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Tag Archives: Learn

Learn Things So You Can Build Things — A Data Analyst’s Opinion

December 6, 2020   BI News and Info
pexels negative space 169573 2 Learn Things So You Can Build Things — A Data Analyst’s Opinion

This blog post is guest written by Tomi Mester of data36.com. 

When it comes to data science, it’s not about what you learn. It’s about what you are able to build with what you’ve learned.

The field of data science has been growing rapidly—especially in the last few years. We see exciting new tools and methods emerge all the time. And while these can be great, I feel that these can cause some confusion as well. Why? Because they make data professionals think about the wrong questions.

Asking the wrong questions

What do I mean by asking the wrong questions?

Examples of wrong questions might be:

  • What are the coolest new tools to try out?
  • What are the most exciting data science problems nowadays?
  • How can we fit these into our business (to experiment with them)?

Instead, we want to ask better questions like:

  • What business problems (or opportunities) do we have right now?
  • How can data help with this?
  • Why and how will our data project be useful for the company?
  • What should I learn to start building it?

Within data science, there is enormous hype around new tools every time a new machine learning algorithm is released. Or a new cloud-based solution is available. Or a new module is implemented for this or that programming language. And so on.

But aren’t these new tools important? Well, yes, but…

Tools are important, but with a caveat

Let’s think about an example from cook. You can’t cook soup without a spoon. But when eating the soup, very few people will say: “Hmmm, you have a pretty nice wooden spoon.” Instead, most of them will say: “Yum, this food tastes really good!”

And that’s because, at the end of the day, tools are just tools. You have to learn how to use them…

But that’s not the full sentence. It’s rather:

You have to learn how to use them so you can build useful things with them…

And that’s still not quite all.

You have to learn how to use them so you can build useful things with them that will have a positive impact on your business’s bottom line.

Maybe it sounds obvious written down. And if it is for you, that’s great. But I see many data professionals choose to focus on fancy data science solutions over the data science solutions they actually need. And then they hit a wall.

Unpopular opinion: most data scientists won’t need to know anything about deep learning

Let me give you just one example: deep learning.

I run a data science blog where I publish tutorials for aspiring data scientists on topics like the basics of Python or the basics of SQL, and so on.

And I get this question every week from someone: “When will you publish a tutorial on deep learning?”

And the answer is always the same: never.

Okay, I have to admit, I played around with the idea to quickly draft an introductory article on the topic… But it was tempting only for one reason: I know I’d get a lot of clicks for that article.

Most people want to learn about deep learning only because it’s popular. Why is it popular? Because it’s used for cool stuff, like self-driving cars at Tesla—and for that reason it gets a huge amount of media attention. That makes people excited and suddenly everyone wants to apply deep learning in their own projects.

But (at least in my opinion) it doesn’t work that way! A data science project should always start by defining the problem you want to solve. And once you have that, then you can choose the best tool to get the job done!

The naked reality is that in, most data science projects, there is a much higher demand for more traditional tools, like:

  • descriptive analytics and reporting
  • data cleaning and data wrangling
  • automating your processes
  • simple predictions and forecasting
  • simple classification methods

I know, at first, these sound less cool than deep learning… But believe me, when you are working on a real project, they are just as exciting (if not more)! Why? Because they get you useful information a lot more quickly than building trying to tackle a project with something complicated like deep learning. 

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AI Weekly: Can language models learn morality?

August 8, 2020   Big Data
 AI Weekly: Can language models learn morality?

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The fervor around state-of-the-art AI language models like OpenAI’s GPT-3 hasn’t died down. If anything, it’s gaining steam. Melanie Mitchell, a professor of computer science at Portland State University, found evidence that GPT-3 can make primitive analogies. Raphaël Millière, a philosopher of mind and cognitive science at Columbia University’s Center for Science and Society, asked GPT-3 to compose a response to the philosophical essays written about it. Among other applications, the API providing access to the model has been used to create a recipe generator, an all-purpose Excel function, and a comedy sketch writer.

But even language models as powerful as GPT-3 have limitations that remain unaddressed. Morality aside, countless studies have documented their tendency to reinforce the gender, ethnic, and religious stereotypes explicit within the data sets on which they’re trained. Shortcomings like these could lead to headline-generating models with a negative slant against people of color, for example, or news-summarizing models with warped concepts of gender.

