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1/18 Webinar: Using PowerApps and Flow to create Line of Business “portals” by Vishwas Lele

This webinar is designed to show you how to more easily create PowerApps applications and take advantage of the recently introduced PowerApps custom visual for Power BI

Using PowerApps and Flow to create Line of Business Enterprise “portals” by Vishwas Lele

 

Vishwas will showcase a PowerApp application that is essentially a “portal” for existing Line of Business Enterprise Applications (inventory, contracts etc.) and Services ( Dynamics, O365, DropBox etc. )Through the use of PowerApps features like the out of the box connectors, integration with Flow and mobile enablement, learn how easy it is to build an app for the information workers that allows them to  have all the information in one location and on a device of their choice. 

When 1/18/2018 10AM

Where: https://www.youtube.com/watch?v=eSMAAFHK44c

About Vishwas Lele

vishwas lele v1 1/18 Webinar: Using PowerApps and Flow to create Line of Business “portals” by Vishwas Lele

Vishwas Lele serves as Chief Technology Officer at Applied Information Sciences, Inc. Mr. Lele is responsible for assisting organizations in envisioning, designing, and implementing enterprise solutions. Mr. Lele brings close to 24 years of experience and thought leadership to his position, and has been at AIS for 18 years. A noted industry speaker and author, Mr. Lele serves as Microsoft Regional Director for the Washington, D.C. area and is a member of Windows Azure Insiders group. Additionally, Mr. Lele received an MVP (Most Valuable Professional) for Solution Architecture.

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Microsoft Power BI Blog | Microsoft Power BI

5 Ideas for Using Personalization in Emails with Dynamics 365

Email marketing is no longer a one-size-fits-all initiative. Consumers now expect customized communications from the organizations they do business with. And when businesses meet those expectations, they reap the benefits. Research from Experian reveals that emails with personalized subject lines are 26 percent more likely to be opened. And according to Aberdeen Group, personalized emails improve click-through rates by 14 percent and conversion rates by 10 percent.

So, what can you personalize in an email? You are really only limited by the information that you have available in Microsoft Dynamics 365. While that’s great news in terms of the flexibility you have for customizing campaigns, it can also be a little daunting to figure out where to start. To help you get started with personalization and take your emails to the next level, here are five ideas for how to use personalization in your emails:

1. Name. Using a lead or contact’s name in an email is likely the most common use of email personalization, and rightly so. It’s a simple and effective way to show your recipients that you know who they are, and it helps create a connection with recipients immediately. You can use names in an email subject line or preheader, as seen here:

 5 Ideas for Using Personalization in Emails with Dynamics 365

Or choose to greet your customers or leads by name in the content of the email:

 5 Ideas for Using Personalization in Emails with Dynamics 365

2. Important dates. Our lives are filled with dates worth remembering and recognizing, both professionally and personally. So, what better way to endear yourself to your customers or prospects than to acknowledge these important dates too? An anniversary as it relates to your organization is a popular pick for a date to recognize. In the example email below, you can see how a professional association incorporated a date to recognize how long an individual had been a member of the association.

 5 Ideas for Using Personalization in Emails with Dynamics 365

Birthdays are another important milestone to recognize. Other important dates to recognize could be the birth of a child or a wedding date. Again, you are really only limited by the data you have available in Dynamics 365.

3. Location. Consider these two subject lines: “Check out these hot new restaurants” versus “Check out these hot new Atlanta restaurants.” If you’re a foodie, you might be inclined to open the email either way, but the subject line that references the city you live in is more attention-grabbing because you know that the content is localized to you and very relevant. Localization also works well in the body of an email, as seen in the example below for a real estate brokerage, which references the city where the recipient wants to buy a new home and displays a few homes available in that location.

