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PowerBIWorldTour.com #PBIWorldTour
Self-service data prep in
Power BI with Dataflows
Adam Saxton
@GuyInACube
PowerBIWorldTour.com #PBIWorldTour
Where we were…
A quick review
OLTP systems Reports and dashboards
Data Warehouse
Data Preparation / ETL OLAP / Analytics Models
Data Lake
Self-Service BI
Requires a team of specialists
Creating E2E BI solutions requires
multiple BI tools. This requires specific
knowledge of each of the tools and
complex integration to build and
maintain an E2E BI solution.
Fragmented, incomplete data
Pulling together data from traditional and
cloud data sources, and figuring out how
to enrich it is extremely difficult.
Complex system integration
Traditional BI solutions span multiple
applications and services. Sharing data
across systems requires each system to
understand the location, structure and
meaning of the data.
Business data has no structural or
semantic consistency
Different applications, departments, and
analysts define data in different ways,
which makes data exploration, and reuse
of data and apps extremely challenging.
Modern BI challenges
PowerBIWorldTour.com #PBIWorldTour
Power BI is evolving
Power BI introduces self-service data-prep capabilities
Self-service low code/no code Integral part of Power BI stack
Cloud and on-premises
connectors
Standard schema
(Common Data Model)
Data reuse In-lake transformations
Power BI introduces self-service data-prep capabilities
Self-service low code/no code Integral part of Power BI stack
Cloud and on-premises
connectors
Standard schema
(Common Data Model)
Data reuse In-lake transformations
Dataflows
Power BI introduces dataflows
BI models
Visualizations
Data prep
Data (Azure Data Lake)
Requires a team of specialists
Creating E2E BI solutions requires multiple BI
tools. This requires specific knowledge of
each of the tools and complex integration to
build and maintain an E2E BI solution.
Fragmented, incomplete data
Pulling together data from traditional and
cloud data sources, and figuring out how to
enrich it is extremely difficult.
Complex system integration
Traditional BI solutions span multiple
applications and services. Sharing data
across systems requires each system to
understand the location, structure and
meaning of the data.
Business data has no structural or
semantic consistency
Different applications, departments, and
analysts define data in different ways, which
makes data exploration, and reuse of data
and apps extremely challenging.
CDS for Apps Power BI
Data Integration
Common Data Model
ADLS v2
Enrichment and AI
Intelligence
Integration
Dynamics 365 Apps PowerApps Power BI Apps
Announcing the
Open Data Initiative
Deliver unparalleled business
insight from your behavioral,
transactional, financial, and
operational data with the Open
Data Initiative—a jointly-
developed vision by Adobe,
Microsoft, and SAP.
PowerBIWorldTour.com #PBIWorldTour
The bigger picture
…we want to leverage the data created by the
dataflow in applications or services outside of
Power BI?
Azure
Databricks
Azure ML
Azure SQL
DW
Azure Data
Factory
Business analysts
Low/no code
Data scientists
Data engineers
Low to high code
CDM folder CDM folder CDM folder
…we want to leverage the data created by the
dataflow in applications or services outside of
Power BI?
…or to provide an easy way to consume
analytical data created outside of Power BI, in
Power BI?
Azure Data
Factory
Azure
Databricks
Azure SQL
DW
Azure ML
CDM folder
…we want to leverage the data created by the
dataflow in applications or services outside of
Power BI?
…or to provide an easy way to consume
analytical data created outside of Power BI, in
Power BI?
…or to eliminate the complexity of data prep
orchestration?
The difficult part of data preparation is usually not the individual job,
package, or query – it is ensuring all of the jobs, packages, and
queries run as a logical and reliable system
Each Dataflow Entity has a formula
The Power BI Dataflow calculation engine understands the
relationships between all entities
Update one, the rest update automatically and consistently – just like
Excel
20
4
1
8
10
4
1
1
Twitter: @GuyInACube
Questions?
Don’t forget to join your local PUG to enjoy
year-round networking and learning.