In an effort to highlight models’ ethical dilettantism, researchers at Microsoft; the University of California, Berkeley; Columbia University; and the University of Chicago coauthored a preprint paper that assesses language models’ knowledge of moral concepts. They claim the benchmark they devised — dubbed ETHICS — provides a stepping stone to AI that’s better aligned with human values.

Some scientists argue improvements in language processing won’t necessarily lead to ethical AI because intelligence is divorced from moral behavior. Others claim that while ethical AI will be an important problem in the future, it’s outside the scope of data science and machine learning capabilities today. In any case, few (if any) methods of measuring a natural language system’s grasp of human values currently exist, which is what motivated the study.

The coauthors note that fairness is a concept of justice that more broadly encompasses concepts like impartiality and desert. (In philosophy, “desert” is the condition of deserving something.) Having systems abide by safety constraints is similar to deontological ethics in which right and wrong are determined by a collection of rules. Imitating prosocial behavior and demonstrations is an aspect of virtue ethics, which locates moral behavior in the imitation of virtuous agents. And improving utility by learning human preferences can be viewed as part of utilitarianism, or the theory that advocates maximizing the aggregate well-being of all people. ETHICS attempts to tie these separate strands — justice, deontology, virtue ethics, utilitarianism, and commonsense moral judgments — together by confronting the challenges posed by open-world scenarios and covering applicable theories in normative ethics.

ETHICS requires models to learn how basic truths about the world connect with human values, like the fact that although everyone coughs, people don’t want to be coughed on because it might make them sick. It’s the researchers’ assertion this contextualized setup captures the type of nuance necessary for a more general understanding of ethical principles.

To perform well on the ETHICS data set’s over 130,000 scenarios, models must reason about morally relevant factors emphasized by each of several ethical systems. The scenarios regarding justice underline notions of impartiality. The deontological scenarios emphasize rules, obligations, and constraints. Character traits like benevolence and truthfulness are paramount in the virtue ethics examples. And while happiness or well-being are the sole factors for the utilitarian scenarios, both are involved in the commonsense moral intuition scenarios.

The researchers took steps to ensure that scenarios within ETHICS didn’t involve ambiguous moral dilemmas. (For instance, “I broke into a building” is treated as morally wrong in the ETHICS data set, even though there might be situations where it isn’t wrong, such as if you’re a firefighter trying to save someone from a burning building.) They had Amazon Mechanical Turk workers relabel each scenario and discard those scenarios with low agreement, collecting data from English speakers in the U.S., Canada, and Great Britain and focusing on uncontroversial topics.

Over the course of several experiments, the researchers tested leading language models, including Google’s BERT and ALBERT, Facebook’s RoBERTa, and GPT-3. They found that all four achieved low performance on most moral reasoning tasks — one BERT variant answered questions about justice with 11.9% to 15.2% accuracy — but bigger models trained on more data tended to do “significantly” better than smaller models. For instance, the largest RoBERTa model answered questions about the scenarios ethically 44.1% to 68% of the time, which was far better than chance (24.2%).

The researchers posit that aligning AI with human values appears difficult in part because those values contain preferences intertwined with subconscious desires. It’s also true that popular language models trained with large corpora demonstrate several forms of bias. Recently, Facebook AI head Jerome Pesenti found a rash of negative statements from GPT-3, including several that targeted Black people, Jewish people, and women. Emily Bender, a professor at the University of Washington’s NLP group, recently told VentureBeat that even carefully crafted language data sets can carry forms of bias.

The ETHICS work coauthors believe representations could imbue language models with a broader set of human preferences about the world. In tandem with techniques to mitigate the effects of prejudiced data, these representations could also bolster efforts within the AI research community to create more equitable, less potentially harmful applications of AI.

“Systems would do well to understand the ethical factors at play to make better decisions within the boundaries of the law,” the coauthors wrote. “Our work is just a first step that is necessary but not sufficient for creating ethical AI, as we must engage more stakeholders and successfully implement their values. Future work should also make sure these models are explainable, and should test model robustness to optimization pressure.”

Indeed, work to imbue models with morality is likely necessary on the path toward sophisticated AI assistants. In remarks at MIT’s Computing Community Consortium in March 2019, Eric Schmidt, former executive chairman of Google and Alphabet, described his vision of assistants of the future that might help children to learn language and math; help adults plan their day; and provide warmth and companionship to the elderly. If such assistants were to lack a moral compass of any kind, the impact could be harmful particularly on young children, who lack a nuanced understand of right and wrong.

