000 feet below the surface and to describe activitiesaround the wells themselves, such as machinery performance, oil flow rates andpressures. Prescriptive analytics software has the potential to help during each phase

of the oil and gas business through its ability to take in seismic data, well log data,and their related data sets to prescribe where to drill, how to drill there and how tominimize the environmental impact. Prescriptive analytics software can accuratelypredict production issues by modeling numerous internal and external variablessimultaneously.” ---Davenport, T., Harris, J. and Morison, R (qtd in the book fromAnalytics at Work: Smarter Decisions, Better results. 2012).


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“Every organization needs to answer some fundamental questions about its business.Taking an analytical approach begins with anticipating how information will be usedto address common questions (see figure 1-1). These questions are organized across

two dimensions:Time frame. Are we looking at the past, present, or future?

Innovation. Are we working with known information or gaining new insight?

The matrix in figure 1-1 identifies the six key questions that data and analytics canaddress in organizations. The first set of challenges is using information moreeffectively. The “past” information cell is the realm of traditional business reporting,rather than analytics. By applying rules of thumb, you can generate alerts about thepresent —what’s happening right now (like whenever an activity strays outside of itsnormal performance pattern). Using simple extrapolation of past patterns createsinformation about the future, such as forecasts. All of these questions are useful toanswer, but they don’t tell you why something happens or how likely it is to recur.

The second set of questions requires different tools to dig deeper and produce newinsights. Insight into the past is gained by statistical modeling activities, which explainhow and why things happened.

Insight into the future comes from prediction, optimization, and simulation

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techniques to create the best possible future results.” ---Davenport, T., Harris, J. andMorison, R (qtd in the book from Analytics at Work: Smarter Decisions, Better results.2012).


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“The key consumer of these analytics is the business user , a person whose job is notdirectly related to analytics per se, but who typically must use analytical tools toimprove the results of a business process along one or more dimensions.”

[3] : Mohanty, S. (2012) , Analytics in PracticeImage used. http://www-03.ibm.com/press/us/en/photos.wss

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“The gap between the relevant analytics and the critical needs of the intendedbusiness users still remains significant.”The following challenges highlight characteristics of this gap:

The time to perform the overall cycle of collecting, analyzing, and acting onenterprise 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 withthese tasks. Within this cycle, the time and analytic expertise necessary to analyzedata must be reduced.

Clear business goals and metrics must be defined.

- Data collection efforts must have clear goals. Once metrics are identified,organizations must strive to collect the appropriate data and transform them. In manysituations, data analysis is often an afterthought, restricting the possible value ofanalysis.

Analytics results must be distributed to a wider audience.- Most analysis tools are designed for quantitative analysts —very rich in statisticalalgorithms, not for the broader base of business users who need the output to betranslated into language and visualizations that are appropriate for the businessneeds.

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Verticalization

Analytics solutions are becoming increasingly focused on industry specificproblem solving.

The use of industry-specific knowledge is not limited to the components of theanalytic applications, but also affects how the extracted information ispresented and accessed in each industry.

Comprehensive Models and Transformations for Insight

Business users prefer simple yet comprehensive models with rich visualizationfunctionalities that show only a few and the most important attributes from alarger set of attributes that were determined by the analytics module.

[3] : Mohanty, S. (2012) , Analytics in PracticeImage used. http://www-03.ibm.com/press/us/en/photos.wss

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“Most companies today have massive amounts of data at their disposal. The datamay come from transaction-oriented applications such as ERP (enterprise resourceplanning) systems, scanner data in retail environments, customer loyalty programs,

financial transactions, or clickstream data from customer Web activity. But what dothey do with all this information? Not nearly enough.

Companies, governments, and nonprofits, in sophisticated economies and developingnations alike, stumble over the same ineffective strategies as this retailer. They collectand store a lot of data, but they don’t use it effectively. They have information andthey make decisions, but they don’t analyze the information to inform their decisionmaking.

Of course, companies won’t become analytical all at once. Instead, they will do soone decision at a time. They’ll look at individual decisions and say, “We can dobetter.” Then they’ll apply fact -based and quantitative analysis to make that decisionmore accurately, more consistently, and with an eye toward the future, rather than just reporting on the past. When they realize how much better they make that onedecision, they’ll move on to others. To some extent, this creeping improvement ofdecisions is inevitable as our society becomes more computerized, data-rich, andanalytical. However, those who move forward with intent and urgency have an edgeover those who simply evolve.” ---Davenport, T., Harris, J. and Morison, R (qtd in the

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“Companies, governments, and nonprofits, in sophisticated economies anddeveloping nations alike, stumble over the same ineffective strategies as this retailer.They collect and store a lot of data, but they don’t use it effectively. They have

information and they make decisions, but they don’t analyze the information toinform their decision making.