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Datafl

  • 1.
  • 2. PowerBIWorldTour.com #PBIWorldTour Self-service data prep in Power BI with Dataflows Adam Saxton @GuyInACube
  • 4. OLTP systems Reports and dashboards Data Warehouse Data Preparation / ETL OLAP / Analytics Models Data Lake Self-Service BI
  • 5. Requires a team of specialists Creating E2E BI solutions requires multiple BI tools. This requires specific knowledge of each of the tools and complex integration to build and maintain an E2E BI solution. Fragmented, incomplete data Pulling together data from traditional and cloud data sources, and figuring out how to enrich it is extremely difficult. Complex system integration Traditional BI solutions span multiple applications and services. Sharing data across systems requires each system to understand the location, structure and meaning of the data. Business data has no structural or semantic consistency Different applications, departments, and analysts define data in different ways, which makes data exploration, and reuse of data and apps extremely challenging. Modern BI challenges
  • 7. Power BI introduces self-service data-prep capabilities Self-service low code/no code Integral part of Power BI stack Cloud and on-premises connectors Standard schema (Common Data Model) Data reuse In-lake transformations
  • 8. Power BI introduces self-service data-prep capabilities Self-service low code/no code Integral part of Power BI stack Cloud and on-premises connectors Standard schema (Common Data Model) Data reuse In-lake transformations Dataflows
  • 9. Power BI introduces dataflows BI models Visualizations Data prep Data (Azure Data Lake)
  • 10. Requires a team of specialists Creating E2E BI solutions requires multiple BI tools. This requires specific knowledge of each of the tools and complex integration to build and maintain an E2E BI solution. Fragmented, incomplete data Pulling together data from traditional and cloud data sources, and figuring out how to enrich it is extremely difficult. Complex system integration Traditional BI solutions span multiple applications and services. Sharing data across systems requires each system to understand the location, structure and meaning of the data. Business data has no structural or semantic consistency Different applications, departments, and analysts define data in different ways, which makes data exploration, and reuse of data and apps extremely challenging.
  • 11. CDS for Apps Power BI Data Integration Common Data Model ADLS v2 Enrichment and AI Intelligence Integration Dynamics 365 Apps PowerApps Power BI Apps
  • 12.
  • 13. Announcing the Open Data Initiative Deliver unparalleled business insight from your behavioral, transactional, financial, and operational data with the Open Data Initiative—a jointly- developed vision by Adobe, Microsoft, and SAP.
  • 15.
  • 16. …we want to leverage the data created by the dataflow in applications or services outside of Power BI?
  • 17. Azure Databricks Azure ML Azure SQL DW Azure Data Factory Business analysts Low/no code Data scientists Data engineers Low to high code CDM folder CDM folder CDM folder
  • 18. …we want to leverage the data created by the dataflow in applications or services outside of Power BI? …or to provide an easy way to consume analytical data created outside of Power BI, in Power BI?
  • 20. …we want to leverage the data created by the dataflow in applications or services outside of Power BI? …or to provide an easy way to consume analytical data created outside of Power BI, in Power BI? …or to eliminate the complexity of data prep orchestration?
  • 21. The difficult part of data preparation is usually not the individual job, package, or query – it is ensuring all of the jobs, packages, and queries run as a logical and reliable system Each Dataflow Entity has a formula The Power BI Dataflow calculation engine understands the relationships between all entities Update one, the rest update automatically and consistently – just like Excel
  • 24. Don’t forget to join your local PUG to enjoy year-round networking and learning.

