Business Intelligence Course Notes (PDF)

Business Intelligence Course Edoardo Amadori SDG Group Italy March – April 2024 Copyright © SDG Group  I’m Nice Edoardo Amadori To Bachelor Degree...

Business Intelligence Course Edoardo Amadori SDG Group Italy March – April 2024 Copyright © SDG Group  I’m Nice Edoardo Amadori To Bachelor Degree International Development Meet and Cooperation You! Master Degree Management and Economics Executive Master Product Management Microsoft Certified 9x Copyright © SDG Group  My Profesional Path 2018 2022 Enterprise Solutions MS Senior Consultant Consultant Dedagroup @ Trento SDG @ Milano 2021 2023 MS Senior ABA Consultant Qualified SDG @ Milano SDG @ Milano Copyright © SDG Group  Syllabus Copyright © SDG Group  Advanced Business Analytics 18-03-2024 Copyright © SDG Group Lecture overview INTRODUCTION SDG W H AT 01 TO A D VA N C E D BUSINESS 02 APPROACH 03 IS BUSINESS INTELLIGENCE A N A LY T I C S BI D ATA 04 A N A LY S T: TOOLS AND 05 = VA L U E ? SKILLS Lecture overview INTRODUCTION SDG W H AT 01 TO A D VA N C E D BUSINESS 02 APPROACH 03 IS BUSINESS INTELLIGENCE A N A LY T I C S BI D ATA 04 A N A LY S T: TOOLS AND 05 = VA L U E ? SKILLS  Introduction to Advanced Business Analytics Definition Advanced analytics refers to a set of sophisticated data analysis techniques used in the context of business analysis. It covers the full analysis spectrum, in terms of value and complexity: Descriptive, Diagnostic, Predictive, Prescriptive. The goal is to uncover hidden patterns, forecast trends, and make informed decisions based on data. Copyright © SDG Group  Introduction to Advanced Business Analytics Analysis spectrum DESCRIPTIVE DIAGNOSTIC PREDICTIVE PRESCRIPTIVE A N A LY T I C S A N A LY T I C S A N A LY T I C S A N A LY T I C S What happened Why it Will it happen How should I in the past happened again intervene Examines historical Focuses on understanding Predicts likely future Recommends data sets to identify the causes behind outcomes using actions to influence patterns and trends. observed events. historical data. future outcomes. Provides a foundational Helps uncover Extends trends into the Suggests strategies to understanding of relationships and future by leveraging achieve desired goals. business performance correlations.. probabilities. Copyright © SDG Group  Introduction to Advanced Business Analytics Data, Information Raw and unprocessed facts Processed and organized data Includes numbers, images, audio or Helps us understand what is video files, readings from machines, being observed and etc. measured. Basic unit of measurable facts. Reveals meaning when seen Can be stored and transmitted. in context. Not particularly useful in its raw form. Observes what is happening. Every business generates data Provides a foundation for constantly, but it needs processing and decision-making. analysis to become valuable. Copyright © SDG Group  Introduction to Advanced Business Analytics Example Imagine you’re analyzing lead data for your marketing team: Data: You received 2,000 leads last month. Information: Leads grew by 20% compared to the past 6 months. Can you action something? Insight: The growth came from the live chat window on your website Knowledge: Indicating a preference for chat over forms or emails. Now you can optimize your lead generation process based on this knowledge. Copyright © SDG Group  Introduction to Advanced Business Analytics Areas and capabilities Copyright © SDG Group  Introduction to Advanced Business Analytics An iterative process Copyright © SDG Group Lecture overview INTRODUCTION SDG W H AT 01 TO A D VA N C E D BUSINESS 02 APPROACH 03 IS BUSINESS INTELLIGENCE A N A LY T I C S BI D ATA 04 A N A LY S T: TOOLS AND 05 = VA L U E ? SKILLS  SDG Approach Subtitle PRESENCE We Are Pioneers In Data & Analytics consultancy, and we are committed to unlocking the organizations’ hidden GLOBAL potential by offering in-depth analytics expertise. Insights Business agility is the ability of an organization to adapt EMPLOYEES +1700 Beyond quickly to market changes, both internally and externally. This cannot exist without becoming a truly data-driven Analytics company. VOLUME OF ACTIVITY SDG Group achieves this by co-creating optimal +150M solutions with its customers, leveraging Data & Analytics services through a unique combination of CUSTOMER BASE business domain expertise and state-of-the-art technologies delivered by industry-leading talent. +700 Copyright © SDG Group  Value Proposition Why SDG Copyright © SDG Group Copyright © SDG Group  SDG Corporate Identity Why SDG Strong Integration We Help Companies Passionate about Data between Technical and become Data Driven Business skills Technical Knowledge greater Efficiency direct relationship with Business team Scalability and Minimum Reaction Time to the requests Copyright © SDG Group  AND FASTEST GROWING ANALYTICS CONSULTING FIRM Strategy. Decision. Governance. SDG Group Alten Copyright © SDG Group.  Milano Verona Firenze AND FASTEST GROWING ANALYTICS CONSULTING FIRM Strategy. Decision. Governance. SDG Group Copyright © SDG Group. Lecture overview INTRODUCTION SDG W H AT 01 TO A D VA N C E D BUSINESS 02 APPROACH 03 IS BUSINESS INTELLIGENCE A N A LY T I C S BI D ATA 04 A N A LY S T: TOOLS AND 05 = VA L U E ? SKILLS  What is Business Intelligence From Advanced Analytics to Business Intelligence 📊 BI focuses has a focus on reporting, leveraging on descriptive and diagnostic analytics. 💡 Empowers organizations to gain competitive advantages, make informed decisions, and achieve long-term stability. Advanced analytics adds predictive and prescriptive capabilities. Copyright © SDG Group  What is Business Intelligence History “The ability to collect and react accordingly based on the information retrieved is central to business intelligence” The earliest known use of the term business intelligence is in Richard Millar Devens' Cyclopædia of Commercial and Business Anecdotes (1865). Devens used the term to describe how the banker Sir Henry Furnese gained profit by receiving and acting upon information about his environment, prior to his competitors. “The ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal” When Hans Peter Luhn, a researcher at IBM, used the term business intelligence in an article published in 1958, he employed the Webster's Dictionary definition of intelligence. “Concepts and methods to improve business decision making by using fact-based support systems." In 1989, Howard Dresner (later a Gartner analyst) proposed business intelligence as an umbrella term. Copyright © SDG Group  What is Business Intelligence Definition “Business intelligence is a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making” Forrester Research Copyright © SDG Group  What is Business Intelligence Foundation Copyright © SDG Group  What is Business Intelligence Questions through processes and industries Copyright © SDG Group  What is Business Intelligence Answers through processes and industries Copyright © SDG Group  What is Business Intelligence Standard architecture Copyright © SDG Group  What is Business Intelligence Impact & Adoption Pixel Perfect Reporting Ad-Hoc Analysis Dashboard & Mobile Self-Service Data Exploration PIXEL PERFECT REPORTING & AD HOC SELF SERVICE DATA EXPLORATION ANALYSIS Self Service is not providing access to the user It allows