Of course, companies won’t become analytical all at once. Instead, they will do soone decision at a time. They’ll look at individual decisions and say, “We can dobetter.” Then they’ll apply fact -based and quantitative analysis to make that decisionmore accurately, more consistently, and with an eye toward the future, rather than just reporting on the past. When they realize how much better they make that onedecision, they’ll move on to others. To some extent, this creeping improvement ofdecisions is inevitable as our society becomes more computerized, data-rich, andanalytical. However, those who move forward with intent and urgency have an edgeover those who simply evolve.” ---Davenport, T., Harris, J. and Morison, R (qtd in thebook from Analytics at Work: Smarter Decisions, Better results. 2012).

[1] Davenport, T., Harris, J. and Morison, R. (2007) Analytics at Work: SmarterDecisions, Better results.Image used. http://www-03.ibm.com/press/us/en/photos.wss, www.123f.com

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and OLAP : Concept, Architectures and Solutions 2012).


[5] : Wrembal, R. Koncilla, K. Data Warehouses and OLAP: Concepts, Architecturesand Solutions (2012)Image used. http://www-03.ibm.com/press/us/en/photos.wss

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“Revolutions come and revolutions go; while their influence may be obvious inretrospect, it is rare that we appreciate their impact at the time. The one thing theyhave in common is disruption at all levels of society. First, the agricultural revolution;

then the industrial revolution; and now, 60 years into the latter, we have theinformation revolution.

Things change. At the time of this book's publication, the United States Library ofCongress had over 33 million books (including other printed material) and 63 millionmanuscripts. The Internet Archive, capturing only a subset of all the informationcontained on the Web, has already cataloged almost 2 petabytes of text and isgrowing at approximately 20 terabytes a month, in itself a larger amount ofinformation than that held by the Library of Congress.

And, at a professional level, where we once struggled with a paucity of informationwe now struggle to pick which pieces of information are important out of the millionsof measures at our fingertips. Regardless of where you start, this ever-increasingamount of information has changed the way we view the world, the way we live, andthe way we do business.

Without his research, our world would be vastly different. Among other things, it isunlikely that Voyager would have launched or that the Internet would exist.

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Even though they’re not always feasible, analytics are valuable enough that theyshould be the first, rather than the last, resort in making decisions. In many intuitiveorganizations, analytics are merely a rationalization, wherein data is selected tosupport a decision that’s already been made.

But sometimes testing isn’t possible, either. Mike Linton, formerly the chief marketingofficer for Best Buy, says that it’s not always possible to use analytics before makingmarketing decisions: “You have to mix the ‘ready/aim/fire’ analytical decision makingwith the ‘ready/fire/aim’ approaches sometimes. For example, we tied a new PaulMcCartney CD we were selling exclusively with promotion of his concert tour. Thathad never been done before, to our knowledge, and we couldn’t test it. You use allthe decision tools at your disposal, but sometimes you have to go with intuition.” It isworth noting, though, that Best Buy defines beforehand how it will evaluate the

success and impact of such experiments, thus creating new insights and building aplatform for making fact-based decisions the next time.

. Though Taleb unwisely discounts all statistical analysis because of these anomalies,statistical analysis is very useful most of the time. Rather than abandoning statisticsaltogether, companies should try to identify those unusual times when the past is nota good guide to the present.

.If you’re an experienced home appraiser, for example, you can approximate what a

home is worth without feeding data into an algorithm.

. For example, while the process of finding a romantic partner or spouse has been thesubject of considerable quantitative analysis (as employed by firms such aseHarmony), we’re not strong believers in the power of analytics to help you choose amate. Analytics can be a start, but cannot replace intuitive judgments in suchdomains; you may want to meet your “match” in person before buying a ring!

. As the Scottish writer Andrew Lang commented, “Statistics are often used as adrunken man uses a lamppost —for support rather than illumination.” Intuition, toooften the default tool of decision makers, should be employed only when there is noalternative. Even in the circumstances listed above, in which intuition is appropriate,it’s worthwhile to track the intuition applied, the decisions made, and the results.Over time, such recordkeeping can turn intuition into rules of thumb and evenalgorithms.” ---Davenport, T., Harris, J. and Morison, R (qtd in the book from Analyticsat Work: Smarter Decisions, Better results. 2012).