Editor's Notes

  1. As customer strive to take advantage of the digital transformation that is occurring in virtually every industry, they need to re-evaluate how they engage with their customers/prospects, how they transform their products and operations, and how they empower and understand their employees.   In today’s world, doing each of these things is more and more reliant upon data…traditionally, everything you knew about your customers and prospects was available in your business application systems and in the heads of employees. You learned almost everything about your products BEFORE they left your warehouse. Employees used technology more to enter data than to learn from it.   In the data-driven world we live in today, leveraging intelligent insights from data across customers, products, and employees is critical to be able to stay competitive and keep up with or lead digital transformation in any industry. And this isn’t just a customer’s typical business application data – it’s also about augmenting the customer’s data with additional data (e.g. search, employee behavioral data, sentiment data, benchmark data, etc..) – and applying the right intelligence to drive meaningful insights. Let’s take a look at how this might help businesses(next slide – top of mind)   _______________________________ ORIGINAL TALKING POINTS: The lower left-hand side customers. If you think about -- and this is sort of traditionally the domain of CRM, customer engagement. If you think about CRM traditionally, it was a system largely about the "known knowns." You know, you were in my CRM system if I knew your name, maybe had some contact information. But, increasingly, our customers -- or even our prospects before they are customers -- are showing up to us in anonymous form first. They're a profile on the other side of a Web session, some random Twitter handle tweeting about my brand, and they're coming at me from all of these various channels that are digitally delivered. And that allows our customers, and in many ways mandates that our customers think differently about how they engage their customers. Collecting all of this information from these various sources, even prior to knowing who this individual is or being able to identify them, and being able to sort of take them on a journey intelligently based on these learnings. That's a fundamental change in the way that we're engaging our customers. On the right-hand side at the bottom, products. I would argue this is the traditional ERP environment, where traditionally ERP was about collecting supply chain, manufacturing some products, warehousing them, shipping them out, hopefully getting paid. But once the product left the loading docks, hit the truck, it was gone. Today, that is increasingly not the case. When I ship a product to a customer, that product is often connected back. It's sending lots of telemetry, and it's allowing me to rethink fundamentally the nature of the products and services. And it has huge implications for what's possible. Think about manufacturing defects or quality, being able to assess your posture as a result of real data for real customer use. Watching the use of consumables, doing a better job of planning inventory. Predicting when a piece of capital equipment is going to fail, and then proactively managing it, versus allowing the customer to get into a situation of dissatisfaction. Again, fundamental reimagination of what's possible on the product side. And these two things are inextricably linked. And I'm learning something about my customers I never could if I can observe their use of my products and services, I can feed that right back into the customer engagement cycle. I can recommend new products and services. So this sort of interconnected set of business processes is brand new, powered by data, artificial intelligence, and as we'll talk, delivered through Dynamics 365. But this last loop is the people loop. When you think about the kinds of people that we need in our organization that can operate in this digitally transformed environment, the kind of tools that you need to allow them to create, collaborate, communicate, and to do that connected with the business processes that transformed, and to do it with data. Again, fundamental change I how we find these people, convince them to join our organization, provide them with tools that they need in order to connect with customers and connect with products. This opportunity is real, and it is what's driving all the growth.
  2. The Microsoft Common Data Model (CDM) provides well-defined, modular, and extensible business entities at the center of the CDS application and intelligence platforms. Customers, system integrators, and ISVs can build on and depend on these standard CDM definitions, and also easily extend the models to capture additional business-specific ideas. The Microsoft Common Data Model (CDM) provides a common data structure ISV’s, BI Pros and analysts can collaborate on.