users to explore raw data to pilot ML Pixel Perfect Reporting, and Ad Hoc Analysis are normally to every single table and column in the Models, or new Analysis. already satisfied by the current adopted Reporting datawraehouse. Platform. The Key impact on the platform: Impact is on performance on the architecture, Self Service requires an ad-hoc computation it requires large amount of raw data ▪ Generated SQL by the Platform system, and model in order not to impact on the platform. ▪ Complexity of the Data Model (eg Joins or Nested Queries) DASHBOARD & MOBILE Dashboard do require excellent performance, so it should point to highly denormalised structure. Copyright © SDG Group  What is Business Intelligence Examples Copyright © SDG Group Lecture overview INTRODUCTION SDG W H AT 01 TO A D VA N C E D BUSINESS 02 APPROACH 03 IS BUSINESS INTELLIGENCE A N A LY T I C S BI D ATA 04 A N A LY S T: TOOLS AND 05 = VA L U E ? SKILLS  BI Analyst: tools and skills Toolbelt and Skill set Copyright © SDG Group Lecture overview INTRODUCTION SDG W H AT 01 TO A D VA N C E D BUSINESS 02 APPROACH 03 IS BUSINESS INTELLIGENCE A N A LY T I C S BI D ATA 04 A N A LY S T: TOOLS AND 05 = VA L U E ? SKILLS  Data = Value ? Understanding the Journey In today’s data-driven landscape, the transition from raw data to knowledge and tangible value is crucial. Key Considerations: 1. Quality Data: Start with accurate, reliable data. 2. Context Matters: Understand the context in which the data operates. 3. Business Relevance: Focus on data that directly impacts business goals. 4. Agility: Adapt quickly to changing data landscapes. Copyright © SDG Group  Data = Value ? BI Lifecycle Copyright © SDG Group Edoardo Amadori [email protected] +39 324 05 88 037  From data to value 18-03-2024 Copyright © SDG Group Lecture overview D ATA PROJECT THE 01 ARCHITECTURES 02 METODOLOGIES 03 ART OF REQUIREMENTS GARBAGE D ATA D ATA 04 IN GARBAGE OUT 05 MODELLING 06 V I S U A L I Z AT I O N Lecture overview D ATA PROJECT THE 01 ARCHITECTURES 02 METODOLOGIES 03 ART OF REQUIREMENTS GARBAGE D ATA D ATA 04 IN GARBAGE OUT 05 MODELLING 06 V I S U A L I Z AT I O N  Data architectures Definition A data architecture describes how data is managed from collection through to transformation, distribution, and consumption. It sets the blueprint for data flow through storage systems. Copyright © SDG Group  Data architectures Traditional ETL Copyright © SDG Group  Data architectures ETL Example CRM ERP External Sources Copyright © SDG Group  Data architectures Modern ELT Copyright © SDG Group  Data architectures ELT Example Copyright © SDG Group  Data architectures Real time analytics Copyright © SDG Group Lecture overview D ATA PROJECT THE 01 ARCHITECTURES 02 METODOLOGIES 03 ART OF REQUIREMENTS GARBAGE D ATA D ATA 04 IN GARBAGE OUT 05 MODELLING 06 V I S U A L I Z AT I O N  Project methodologies Definitions A project is a set of tasks that must be completed within a defined timeline to accomplish a specific set of goals. This goals are defined inside a perimeter that it’s called scope. It involves several stakeholders and it’s conducted by a project team. A project methodology is a framework of processes, methods, and best practices used to design, plan, implement, and achieve project objectives. Copyright © SDG Group  Project methodologies Waterfall: Stakeholder and Project team Copyright © SDG Group  Project methodologies Waterfall: Scope definition A blueprint in the context of data projects serves as a detailed plan or roadmap. It outlines the essential components, processes, and guidelines necessary to successfully execute a data-related initiative. It acts as a reference for project stakeholders, including data engineers, analysts, scientists, and project managers. Contents of a Blueprint Document: Project Overview: Briefly describe the project’s purpose, stakeholders, and expected outcomes. Architecture Diagram: Visualize the data flow, components, and interactions. Include databases, APIs, data pipelines, and external services. Data Model: Define the data schema, tables, relationships, and data transformations. Security and Privacy: Address data security, access controls, encryption, and compliance. Performance Considerations: Discuss optimization strategies. Monitoring and Maintenance: Specify monitoring tools, alerts, and maintenance procedures. Testing and Validation: Describe testing methodologies, data quality checks, and validation processes. Copyright © SDG Group  Project methodologies Waterfall: Project phases Copyright © SDG Group  Project methodologies Waterfall vs Agile Copyright © SDG Group  Project methodologies Waterfall vs Agile Copyright © SDG Group  Project methodologies Waterfall vs Agile quality is not negotiable Copyright © SDG Group  Project methodologies Agile: Stakeholders and Project Team PRODUCT OWNER DEVELOPERS Developing and explicitly Creating a plan for the communicating the Product Goal Sprint, the Sprint Backlog; Creating and clearly Instilling quality by communicating Product Backlog adhering to a Definition of items Done; Prioritization of Product Adapting their plan each Backlog items day toward the Sprint Goal; SCRUM MASTER Coaching the team members in self-management and cross-functionality; Helping the Scrum Team focus on creating high-value Increments that meet the Definition of Done; Causing the removal of impediments to the Scrum Team’s progress Copyright © SDG Group  Project methodologies Agile: Scope definition Epic High Level description of a product Macro functionality which creates value Feature Feature for the end user Product Backlog Product Backlog Product Backlog Product Backlog Small business increment that can be Item Item Item Item delivered during 1 sprint (2 weeks) Technical work to be done related to a Tasks Tasks Tasks Tasks Tasks Tasks Tasks Tasks User Story Copyright © SDG Group  Project methodologies Agile: Project Phases Copyright © SDG Group  Project methodologies Hybrid Approach Copyright © SDG Group Lecture overview D ATA PROJECT THE 01 ARCHITECTURES 02 METODOLOGIES 03 ART OF REQUIREMENTS GARBAGE D ATA D ATA 04 IN GARBAGE OUT 05 MODELLING 06 V I S U A L I Z AT I O N  The art of requirements Requirements gathering Data teams work with the business to understand business needs and intended outcomes of an analytics project. A data team won't be able to determine the appropriate type of analysis and/or the correct solution without having followed a structured requirements gathering process. Requirements gathering includes identification of: What are the key business questions? What data are available? Will available data respond to the needs or does more data need to be collected? What are the essential dimensions - how to slice and dice the data? What are the key performance indicators or performance metrics? How will users consume the analysis? What is the frequency of data ingestion? What is the frequency of reporting? Copyright © SDG Group  The art of requirements The importance of the “Why” Copyright © SDG Group Lecture overview D ATA PROJECT THE 01 ARCHITECTURES 02 METODOLOGIES 03 ART OF REQUIREMENTS GARBAGE D ATA D ATA 04 IN GARBAGE OUT 05 MODELLING 06 V I S U A L I Z AT I O N  Garbage in, garbage out The importance of Data Quality Data quality refers to the overall reliability, accuracy, and completeness of data. It encompasses factors