[1] Davenport, T., Harris, J. and Morison, R. (2007) Analytics at Work: Smarter

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Decisions, Better results.Image used: *http://www-03.ibm.com/press/us/en/photos.wss?rN=61&topic=461

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Instructors to double-click document when on edit mode.

“You may be motivated to learn some of the latest techniques and best practices of

how to use different types of information across the enterprise. You may be ananalytical professional and want to learn how to take your organization's analytics tothe next level. You may be an HR leader who wants to learn about data across theenterprise so you can decide how best to use it to make strategic human capitaldecisions. Whatever your motivation for reading this Module, we assume yourorganization has business challenges that you hope data and the practice of businessanalytics will help you overcome.

Also, a recent International Data Corporation (IDC) report predicts that the businessanalytics market will grow 8.2% in 2012 to $33.9 billion. It is gradually becoming clearthat in today's cutthroat business climate, failing to leverage business analyticseffectively in your organization can be the difference between thriving or slow death.

Because business analytics is rapidly evolving and often indicates different things todifferent people, we think it is important to outline what we mean by "businessanalytics" for the purpose of this Module. We define business analytics as theintegration of disparate data sources from inside and outside the enterprise that arerequired to answer and act on forward-looking business questions tied to key

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business objectives. We realize this is a fairly broad definition; however, ourexperience in practicing business analytics, as well as the hundreds of companies thathave provided input, indicates to us that business analytics is moving away from anisolated reporting and dashboard mentality and toward an integration of varioustypes of information across the organization in tighter alignment with the businessgoals of C- level executives.” ---Isson, J. P. and Harriot, J. (qtd in the book from Winwith Advance business Analytics: Creating Business value from Your Data 2012).

[6]: Isson, J.P. and Harriott, J. (2012) , Win with Advance business Analytics: CreatingBusiness value from Your DataImage used. http://www-03.ibm.com/press/us/en/photos.wss

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“There are several key components worth noting in our definition that may differfrom more traditional definitions of business intelligence, research, Web analytics,information retrieval, data mining, or other related disciplines.

First = The amount of data available to businesses is overwhelming and is growing atan exponential rate, and it's easy to enter analysis paralysis or drift into intellectualcuriosities. Therefore, organizations must articulate and prioritize the key questionsthey want business analytics to answer.

Second = In other words, business analytics is most useful when it is predictive andprovides a lens into the future regarding likely business outcomes.

Third = . If you recall, from our definition, all effective business analytics should begrounded in key business questions and objectives. Those business questions andobjectives do not care about your organization's structure —that some of the data arein finance, some are in marketing, and some are in product. Those business questionssimply demand an answer, and whichever organization can answer them consistently,with speed and accuracy, will win. Will that be you or your competition?

Even though business analytics is a relatively new field, we see it as having thepotential for great organizational impact and importance, much beyond that of the

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“Environment - We all know that the economic environment has been more intenseand challenging than ever before.

Competition - Part of addressing competitive threats is to monitor and stay one stepahead of your competition —tracking, analyzing, and integrating everything you knowabout your competitors into the analytics of your own company.

Customers - Another business challenge that's leading to an increase in companiesrelying on business analytics to drive their strategy is that customers are becomingmore fickle, and loyalty to products and services is rarer than ever before

With customer loyalty elusive, the number of sales and marketing messages seen by

your customers is also ever-increasing and is another business challenge driving theimportance of business analytics. In the United States, marketers send more than 90billion pieces of direct mail each year, trying to influence the behavior of customers.Also, the Radicati Group estimates that nearly 90 trillion e-mails are sent each year,and certainly a large percentage of these are from businesses trying to get yourcustomers to try their products.Furthermore, eMarketer expects that U.S. onlineadvertising spending will grow 23.3% to $39.5 billion during 2012, pushing it ahead ofadvertising spending in print newspapers and magazines.

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. In the first quarter of 2012, the chairman of the Federal Reserve, Ben Bernanke, wasstill predicting only modest growth during 2012, expecting economic and job growthto remain somewhat muted through the remainder of 2012. Those companies thatidentify with the Fed's cautious outlook see the economic glass as half-empty and aretrying to hold market share, stem losses, and keep their current customers happy.

Yet business and consumer confidence is showed signs of improvement during 2012,and the long-term payroll data trend from the Bureau of Labor Statistics indicatesthat companies have started to create new jobs. Therefore, optimistically mindedcompanies are eagerly trying to be smart about staying ahead of business trends, aswell as about how to capture some of the impending economic growth. Regardless ofwhether your future business outlook is optimistic or pessimistic, effective businessanalytics is becoming a required component of business success.

