Microsoft Crm Analytics

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The document discusses business intelligence and analytics solutions from Microsoft. It outlines how different roles like executives, managers, and end-users have varying objectives that analytics can help achieve. It then describes Microsoft's full spectrum of BI capabilities, which range from Excel and dashboards to predictive modeling and data mining. These solutions can help organizations by providing more agility, loyalty, revenue, and productivity.

Technology Business

Nic Smith BI Solutions Marketing Microsoft Corporation

Executives Managers End-users USERS OBJECTIVES Tactical Strategic Business Scorecard Operational Analysis Embedded Guidance Analytic Needs Differ by Role and Objective

When Do I Need Analytics? Key Indicators ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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[object Object],[object Object],[object Object],[object Object],Full spectrum of business intelligence capabilities ranging including Excel, performance dashboards, ad hoc reporting, OLAP, predictive modeling, and data mining.

More Agility More Customer Loyalty Empower employees with customer metrics that guide employees to make decisions that will help build customer trust . More Revenue Prioritize marketing and sales initiatives in those that show the greatest return on investment. More Productivity Strategically align all customer-facing operations and empower employees from the top down to quickly adjust to changing environments . Eliminate learning curve issues while realizing fast adoption rates by delivering familiar applications that work the way your employees work.

“ ” The true value of Microsoft Dynamics is about how successful the tool can make employees in their jobs.” Greg Lush, Chief Information Officer, The Linc Group Microsoft Dynamics enables multinational service provider to optimize customer relationship management, and increase sales. ,[object Object]

The Magic Quadrant is copyrighted February, 2008 by Gartner, Inc. and is reused with permission. The Magic Quadrant is a graphical representation of a marketplace at and for a specific time period. It depicts Gartner’s analysis of how certain vendors measure against criteria for that marketplace, as defined by Gartner. Gartner does not endorse any vendor, product or service depicted in the Magic Quadrant, and does not advise technology users to select only those vendors placed in the “Leaders” quadrant. The Magic Quadrant is intended solely as a research tool, and is not meant to be a specific guide to action. Gartner disclaims all warranties, express or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. Gartner, Inc. “Gartner Magic Quadrant for Business Intelligence Platforms, 2008,” James Richardson et al., Feb. 1, 2008

Mainframe/ Departmental Systems END USER TOOLS AND PERFORMANCE MANAGEMENT APPLICATIONS BI PLATFORM (RDBMS, ETL, OLAP, Reporting) DELIVERY

What happened? Reporting, Consolidation What is happening? Scorecards and Dashboards Why did it happen? Analytics What will happen? Forecasting What do I want to happen? Planning, Budgeting

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It’s time to think differently about using data and analytics. While many organizations have progressed from relying on static reports generated by IT teams to using some version of a self-service model, most still struggle to reap the world-changing benefits long-promised by experts. Unfortunately, each evolution has delivered only incremental improvements over the last wave of analytics, leaving the biggest problems half-solved. Join this webinar to learn more about the next generation of embedded analytics: Analytics Infusion. Learn more about: • Developing a new approach to solving the analytic adoption challenge • Differentiating your product and customer experience • Achieving business outcomes through data

When and How Data Lakes Fit into a Modern Data Architecture

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Whether to take data ingestion cycles off the ETL tool and the data warehouse or to facilitate competitive Data Science and building algorithms in the organization, the data lake – a place for unmodeled and vast data – will be provisioned widely in 2020. Though it doesn’t have to be complicated, the data lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the data swamp, but not the data lake! The tool ecosystem is building up around the data lake and soon many will have a robust lake and data warehouse. We will discuss policy to keep them straight, send data to its best platform, and keep users’ confidence up in their data platforms. Data lakes will be built in cloud object storage. We’ll discuss the options there as well. Get this data point for your data lake journey.

Data Architecture, Solution Architecture, Platform Architecture — What’s the ...

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A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.

A Practical Guide to Implementing Effective BI Governance

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Organizations continually fail to generate ROI on their governance initiatives because they are too narrow in scope. To be effective, Business Intelligence (BI) governance must cover both data and visualizations. This presentation will provide a practical step-by-step guide for implementing effective BI governance and a toolkit for addressing the three critical aspects of any program: • People - Roles and responsibility in the governance process • Process - A matrix to map out the right process for each type of information asset • Technology - The essential components required in your technology stack

Best Practices in Metadata Management

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Metadata is hotter than ever, according to a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information. While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.

