Business Intelligence. Yeow Wei Choong Anne Laurent

Transcription

1 Business Intelligence Yeow Wei Choong Anne Laurent

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3 Business Intelligence Business intelligence (BI) is a broad category of applica>on programs and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make beder business decisions. BI applica>ons include the ac>vi>es of decision support, query and repor>ng, online analy>cal processing (OLAP), sta>s>cal analysis, forecas>ng, and data mining. Essen>ally, exploi>ng data to make a business more profitable

4 Business Intelligence Business intelligence (BI) is a broad category of applica>on programs and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make beder business decisions. BI applica>ons include the ac>vi>es of decision support, query and repor>ng, online analy>cal processing (OLAP), sta>s>cal analysis, forecas>ng, and data mining. Essen>ally, exploi>ng data to make a business more profitable

5 Business Intelligence Business intelligence (BI) is a broad category of applica>on programs and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make beder business decisions. BI applica>ons include the ac>vi>es of decision support, query and repor>ng, online analy>cal processing (OLAP), sta>s>cal analysis, forecas>ng, and data mining. Essen>ally, exploi>ng data to make a business more profitable

6 Business Intelligence Business intelligence (BI) is a broad category of applica>on programs and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make beder business decisions. BI applica>ons include the ac>vi>es of decision support, query and repor>ng, online analy>cal processing (OLAP), sta>s>cal analysis, forecas>ng, and data mining. Essen>ally, exploi>ng data to make a business more profitable

7 Business Intelligence Business intelligence (BI) is a broad category of applica>on programs and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make beder business decisions. BI applica>ons include the ac>vi>es of decision support, query and repor>ng, online analy>cal processing (OLAP), sta>s>cal analysis, forecas>ng, and data mining. Essen>ally, exploi>ng data to make a business more profitable

8 Business Intelligence Business intelligence (BI) is a broad category of applica>on programs and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make beder business decisions. BI applica>ons include the ac>vi>es of decision support, query and repor>ng, online analy>cal processing (OLAP), sta>s>cal analysis, forecas>ng, and data mining. Essen>ally, exploi>ng data to make a business more profitable

9 Business Intelligence Business intelligence (BI) is a broad category of applica>on programs and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make beder business decisions. BI applica>ons include the ac>vi>es of decision support, query and repor>ng, online analy>cal processing (OLAP), sta>s>cal analysis, forecas>ng, and data mining. Essen>ally, exploi>ng data to make a business more profitable

10 Business Intelligence Business intelligence (BI) is a broad category of applica>on programs and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make beder business decisions. BI applica>ons include the ac>vi>es of decision support, query and repor>ng, online analy>cal processing (OLAP), sta>s>cal analysis, forecas>ng, and data mining. Essen>ally, exploi>ng data to make a business more profitable

11 Business Intelligence Business intelligence (BI) is a broad category of applica>on programs and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make beder business decisions. BI applica>ons include the ac>vi>es of decision support, query and repor>ng, online analy>cal processing (OLAP), sta>s>cal analysis, forecas>ng, and data mining. Essen>ally, exploi>ng data to make a business more profitable

12 Business Intelligence Business intelligence (BI) is a broad category of applica>on programs and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make beder business decisions. BI applica>ons include the ac>vi>es of decision support, query and repor>ng, online analy>cal processing (OLAP), sta>s>cal analysis, forecas>ng, and data mining. Essen>ally, exploi>ng data to make a business more profitable

13 Business Intelligence Business intelligence (BI) is a broad category of applica>on programs and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make beder business decisions. BI applica>ons include the ac>vi>es of decision support, query and repor>ng, online analy>cal processing (OLAP), sta>s>cal analysis, forecas>ng, and data mining. Essen>ally, exploi>ng data to make a business more profitable Profits!

14 Harrah s Entertainment Harrah s maintains a database that contains data on customer ac>vity slot machines, restaurants and other retail outlets as well as demographic data and gambling habits Harrah s used this data to determine that 26% of gamblers generate 82% of their income and those gamblers were not the high rollers From this they generate promo>ons targeted at specific groups or even specific customers

15 The Tesco Project started in 2008 is saving 100 m a year today Through a reduc>on in wasted stock Combines data from weather records with detailed sales data broken down by store and products etc models that predict future demand for product lines Tesco can avoid holding too much stock, or running out of stock all together.

