The document provides an overview of the role of business intelligence (BI) in the retail and fast-moving consumer goods (FMCG) industry. It discusses key aspects of the industry including major changes, current needs, and key performance indicators. The document then covers BI applications in retail, the BI system framework, evolution of BI, and advantages of BI for the retail industry. It also provides an example of dimensional data modeling for a retail scenario and discusses major BI tools and players in the retail BI market.
1. The document discusses dimensional modeling for a retail business with 100 stores selling 60,000 individual products.
2. It outlines the four steps to dimensional modeling: selecting the business process (point-of-sale retail sales), declaring the grain (individual line items), choosing dimensions (date, product, store), and identifying facts (sales quantity, price, amount).
3. Key recommendations include selecting the process that answers the most important questions, using the lowest level of granularity, avoiding too many dimensions, and not including ratios in the fact table.
Business intelligence (BI) is a tool that analyzes raw business data from various sources like social media and online stores to provide insights. BI helps eCommerce businesses bridge gaps with customers, strengthen relationships with data, transform unstructured into structured data, discover opportunities, and guide future strategies. BI solutions allow businesses to perform customer, sales, inventory, and marketing analysis with just a few clicks.
Business Intelligence for Retail - ScienceSoftScienceSoft
油
ScienceSoft builds ad-hoc analytic tools that help retail companies of all sizes to address challenges in product assortment & placement planning, consumer behavior prediction as well as supply chain optimization.
Business Intelligence tool recommendation for ecommerceRud Boruah
油
This document analyzes and compares several business intelligence tools for ecommerce retailers: QlikView, Tableau/BIME, Splunk, and e-Commera. It finds that e-Commera is the best choice due to its retail focus, action-based recommendations, and low learning curve. BIME is recommended if cost is the primary factor. The analysis was conducted by Dhruv Boruah for a client using Magento and considers implementation time, costs over 1 and 3 years, features, and benefits of each tool.
Business intelligence (BI) involves collecting data from various sources, analyzing it to gain insights, and presenting the findings to help make better business decisions. It aims to provide the right information to decision-makers at the right time. The document outlines the five stages of BI - collecting data, extracting and transforming it, loading it into a data warehouse, analyzing it, and presenting insights through dashboards, reports and alerts. It also provides examples of how a retail company uses BI tools to gain insights from customer and sales data to improve performance.
Data Mining and Business Intelligence ToolsMotaz Saad
油
This document provides an outline for a presentation on data mining and business intelligence. It discusses why data mining is important due to the explosive growth of data from various sources like business transactions, scientific research, and social media. It also gives an overview of some popular open source and non-open source data mining tools, including WEKA, Rapid Miner, SPSS, SQL Server Analysis Services, and Oracle Data Miner.
Business Intelligence made easy! This is the first part of a two-part presentation I prepared for one of our customers to help them understand what Business Intelligence is and what can it do...
The document discusses business intelligence and the decision making process. It defines business intelligence as using technology to gather, store, access and analyze data to help users make better decisions. This includes applications like decision support systems, reporting, online analytical processing, and data mining. It also discusses key concepts like data warehousing, OLTP vs OLAP, and the different layers of business intelligence including the presentation, data warehouse, and source layers.
Business intelligence (BI) provides processes, technologies, and tools to help organizations analyze data and make better business decisions. BI technologies gather, store, analyze and provide access to enterprise data. This helps users understand what happened in the past, what is happening currently, and make plans to achieve desired future outcomes. BI provides a single point of access to information, timely answers to business questions, and allows all departments to use data for decision making. Key BI tools include dashboards, key performance indicators, graphical reporting, forecasting, and data visualization. These tools help analyze trends, customer behavior, market conditions, and support risk analysis and decision making.
Hacker Halted 2014 - Why Botnet Takedowns Never Work, Unless Its a SmackDown!EC-Council
油
Why Botnet Takedowns Never Work, Unless Its a SmackDown!
If organizations are truly working to limit Internet abuse and protect end users, we need to take a more thoughtful approach to botnet takedowns or once again bots will veer their ugly heads.
There are three main causes of ineffective takedowns:
The organizations performing botnet takedowns do so in a haphazard manner.
The organizations do not account for secondary communication methods, such as peer-to-peer or domain generation algorithms (DGA) that may be used by the malware.
The takedowns do not result in the arrest of the malware actor.