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What Manufacturers Can Learn From Formula One’s Industrial Optimization Model

July 6, 2020   TIBCO Spotfire
TIBCO Formula1 ModelOps 696x522 What Manufacturers Can Learn From Formula One’s Industrial Optimization Model

Reading Time: 3 minutes

When it comes to optimizing manufacturing processes, no one is better at it than the Mercedes-AMG Petronas Formula One team. From the way the team collects data, to how it analyzes that data and optimizes its systems and processes, it serves as a model for manufacturers. Let’s take a look at what manufacturers can learn from Mercedes-AMG Petronas F1’s industrial optimization model in order to improve their own factories.

Data Collection

When it comes to data, like other manufacturers, the team produces a plethora of data that needs to be collected and analyzed.  This translates to 45 terabytes of data produced during the course of a race week, comprised of 50,000 data points from over 300 sensors. Similarly, in a factory, production machines generate large volumes of data that need to be analyzed and quickly. For example, a CPG company can generate 5,000 data samples every 33 milliseconds. Manufacturers can learn from F1’s amazing ability to collect, analyze, and act on that tremendous amount of data in near real time.

Manufacturers can learn from F1’s amazing ability to collect, analyze, and act on that tremendous amount of data in near real time. Click To Tweet

Data Analysis

For the Mercedes-AMG Petronas F1 team, one of the ways data is collected is from a digital twin simulator, which tests overall car performance. There are billions of combinations of car set-ups that are possible, so the team needs to use analysis and experience to figure out the best ones to test. 

Like F1, in a factory, Industrial Internet of Things (IIoT) data must be analyzed in real time to understand how a process is performing in order to detect anomalies. Digital twins are also used in factories to reduce waste and improve product quality; a faulty product can lead to increased costs, rework, and unhappy customers, in addition to hefty fines and business closures. Digital twins are able to achieve this by mimicking real-world processes by utilizing sensors data in real-time to hone in and predict the key elements and attributes to optimize production, or prevent unnecessary failures.

Optimization 

When everything is properly optimized, the Mercedes-AMG Petronas F1 team sees the most benefit at the track. After careful analysis of the data, the team is able to find the optimum car setup in rapidly changing circumstances leading to significant gains in performance. Other examples include a reduction in anomalies in gearbox changes, resulting in great track performance improvements, helping ensure the best race and qualifying lap times.

 Imagine what that kind of time-saving that type of optimization could do for your company.

In fact, without proper manufacturing optimization, manufacturers face unplanned outages, which translate in a lower Overall Equipment Effectiveness (OEE). However, when optimized, manufacturers see increased performance and higher quality products. 

Looking Ahead

In the coming decade, many manufacturers are going to be switching their smart factory strategy from one that was focused on technology implementation to one that is focused on process-change management. This will result in manufacturers treating their own IIoT assets like internal customers, reducing downtime, equipment failures, and diagnosing and resolving issues. Manufacturers will increasingly leverage digital twins driven by IIoT and machine learning in order to save operational expenses and optimize supply chains. 

In the coming decade, many manufacturers are going to be switching their smart factory strategy from one focused on technology implementation to one that is focused on process-change management. Click To Tweet

While Mercedes-AMG Petronas F1 pioneered the modern industrial optimization model, manufacturers are starting to implement these best practices into their own factories. From data collection, data analysis, and optimization, manufacturers have an opportunity for greater industrial optimization going forward. When utilized properly and with the right technology, factories can increase performance, reduce costs, and produce higher quality products.

Download this infographic to see in greater detail what manufacturers can learn from Mercedes-AMG Petronas F1’s industrial optimization model. And, to learn more about how TIBCO gives the team a competitive advantage, visit our partnership page. 

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Community Summit and extreme365 Europe 2020 – Join virtual expo 30 June to 03 July – Lets together learn, connect and collaborate

June 29, 2020   CRM News and Info

Community Summit, extreme 365 is just around the corner and we can’t hold our excitement to be a part of this year’s very first virtual event. 300+ sessions, inspiring key notes and much more, this Summit is  a great way to learn, connect, discover and collaborate without stepping out of your home!

Summits are always the place where you can discover the latest Microsoft Business Applications and related 3rd party apps while speaking with the innovators who created them. It’s a place to find your technology partner and enhance your Microsoft technology stack. And like every year, Inogic has some new productivity app releases and is now a one stop hub to assist you with your Dynamics CRM, Power Platform (PowerApps, Power BI, Power Automate), Field Service, Microsoft Portals Development/Integrations requirements, that you always had in mind!