 5 Ideas for Using Personalization in Emails with Dynamics 365

4. Dollar amounts. As seen in the example below, nonprofits in particular can use dollar amounts as an effective way to personalize their emails. This personalization allows the organization to recognize a donor’s specific contribution rather than just a general reference to an unspecified donation amount. Other examples of this personalization in action could be retailers with loyalty programs using dollar amount personalization to thank a customer for spending a certain amount or a financial institution could employ this type of personalization to show how much a customer has saved using a round-up on purchases savings account.

 5 Ideas for Using Personalization in Emails with Dynamics 365

5. How you met. Sometimes people need a little reminder of how they met your organization, and they also like seeing that you remember too. This is particularly true for new leads who are having their first few interactions with your business. Did they visit your website? Make a purchase from your store? Attend an event? For example, a college wanted to send an email blast to prospective students that their representatives had spoken with at a series of college fairs. Using personalization, they were able to incorporate the name of the specific event each prospective student attended, providing a more customized email experience.

 5 Ideas for Using Personalization in Emails with Dynamics 365

These are just a few examples of email personalization you can try in your communications. As you gather data in CRM, think of other opportunities for personalization that would suit your business and audience.

This post was contributed by ClickDimensions.

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CRM Software Blog | Dynamics 365

Automate Business Process Flow stages using workflows

The July 2017 Update for Dynamics 365 introduced a new feature that supports Business Process Flows as an entity. You can now work with each Business Process Flow as its own entity through dashboards, grids, and charts. This also means that you have the ability to interact with them through Dynamics 365 workflows.

In this article, I am going to focus on how you can create a Workflow for the Business Process Flow entity record to change the Active Stage when a field on the Opportunity entity record is updated. Previously this was only possible using client-side APIs or use of a plugin.

Triggers include:
• Process is applied
• Process status changes
• Process is assigned
• Process changes
• Process is deleted

In order to trigger the workflow to fire for a related, parent entity, such as Opportunity, you will need to keep this an asynchronous workflow. Select Process changes as a trigger, which will give you an option of which record type of the field you want the workflow to fire on.

Suppose that with the Out-of-Box Opportunity entity, when an Opportunity is created that is tied to an Existing Contact or Existing Account, the stage should move from the Qualify stage to the Develop stage.

When selecting the Record Type in the workflow, select Opportunity(Opportunity) and select both Account and Contact. This will kick off the workflow when these fields are populated.

Next, choose to Update Record to set the stage and choose to update the Business Process Flow entity. In this case, the Opportunity Sales Process entity.

In the Set Properties window, choose the Develop stage for Active Stage:

After you Save and Activate the Workflow, this should now trigger on update of the Account or Contact fields on the Opportunity record.

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Dynamics CRM in the Field

Webinar 12/7 -Using Microsoft Flow to automate a B2B approval process by Paul Schaeflein

Recently one of our Office MVPs, Paul Schaeflein, was asked how to automate a process to request and approve external users having access to content inside an Office 365 Group.   In this webinar Paul will go through how to use Microsoft Flow, Azure B2B and Microsoft’s Cloud services to solve this scenario.   Out of the box, Microsoft Flow allows you to connect to many cloud-services. But what about your line-of-business systems? With a JSON-capable web service, your employees can use Flow to automate their processes without development skills. Come learn how to connect Flow, APIs, Azure AD and Office 365 in this demo-heavy session.  Note: Note with PowerApps and PowerBI adoption of B2B this scenario will become mainstream.

 

When: 12/7/2017 10AM PST

Subscribe to watch https://www.youtube.com/watch?v=AIImcUZyP7U

 

 

 

Schaeflein Paul Webinar 12/7  Using Microsoft Flow to automate a B2B approval process by Paul Schaeflein

 

Paul Schaeflein is a solution architect/developer with more than three decades experience in architecting, designing and developing software solutions. This experience covers a vast range of technologies, languages and industries. Paul is an independent consultant, helping organizations of all sizes with their application development, ALM and SharePoint/Office 365 projects. Paul is a top-rated speaker, having presented at SharePoint Evolutions, the Microsoft SharePoint Conference and TechEd conferences, as well as user groups. In recognition of these community efforts, he is recognized as a Most Valuable Professional (MVP). Paul has a blog at http://www.schaeflein.net/blog, and is active on Twitter as @paulschaeflein. You can also reach him at paul@schaeflein.net.