such as consistency, timeliness, relevance, and absence of errors. High-quality data is essential for informed decision-making, effective analysis, and reliable outcomes in various fields, including No Data quality business, research, and technology. Ensuring data quality involves validation, Tech Data quality + cleansing, and maintaining data integrity Business data quality throughout its lifecycle. Copyright © SDG Group  Data Observability S AV I N G E N G I N E E R I N G TIME What & Why Reduce number of Data Quality issues, time-to-identification and time- to-resolution. DO can solve problems faster and prevent them from recurring The ability to understand the health of an REDUCING LAPSES IN D ATA Q U A L I T Y organization’s The cost of poor data quality can directly affect the top-line and bottom- ▪ Data line of a company. DO reduces downtime of applications and number of ▪ Data pipelines impacted users ▪ Data landscape I N C R E A S I N G D ATA ▪ Data infrastructure TEAM LEVERAGE Data teams can spend their time delivering data products to the business. DO improves focus time of data teams By continuously monitoring, tracking, alerting, analyzing and troubleshooting problems to E X P A N D I N G D ATA AW A R E N E S S reduce and prevent data errors or downtime. DO provides the metadata needed to data team to optimize their own workflows Copyright © SDG Group Lecture overview D ATA PROJECT THE 01 ARCHITECTURES 02 METODOLOGIES 03 ART OF REQUIREMENTS GARBAGE D ATA D ATA 04 IN GARBAGE OUT 05 MODELLING 06 V I S U A L I Z AT I O N  Data modelling Definition Data modeling is the process of creating a structured representation of data to understand its relationships, constraints, and organization. It involves defining entities (such as tables), attributes (columns), and their interconnections in a database. Data models help ensure data consistency, improve query efficiency, and guide database design. Common types include data dictionaries, entity- relationship diagrams (ERDs), and semantic models. Copyright © SDG Group  Data modelling Data dictionary Comprehensive reference and description of each data element within a system or database Copyright © SDG Group  Data modelling Entity-relationship diagrams (ERDs) Visually represents the structure of a relational database Copyright © SDG Group  Data modelling Semantic models Logical layer containing the transformations, calculations, and relationships between data sources needed to create reports and dashboards The “Star Schema” Copyright © SDG Group  Data modelling Dimensional modelling Dimensions Describe the entities in the model (such as products, people, and dates). Each table of this type has a code that acts as a unique identifier and a description (ex: a product will appear only once within this table and will have all the related information). Facts Store information related to observations or events, such as sales, consumption, and temperatures. These tables will therefore only contain the code columns of the dimensions so that they can be related to the dimensional tables in addition to numerical columns Copyright © SDG Group  Data modelling Techniques Normalization and denormalization are two opposing strategies for organizing data in a relational database, and the choice between them depends on the specific needs and goals of your application. Copyright © SDG Group  Data modelling Techniques Use normalization when data integrity is a top priority, and you want to minimize data redundancy and avoid anomalies (insertion, update, and deletion anomalies). It is most suitable for transactional databases where data accuracy and consistency are crucial: Reduces data redundancy: Normalization splits data into separate tables to avoid duplicating the same information, which saves storage space and ensures consistency. Simplifies updates: With normalized data, you only need to update information in one place, reducing the risk of inconsistent data. Supports complex relationships: Normalization allows you to represent complex relationships between entities accurately. There are several normalization forms, including 1NF, 2NF, 3NF and so on, each with specific rules to achieve progressively higher levels of data integrity and reduced redundancy. Copyright © SDG Group  Data modelling Techniques Use denormalization when you need to optimize query performance, especially for read-heavy workloads or reporting databases. It is suitable for cases where data redundancy is acceptable if it leads to significantly faster query execution. Improves query performance: By reducing the number of joins and minimizing the need to fetch data from multiple tables, denormalization can speed up data retrieval. Aggregations and reporting: Denormalized structures are often better suited for reporting and analytics because they can reduce the complexity of queries. Caching: Denormalization can facilitate data caching, which can further improve performance. In practice, many databases use a combination of normalization and denormalization. You can selectively denormalize specific parts of the database to improve performance while keeping other parts normalized for data integrity. Copyright © SDG Group  Data modelling Techniques Copyright © SDG Group Lecture overview D ATA PROJECT THE 01 ARCHITECTURES 02 METODOLOGIES 03 ART OF REQUIREMENTS GARBAGE D ATA D ATA 04 IN GARBAGE OUT 05 MODELLING 06 V I S U A L I Z AT I O N  Data visualization Definition Our brains don’t function like calculators; they can’t process hundreds of thousands of data points in parallel. Data visualization allows us to explore and analyze data visually. Data visualization is the interactive exploration and graphical representation of data of any size (small and big data), nature, and origin. It allows managers and decision-makers to identify patterns, trends, and insights that might remain hidden during initial data analysis. Copyright © SDG Group  Data visualization Examples Copyright © SDG Group  Data visualization Examples Copyright © SDG Group  Data visualization Examples Copyright © SDG Group  Data visualization Examples Copyright © SDG Group  Data visualization Examples Copyright © SDG Group  Data visualization Examples Copyright © SDG Group  Data visualization Examples Copyright © SDG Group  Data visualization Market Copyright © SDG Group  Introduction to Power BI Power BI Desktop installation 1. Open Microsoft Store 2. Search for Power BI Desktop 3. Click on Download Copyright © SDG Group Edoardo Amadori [email protected] +39 324 05 88 037  Business case: Sell-Out 25-03-2024 Copyright © SDG Group Lecture overview I N D U S T R Y, FUNCTIONAL D ATA 01 CUSTOMER, BUSINESS USERS 02 REQUIREMENT AND PROJECT 03 TEAM ARCHITECTURE GOALS 04 AND PROCESS 05 ACHIEVED Lecture overview I N D U S T R Y, FUNCTIONAL D ATA 01 CUSTOMER, BUSINESS USERS 02 REQUIREMENT AND PROJECT 03 TEAM ARCHITECTURE GOALS 04 AND PROCESS 05 ACHIEVED  Industry, Customer, Business users Sector - Industry Sector Industry A sector refers to a large segment of the An industry is a more specific group of economy. It encompasses various related companies that share common business industries and companies. activities or operate in a similar business sphere. Sectors are broad classifications that Companies within the same industry offer similar group together businesses with similar