16 Business Intelligence Process Analysis Insight Business Intelligence Measurement Action

17 Analysis People analyze the world using mental models Mental models are a result of experience, educa>on, etc. but are also constrained by the informa>on available BI systems (should) allow free- form acquisi>on to informa>on so allowing less restric>ve mental models Exper>se Informa>on

18 Insight Insight is the product of broad, free- ranging analysis born of ques>ons that only humans can ask and discovery of paderns that only humans can recognize as useful BI enables people to ask ques>ons and look for paderns and also allows them to convince others of their insights Knowledge Discovery

19 Ac>on Well- reasoned, supported analysis allows organiza>ons to act more quickly with confidence so they can be more nimble and responsive to changing condi>ons Ac#onable

20 Measurement BI provides for more thorough and >mely measurement A wider variety of measures taken from a broader range of data sources can be accessed Timeliness of measures can be tailored to requirements of each level of management Measureable Timeliness

21 Manager s Informa>on Requirements Line Managers Middle Management Upper Management Goals Day-to-Day Short Term Long Term Concrete Measures Detail-level drilldown Summarized data with drilldown Highly summarized KPIs Timing Hourly or daily Weekly or monthly Weekly, monthly or longer

22 BI Goals Making beder decisions faster Conver>ng data into informa>on Difference between the informa>on that managers require and the large amount of informa>on available has been called the analysis gap Using a ra>onal approach to management

23 Analysis Gap More and more data Faster and faster analysis

24 Increasing the Pace of Decisions Organiza>ons must constantly engage in a process of planning implemen>ng plans, monitoring the status of plans, evalua>ng results against the plan and reevalua>ng the plans. Evaluate Plan Implement One of the goals of BI is to increase the rate at which this cycle can be performed. BI allows managers to monitor, provides informa>on to evaluate and provides informa>on as input for planning. Monitor

25 Data Informa>on - Knowledge Level of Abstraction Knowledge and Intelligence Information Data Size of Data

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27 Data Data is a collec>on of raw value elements or facts used for calcula>ng, reasoning, or measuring. Data may be collected, stored, or processed but not put into a context from which any meaning can be inferred

28 Data Informa>on Informa*on is the result of collec>ng and organizing data in a way that establishes rela>onships between data items, which thereby provides context and meaning. Turning Data into Informa>on Process of determining what data can be collected and in what context For example, designing a database that models a real world set of en>>es and rela>onships among the en>>es Requires technical and some business exper>se

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30 Informa>on Knowledge Knowledge is the concept of understanding informa>on based on recognizing paderns in a way that provides insight to informa>on. Turning Informa>on into Knowledge Informa>on becomes knowledge when it can be used to address problems confronted by a business For example, using analy>cal systems to find paderns in data that suggest courses of ac>on Requires business exper>se

31 From Data to Ac>on Data - Lifestyle - Point of sale - Demographic - Geographic Information - X lives in Z - S is Y years old - X and S moved - W has money Z Knowledge - Product A is bought X% of time if product B is bought - Amount of matter Y is mostly used in region Z - Customers of class Y will use X% of C during period D Decision - Let us promote product A in region Z in stores - Send catalogs to houses of profile P - Allocate X% of funds to population B - Offer additional services to clients P

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33 Informate Use informa>on to transform work. In the context of enterprise solu>ons, organiza>ons informate by transforming enterprise solu>ons data into context rich informa>on and knowledge that supports the unique business analysis and decision- making needs of mul>ple work forces Solu#ons

34 End User Access to Data Very little access to data and no analytics End user use of ad hoc reports with data warehouse Some analytic use with a data warehouse Significant use of analytics Extensive use of analytics % of respondents

35 Informa>ng Organiza>ons and users require experience with a new enterprise system to understand what data is available and to learn what they can do with it Ocen requires adding bolt- ons that provide analy>c or DSS capabili>es (e.g. Business warehouse or CRM) Informa>on portals are ocen a key component of systems that give users access to data and analy>cal tools Availability of Data What/How to do with the Data

36 The BI Aftude Seeking objec>ve measurable quan>ta>ve facts about the business Using organized methods and technologies to analyze the facts Inven>ng and sharing models that explain the cause and effect rela>onships between opera>onal ac>ons and the effects these have on reaching the goals of the business Experimen>ng with alterna>ve approaches and monitoring feedback on results Understanding that people are not always ra>onal Running the business based on all these characteris>cs

37 The BI Aftude Seeking objec>ve measurable quan>ta>ve facts about the business Using organized methods and technologies to analyze the facts Inven>ng and sharing models that explain the cause and effect rela>onships between opera>onal ac>ons and the effects these have on reaching the goals of the business Experimen>ng with alterna>ve approaches and monitoring feedback on results Understanding that people are not always ra>onal Running the business based on all these characteris>cs

