Business Intelligence

Data refers simply to raw facts and figures. Alone it tells you nothing. The real goal is to turn data into information. Data becomes information when it’s presented in a context so that it can answer a question or support decision making. And it’s when this information can be combined with a manager’s knowledge—their insight from experience and expertise—that stronger decisions can be made.

 

Business Intelligence Systems are information systems

(Hardware, Software, Data, Procedures, People)

That process operational data and other data to analyze past performance and to make predictions

Purchased Data, Scanner Data, Social Media, SCADA, Weather Temperature, Performance Data, Market Data, Building permits, Cell Phone Data, etc.

Business Intelligence = patterns, relationship, trends and insights exposed or identified by BI systems.

Hunch in the field, with data becomes strategic insight

 

 

How do Organizations use Business Intelligence?

 

 

Business intelligence applications flowchart. Operation databases, purchased data, social data, and employee knowledge flow into business intelligence applications. From the business intelligence applications, through the analyzation of data (data mining, reports, big data), and the publication of said business intelligence, it reaches knowledge workers.

Sales Vs Forecast

By Part, By Supplier, By Category

Changes in Purchasing Patterns

Scanner Data, Target?

Consumer Preferences for investment

Netflix, Amazon

 

 

  • Expose Patterns
    • Purchasing (people who bought what you’re buying also bought…
      • Diapers and Beer
      • Target – Lotions and vitamins = pregnant?
    • Materials usage/Supply chain – efficiencies, early buys
      • WalMart buying patterns and data reduce warehouses and push cost onto suppliers.
    • AMAZON, Netflix producing shows?
    • Buyers are Liars?
  • Monitor, exception reporting
    • Gunshot noise detection
    • Influenza

As processing and storage continue to become cheaper we can store more events and understand more norms.

 

 

Tesco Mines Data

Tesco: Tracked Transactions, Increased Insights, and Surging Sales UK grocery giant Tesco, the planet’s third-largest retailer, is envied worldwide for what analysts say is the firm’s unrivaled ability to collect vast amounts of retail data and translate this into sales (Capell, 2008).

Tesco’s data collection relies heavily on its ClubCard loyalty program, an effort pioneered back in 1995. But Tesco isn’t just a physical retailer. As the world’s largest Internet grocer, the firm gains additional data from Web site visits, too. Remove products from your virtual shopping cart? Tesco can track this. Visited a product comparison page? Tesco watches which product you’ve chosen to go with and which you’ve passed over. Done your research online, then traveled to a store to make a purchase? Tesco sees this, too.

Tesco then mines all this data to understand how consumers respond to factors such as product mix, pricing, marketing campaigns, store layout, and Web design. Consumer-level targeting allows the firm to tailor its marketing messages to specific subgroups, promoting the right offer through the right channel at the right time and the right price. To get a sense of Tesco’s laser-focused targeting possibilities, consider that the firm sends out close to ten million different, targeted offers each quarter (Davenport & Harris, 2007). Offer redemption rates are the best in the industry, with some coupons scoring an astronomical 90 percent usage (Lowenstein, 2002)!

The firm’s data-driven management is clearly delivering results. In April 2009, while operating in the teeth of a global recession, Tesco posted record corporate profits and the highest earnings ever for a British retailer (Capell, 2009).

 

 

Brinker uses Outside Data to Correlate Results

Consider restaurant chain Brinker, a firm that runs seventeen hundred eateries in twenty-seven countries under the Chili’s, On The Border, and Maggiano’s brands. Brinker (whose ticker symbol is EAT), supplements their own data with external feeds on weather, employment statistics, gas prices, and other factors, and uses this in predictive models that help the firm in everything from determining staffing levels to switching around menu items (King, 2009).

 

 

Business Intelligence Basics

Data Acquisition – Obtaining, Cleansing, organizing, relating

Analytics – Converting data to information reporting, data mining and Big Data

Publish – Delivery – Push, Pull

 

 

Data sources flow to - acquire data (obtain, organize and relate, cleanse, and catalog). Acquire data flows to data analysis (big data, data mining). Data analysis flows to publish result (printing, web servers, report servers, automation). publish results flow to and from knowledge workers as pushes and pulls. Published results also flows and loops back to the starting - data sources.