products or services and compete for characteristics or functions. customers with specific needs. For example, the financial sector includes For example, within the financial sector, we entities that operate within the same have various industries such as banks, asset economic sphere. management companies, insurance companies, and brokerages. Copyright © SDG Group  Industry, Customer, Business users Sector - Industry In summary, while both terms relate to business categories, a sector is broader and contains multiple industries, each representing a more specific group of related companies. When comparing investment opportunities, investors often find it advantageous to compare different companies within the same industry. This allows for an apples- to-apples comparison based on shared production processes, customer types, financial reporting, and responsiveness to policy changes. Copyright © SDG Group  Industry, Customer, Business users Sector The consumer goods sector encompasses companies that produce final products intended for direct use by individuals and households. These products are purchased for personal enjoyment and consumption, rather than for further manufacturing or industrial purposes. Consumer Behavior Impact: The performance of the consumer goods sector heavily depends on consumer behavior. When the economy grows, demand for higher-end products increases. Conversely, during economic downturns, there’s a relative demand for value products. Advertising and Brand Differentiation: Many companies in this sector rely on advertising and brand differentiation. Developing new flavors, fashions, and styles and effectively marketing them to consumers is a priority. Technological Trends: Modern internet technology has significantly impacted how products are manufactured, distributed, marketed, and sold in the consumer goods sector. Copyright © SDG Group  Industry, Customer, Business users Sector Product Categories Product Nature Packaged goods: Items like food, beverages, Durable goods: These are big-ticket items toiletries, and cleaning supplies. with longer lifespans. Clothing and apparel: Fashion items for Nondurable goods: These are fast-moving personal use. consumer goods with high sales volume, rapid inventory turnover, and shorter Automobiles: Vehicles used by consumers. shelf lives. Electronics: Devices like televisions, smartphones, and home appliances. Copyright © SDG Group  Industry, Customer, Business users Industry Global Alcohol Industry Outlook The worldwide alcohol market was valued at USD 1,609 billion in 2023. The industry encompasses a diverse range of alcoholic beverages, each with its unique characteristics and consumer base. Key Considerations While building an alcohol industry report, it’s crucial to consider factors such as market trends, regulatory frameworks, and consumer preferences. Limitations may include data availability, regional variations, and changing consumer behaviors. Copyright © SDG Group  Industry, Customer, Business users Identikit Copyright © SDG Group  Industry, Customer, Business users Main players Diageo is a global leader in the Pernod Ricard is known for its diverse alcoholic beverages industry. spirits portfolio. Their brand portfolio includes Johnnie Brands like Absolut Vodka, Chivas Walker, Smirnoff, Baileys, Regal, and Jameson contribute and Guinness. significantly. Diageo’s revenue is substantial driven They focus on premium and super- by premium spirits. premium spirits. Copyright © SDG Group  Industry, Customer, Business users Main players https://www.camparigroup.com/it/pages/brands Copyright © SDG Group  Industry, Customer, Business users Case study GLOBAL PLAYER The client is an international player in the CPG industry. Most of the countries are based on a 3-tier market structure, composed by Sell-In, Depletions and Sell-Out Copyright © SDG Group  Industry, Customer, Business users Case study SOURCES OF DATA Sell-In data is generated in the ERP system Copyright © SDG Group  Industry, Customer, Business users Sell-In example FULL YEAR 2023-RESULTS HIGHLIGHTS Net sales of €2,918.6 million, full year organic growth of +10.5%. EBIT-adjusted of €618.7 million, 21.2% on net sales. EBITDA-adjusted of €728.9 million, 25.0% on net sales, up +15.5% organically. Group net profit-adjusted of €390.4 million, up +0.7%. Proposed full year dividend of €0.065 per share, an increase of +8.3% vs. the previous year. Best-in-class performance in terms of Total Shareholder Return: +13.6% since IPO, +11.2% since May 2007, +6.6% since January 20192. https://www.camparigroup.com/sites/default/files/downloads/Campari%20Group%20FY%202023%20Results%20Press%20Release.pdf Copyright © SDG Group  Industry, Customer, Business users Case study SOURCES OF DATA Depletion data is currently integrated from external data providers Copyright © SDG Group  Industry, Customer, Business users Case study SOURCES OF DATA Sell-Out data is currently integrated from external data providers Copyright © SDG Group  OUR REFERENCES BY INDUSTRY CONSUMER PRODUCTS Copyright © SDG Group.  Industry, Customer, Business users Case Study Copyright © SDG Group Lecture overview I N D U S T R Y, FUNCTIONAL D ATA 01 CUSTOMER, BUSINESS USERS 02 REQUIREMENT AND PROJECT 03 TEAM ARCHITECTURE GOALS 04 AND PROCESS 05 ACHIEVED  SUCCESS STORIES Background The Client is a global CPG player selling its products across 200 countries. The Client was looking for a worldwide unified data model to monitor sell-out data across countries. SDG designed and built an end-to-end solution based on Azure Stack to integrate, model, govern and present sell-out information. SDG provided its business knowledge in the CPG industry and thanks to the Microsoft technological expertise built a strong solution. Copyright © SDG Group. Copyright © SDG Group.  Functional requirements and Project team Requirement Current Situation Objectives ❑ Each provider is selling data in its own format, with specific ❑ Define a global template to ingest data into the client Data Lake dimensions and KPIs ❑ There is no data harmonization across countries and even ❑ Normalize dimensions and KPIs to be used at local and global inside the same country level ❑ Data gathering is not automated and centralized ❑ Label source systems and standardize methodology to ingest data into Data Lake ❑ Reporting is performed at local level, involving a lot of manual ❑ Provide specific local reporting plus global Sell-Out reporting activities ❑ Enable analysis that combine Sell-In, Depletions and Sell-Out data Copyright © SDG Group  SUCCESS STORIES 3… 2… 1… The starting point of the project was the understanding of what data was available in each specific country and how to receive it automatically, so that the project did not represent an additional burden for the individual country. This phase led SDG Group and the Customer team to deal directly with local counterparts and data providers to understand how to proceed in an optimal way. Copyright © SDG Group.  