Another business challenge driving the increased importance of business analytics isthat business competition has become more intense. It's easier to start a businesswith little capital and, in some cases, gradually disrupt an entire industry or invent anew one. Take the case of Amazon, the well-known online retailer based inWashington State. Started in 1994, it spurred the rise in the online purchase of booksand music and was, in part, responsible for the relatively rapid decline of bricks-and-mortar stores in the Module and music industries. These types of examples shouldmotivate most organizations to acquire as much data about competitors and theirindustries as possible.

For example, do you know your market share trend over time, the strategies andtactics your competitors use to sell to customers, how your products are perceivedcompared to theirs, which of your customer segments are more likely to defect to thecompetition, or why some customers use only your competition and not you? If yourorganization has timely and thorough answers to these types of questions, thenbravo. Many companies rely on informal feedback about the competition and do nothave solid analytical systems in place to address these issues.

Interestingly, there is a similar trend starting to occur among employees —more and

more employees are becoming less engaged, and are planning to look for new workwhen the recession ends. "The decline in employee loyalty is also seen to be affectingthe quality of the service provided to customers. Given all of this, it's extremelycrucial for businesses to understand customer issues, such as what drives purchaseintent, purchase preference, and purchase behavior. Doing this without systematicanalytics and voice-of-the-customer input is almost impossible —unless you have onlyone or two customers. In that case, you may have business challenges to addressbeyond just analytics.

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Given intense business competition, existing companies must continually monitortheir customers' behaviors and feedback, remaining on guard for new entrants intothe marketplace. Companies are under great pressure to continually and rapidlyreinvent themselves and how they offer value to customers, and failing to accuratelylisten to customers and track their behavior often results in certain and swift demise.Take the case of Polaroid, the well-known brand of instant photographic equipmentthat failed to capitalize on the growing trend of digital photography. Polaroid wasfounded in 1937 by Edwin Land and was one of America's early high-tech successstories. The catapult of its success was the invention of camera film in 1948 thatdeveloped a photograph in minutes —much faster than other methods at the time.This competitive strategy was successful for Polaroid through 2001, when Polaroidfiled for bankruptcy due to the rapid decline in the sale of photographic film. Theirony is that Polaroid had been investing heavily in digital photography technologyand was actually a top seller of digital cameras into the late 1990s. Yet although

Polaroid invested a lot in technology R&D, the company failed to take a businessanalytics approach and understand that customers were relying more on storingdigital photos on their computers, rather than printing a paper copy of each picture. IfPolaroid had integrated accurate voice-of-the-customer input and customer analyticsinto its business analytics strategy at the senior executive level, it may have been ableto adapt its strategy away from photographic print film and toward a successfuldigital photography play.

This new media is taking a lot of the friction out of learning about a product andabout choosing a company brand. Yet with the increase of new media and themultitude of ways to interact online comes a flood of new data into the organization.Every interaction someone has with your brand or product in an electronic medium,such as an Internet search engine, a website, a social media platform, an electroniccoupon provider, a blog post, or a mobile device, generates a data trail. “ ---Isson, J. P.and Harriot, J. (qtd in the book from Win with Advance business Analytics: CreatingBusiness value from Your Data 2012).

[6]: Isson, J.P. and Harriott, J. (2012) , Win with Advance business Analytics: CreatingBusiness value from Your Data

Image used. http://www-03.ibm.com/press/us/en/photos.wss

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“Inside the organization: Whatever your specific outside challenges driving youtoward business analytics, there are also challenges for analytics inside theorganization. In other words, how do you unleash the power of analytics to address

the business challenges that are most critical to your organization, while overcomingtypical pitfalls inside your company?

Evolving Business Analytics: The field of business analytics is evolving. It's becomingless about data silos and more about the integration of different data assets acrossthe company.

Less Subject Matter Experts : There is a skills shortage for knowledgeable dataprofessionals. It's expected to get worse, not better.

External Factors: Business analytics is being driven by several external factors, such asincreased competition, decreased customer loyalty, economic woes, and theproliferation of new media.

Internal factors: Business analytics requires many internal factors to succeed,including strong executive leadership support for analytics, effective technologyinfrastructure and tools, alignment with corporate priorities, and effectivecommunication across departments.

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Many internal challenges will crop up on the way. These are just some of the internalchallenges a business analytics function must rise to meet in order to becomebusiness relevant, fast, insightful, and predictive; have a bias toward action; andbecome part of the corporate culture.