5 Steps to Transform into a Data-Driven Organization - Ganes Kesari - Gramen...

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This session was presented on May 27th, 2021, in a Webinar organized by Gramener. https://info.gramener.com/5-steps-to-transform-into-data-driven-organization Session Details: Today, organizations struggle to get value from data despite significant investments. Did you know that there's one factor that influences the outcomes of all your data initiatives? This webinar will highlight how an organization's data maturity influences its performance. It will show how you can assess your data maturity and plan the five steps for data-driven business transformation. Pain points we would be discussing: Most organizations stagnate midway in their data journey. Gartner says that over 87% of organizations in the industry are at lower levels of data maturity (levels 1 and 2 on a scale of 5). Just doing more data science projects will not improve your capabilities or outcomes. The fact is that the top challenges reported by CDOs fall into five common areas. This webinar will show what they are and how you can tackle them. Who should attend - Executives, Chief Data/Analytics Officers, Technology leaders, Business heads, Managers What Will You Learn? - What is data science maturity, and why does it matter? - How do you assess data science maturity and limitations of the assessment? - How can data science maturity help your organization level up (explained with an example)?

Do-It-Yourself (DIY) Data Governance Framework

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A worthwhile Data Governance framework includes the core component of a successful program as viewed by the different levels of the organization. Each of the components is addressed at each of the levels, providing insight into key ideas and terminology used to attract participation across the organization. A framework plays a key role in setting up and sustaining a Data Governance program. In this RWDG webinar, Bob Seiner will share two frameworks. The first is a basic cross-reference of components and levels, while the second can be used to compare and contrast different approaches to implementing Data Governance. When this webinar is finished, you will be able to customize the frameworks to outline the most appropriate manner for you to improve your likelihood of DG success. In this webinar, Bob will discuss and share: - Customizing a framework to match organizational requirements - The core components and levels of an industry framework - How to complete a Data Governance framework - Using the framework to enable DG program success - Measuring value through the DIY DG framework

Data Centric Development: Supercharge your web & mobile application development

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Many businesses are finding that their web and mobile applications aren’t providing the long-term solution they were hoping for. As consumers provide more and more useful data, these digital platforms don’t allow businesses to take advantage of the huge opportunities that data presents. Our new whitepaper details the practical steps you can take to supercharge your web and mobile application development and stay ahead of the data revolution.

The last year has put a new lens on what speed to insights actually mean - day-old data became useless, and only in-the-moment-insights became relevant, pushing data and analytics teams to their breaking point. The results, everyone has fast forwarded in their transformation and modernization plans, and it's also made us look differently at dashboards and the type of information that we're getting the business. Join this live event and hear about the data teams ditching their dashboards to embrace modern cloud analytics.

The Evolving Role of the Data Architect – What Does It Mean for Your Career?

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If you’re a data architect, you’ve heard it all—from ‘data management is the sexiest job of the 21st century’ to ‘data management is dead’. The truth almost certainly lies somewhere in the middle of the extremes, but how can you make sense of the true future of the data architect’s role in the rapidly-changing data landscape? The Data Architect holds a unique position as the translator between business value and technical implementation. Join this webinar to learn how you can take advantage of the uniqueness of this role to catapult your career to the next level.

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Building a Data Strategy – Practical Steps for Aligning with Business Goals

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Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace, from digital transformation to marketing, customer centricity, population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.

RWDG Slides: Using Tools to Advance Your Data Governance Program

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Data Governance tools can be used to advance your Data Governance program … or they can become the reason why Data Governance fails to meet people’s expectations. Tools can be developed internally or acquired from reliable vendors to attempt to address your organization’s needs. Sometimes the best environment is made up of a hybrid of tools developed internally and tools acquired. Join Bob Seiner for this month’s RWDG webinar where he will share tools that you can build yourself and talk about how the tools can be used to determine requirements to acquire outside tools. Tools developed internally at little or no cost have helped to solve many Data Governance problems. Several of these problems and their solutions will be described in detail during this webinar. In this webinar, Bob will discuss: • Several easy-to-build Data Governance tools • Customizing these tools to address specific issues • How internally developed tools can lead to tool acquisition • Knowing when it is time to acquire tools • Integrating DIY tools with acquired tools

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Data strategy is a necessary component of every company but the approach and skills can vary widely as a product and its users grow. Data ensures that each product feature released can be measured as to its impact and effectiveness. Data also surfaces latent market needs that can be leveraged into further product value. Carbon Five has been using data to solve tricky product problems with companies like Square, Altschool, StitchFix, Prosper, and Fandango for over 15 years. Come join the conversation if you are interested in what skills are necessary to drive data science at your company, how to hire data science talent, and what data strategy looks like for different companies. - See more at: http://schedule.sxswv2v.com/events/event_V2VP46093#sthash.oPukb2oW.dpuf

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The Environment Agency uses data models as a key part of their digital journey in reporting scientific results for water quality, fisheries, conservation and ecology, flood management, and more. Join special guest Becky Russell from the Environment Agency along with host Donna Burbank as they discuss how they were able to gain buy-in from various departments across the organization using data models and data standards.