Functional requirements and Project team “Magneto” Team Starting from 2018, more than 40 SDG consultants have contributed to make the customer a global client, focusing on different technologies and several practice and communicating with business from local team to global heads. HEADS Nicola Bronzino Fabio Sist DATA VISUALIZATION DATA ENGINEERING Edoardo Amadori Giacomo Figueroa Cuneo Leonardo Pigoni Stefano Provera Copyright © SDG Group The Challenge The sell-out data was purchased from the various countries of the Customer directly, turning to external providers, and there was no sharing of their flow on a global level. This made determining the overall market share of a specific product laborious and time-consuming. In fact, being able to have homogeneous sell-out data required very long times, a cumbersome exchange of different Excel files and a complex review to determine whether the data provided by a single Country actually corresponded to the information requested, considering, for example, that the definition of a product and the category it belongs to tends to vary significantly depending on the country examined. That’s when SDG stepped in. Copyright © SDG Group  Functional requirements and Project team Considerations DATA ❑ When manual actions is required, it will be minimized to avoid human error and time consumption INTEGRATION ❑ Full data sets will be integrated from providers in order not to lose any information DATA ❑ Harmonization will include dimensions and KPIs mapping plus products harmonization HARMONIZATION ❑ Providers will be required to provide data as close as possible to the defined global template DATA QUALITY ❑ When providers send codes, those will be checked towards Master Data DATA ❑ Design Dashboards for different audiences (Global-Local; Executives-Operatives) VISUALIZATION Copyright © SDG Group  Functional requirements and Project team Steps to success GLOBAL TEMPLATE ROLL OUT ARCHITECTURE DESIGN Create a Global Define a Roll-Out Technological Design of valuable Template to define Strategy on the other compliance with dashboards at Group the list of analysis countries that allows respect to the Group level, which can then dimensions and KPIs to extend the solution guidelines, with the be fed with data from to be integrated from in a short time and possibility, if possible, the different the providers. The with certain costs of its evolution and countries, as they are global template will be optimization acquired common to all countries BUSINESS PROJECT SOLUTION UI/UX ANALYST MANAGER ARCHITECT DESIGNER Copyright © SDG Group Lecture overview I N D U S T R Y, FUNCTIONAL D ATA 01 CUSTOMER, BUSINESS USERS 02 REQUIREMENT AND PROJECT 03 TEAM ARCHITECTURE GOALS 04 AND PROCESS 05 ACHIEVED  Data Challenges The creation of a central database naturally leads to the need of data standardization in terms of: Dimensions of analysis KPI definitions Time Periods Language Currency Unit of measure Copyright © SDG Group  Data Examples Copyright © SDG Group  Data Examples Copyright © SDG Group  Magneto Data Model Copyright © SDG Group Lecture overview I N D U S T R Y, FUNCTIONAL D ATA 01 CUSTOMER, BUSINESS USERS 02 REQUIREMENT AND PROJECT 03 TEAM ARCHITECTURE GOALS 04 AND PROCESS 05 ACHIEVED  Architecture and Process High Level Architecture Copyright © SDG Group  Architecture and Process Data Visualization Approval Single country report report Approval model Approval Cross countries report ROM report Official model RSP Monitoring Operative report and more in the making Operative model Copyright © SDG Group  Architecture and Process “Power Heroes Approval App” Copyright © SDG Group  Architecture and Process “Power Heroes Approval App” Local Users Global Users Experience the process Full experience from their country of responsibility perspective In charge of the datasets refresh Approve data only for their country of responsibility Cannot refresh the datasets Copyright © SDG Group  Architecture and Process “Power Heroes Approval App” Bridge Table App External Data ELT Visualize and Approval approve data Table Approval Dataset Email Official Table Visualize data Official Dataset Copyright © SDG Group  Bridge App Table External Data ELT Visualize and approve data Approval Table Approval Dataset Email Official Table Visualize data Official Dataset  Architecture and process “Power Heroes Approval App” Copyright © SDG Group  Architecture and Process Data Visualization Market share, trend, market positioning, (both single country and cross-countries, Approval both local and global attributes) report Single country Numeric data check, new report brand, manufacturer, Cross countries categories… mapping report RSP Monitoring Ad hoc ROM report analysis Operative Final market price, price range, report positioning w.r.t. competitors and more in the making Copyright © SDG Group  Architecture and Process Example Countries comparison Customer performance cross countries Customer VS Market Copyright © SDG Group  Architecture and Process Agile approach Copyright © SDG Group Lecture overview I N D U S T R Y, FUNCTIONAL D ATA 01 CUSTOMER, BUSINESS USERS 02 REQUIREMENT AND PROJECT 03 TEAM ARCHITECTURE GOALS 04 AND PROCESS 05 ACHIEVED  SDG built a full cloud solution on Azure stack with the following features: ✓ Fully automatic data ingestion of new data Data Volume ✓ Harmonized data model across countries 20 millions rows from 30 databases ✓ Approval flow to accept or reject new data ✓ New products data mapping solution Users More than 80 users across the world ✓ Unified reporting for operational teams and directors Copyright © SDG Group SUCCESS STORIES The Result SDG Group achieved this goal by harmonizing 1,200 different brands and mapping 200 competitors through a mapping file updated monthly, which allowed all dashboards to be populated and a set of common KPIs to be defined, such as market share in value. In this way, who access the dashboards can now more easily grasp common insights at a global level and better understand how the business will perform compared to competitors or the strategic plan of the company. Thanks to MAGNETO, the Customer was able to add important and common business indicators to its analyzes to be exploited in a strategic way by harmonizing the very concepts underlying the KPIs, thanks to coherent guidelines that allow everyone to understand the nature of the data being taken under exam. Copyright © SDG Group.  Looking Forward A highly valuable result achieved thanks to MAGNETO is the integration of sell-out data into the business planning process to define the budget for the following years. By offering immediate visibility into data, MAGNETO allows you to take business actions based on market shares or other relevant insights and gain a better understanding of the correspondence between sell-in and sell-out. These insights also prove to be key elements of an advanced analytics project to carry out the statistical forecast of the Customer’s Group. Copyright © SDG Group. SUCCESS STORIES In many business areas the advent of MAGNETO represented a 100% improvement, providing us with information that we either didn't have at all before, or were very difficult and time- consuming to find. We have been able to replace Excel files with Power BI dashboards, which allow us to have new metrics available, bring together more data and workloads, and thus help us create a new data culture in the company. Together with SDG Group we have created a real data journey for our company which today can use data to create new insights for its business. Product Owner Copyright © SDG Group. 