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Microsoft Power BI Blog | Microsoft Power BI

Benefits Of Using A Third-Party Logistics Vendor

Businesses share something important with lions. When a lion captures and consumes its prey, only about 10% to 20% of the prey’s energy is directly transferred into the lion’s metabolism. The rest evaporates away, mostly as heat loss, according to research done in the 1940s by ecologist Raymond Lindeman.

Today, businesses do only about as well as the big cats. When you consider the energy required to manage, power, and move products and services, less than 20% goes directly into the typical product or service—what economists call aggregate efficiency (the ratio of potential work to the actual useful work that gets embedded into a product or service at the expense of the energy lost in moving products and services through all of the steps of their value chains). Aggregate efficiency is a key factor in determining productivity.

SAP Q417 DigitalDoubles Feature2 Image2 Benefits Of Using A Third Party Logistics VendorAfter making steady gains during much of the 20th century, businesses’ aggregate energy efficiency peaked in the 1980s and then stalled. Japan, home of the world’s most energy-efficient economy, has been skating along at or near 20% ever since. The U.S. economy, meanwhile, topped out at about 13% aggregate efficiency in the 1990s, according to research.

Why does this matter? Jeremy Rifkin says he knows why. Rifkin is an economic and social theorist, author, consultant, and lecturer at the Wharton School’s Executive Education program who believes that economies experience major increases in growth and productivity only when big shifts occur in three integrated infrastructure segments around the same time: communications, energy, and transportation.

But it’s only a matter of time before information technology blows all three wide open, says Rifkin. He envisions a new economic infrastructure based on digital integration of communications, energy, and transportation, riding atop an Internet of Things (IoT) platform that incorporates Big Data, analytics, and artificial intelligence. This platform will disrupt the world economy and bring dramatic levels of efficiency and productivity to businesses that take advantage of it,
he says.

Some economists consider Rifkin’s ideas controversial. And his vision of a new economic platform may be problematic—at least globally. It will require massive investments and unusually high levels of government, community, and private sector cooperation, all of which seem to be at depressingly low levels these days.

However, Rifkin has some influential adherents to his philosophy. He has advised three presidents of the European Commission—Romano Prodi, José Manuel Barroso, and the current president, Jean-Claude Juncker—as well as the European Parliament and numerous European Union (EU) heads of state, including Angela Merkel, on the ushering in of what he calls “a smart, green Third Industrial Revolution.” Rifkin is also advising the leadership of the People’s Republic of China on the build out and scale up of the “Internet Plus” Third Industrial Revolution infrastructure to usher in a sustainable low-carbon economy.

The internet has already shaken up one of the three major economic sectors: communications. Today it takes little more than a cell phone, an internet connection, and social media to publish a book or music video for free—what Rifkin calls zero marginal cost. The result has been a hollowing out of once-mighty media empires in just over 10 years. Much of what remains of their business models and revenues has been converted from physical (remember CDs and video stores?) to digital.

But we haven’t hit the trifecta yet. Transportation and energy have changed little since the middle of the last century, says Rifkin. That’s when superhighways reached their saturation point across the developed world and the internal-combustion engine came close to the limits of its potential on the roads, in the air, and at sea. “We have all these killer new technology products, but they’re being plugged into the same old infrastructure, and it’s not creating enough new business opportunities,” he says.

All that may be about to undergo a big shake-up, however. The digitalization of information on the IoT at near-zero marginal cost generates Big Data that can be mined with analytics to create algorithms and apps enabling ubiquitous networking. This digital transformation is beginning to have a big impact on the energy and transportation sectors. If that trend continues, we could see a metamorphosis in the economy and society not unlike previous industrial revolutions in history. And given the pace of technology change today, the shift could happen much faster than ever before.