So what does a successful botnet take down actually look like? In his presentation on Botnet SmackDowns, Brian Foster, CTO of Damballa will share with attendees how to effectively takedown botnets for good. The only way botnet takedowns will have a lasting impact on end user safety is if security researchers use a comprehensive and systematic process that renders the botnet inoperable.
1. 多Qu辿 es BI y que necesitan los usuarios?
2. 多Qu辿 productos de Cognos se han implementado?
3. Definici坦n de Plantillas
4. Construcci坦n de Reportes en Base a Plantillas
5. BI Embebido
Data Mining: Concepts and techniques: Chapter 13 trendSalah Amean
油
Mining Complex Types of Data,
Other Methodologies of Data Mining,
Data Mining Applications,
Data Mining and Society,
Data Mining Trends,
Summary
by
Jiawei Han, Micheline Kamber, and Jian Pei,
University of Illinois at Urbana-Champaign &
Simon Fraser University,
息2013 Han, Kamber & Pei. All rights reserved.
Business Intelligence tool recommendation for ecommerceRud Boruah
油
This document analyzes and compares several business intelligence tools for ecommerce retailers: QlikView, Tableau/BIME, Splunk, and e-Commera. It finds that e-Commera is the best choice due to its retail focus, action-based recommendations, and low learning curve. BIME is recommended if cost is the primary factor. The analysis was conducted by Dhruv Boruah for a client using Magento and considers implementation time, costs over 1 and 3 years, features, and benefits of each tool.
Business intelligence (BI) involves collecting data from various sources, analyzing it to gain insights, and presenting the findings to help make better business decisions. It aims to provide the right information to decision-makers at the right time. The document outlines the five stages of BI - collecting data, extracting and transforming it, loading it into a data warehouse, analyzing it, and presenting insights through dashboards, reports and alerts. It also provides examples of how a retail company uses BI tools to gain insights from customer and sales data to improve performance.
Data Mining and Business Intelligence ToolsMotaz Saad
油
This document provides an outline for a presentation on data mining and business intelligence. It discusses why data mining is important due to the explosive growth of data from various sources like business transactions, scientific research, and social media. It also gives an overview of some popular open source and non-open source data mining tools, including WEKA, Rapid Miner, SPSS, SQL Server Analysis Services, and Oracle Data Miner.
Business Intelligence made easy! This is the first part of a two-part presentation I prepared for one of our customers to help them understand what Business Intelligence is and what can it do...
The document discusses business intelligence and the decision making process. It defines business intelligence as using technology to gather, store, access and analyze data to help users make better decisions. This includes applications like decision support systems, reporting, online analytical processing, and data mining. It also discusses key concepts like data warehousing, OLTP vs OLAP, and the different layers of business intelligence including the presentation, data warehouse, and source layers.
Business intelligence (BI) provides processes, technologies, and tools to help organizations analyze data and make better business decisions. BI technologies gather, store, analyze and provide access to enterprise data. This helps users understand what happened in the past, what is happening currently, and make plans to achieve desired future outcomes. BI provides a single point of access to information, timely answers to business questions, and allows all departments to use data for decision making. Key BI tools include dashboards, key performance indicators, graphical reporting, forecasting, and data visualization. These tools help analyze trends, customer behavior, market conditions, and support risk analysis and decision making.
Hacker Halted 2014 - Why Botnet Takedowns Never Work, Unless Its a SmackDown!EC-Council
油
Why Botnet Takedowns Never Work, Unless Its a SmackDown!
If organizations are truly working to limit Internet abuse and protect end users, we need to take a more thoughtful approach to botnet takedowns or once again bots will veer their ugly heads.
There are three main causes of ineffective takedowns:
The organizations performing botnet takedowns do so in a haphazard manner.
The organizations do not account for secondary communication methods, such as peer-to-peer or domain generation algorithms (DGA) that may be used by the malware.
The takedowns do not result in the arrest of the malware actor.
So what does a successful botnet take down actually look like? In his presentation on Botnet SmackDowns, Brian Foster, CTO of Damballa will share with attendees how to effectively takedown botnets for good. The only way botnet takedowns will have a lasting impact on end user safety is if security researchers use a comprehensive and systematic process that renders the botnet inoperable.
1. 多Qu辿 es BI y que necesitan los usuarios?
2. 多Qu辿 productos de Cognos se han implementado?