132 Edoardo Amadori [email protected] +39 324 05 88 037  Power BI Lab pt. I 25-03-2024 Copyright © SDG Group Lecture overview INTRODUCTION HANDS 01 TO POWER BI 02 ON Lecture overview INTRODUCTION HANDS 01 TO POWER BI 02 ON  Introduction to Power BI Microsoft Business Application Ecosystem Copyright © SDG Group  Introduction to Power BI Microsoft Power Platform Copyright © SDG Group  Introduction to Power BI Microsoft Power BI It’s a unified and scalable platform for self-service and enterprise business intelligence (BI). Connect to and visualise any data, and seamlessly infuse the visuals into the apps you use every day. Copyright © SDG Group  Introduction to Power BI Why Power BI 1 2 3 4 5 Market Leader Licensed for your needs Stunning visualizations AI powered Endless Integrations Recognized in Choose the best Be it real time Uncover insights 150+ connectors 2023 as “Leader” fit for you dashboards or faster with for your data by Gartner for the between three interactive report, Copilot, Natural sources, sixteenth different you can visualize Language Query increasing every consecutive year versions: free, your data like & AI insights month pro, premium never before Copyright © SDG Group  Introduction to Power BI Common development methodology 1. Query Creation: Filtering, formatting, and refining data. 2. Relationship Configuration: Establishing the foundations of a data model. Publishing 3. Data Model Enrichment: Adding calculation logic and formatting to the data model. 4. Data Exploration: Using Canvas drag-and-drop to explore data in a Data Visualization new way. 5. Interactive Report Design: Creating reports with a wide range of Data Model data visualizations. Data Load 6. Publishing: Sharing and consuming the report content online. Copyright © SDG Group  Introduction to Power BI Components Copyright © SDG Group  Introduction to Power BI Power BI Desktop Power BI Desktop is a free tool that empowers you to create rich, interactive reports with visual analytics from hundreds of data sources. Key Features Interactive Reports: Design visually appealing reports with a wide range of customizable visuals. Connectivity: Access your data coming from different sources. Data Modelling: Easily model and shape your data. AI-Driven Insights: Explore data, uncover hidden patterns, and predict outcomes using built-in AI features. Consumption Optimization: Create reports optimized for viewing on web and mobile devices Use Power BI Desktop Install Power BI Desktop from Microsoft Store or visit https://powerbi.microsoft.com/it-it/downloads/ Log in to share your reports and online publishing Copyright © SDG Group  Introduction to Power BI Power BI Desktop Report view Copyright © SDG Group  Introduction to Power BI Power BI Desktop Data view Copyright © SDG Group  Introduction to Power BI Power BI Desktop Model view Copyright © SDG Group  Introduction to Power BI Data Sources and Data Loading Power Query is a data transformation and data preparation engine. It comes with a graphical interface for getting data from sources and a Power Query Editor for applying transformations. Using Power Query, you can perform the extract, transform, and load (ETL) processing of data. Copyright © SDG Group  Introduction to Power BI Data Sources and Data Loading When we automatically load the data source, we create a Query in Power BI Desktop. The Query is defined as data retrieval from a data source, characterized by a unique name, defining steps using the M language, and used to load data into a table within a data model Power Query consists of four sections: 1) Menu Bar: Contains all fundamental functions such as data transformation or column addition. 2) Queries Pane (Left Panel): Displays the number of active queries and their respective names. 3) Data Pane (Central Panel): Shows the data based on the selected query from the left panel. 4) Query Settings Pane (Right Panel): Displays all the steps and modifications made to the query. Copyright © SDG Group  Introduction to Power BI Data Sources and Data Loading Power BI allows you to connect to various data sources, enabling you to create insightful reports and dashboards For data connection, it is necessary to: 1. Select the Get Data function from Home in the multifunctional bar. 2. Choose the type of source to which you want to connect (e.g., Oracle/Excel/SQL/etc.). 3. Provide the required connection settings based on the data source (e.g., Server/Database/credentials/etc.). Every month new connectors are released, for the full connector list, refer to: https://learn.microsoft.com/en-us/power-query/connectors/ Copyright © SDG Group  Introduction to Power BI Data Sources and Data Loading After connecting to the data, the first screen that will appear is the Navigator This screen displays all the tables within the data source. By selecting a table, you’ll get a preview of its contents. You have two options: 1. Load: Import the table immediately. 2. Transform Data: Choose this option to perform further data transformations. Copyright © SDG Group  Introduction to Power BI Data Sources and Data Loading For some types of connectors, after selecting Get Data, a connection window will appear, allowing you to choose the connection mode 1. Import Mode: Power BI takes a snapshot of the data and stores it within Power BI Desktop. Useful when you want to work with a static dataset. Data is loaded into memory, allowing for faster visualizations and calculations. 2. DirectQuery Mode: Queries are executed directly at the data source during runtime. Useful when you want real-time access to the data. Data remains in the source system; no local storage in Power BI. Copyright © SDG Group  Introduction to Power BI Data Modelling In Power Query, we can implement simple and complex Data Transformation operations like changing a column’s data type or adding new columns: Copyright © SDG Group  Introduction to Power BI Data Modelling Furthermore, we can implement also Data Shaping solutions, which involve data modeling. Beyond the classic functionalities, you can perform merge and append operations. Merge: combines data from two or more queries based on common columns (similar to a database join operation). Append: append stacks rows from multiple queries on top of each other (similar to stacking tables vertically). Copyright © SDG Group  Introduction to Power BI Data Modelling In Power Query, besides the graphical interface, you can also write code in the M language within the Advanced Editor. This allows you to perform more advanced data transformations and customizations beyond the standard features like adding columns or correcting errors Copyright © SDG Group  Introduction to Power BI Data Modelling As the complexity of the analysis increases, it is likely that you will need to import more than one fact table and several dimension tables. Within Power BI Desktop, you can define the semantic model by creating relationships between different tables (by drag and drop). The key characteristics in relationships are: 1. Cardinality: Describes the relationship between tables, such as one-to-one, one-to-many, or many-to-many. 2. Filter Direction: Indicates how the filter propagates through the relationship (e.g., from one table to another or in both directions). 