 

A table showing departments and their applications
Department Application
Sales and Marketing
  • Lead generation
  • Lead tracking
  • Customer management
  • Sales forecasting
  • Product and brand management
Operations
  • Order entry
  • Order management
  • Finished-goods inventory management
Manufacturing
  • Inventory (raw materials, goods-in-process)
  • Planning
  • Scheduling
  • Operations
Customer Service
  • Order tracking
  • Account tracking
  • Customer support
Human Resources
  • Recruiting
  • Compensation
  • Assessment
  • HR Planning
Accounting
  • General Ledger
  • Financial reporting
  • Cost accounting
  • Accounts receivable
  • Accounts payable
  • Cash Management
  • Budgeting
  • Treasury management

 

Sales Order, Customer, Product, Inventory, Manufacturing, AR, AP, Budgets, Human Resources, flow to - Extract, transform, load, which then flows to - Analytics Engine, which then flows to - publish to users. Sales Order, Customer, Product, Inventory, Manufacturing, AR, AP, Budgets, Human Resources, flow to - Extract, transform, load, which then flows to - Data Warehouse, which then flows to - Analytics Engine, which then flows to - publish to users. Sales Order, Customer, Product, Inventory, Manufacturing, AR, AP, Budgets, Human Resources, flow to - Extract, transform, load, which then flows to the following data marts: CRM, Orders, Inventory, Human Resources. These data marts then flow to - Analytics Engine, which then flows to - publish to users. Sales Order, Customer, Product, Inventory, Manufacturing, AR, AP, Budgets, Human Resources, flow to - Extract, transform, load, which then flows to the following data marts: CRM, Orders, Inventory, Human Resources. These data marts then flow to - Analytics Engine, which then flows to the business intelligence server, and is published to users.

 

A screenshot of an order extract. Fields include: CustomerName, Contact, Title, Bill Year, number orders, units, revenue, source, part number.

 

Part Data table in a database, fields include: ID, Part number, Shipping weight, vendor.

 

Ordders andd Parts view with fields: customer name, contract, title, bill year, number orders, units, revenue, source, part number, shipping weight, vendor. Shipping weight and vendor are highlighted in the screen shot.
The previous screen shots of order extract, part data, and the order and part view.

 

The orders an parts view in a different formats. Two boxes with a line connecting them. One is the order extract table, the other is the part data table. the ID in both tables has a key icon next to them. indicating this is how they are connected, and represented through the visual line.

 

Customer Summary table, fields include customer name, sum of revenuem sum of units, and average price

 

Qualifying parts query with fields: number orders, average order size, unit price, shipping weight, part number

 

 

What does the Join Look Like?

Select   CustomerName, Revenue, Units, Price

Into # temp1

From Order_Extract Left Outer Join Parts

On Order_Extract.partnumber = Parts.Partnumber

 

Inner Join

Left Outer Join

Right Outer Join

(Full) Outer Join

Cross Join

Finishing the Query?

Select PartNumber, SUM(revenue) as Sum_Rev, SUM(NumberOrders) as Sum_Orders, SUM(units) as Sum_Units,

Into #temp_summary

From Order_Extract

Group by PartNumber

 

Select Sum_Order, AVG_Order =Sum_Units/Sum_Orders,

Unit Price =Sum_Rev/Sum_Units,

Shipping_weight

From #Temp_Summary Left Outer Join Parts_data  on

#Temp_Summary.PartNumber = Parts_Data.PartNumber

Where?

 

 

Homework – AllRoads

Using thhe SUM and AVG functions withh a - Group by, clause.
When used with a group by clause, each aggregate function produces a single value for each group, instead of for the whole table. The following example produces summary values for eachh sales territory in the aventure works 2012 database. The summary lists the average bonus received by the sales people in each territory and the sum of year to dat sales for each territory.

 

 

Customer Summary

Customer Summary query with orders and parts view table shown.

 

Business Focus is Important

The old saying from the movie Field of Dreams, “If you build it, they will come,” doesn’t hold up well for largescale data analytics projects. This work should start with a clear vision with business-focused objectives. When senior executives can see objectives illustrated in potential payoff, they’ll be able to champion the effort, and experts agree, having an executive champion is a key success factor. Focusing on business issues will also drive technology choice, with the firm better able to focus on products that best fit its needs.

 

 

Data Issues?

  • Clean – Cleansing
    • Missing
    • Address Info – scrub with mail database?
  • Consistency – Source System of Record
  • Timing
  • Granularity
  • Aggregated
  • Sprawl

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