Data management trends

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Current data management trends. Companies are moving from business intelligence standardization to experimentation, data-quality concerns are easing a bit, and cloud-based, real-time, and big-data platforms are on the rise. Amongst them are both structured and unstructured database trends which are professionally validated. Here then are the trends to watch out for: Internal Systems Data Analytical Databases Data Integration Management Business Intelligence Development Data Governance and Visualization Data Storytelling and Collaboration Mobile Business Data Intelligence Analytics and BI standardization Cloud-based Data Warehousing Real-time Technology Data Quality Concerns

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Today, the role of Chief Data Officers and their teams has expanded from risk and compliance-based activities to delivering business value through trusted data. With the exponential growth in data and data consumers throughout an organization, ensuring that everyone gets the information that they need — and that it can be relied upon — is no small feat. CDOs need to rely on modern Data Governance leaders to discover where all of the data lives, define the context, measure the quality, ensure privacy, and then democratize data to empower the rest of the organization. Join us for this informative webinar as we highlight the challenges of today’s data leaders, how they can democratize trusted, secure data, and ultimately discover how to deliver business value.

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This session will discuss how the master data management platforms are evolving to meet needs of digital economy. A modern master data management platform incorporates graph technology, infuses insights from the data using advanced analytics and ML, and offer big data scale performance in the cloud. Join this webinar to learn about these and other critical capabilities that power connected customer experience, compliance, and business alignment.

Data Modeling Techniques

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Data modeling continues to be a tried-and-true method of managing critical data aspects from both the business and technical perspective. Like any tool or methodology, there is a “right tool for the right job”, and specific model types exist for both business and technical users across operational, reporting, analytic, and other use cases. This webinar will provide an overview of the various data modeling techniques available, and how to use each for maximum value to the organization.

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The majority of successful organizations in today’s economy are data-driven, and innovative companies are looking at new ways to leverage data and information for strategic advantage. While the opportunities are vast, and the value has clearly been shown across a number of industries in using data to strategic advantage, the choices in technology can be overwhelming. From Big Data to Artificial Intelligence to Data Lakes and Warehouses, the industry is continually evolving to provide new and exciting technological solutions. This webinar will help make sense of the various data architectures & technologies available, and how to leverage them for business value and success. A practical framework will be provided to generate “quick wins” for your organization, while at the same time building towards a longer-term sustainable architecture. Case studies will also be provided to show how successful organizations have successfully built a data strategies to support their business goals.

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Modern Metadata Strategies

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Metadata is hotter than ever, according a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information. While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.

Data-Centric Analytics and Understanding the Full Data Supply Chain

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While model development is an important part of analytics, this activity can be compromised by a lack of understanding of the data used in these models and poor Data Quality. For insights to be relied upon and truly actionable, data-related issues must be addressed. The data supply chain (the set of architectural components that moves data around the enterprise from points where it is created or acquired to points where it is used) must be managed to supply the needs of analytics and other constituencies. This webinar describes how the data supply chain should be designed and operated to provide analytics with the data it needs, and how Data Scientists should interact with the data supply chain to obtain the data they need. It also covers: Data-centric considerations that must be taken into account in the development of analytic models Features of a modern data supply chain Major components in the data supply chain, with a focus on Data Lakes Major roles and responsibilities in the data supply chain How analytics must interact with the data supply chain

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1. Nic Smith BI Solutions Marketing Microsoft Corporation

3. Executives Managers End-users USERS OBJECTIVES Tactical Strategic Business Scorecard Operational Analysis Embedded Guidance Analytic Needs Differ by Role and Objective

9. More Agility More Customer Loyalty Empower employees with customer metrics that guide employees to make decisions that will help build customer trust . More Revenue Prioritize marketing and sales initiatives in those that show the greatest return on investment. More Productivity Strategically align all customer-facing operations and empower employees from the top down to quickly adjust to changing environments . Eliminate learning curve issues while realizing fast adoption rates by delivering familiar applications that work the way your employees work.

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16. The Magic Quadrant is copyrighted February, 2008 by Gartner, Inc. and is reused with permission. The Magic Quadrant is a graphical representation of a marketplace at and for a specific time period. It depicts Gartner’s analysis of how certain vendors measure against criteria for that marketplace, as defined by Gartner. Gartner does not endorse any vendor, product or service depicted in the Magic Quadrant, and does not advise technology users to select only those vendors placed in the “Leaders” quadrant. The Magic Quadrant is intended solely as a research tool, and is not meant to be a specific guide to action. Gartner disclaims all warranties, express or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. Gartner, Inc. “Gartner Magic Quadrant for Business Intelligence Platforms, 2008,” James Richardson et al., Feb. 1, 2008

17.

18. Mainframe/ Departmental Systems END USER TOOLS AND PERFORMANCE MANAGEMENT APPLICATIONS BI PLATFORM (RDBMS, ETL, OLAP, Reporting) DELIVERY

19. What happened? Reporting, Consolidation What is happening? Scorecards and Dashboards Why did it happen? Analytics What will happen? Forecasting What do I want to happen? Planning, Budgeting

20.

Editor's Notes

06/06/09 07:36 © 2007 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

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