We don't claim this Module will solve all of these issues for everyone. Yet we knowthat the best practices, lessons learned, and assessment tools within will go a longway toward helping you make sure your business analytics is world-class

If you could only find that brilliant data scientist and woo him or her into yourorganization, then everything would be all right, and your company could do brilliantthings with its data.

Question : That one genius could help you segment your market effectively, increaseyour number of customers, reduce the customer attrition rate, predict what will makenew customers buy, predict online customer behavior, and increase your companymarket cap by 30%, right?

Wrong . Certainly, smart and knowledgeable staff is important in helping you makegood use of your data —but that is nowhere near enough. Several other challengesfrom within your organization need to be addressed before you can reach datanirvana using brilliant data scientists. “ ---Isson, J. P. and Harriot, J. (qtd in the book

from Win with Advance business Analytics: Creating Business value from Your Data2012).

[6]: Isson, J.P. and Harriott, J. (2012) , Win with Advance business Analytics: CreatingBusiness value from Your DataImage used. http://www-03.ibm.com/press/us/en/photos.wss

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“Establish business analytic culture: When one looks globally, some organizations seem not to mistrust analytics.Somehow, they seem to create renewable value through applying their competencies

across many different business problems. Somehow, they succeed, not once, butrepeatedly.

Understand analytic in playChange comes from two directions: top down or bottom up. If the organizationalready has the right management culture, using insight as a competitive advantage isrelatively straightforward. As Jack Welch is reputed to have said, “An organization'sability to learn, and translate that learning into action rapidly, is the ultimatecompetitive advantage.”

Recognize the insights as a competitive advantage.In these organizations, applying business analytics is relatively easy. There ismanagement commitment to the use of insight, there is an understanding of the roleanalytics plays in creating competitive advantage, and there is often a culture ofcontinuous improvement. For those of us lucky enough to work in this context, life iseasy.

. And somehow, they translate their experience and skills into sustainable competitive

8/15/2019 Bafban1 - Week 01


advantage. As researchers such as Thomas Davenport and Jeanne Harris have rightlypointed out, overall success can often be linked to a variety of factors, includingorganizational structure, management commitment, and successful strategicplanning. However, it is often “where the rubber hits the road” that the greatestimpact can occur.

Unfortunately, most of us do not work with these types of organizations. Instead, wework in environments that are unfamiliar with the use of analytics. Environments thatstruggle to differentiate between intuition and fact, often taking pride in makingdecisions based on “gut feel.” Environments that, as frustrating as it can be, cannotunderstand the value that business analytics provides.

This Module is written to help those who want to change the environment in whichthey operate.

Analytics is a multidisciplinary activity: The value from insight comes not from theactivity but from the execution. Often, this crosses a variety of departments within anorganization. Few analytics groups have responsibility for both the insight creationand the execution of that insight. Because of this, selling the value of analytics is not

just an aspirational goal for managers; it is a necessary criterion for success.” ---Stubbs, E. (qtd in the book from The Value of Business Analytics: Identifying the Pathto Profitability. 2012).

[2]: Stubbs, E. (2012) , The Value of Business Analytics: Identifying the Path toProfitabilityImage used. http://www-03.ibm.com/press/us/en/photos.wss?rN=61&topic=461

8/15/2019 Bafban1 - Week 01



8/15/2019 Bafban1 - Week 01


“In this module we occasionally refer to analytical competitors, such as Cincinnati Zoo, Papa Gino’s , Kraft Vegemite, Boerse Stuttgart, Globe Telecom, University ofMaryland and other business, because they are great repositories of leading

analytical practices. But most of the companies we describe in this module are notaggressive analytical competitors —they’ve just figured out how to make moreanalytical decisions and have profited from them.

While we suspect this module will be of great interest to analytical competitors, itspeaks more directly to a broader base of organizations: those aiming to becomemore analytical. If you think that your organization ought to make more decisionsbased on facts (not unaided intuition or prejudice), or if you want to unleash thepotential buried in your company’s data, this module will help you. We still urgecompanies, over time, to move toward a mentality and strategy of competing onanalytics —we think that’s where the greatest benefits lie. But those who seek a moreincremental approach can still be more analytical, even if primarily competing onother factors, such as product innovation, customer relationships, operationalexcellence, and so forth.” ---Davenport, T., Harris, J. and Morison, R (qtd in the bookfrom Analytics at Work: Smarter Decisions, Better results. 2012).


8/15/2019 Bafban1 - Week 01


8/15/2019 Bafban1 - Week 01


Image used.

http://www-03.ibm.com/press/us/en/photos.wss?rN=61&topic=461

8/15/2019 Bafban1 - Week 01


bafban1 - week 01

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