3. Active/Inactive Relationship: Power BI Desktop allows only one active relationship between two tables at a time. By the way, you can create multiple inactive relations. Copyright © SDG Group  Introduction to Power BI Data Modelling Copyright © SDG Group  Introduction to Power BI Data Modelling Copyright © SDG Group  Introduction to Power BI Data Modelling Copyright © SDG Group  Introduction to Power BI DAX The DAX language is a functional language in which instructions are provided in the form of functions. Each can consist of nested functions. DAX (Data Analysis Expressions) is a library of functions and operators that can be combined to build formulas and expressions in Power BI. Elements of DAX formula: A. The name of the measure: Total Sales. B. The Equal (=) operator indicates the beginning of the formula. Once calculated, it will return a result. C. The DAX SUM function sums all values from the SalesAmount column in the Sales table. D. Parentheses () surround an expression containing one or more arguments. All functions require at least one argument. E. An argument passes a value to the function. F. The reference column [SalesAmount] in the Sales table allows SUM to aggregate data. Copyright © SDG Group  Introduction to Power BI DAX 1. Measures: 1. Dynamic calculation formulas whose results change based on context. 2. Used for creating reports that support data model combination and filtering. 3. Example: Calculating total sales, average revenue, etc. 2. Calculated Columns: 1. Added to an existing table with a new DAX formula defining their values. 2. Values are calculated as soon as you enter the formula. 3. Stored in the data model memory. 4. Example: Creating a calculated column for custom dates format. 3. Calculated Tables: 1. Derived from other tables in the same model. 2. Defined through a DAX formula. 3. Example: Creating a new table based on existing data. 4. Row-Level Security (RLS): 1. Uses a TRUE/FALSE boolean condition. 2. Defines which rows can be returned from query results based on specific role members. 3. Enhances data security by restricting access at the row level. Copyright © SDG Group  Introduction to Power BI Data Visualization There are two ways to create new visualizations in Power BI Desktop: 1. Drag and Drop Fields: Drag field names from the Fields panel and drop them onto the report canvas. By default, the visualization appears as a data table. 2. Visualizations Pane: In the Visualizations pane, select the type of visualization you want to create. This way, the default visualization is an empty image similar to the object you’re going to create. Copyright © SDG Group  Introduction to Power BI Data Visualization After creating the visual object, you can customize it using the three tabs 1 2 3 available in the lower pane of visualizations. In order, you will find: 1. Fields Pane: Used to manage fields within a visualization. The buckets (also known as areas) in this pane vary depending on the selected visualization type. 2. Format Pane: Used to format visual objects. The available options depend on the selected visualization type. 3. Analytics Pane: Allows you to add dynamic reference lines to visualizations and focus attention on trends or important information. Copyright © SDG Group  Introduction to Power BI Power BI Service Power BI Service is a cloud-based platform that allows you to create, share, and collaborate on interactive reports and dashboards using your data. It’s an integral part of the broader Power BI ecosystem and complements the Power BI Desktop application. Key Features Dashboards: Create dynamic dashboards by pinning visuals from different reports. Data Exploration: Dive deep into your data using filters, slicers, and drill-through capabilities. Collaboration: Share reports with colleagues, collaborate on datasets, and annotate insights. Scheduled Refresh: Keep your data up-to-date by configuring automatic data refreshes. Create Reports: Design visually appealing reports with interactive visuals, charts, and tables. (Limited experience in comparison with Power BI Desktop) Accessing Power BI Service Sign In: Access your Power BI account by signing in with your email or start with a free trial Copyright © SDG Group  Introduction to Power BI Publishing This is how the Power BI Service home page looks like: Copyright © SDG Group  Introduction to Power BI Publishing Reports created in Power BI Desktop can be published to either your personal workspace or a Power BI workspace. When you publish a report for the first time, a new semantic model with the same name will be uploaded. Important Notes: You cannot save two different datasets with the same name; the previous one will be overwritten. Renaming fields or measures in an already published dataset may cause errors in reports that used the previous names. Copyright © SDG Group  Introduction to Power BI Power BI Mobile Power BI Mobile provides a seamless experience for accessing and interacting with your Power BI content on the go. It’s available as native apps for Windows, iOS, and Android devices. Key Features Anywhere Access: View live Power BI dashboards and reports securely from any location. Interactive Exploration: Use touch-enabled visuals to explore data and gain insights. Collaboration: Annotate reports, share insights, and collaborate with your team. Real-Time Updates: Receive push notifications for data alerts and stay informed. Stay Informed Anytime, Anywhere iOS 3D Touch: Quickly access links from your home screen. Apple Watch Support: Get real-time data updates on your wrist. Phone, Tablet, and More: Power BI Mobile adapts to various devices. Copyright © SDG Group  SDG Group Italy Power BI successful projects Copyright © SDG Group  Static Demo VGI – Monitoraggio concessionari Copyright © SDG Group  Static Demo OTB – Business Review Copyright © SDG Group  Static Demo Calzedonia – CRM Analytics Copyright © SDG Group  Static Demo Bata – Store Dashboard Copyright © SDG Group  Static Demo Pomellato – Mobile Sales Dashboard Copyright © SDG Group Lecture overview INTRODUCTION HANDS 01 TO POWER BI 02 ON  Hands on Laptop time Copyright © SDG Group Edoardo Amadori [email protected] +39 324 05 88 037  Power BI Lab pt. II 03-04-2024 Copyright © SDG Group Lecture overview HANDS AI 01 ON 02 AND COPILOT Lecture overview HANDS AI 01 ON 02 AND COPILOT  Hands on Laptop time Copyright © SDG Group Lecture overview HANDS AI 01 ON 02 AND COPILOT  AI and Copilot Generative AI “Generative artificial intelligence describes algorithms that can be used to create new content, including audio, code, images, text, simulations, and videos” Copyright © SDG Group  AI and Copilot A global revolution Copyright © SDG Group  AI and Copilot Prompt Engineering Copyright © SDG Group  AI and Copilot Image generation Copyright © SDG Group  AI and Copilot ChatGPT Copyright © SDG Group  AI and Copilot Copilots Copilots are a series of AI companions developed by Microsoft that can complete a wide range of tasks to make your life easier. They are essentially collaboration tools, built using large language models and generative AI, that are typically seamlessly integrated into existing applications to help users save time and boost productivity. Copilots can handle a wide range of tasks, from routine information summary to the analysis of extensive data collections. Copyright © SDG Group  AI and Copilot Copilots vs ChatGPT ChatGPT Microsoft Copilot works like a chatbot as it was is an AI-powered digital assistant that aims to designed to hold a