3. Definici坦n de Plantillas
4. Construcci坦n de Reportes en Base a Plantillas
5. BI Embebido
Data Mining: Concepts and techniques: Chapter 13 trendSalah Amean
油
Mining Complex Types of Data,
Other Methodologies of Data Mining,
Data Mining Applications,
Data Mining and Society,
Data Mining Trends,
Summary
by
Jiawei Han, Micheline Kamber, and Jian Pei,
University of Illinois at Urbana-Champaign &
Simon Fraser University,
息2013 Han, Kamber & Pei. All rights reserved.
1. 舒亟舒亳 亳 仂亳弍从亳舒仆舒仍亳亳从亳 于 亳亠亶仍亠.Business Intelligence in Retail. Typical task and mistakes .Dmitry Liakhovets
2. 仆仂于仆舒 亰舒亟舒舒 舒仆舒仍亳亳从亳 于 亳亠亶仍亠The basic task of Business Intelligence in Retail亳亠亶仍 亰舒舒弍舒于舒亠 亟亠仆亞亳 舒仄, 亞亟亠 仗亠亟于亳亟亳/仂仄亳亠 仗仂 亳 仂仂仂 亰仆舒亠 仂亳仄仂 亟仂于仍亠于仂亠仆亳 仂亞仂 仗仂舒 ( 仆亠仂弍仂亟亳仄仄 从舒亠于仂仄) / Retail makes money, where anticipates / creates demand and knows well the cost to meet this demand (with the necessary quality)仆舒仍亳亳从舒 仗仂仄仂亞舒亠 仗亠亟于亳亟亠 仗仂 亳 仂仗亠亟亠仍亳 仂亳仄仂 亠亞仂 亟仂于仍亠于仂亠仆亳 ( 仆亠仂弍仂亟亳仄仄 从舒亠于仂仄) /Business Intelligence helps to anticipate demand and to determine the costs on ensure this demand (with the required quality).
3. 亠亠仆亳 / SolutionsHead of MarketingBrand & AdvertisingManagerMarketData AnalysisManagerMulti ChannelManagerVisualMerchandisingManager亟亳仆仂亠 仗仂仆亳仄舒仆亳亠 亳 亟仂仗仆仂 舒仆舒仍亳亳亠从亳 仗仂从舒亰舒亠仍亠亶 - 仂仆仂于舒 亠从亳于仆仂亶 舒弍仂 仂亟仆亳从仂于 / A common understanding analytical indicators and availability of analytical indicators is the basis of staff performance丶亠仆舒仍亳亰仂于舒仆仆亶 从仂仆仂仍 亳 舒仆仂于从舒 舒仆亟舒仂于 舒仆舒仍亳亳从亳 于 亟亠仗舒舒仄亠仆亠 舒从亠亳仆亞舒 / Centralized management anddefinition standards for analytical reporting and standard indicators in the Department of Marketing 个仆从亳仂仆舒仍仆仂亠 仗仂亟亳仆亠仆亳亠 舒仆舒仍亳亳从仂于, 仆舒仂亟亳 于 仆从亳仂仆舒仍仆 亟亠仗舒舒仄亠仆舒 / Data Analysis Manager in other departments are in functionally subordinate Market Data Analysis Manager
4. 亠亠仆亳 / Solutions亠亠仆舒仍亳亰舒亳 从仂仄仗亠亠仆亳亶 仗仂 舒仆舒仍亳亳亠从仂亶 仂亠仆仂亳 仗亳于仂亟亳 从 仆亠于仂亰仄仂亢仆仂亳 从仂仆从亳于仆仂亶 从仂仄仄仆亳从舒亳亳 仄亠亢亟 亟亠仗舒舒仄亠仆舒仄亳 于 仂仄 仍舒亠 从仂仍亳亠于仂 仗仂从舒亰舒亠仍亠亶 亳 舒仆亟舒仆 仂亠仂于 仗亠于舒亠 仆亠从仂仍从仂 亳 弍仂 舒亠/ The decentralization of competences in analytical reporting leads to the impossibility of constructive communication between departments - in this case the number of indicators and reports more than a few thousand and is growing rapidly.亟亳仆舒 于亳亳仆舒 仗仂从舒亰舒亠仍亠亶 亳 亠亟亳仆亶 亠亠 仂亠仂于 亟仂仗仆亠 仆舒 从仂仗仂舒亳于仆仂仄 仗仂舒仍亠 / Unified of data mart and unified of repository analytic reports are available on the corporate portal
