Topic 11: Business Intelligence, Business Analytics, and Data Mining

business/big data analytics

"the use of math and statistics to drive meaning from data in order to make better business decisions" 3 types: 1. descriptive (for example, *dashboards*) 2. predictive (use past data to model future outcomes) 3. prescriptive (*optimization- how best to do your job*)

best practices and challenges with BA, continued

*executive ownership*- BA requires buy in from sr. leadership and a clear corporate strategy for integrating predictive models *IT involvement*- technology infrastructure and tools must be able to handle the data and BA tools *availability of production data vs cleansed modeling data*- historical data for model development and real-time data *end-user involvement and buy-in* *change management* *explainable vs "the perfect lift"*- balance building precision vs time to produce acceptable results

types of data to mine

- relational databases - *transactions* - temporal/ (date, time) - spatial (geographical) - *space and time* (land o lakes) - *text* - *web* (object oriented databases) - *graphic* - *streaming*

differences between BI and BA

BI involves the process of collecting data from all sources and preparing it for analysis. BI is more of a first step for companies to take when they need the ability to make data-driven decisions. BA is the *analysis of the answers provided by BI*. BI answers what happened, BA answers why it happened and whether it will happen again.

use cases

Mamas & Papas is the UK's most loved baby brand- marketing - company began to grow, issues with data *accuracy and quality* - analyze sales by product, category, and store - balance the stock within each store - future geographical locations to expand based on sales per capita per postcode infrastructure investor in Turkey- finance - funding many of the country's major developments such as seaports, airports - financial data is transmitted from multiple sources/projects/companies - shared with stakeholders, shareholders, and the media - turn raw data into an executive analysis format in a quick and effective manner provides our leadership with valuable insight for what is really taking place on the ground on a real time basis

use cases, cont.

Queensland Health- operations - managing over 30k patients per day with 70k staff - start utilizing data to make operational improvements - a single source, where it previously needed *multiple inefficient platforms* - insight into both *internal operations* such as staffing levels and compensation benchmarks, and *patient operations* such as their nurse-to-bed ratio and analyses comparing pathology reports - extract value from the massive amounts of data we generate every month Austin Fire Dept- HR - several challenges to overcome - now analyze attendance and payroll trends - staff demographics to better understand their workforce - *succession planning* to enhance internal leadership - compensation is more carefully negotiated based on *skills and talents* such as education, certifications, bilingual abilities

diaper and beer example

a Midwest grocery chain used data mining software to analyze local buying patterns they discovered that when men bought diapers on Thus and Sat, they also tended to buy beer further analysis showed that these shoppers typically did their weekly grocery shopping on Saturdays. On thursdays, they only bought a few items the retailer concluded that they purchased the beer to have it available for the upcoming weekend the chain used this newly discovered info in various ways to increase revenue - move the beer display closer to the diaper display - make sure beer and diapers were sold at full price on thurs

definitions: Business Intelligence

an umbrella term that includes the *applications, infrastructure, and tools, and best practices* that enable access to and analysis of information to improve and optimize decisions and performance at all levels in an organization.

process for analyzing data

before data can be analyzed, data has to be processes knowledge discovery in data - cleaned - integration - selection - transformation - mining - evaluation - visualization

best practices & challenges with BA

best practices: - know the objectives for using Business Analytics. Define your *business use case* and the goal ahead of time. - define your criteria for *success and failure*. - select your *methodology* and be sure you know the data and relevant internal and external factors. - validate models using your predefined success and failure criteria. challenges: - potential for privacy invasion - financial exposure in fast-moving markets - mistaking noise for true insight - risk of spending lots of money and time chasing poorly defined problems or opportunities

what does Business Intelligence offer? continued

companies are able to determine relationships among "internal" factors such as *price, product positioning*, and "external" factors such as *economic indicators, competition, and customer demographics* using BI, a retailer could use point of sale records of customer purchases to send targeted promotions based on an individuals purchases history its not enough that BI reports sales were X yesterday and Y a year ago that same day. They need to explain what factors influence the businesses caused sales to be X one day and Y on the same date the previous year - Jan 17 2018 vs 2019: temperature, traffic, hotel rates, Uber