conversation with provide personalized assistance to users for a you. After giving it a question or range of tasks and activities. Copilot doesn’t prompt, ChatGPT uses machine learning algorithms to understand the context of the conversation and vs just connect ChatGPT with Microsoft 365; it combines the power of large language models (LLMs) with your data in the Microsoft generate the appropriate responses. Graph (think of Outlook, Teams) and the Unlike a search engine, ChatGPT Microsoft 365 apps to turn your words into the does not have the ability to search the most powerful productivity tool on the planet. internet. Copyright © SDG Group  AI and Copilot Copilots The Copilot stack is available also in Power BI, accessible via Microsoft Fabric. Generative AI features within Microsoft Power BI can help users in creating reports, generating insights, and exploring their data. Copilot for Microsoft Fabric is in Public Preview and it’s ready for use if your tenant admin enabled the Copilot setting at the tenant level and the workspace you’re utilizing it’s running on F64 or Premium capacity. Copyright © SDG Group Copyright © SDG Group  AI and Copilot Power BI Copilot – Use Cases Create an entire report with natural language Copyright © SDG Group  AI and Copilot Power BI Copilot – Use Cases Create an entire report with natural language Copyright © SDG Group  AI and Copilot Power BI Copilot – Use Cases Smart Narratives Copyright © SDG Group  AI and Copilot Power BI Copilot – Use Cases DAX with natural language Copyright © SDG Group Edoardo Amadori [email protected] +39 324 05 88 037  Business Information Systems 1 CRM Chiara Francalanci Functional architecture of ERP systems: overview COMPANY ADMINIST. OPERATIONAL PORTFOLIO EXECUTIVE INTER-ORGANIZ. PORTAL PORTFOLIO Industry-specific modules PORTFOLIO SYSTEMS Web site Accounting Strategic (eCommerce) Human PLM - Product Life planning Cycle Management Unified access resources Budgeting Production SCM - Supply Chain to company Finance MRP Distribution Activity Based information Project planning Management Costing Infrastrutture per CRM management Balanced (Customer market place Score Card E-Procurement Relationship Dashboards Management) Procure- Production Delivery ment ERP core cross- Inbound Inventory Outbound industry logistics mgmt logistics ERP core industry-specific ERP extended Information integration: ERP vision Decision-making applications, functionalities, user interfaces Data warehouse ETL Operational DB Operations support applications, functionalities, user interfaces Horizontal data consistency (information sharing) Vertical data consistency (from operations to executive dashboards) Conceptual consistency: one, common, integrated data model 3 Multi-channel integration: CRM vision Operational CRM with different distribution channels: shops/agencies, sales force, call center, Web, mobile, mail… Analytical CRM: Operational DB Mining Customer profiling Legacy systems Campaign management ETL Executive CRM, reporting Service level KPIs Customer data Customer satisfaction Customer profitability Multi-channel integration ensures cross-channel service consistency Value extraction from customer data with analytic CRM Customer understanding and monitoring with executive functionalities 4 Analytical CRM – Mining Mining refers to data analysis with the goal of discovering insights that are relevant for business management. Mining can be performed with any technique: descriptive statistics (e.g. mean values), data visualization techniques (e.g. plotting data on a bidimensional chart), statistical correlations (e.g. correlating IT investments with sales). More recently, also with machine learning techniques. Why mining data? – Several companies have huge operational databases embedding new knowledge (KM information process) – Data mining is used to extract patterns – Patterns should be explained by summarizing findings in an intuitive way (e.g. simple statistical indicators, recurring associations, etc.) Analytical CRM – Mining Example o o o Loans x o o x x o o x x o o x x o x x x o o o o x x o x x x o Salary People who have received a loan from a bank: x: people with missing payments o: people with regular payments  Analytical CRM – Mining Example o o o Loan x o o x x o o x x o o x x o x x x o o o o x x o x x x o k Salary IF salary < k THEN the probability of missing payments is greater than p (e.g. > 0.1) Probability p represents the risk that the bank is willing to take Probability p is estimated with statistical techniques (e.g. by calculating the frequency of missing payments for customers with salary below threshold k Analytical CRM – Customer profiling Indicators on Example of behavioural analysis: customer segmentation customers Customers who can be traced (with a loyalty card) can be segmented according Catalog information to different dimensions: - Loyalty, from the analysis of their purchasing habits, such as frequency, recency and expenditure. Operations - Price sensitivity, such as up, low and mid market. - Lifestyle, that is the behavioural orientation of customers along different dimensions, such as «business», «casual», «classic», «vintage» or «sports Behaviour wear» Financials Customer segmentation represents the basis for targeting promotions, by: - Targeting a segment or a mix of segments. - Using the average characteristics of a segment to complement the Forecasts knowledge on individual customers  Analytical CRM – Campaign management Campaign management is a set of functionalities that support marketing campaigns. Campaigns have four fundamental phases and related functionalities: Planning & Phases Design Execution Evaluation budgeting 1. Budget and 1. Definition of target 1. Channels lists 1. Analysis of data on strategy customers (= list) successful contacts 2. Execution on planning selected channels Functionalities 2. Objective, target 2. Definition of offer 2. Effectiveness and efficiency of contacts 3. Choice of channels 3. Progress reporting results and and monitoring 3. Evaluation of target segments 4. Def. of schedule customer behaviour Campaign management: Example (Salesforce)  Reporting: Example Reports show customer and product segments vs. KPIs, for example: sales of each product segment profitability of each customer segment Data are extracted from the customer data warehouse (or from marts) and shown with the reporting functionalities of the CRM software reporting 20 years ago (Siebel) reporting today (Qlik) Operational CRM – Sales Force Automation (SFA) Goal: to provide CRM front-end functionalities to the sales force in both B2C and B2B Advantages for companies: – Management perspective: governance of the sales force. – Sales force perspective: stronger sales capabilities. Operating objectives : – Reduce the costs of customer acquisition. – Increase customer retention. – Reduce bureacracy for customers and increase responsiveness during the sales process. – Increase the effectiveness of the sales force. Sales process on physical channels Data Planning Control Execution Sales Follow-up mgmt. Strategic Def. of Contacts Visit Offer Service planning operating Configuration Qualification Contract activation Budgeting objectives and pricing of Customer Periodic Discount/poli opportunities care control cies Cross-selling Invoicing CRM provides the data that allow the def