  3. How to get answers to business questions about your data?
  4. How to get answers to business questions about your data?
  5. As customer strive to take advantage of the digital transformation that is occurring in virtually every industry, they need to re-evaluate how they engage with their customers/prospects, how they transform their products and operations, and how they empower and understand their employees.   In today’s world, doing each of these things is more and more reliant upon data…traditionally, everything you knew about your customers and prospects was available in your business application systems and in the heads of employees. You learned almost everything about your products BEFORE they left your warehouse. Employees used technology more to enter data than to learn from it.   In the data-driven world we live in today, leveraging intelligent insights from data across customers, products, and employees is critical to be able to stay competitive and keep up with or lead digital transformation in any industry. And this isn’t just a customer’s typical business application data – it’s also about augmenting the customer’s data with additional data (e.g. search, employee behavioral data, sentiment data, benchmark data, etc..) – and applying the right intelligence to drive meaningful insights. Let’s take a look at how this might help businesses(next slide – top of mind)   _______________________________ ORIGINAL TALKING POINTS: The lower left-hand side customers. If you think about -- and this is sort of traditionally the domain of CRM, customer engagement. If you think about CRM traditionally, it was a system largely about the "known knowns." You know, you were in my CRM system if I knew your name, maybe had some contact information. But, increasingly, our customers -- or even our prospects before they are customers -- are showing up to us in anonymous form first. They're a profile on the other side of a Web session, some random Twitter handle tweeting about my brand, and they're coming at me from all of these various channels that are digitally delivered. And that allows our customers, and in many ways mandates that our customers think differently about how they engage their customers. Collecting all of this information from these various sources, even prior to knowing who this individual is or being able to identify them, and being able to sort of take them on a journey intelligently based on these learnings. That's a fundamental change in the way that we're engaging our customers. On the right-hand side at the bottom, products. I would argue this is the traditional ERP environment, where traditionally ERP was about collecting supply chain, manufacturing some products, warehousing them, shipping them out, hopefully getting paid. But once the product left the loading docks, hit the truck, it was gone. Today, that is increasingly not the case. When I ship a product to a customer, that product is often connected back. It's sending lots of telemetry, and it's allowing me to rethink fundamentally the nature of the products and services. And it has huge implications for what's possible. Think about manufacturing defects or quality, being able to assess your posture as a result of real data for real customer use. Watching the use of consumables, doing a better job of planning inventory. Predicting when a piece of capital equipment is going to fail, and then proactively managing it, versus allowing the customer to get into a situation of dissatisfaction. Again, fundamental reimagination of what's possible on the product side. And these two things are inextricably linked. And I'm learning something about my customers I never could if I can observe their use of my products and services, I can feed that right back into the customer engagement cycle. I can recommend new products and services. So this sort of interconnected set of business processes is brand new, powered by data, artificial intelligence, and as we'll talk, delivered through Dynamics 365. But this last loop is the people loop. When you think about the kinds of people that we need in our organization that can operate in this digitally transformed environment, the kind of tools that you need to allow them to create, collaborate, communicate, and to do that connected with the business processes that transformed, and to do it with data. Again, fundamental change I how we find these people, convince them to join our organization, provide them with tools that they need in order to connect with customers and connect with products. This opportunity is real, and it is what's driving all the growth.
  6. Power BI users can now easily create dataflows to prepare data in a centralized storage, using a standardized schema, ready for easy consumption, reuse, and the generation of business insights. Data is stored in Azure Data Lake Store gen2, and is integrated with Power BI and Azure data services. Dataflows are available to Pro and Premium customers.
  7. Challenge: Provide customers with great ready-made insights in the product –and enable unconstrained self service customizations Dyn365F&O offers best in class ready-made Analytics embedded within the product.  Provide customers with no-cliffs extensibility: Within minutes, Power BI customers should be able to extend solutions leveraging data, reports and dashboards from Dyn365F&O as well as other systems Extensibility of existing solutions is possible at two levels Build customization experiences within your application Enable customization and personalization experiences within the product for users of the App Data is still contained within the application, not visible to other applications and services Mashing up with data from other applications and services is not possible Make data available to applications that specialize in customizations Leverage the power of rich, robust experiences that Power users and developers are familiar with Allows integrating data from multiple sources including Web and IoT sensors Customers may need to copy the data over multiple times or set up ETL infrastructure and storage to make it available Operations and Finance + Power BI + Dataflows win, because companies can: Quickly deploy Power BI template with customizable Reports, Dashboards with data in CDM format Customize or enhance the templates with data from other apps or sources using Power Bi’s rich connectors Deploy other solutions that depend on schematized data with common or custom data model without any ETL required
  8. Bonus: Dataflows deliver authoritative data lineage