Add Inogic to your Show Planner for Special Offers
(Our Team will be online on Chat during the Event hours or please contact us anytime on crm@inogic.com)

You’ve been well versed with our popular productivity apps, most of which are already preferred apps on Microsoft AppSource namely Maplytics – our flagship product which is a native Map Visualization, Routing & Geo-analytics app for Dynamics 365 CRM and our other productivity apps for Power Platform – InoLink, Click2Clone, Click2Export, Attach2Dynamics, SharePoint Security Sync, Alerts4Dynamics, Lead Assignment & Distribution Automation and User Adoption Monitor.

So, what’s special at Inogic Booth this year?

Well not just 1 or 2 but we have 6 new app releases to be proud of. Our team will be available to demonstrate our 6 new apps. You have to just click on the chat option or email crm@inogic.com for a first look on Kanban Board, Map My Relationships, Click2Undo, Recurring Billing Manager, Subscription Management and Auto Tax Calculator.

That’s not all!

DON’T MISS OUR PARTNER SOLUTIONS SHOWCASE

Maps for CRM: Geo-Analytics, Routes, Locational Marketing & Territory Management – Powered by Maplytics™ – , a Microsoft Certified app for Dynamics 365. 

July 3 | 1:30 PM – 2:00 PM Central European Summer Time

Maplytics™ being quite popular in the Dynamics community, has received quite love in the past few years. Its eminent features Territory Management, Optimized Routing, Appointment Planning and Radius Search are just what the users prefer over other apps. Since it is going to be an engaging session be prepared with your questions. To join this insightful session, you should be logged in and registered to the event.

So, don’t forget to register. Be our guest and let’s make this virtual event even more interesting and enriching with knowledge exchange.

Without further ado, mail us at crm@inogic.com so that we can book your spot at once!

See you soon… Virtually

Until then – Stay Safe, Stay Healthy!

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What Your Business Can Learn from an Open Banking Strategy

June 23, 2020   TIBCO Spotfire
TIBCO OpenBanking scaled e1592844442712 696x365 What Your Business Can Learn from an Open Banking Strategy

Reading Time: 2 minutes

Digital businesses today don’t have the luxury of sitting static on old infrastructure. In our current era of digital transformation, it’s not even enough to be a fast follower—you have to be an innovator. And there is one thing every innovative organization has in common that enables them to provide superior customer experience: Data. 

Data is the asset that allows you to keep your customers at the center of your business strategy. However, you have to know how, with a combination of the right tools, strategy, and people, to make smart use of that data. Being an early adopter of the latest technologies means you see those opportunities first, leaving no space for market disruption from competitors. 

How data is used to improve the customer experience might vary across fields, but banking and financial service firms are currently front and center of this business reinvention. By adopting an open banking strategy, these institutions are using data to personalize customer experiences and connect the overall customer journey, This also provides new openings for cross-sell and up-sell opportunities and new revenue streams through partner ecosystems. 

While banking and finance are already taking advantage of this strategy, every industry can benefit from collecting, analyzing, and sharing data. To successfully harness the power of your data, adopt an open banking type mindset. Create personalized customer journeys, introduce new digital products, and discover new channels of revenue; these aren’t limited to core banking alone. 

Data is the asset that allows you to keep your customers at the center of your business strategy. However, you have to know how, with a combination of the right tools, strategy, and people, to make smart use of that data. Click To Tweet

To take the first step, you must have the right foundation in place. This will allow you to connect the data across your enterprise, govern and secure that data, and even bring in outside data from partners through APIs. To learn more about the open banking approach and to gain some insights into how a connected ecosystem can help you take advantage of data can benefit your digital business, download this eBook and watch the solution whiteboard video today!

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Learn AAD when you manage a Power BI environment

December 16, 2019   Self-Service BI

I get many questions around Power BI and security related features. Users and customers often don’t realize that most of their requirements can actually be solved by AAD. Power BI uses AAD to handle authentication and authorization. Because of this we can also leverage all the features of AAD to add additional security and rules to Power BI. If you want to understand how AAD and Power BI work together guy in a cube has a great video on this.

So what kind of features does AAD have that you can use to secure your Power BI even more?