38 The BI Aftude Seeking objec>ve measurable quan>ta>ve facts about the business Using organized methods and technologies to analyze the facts Inven>ng and sharing models that explain the cause and effect rela>onships between opera>onal ac>ons and the effects these have on reaching the goals of the business Experimen>ng with alterna>ve approaches and monitoring feedback on results Understanding that people are not always ra>onal Running the business based on all these characteris>cs

39 The BI Aftude Seeking objec>ve measurable quan>ta>ve facts about the business Using organized methods and technologies to analyze the facts Inven>ng and sharing models that explain the cause and effect rela>onships between opera>onal ac>ons and the effects these have on reaching the goals of the business Experimen>ng with alterna>ve approaches and monitoring feedback on results Understanding that people are not always ra>onal Running the business based on all these characteris>cs

40 The BI Aftude Seeking objec>ve measurable quan>ta>ve facts about the business Using organized methods and technologies to analyze the facts Inven>ng and sharing models that explain the cause and effect rela>onships between opera>onal ac>ons and the effects these have on reaching the goals of the business Experimen>ng with alterna>ve approaches and monitoring feedback on results Understanding that people are not always ra>onal Running the business based on all these characteris>cs

41 The BI Aftude Seeking objec>ve measurable quan>ta>ve facts about the business Using organized methods and technologies to analyze the facts Inven>ng and sharing models that explain the cause and effect rela>onships between opera>onal ac>ons and the effects these have on reaching the goals of the business Experimen>ng with alterna>ve approaches and monitoring feedback on results Understanding that people are not always ra>onal Running the business based on all these characteris>cs

42 Evidence- Based Management EBM is a philosophy of management that: Requires that claims be backed- up by suppor>ng data Parse underlying logic for faulty cause- and- effect Encourage experimenta>on and explora>on Reinforce con>nuous learning

43 Removing Cogni>ve Blinders See informa>on No>ce what is happening in the environment Seek informa>on Don t rely only on the processed and filtered informa>on provided to you Use informa>on Use all relevant data Share informa>on Make sure all team members share their unique informa>on

44 BI Systems ROI The decision to invest in a BI system is a business decision and should be jus>fied as such Costs have to be balanced against the expected value The Gartner Group reports that the average ROI from BI projects is 430%* *

45 Costs Fixed costs of BI infrastructure Servers, storage, socware Fixed costs of development Cleansing data, database development, etc. Variable costs of socware Licenses, training, support Variable costs associated with maintenance

46 Value of Informa>on Companies that manage their data as a strategic resource and invest in its quality are already pulling ahead in terms of reputa>on and profitability PricewaterhouseCoopers Global Management Survey, 2003

47 Determining the Value of Informa>on Historical Cost What did we pay to acquire the informa>on? Market Value How much would someone pay to acquire the informa>on? U>lity Value What value can we derive from this informa>on? Karl Marx ( )

48 Factors Affec>ng Informa>on Value Degradable Time value of data Data represents a snapshot of reality and so its value degrades over >me Informa>on as a sharable resource Data is not degraded (with a few excep>ons) by being shared and its value is ocen increased by being shared Increased value through increased use The more it is used the more likely ac>onable knowledge will be generated Shareable Valuable

49 Factors Affec>ng Informa>on Value Increasing value through quality Informa>on of ques>onable value not only has lidle value but may have nega>ve value Increasing value through merging Merging informa>on from disparate sources increases value because of the informa>on contained in the rela>onships Value versus volume Value is not necessarily increased and may be decreased by volume One can ocen define an op>mum amount of informa>on There is a qualita>ve difference between having lots of data from disparate data sources and having the same amount from the same source

50 Factors Affec>ng Informa>on Value Increasing value through quality Informa>on of ques>onable value not only has lidle value but may have nega>ve value Increasing value through merging Merging informa>on from disparate sources increases value because of the informa>on contained in the rela>onships Value versus volume Value is not necessarily increased and may be decreased by volume One can ocen define an op>mum amount of informa>on There is a qualita>ve difference between having lots of data from disparate data sources and having the same amount from the same source

51 Factors Affec>ng Informa>on Value Increasing value through quality Informa>on of ques>onable value not only has lidle value but may have nega>ve value Increasing value through merging Merging informa>on from disparate sources increases value because of the informa>on contained in the rela>onships Value versus volume Value is not necessarily increased and may be decreased by volume One can ocen define an op>mum amount of informa>on There is a qualita>ve difference between having lots of data from disparate data sources and having the same amount from the same source

52 Overview of BI Models Vendors License Hardware Integrate

53 Overview of BI Models Collaborate Free

54 Overview of BI Models SaaS Subscrip#on Browser

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