5. 亠亠仆亳 / Solutions亠仆从舒 于仍亳礌亳磻仂仆从亠仆仂于 亳 亠从亳于仆仂 从仂仆从亠仆仂于 - 于舒亢仆舒仂舒于仍ム舒 舒仆舒仍亳亳从亳 / Assessing the impact of competitors and the effectiveness of competitors is important of part of the analytical reporting in Retail.弌仂仗仂舒于亳仄仂 亟舒仆仆 从仂仆从亠仆舒仄亳 / Comparability of data with competitors 亠亢亠从于舒舒仍仆亠 亳 亞仂亟仂于亠 亟舒仆仆亠 /Revenue 于从舒 于从仍ム舒亠 于从舒亞亠仆从亳 仍亞 于 于亠仍亳亳仆亠 于仂亰仆舒亞舒亢亟亠仆亳 / agency fees included in revenues without a turnover on agency services
8. LFL 亟仍 仗仂亟舒亰亟亠仍亠仆亳亶, 仂舒弍仂舒于亳 弍仂仍亠亠 亟于 仍亠 / only stores open more than two years ago舒仆仆亠 仆从舒 / Analysis of market goods仂弍亠仄 仗仂亟舒亢 于 舒亰亠亰亠 仂于舒仆 从舒亠亞仂亳亶 / sales in the context of product categories and hits - once a week (GfK, Nielsen 亳 亟.)
9. Price index - twice a week (Gfk, 丼, Nielsen 亳 亟.)
10. Index of Consumer Activity - once a week (Watcom)
11. 丐仂于舒仆亠 仍亳仆亠亶从亳 / comparison of product lines - once a week (IFR 亳 亟.)
13. Mystery Shopping once a month ( NextTep, 4Service 亳 亟.)亠亠仆亳 / Solutions舒亟舒亳 仗亳仆亳 亠亠仆亳亶 亳 亰舒亟舒亳 亳仗仂仍仆亠仆亳 亠亠仆亳亶 亳仍仆仂 仂仍亳舒ム 仗仂 亠弍仂于舒仆亳礆 从 舒仆舒仍亳亳从亠 / The task of decision-making and execution of business processes have different requirements for analytical reporting仍 仗亳仆亳 仂仗亠舒亳于仆 亠亠仆亳亶 仆亠仂弍仂亟亳仄 亠亞仍仆亠 仂亠 亳从亳仂于舒仆仆仂亶 从 仄亳仆亳仄舒仍仆仄 从仂仍亳亠于仂仄 仗仂从舒亰舒亠仍亠亶 (亟仂 10) 仗仍舒仆-舒从, LFL, 舒舒仄亠仆, 仄舒亢亳仆舒仍仆舒, 仂于舒仆舒 亳 亠亳仂亳舒仍仆舒 从舒 / Operational decisions - on the basis of regular reports to the fixed structure, with a minimum number of indicators (up to 10) - plan-fact, LFL, attachment, in the context of goods groups, the context of margin groups and the territorial structure仂仄仗仍亠从 亠亢亠亟仆亠于仆 亠亞仍仆 仂亠仂于 仄仂亢亠 于从仍ム舒 于 亠弍 仂 10 亟仂 60 仂亠仂于 舒亰仍亳仆仄亳 仗亠亳仂亟舒仄亳 舒亞亠亞舒亳亳 亟亠仆, 仆亠亟亠仍, 仄亠, 从于舒舒仍 亳 亞仂亟 / Daily set regular reports may include from 10 to 60 reports - from different periods of aggregation - day, week, month, quarter and year
14. 亠亠仆亳 / Solutions亳仄亠 仂亠舒 舒亰于亠从仂亶 仂亟仆仂亞仂 仗仂从舒亰舒亠仍 / Example report with different views on one indicator:亳仄亠 仂亠舒 舒亰于亠从仂亶 亠亳 仗仂从舒亰舒亠仍亠亶 / Example report with different views on the six indicators:
15. 亠亠仆亳 / Solutions亳弍仂仆仂亠 亠亠仆亳亠 - 于从仍ム舒 于 亠亞仍仆亠 仂亠 亟亠舒仍亳亰舒亳 亟仂 仂于舒仆 仗仂亰亳亳亶 / Wrong decision to include in regular reports to detail the positions of goods !亳弍仂仆仂亠 亠亠仆亳亠 - 于从仍ム舒 于 亠亞仍仆亠 仂亠仂仆亳 仗仂从舒亰舒亠仍亠亶 / Wrong decision to include in regular reports to hundreds of indicators !亳仄亠 仂亠舒 舒亰于亠从仂亶 亟于仂 仗仂从舒亰舒亠仍亠亶 / Example report with different views on the ~200 indicators:亠 仂亳 舒从 亟亠仍舒 / It is not necessary to do so!