Conditional access

You can use AAD conditional access which gives you conditions for your users to authenticate with Power BI:

  • When logging into Power BI the user needs to use 2 factor authentication
  • Make sure they can only connect to Power BI when you are on the corporate network
  • Allow Power BI connections only from machines that are domain joined
  • Only allow connections from machines that are complaint with the network policy
  • Only allow logging in to Power BI from certain AD group (the rest cannot log in)

It also allows mixing and matching from the above so you could say normal users can only log in from VPN or the office but admins can always login, etc.

conditionalaccess overview Learn AAD when you manage a Power BI environment

More AAD options

What else can you do with AAD?

These AAD features will help you secure your Power BI environment even further and more and more features get added all the time.

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Learn how Power BI can help with extracting data from a website

December 12, 2019   Self-Service BI
social default image Learn how Power BI can help with extracting data from a website

Join Indira Bandari, Microsoft Data Platform MVP for a Power BI Webinar on December 10, 2019 11:00 am PST.

Join webinar here

Indira Bandari for a webinar walking us through simple to complex scenarios on how to extract data from a website in Power BI.

In this session Indira will go through the a simple table import scenario using the “Add Data By Example” button to import data from a website when there are multiple page numbers.

More about Indira Bandari here

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Learn how to connect Azure DevOps API in a secure way

December 6, 2019   Self-Service BI
social default image Learn how to connect Azure DevOps API in a secure way

Join Microsoft Data Platform MVP Gaston Cruz  for a great webinar!

In this session Gaston Cruz is going to cover how to connect to Azure DevOps API in a secure way. His example will show us how to extract metrics of a development team and how to get crucial reporting details of daily basis work and deployments.

Of course it’s going to be great to share some of the reports that we can get connecting Power BI Desktop to data flows (using parameters, functions to populate entities)

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Learn How Power BI and Accessibility Work Together

November 27, 2019   Self-Service BI

Power BI’s accessibility documentation has been expanded from one article to five new articles for you to explore. We hope that these new articles will help you learn more about how Power BI can empower your organization, report creators, and report consumers through accessibility.

Here’s a breakdown of how the articles are structured:

Overview of accessibility in Power BI

This article gives you an overview of universal design, and different accessibility standards that Power BI is committed to.

Creating accessible Power BI reports

If your reports are viewed by large audiences or might be viewed by users with disabilities, check out this article on how to create accessible reports. It runs through different accessibility features that Power BI provides and highlights areas you should consider when creating reports with accessibility in mind. This article also has a handy Report accessibility checklist, so you can quickly review your report.

Consuming Power BI reports with accessibility tools

If you are a report consumer who uses keyboard navigation, screen readers, or other assistive technology, this article helps describe how you can navigate through reports, visuals, and the accessibility features that are available to you.

Creating Power BI reports with accessibility tools

If you are a report creator who uses assistive technology, this article helps describe how to navigate around Power BI Desktop, so you can build reports.

Accessibility keyboard shortcuts for Power BI reports

This article gives a full list of the keyboard shortcuts available in Power BI Desktop.

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Learn about Power BI at Microsoft Ignite