Business Analytics

comprised of *solutions* used to build analysis models and simulations to *create scenarios, understand realities, and predict future states*. Business analytics includes *data mining, predictive analytics, applied analytics and statistics*, and is delivered as an application suitable for a business user. BA also provides support for companies in the process of making proactive technical decisions, and BA makes it possible for those companies to automate decision making in order to support real-time responses

data mining

data mining allows users to analyze data from many different dimensions, categorize it, and summarize the relationships identified into useful information that typically is not obvious to the user while descriptive data is important, the most influential variables in a data mining model are typically *behavior-based* there are significant opportunities "hidden" in the data

data analysis

different methods of analysis 1. clustering- recognizing distinct groupings or *sub-categories* within the data 2. classifying- an example of classifying is to examine a customer as credit *worthy or unworthy* 3. estimating and predicting- estimating and predicting are 2 similar activities that normally yield a *numerical measure* at the result. From the set of existing customers we may estimate the overall indebtedness of the candidate customer 4. affinity grouping- a special kind of clustering that identifies events or transactions that occur *simultaneously*. a well-known example is market basket analysis

when is business intelligence effective?

effective business intelligence needs to meet 4 major criteria 1. *accuracy*- this refers to the accuracy of the data inputs as well as the outputs. Any system that requires analysis can fall prey to the GIGO problem. this why human discretion is often used to select the data that is relevant 2. *valuable insight*- not all insights are valuable 3. *timeliness*- BI must also be able to deliver those insights at the right time. There are 2 parts to timeliness: the timeliness of the data *going in* and the timeliness of insights *coming out*. Businesses have different decision time frames depending on what they do. 4. *actionable*- the final step is to provide insights that can be acted upon. Business intelligence should provide insight and within a company's unique constraints to deliver actionable ideas designed to improve a business's processes and profitability BI is only effective if it is *trusted* and used to *guide human decisions*

data warehouse

internal data sources: - *operational* data - *historical* data external data sources - external data extract and transform data warehouse and information directory queries and reports, OLAP, data mining

What does business intelligence offer?

the goal of BI is to help managers make *more informed and better decisions* to guide the business. BI software refers to a variety of vendor applications used to *analyze data from a variety of sources (Big Data)* with todays BI software, functional staff can jump in and start analyzing data themselves, rather than wait for IT to run complex reports this *democratization of information* access helps users make business decisions with data that would otherwise be based only on gut feelings and anecdotes

key points

the goal of BI is to help managers make more informed and better decisions to guide the business organizations that can harness Big Data Analytics effectively will be able to create significant value and differentiate themselves Data Mining is the process of discovering meaningful correlations, patterns, and trends by sifting through large amounts of data data has to be processed to manage the variety and volume to be used for data mining, business intelligence, and generating analytics

the real challenge of business intelligence

the most important step in successful Business Intelligence isnt about the technology, its about *understanding the business* this business phase is focused on gathering requirements and identifying and prioritizing a list of opportunities that can have a significant business impact identifying opportunities must be connected to the realities of the data world. by the same token, the data itself may suggest business opportunities

Data Mining

the process of discovering meaningful correlations, patterns, and trends by sifting through large amount of data stored in repositories data mining employs *pattern recognition* technologies, as well as *statistical and mathematical* techniques

business/(big) data analytics, continued

these technologies could generate productivity gains and an improved quality of life, but they carry the risk of causing *job losses* and dislocations. Research found that 40-70% of work activities could be automated using current technologies organizations that can harness these capabilities effectively will be able to create significant *value and differentiate* themselves, while other will find themselves increasingly at a disadvantage. leading companies are using their capabilities not only to improve their core operations but also to launch entirely *new business models*, and they are actively looking for ways to enter other industries where *digital natives were built for analytics*, legacy companies have to do the hard work of *overhauling or changing* existing systems. Adapting to an era of data-driven decision making is not always a simple proposition the urgency for incumbents is growing, since *leaders are staking* out large advantages, and hesitating increases the risk of being disrupted. disruption is already happening, and it takes multiple forms.