16. 亠亠仆亳 / Solutions亠仆从舒 仗仂仍亠亟于亳亶 仗亳仆亳 亠亠仆亳亶 亳仗仂仍亰仂于舒仆亳亠仄 仄仂亟亠仍亠亶 / Assessing the consequences of decision making with the use of models
17. 亠亠仆亳 / Solutions仗仂仍仆亠仆亳亠 弍亳亰仆亠-亠亠仆亳亶 亠弍亠 仗亠亳舒仍仆仂亞仂 舒仆舒仍亳亰舒 于 仗舒于仍磳仄 舒从舒 仂 亠舒亠 仗亳仄亠仆亠仆亳亠仄 OLAP 亳亠仄 /Execution of operational tasks requires a special analysis of the changing view reports - this is solved with the use of OLAP systems
18. 亠亠仆亳 / SolutionsOLAP-仂亠于从仍ム舒ム 仆亠弍仂仍仂亠 亳仍仂 仗仂从舒亰舒亠仍亠亶 (仂 1 亟仂 5), 仆仂仗仂亰于仂仍ム 仂仗亠舒亳于仆仂 于弍舒 仍ミ英亠 仗仂从舒亰舒亠仍亳 亳亰 亟仂仗仆 (亟仂 ~200 于 从弍亠) 亳 仂仗亠舒亳于仆仂 舒亰于亠仆 仍ミ英 (仗仂 5 20 亳亰仄亠亠仆亳礆) 亟亠舒仍亳亰舒亳, 舒 仆从亳仂仆舒仍仆亠 仂亟仆亳从亳 舒弍仂舒ム 仗亠仂仆舒仍仆仄亳 舒从舒仄亳 OLAP/OLAP-reports include a small number of indicators (from 1 to 5), but can quickly choose any indicators available (up to ~200 in the cube), and rapidly deploy any (according to 5 - 20 measurements), staff use detailing and personal view report in OLAP亳弍仂仆仂亠 亠亠仆亳亠 于从仍ム舒于 OLAP-仂亠弍仂仍亠亠 10 仗仂从舒亰舒亠仍亠亶 / Wrong decision - to include in OLAP-reports more than 10 indicators !舒从 仗舒于亳仍仂, 亳仗仂仍亰亠 仂 1 亟仂 6 OLAP-从弍仂于 (仂于舒, 亟亠仆亠亢仆亠 亠亟于舒, 亰舒舒, 仗亠仂仆舒仍, 仂仆仂于仆亠 亠亟于舒, 舒仆仗仂) 亳 仆舒 仆亳 仂 于亠 (仆亠从仂仍从仂 ) 仗亠仂仆舒仍仆 仂亠仂于 / Usually used from 1 to 6 OLAP-cubes (goods, money, costs, personnel, fixed assets, transport) - on these of cubes of built all (several thousands) of personal reports.
19. 亠亠仆亳 / Solutions弌舒亳亳亠从亳亠 亟舒仆仆亠, 仆舒从仂仗仍亠仆仆亠 于 亳亠亶仍亠,- 仂仆仂于舒 亟仍 仗舒于仍亠仆亳 亟仂仂亟仆仂 亳 亰舒舒舒仄亳 / Statistical data accumulated in the retail trade are the basis for reducing operating costs and increase gross margin弌舒亳亳从舒 仄舒亞舒亰亳仆仂-亟仆亠亶 仗仂亰于仂仍磳 舒仆仂于亳 从仂亠仍亳亳 舒仂亳仄亠仆舒 从仂仆于亠亳亠亶 / Statistics of shopping-days days allows link a correlation of assortment of goods with the conversion
20. 亠亠仆亳 / Solutions弌舒亳亳从舒 仄舒亞舒亰亳仆仂-亟仆亠亶 仗仂亰于仂仍磳 舒仆仂于亳 从仂亠仍亳亳 亳仍亠仆仆仂亳 仂亟仆亳从仂于 从仂仆于亠亳亠亶 / Statistics shopping-days allows link a correlation number of staff with the conversion