November 2, 2019   Self-Service BI
social default image Learn about Power BI at Microsoft Ignite
Session Title Code Session Type Room Time Slot Speaker Name The future of content lifecycle management in Power BI THR3096 Theater: 20 Minute The Hub: Microsoft Showcase – Theater 6 Theater #02:
Monday November 4
1:05p-1:25p Eli Greenwald; Nimrod Shalit Navigate data protection and risk management in the cloud era BRK010 Breakout: 45 Minute OCCC W414 Monday November 4
2.00p-2.45p Alym Rayani Power BI Data modeling fundamentals WRK1000 Technology Workshop OCCC W221 E 75 min #0
Monday November 4
2:15p-3:30p Angela Dobrea; Ayush Rathi; Peter Myers Enable modern analytics and enterprise business intelligence using Microsoft Power BI POWA30 Module: 45 minute OCCC WF 1-2 (Tangerine Ballroom) 45 min #28:
Tuesday November 5
9:15a-10:00a Adam Saxton; Christian Wade Microsoft Power BI: Delivering business value with AI BRK3212 Breakout: 45 Minute OCCC W308 45 min #31:
Tuesday November 5
1:00p-1:45p Justyna Lucznik; Sarina Stevens Microsoft Power BI: Business intelligence strategy, vision, and roadmap update BRK2172 Breakout: 75 Minute OCCC W320 (Chapin Theatre) 75 min #05:
Tuesday November 5
2:15p-3:30p Amir Netz; Arun Ulagaratchagan; Christian Wade; Sarina Stevens; Tim Depledge Exporting CDS data to Azure Data Lake – enable modern analytics and gain insights on your data BRK3325 Breakout: 45 Minute OCCC W209 45 min #33:
Tuesday November 5
3:30p-4:15p Mudit Mittal; Sabin Nair Democratizing self-service data preparation within Microsoft Power BI, PowerApps and Flow using dataflows BRK3208 Breakout: 45 Minute Hyatt Florida Ballroom 45 min #34:
Wednesday November 6
9:15a-10:00a Miguel Llopis Microsoft Power BI and Azure SQL Data Warehouse: Intelligent action over big data BRK3274 Breakout: 45 Minute Hyatt Florida Ballroom 45 min #35:
Wednesday November 6
10:30a-11:15a Charles Feddersen; Christian Wade Help shape the future of Power Query and Dataflows BRK2290 Interactive Session OCCC W333 (Hamlin Boardroom) 75 min #08:
Wednesday November 6
10:45a-12:00p Mahesh Prakriya; Miguel Llopis What’s new and what’s next in Power BI embedded analytics BRK3211 Breakout: 45 Minute Hyatt Florida Ballroom 45 min #36:
Wednesday November 6
11:45a-12:30p Eli Greenwald; Nimrod Shalit Help us help you by driving feedback into the export to data lake service BRK2291 Interactive Session OCCC W333 (Hamlin Boardroom) 75 min #09:
Wednesday November 6
12:30p-1:45p Sabin Nair What’s new in Power BI embedded analytics THR3092 Theater: 20 Minute The Hub: Microsoft Showcase – Theater 4 Theater #36:
Wednesday November 6
1:50p-2:10p Eli Greenwald; Nimrod Shalit Microsoft Power BI: Roadmap for enterprise information management – data lineage and impact analysis, data protection, and data discovery BRK3207 Breakout: 75 Minute Hyatt Orlando Ballroom 75 min #10:
Wednesday November 6
2:15p-3:30p Adi Regev; Anton Fritz; Yaron Canari Microsoft Power BI: Advanced concepts in the Common Data Model THR3140 Theater: 20 Minute The Hub: Microsoft Showcase – Theater 4 Theater #37:
Wednesday November 6
2:30p-2:50p Oleg Ovanesyan; Robert Bruckner Microsoft Power BI Desktop: Data exploration, analysis, and storytelling THR3145 Theater: 20 Minute Hyatt Building Theater Theater #38:
Wednesday November 6
3:05p-3:25p Matt Goswell; Miguel Martinez Delivering a real-time Microsoft Power BI dashboard with the REST API WRK1001 Technology Workshop OCCC W221 E 75 min #13:
Thursday November 7
10:45a-12:00p Linda Larkan; Peter Myers; Tobias Koprowski Power BI and Microsoft Information Protection: The game changer for secure BI THR3094 Theater: 20 Minute The Hub: Microsoft Showcase – Theater 4 Theater #46:
Thursday November 7
10:55a-11:15a Adi Regev; Anton Fritz Microsoft Power BI, Flow, and PowerApps: Connecting to data using the on-premises data gateway BRK3313 Breakout: 45 Minute OCCC W240 45 min #44:
Thursday November 7
2:15p-3:00p Arthi Ramasubramanian Iyer Power BI and Microsoft Information Protection: The game changer for secure BI BRK3314 Breakout: 45 Minute OCCC W209 45 min #44:
Thursday November 7
2:15p-3:00p Anton Fritz Advanced data prep with Power BI dataflows BRK3278 Breakout: 45 Minute Hyatt Plaza International Ballroom G-H 45 min #45:
Thursday November 7
3:30p-4:15p Bogdan Crivat Cristian Petculescu; Delia C. Fernandez; Mohammad Ali Common Data Model (CDM): All you need to know about CDM BRK3268 Breakout: 45 Minute Hyatt Florida Ballroom 45 min #46:
Friday November 8
9:15a-10:00a Oleg Ovanesyan; Robert Bruckner Microsoft Power BI: Distributing insights and governing self-service analytics with the Power BI service BRK3282 Breakout: 45 Minute Hyatt Plaza International Ballroom G-H 45 min #47:
Friday November 8
10:30a-11:15a Adam Saxton

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