SAP Q417 DigitalDoubles Feature2 Image3 1024x572 Benefits Of Using A Third Party Logistics VendorThe speed of change is dictated by the increase in digitalization of these three main sectors; expensive physical assets and processes are partially replaced by low-cost virtual ones. The cost efficiencies brought on by digitalization drive disruption in existing business models toward zero marginal cost, as we’ve already seen in entertainment and publishing. According to research company Gartner, when an industry gets to the point where digital drives at least 20% of revenues, you reach the tipping point.

“A clear pattern has emerged,” says Peter Sondergaard, executive vice president and head of research and advisory for Gartner. “Once digital revenues for a sector hit 20% of total revenue, the digital bloodbath begins,” he told the audience at Gartner’s annual 2017 IT Symposium/ITxpo, according to The Wall Street Journal. “No matter what industry you are in, 20% will be the point of no return.”

Communications is already there, and energy and transportation are heading down that path. If they hit the magic 20% mark, the impact will be felt not just within those industries but across all industries. After all, who doesn’t rely on energy and transportation to power their value chains?

That’s why businesses need to factor potentially massive business model disruptions into their plans for digital transformation today if they want to remain competitive with organizations in early adopter countries like China and Germany. China, for example, is already halfway through an US$ 88 billion upgrade to its state electricity grid that will enable renewable energy transmission around the country—all managed and moved digitally, according to an article in The Economist magazine. And it is competing with the United States for leadership in self-driving vehicles, which will shift the transportation process and revenue streams heavily to digital, according to an article in Wired magazine.

SAP Q417 DigitalDoubles Feature2 Image4 Benefits Of Using A Third Party Logistics VendorOnce China’s and Germany’s renewables and driverless infrastructures are in place, the only additional costs are management and maintenance. That could bring businesses in these countries dramatic cost savings over those that still rely on fossil fuels and nuclear energy to power their supply chains and logistics. “Once you pay the fixed costs of renewables, the marginal costs are near zero,” says Rifkin. “The sun and wind haven’t sent us invoices yet.”

In other words, zero marginal cost has become a zero-sum game.

To understand why that is, consider the major industrial revolutions in history, writes Rifkin in his books, The Zero Marginal Cost Society and The Third Industrial Revolution. The first major shift occurred in the 19th century when cheap, abundant coal provided an efficient new source of power (steam) for manufacturing and enabled the creation of a vast railway transportation network. Meanwhile, the telegraph gave the world near-instant communication over a globally connected network.

The second big change occurred at the beginning of the 20th century, when inexpensive oil began to displace coal and gave rise to a much more flexible new transportation network of cars and trucks. Telephones, radios, and televisions had a similar impact on communications.

Breaking Down the Walls Between Sectors

Now, according to Rifkin, we’re poised for the third big shift. The eye of the technology disruption hurricane has moved beyond communications and is heading toward—or as publishing and entertainment executives might warn, coming for—the rest of the economy. With its assemblage of global internet and cellular network connectivity and ever-smaller and more powerful sensors, the IoT, along with Big Data analytics and artificial intelligence, is breaking down the economic walls that have protected the energy and transportation sectors for the past 50 years.

Daimler is now among the first movers in transitioning into a digitalized mobility internet. The company has equipped nearly 400,000 of its trucks with external sensors, transforming the vehicles into mobile Big Data centers. The sensors are picking up real-time Big Data on weather conditions, traffic flows, and warehouse availability. Daimler plans to establish collaborations with thousands of companies, providing them with Big Data and analytics that can help dramatically increase their aggregate efficiency and productivity in shipping goods across their value chains. The Daimler trucks are autonomous and capable of establishing platoons of multiple trucks driving across highways.

It won’t be long before vehicles that navigate the more complex transportation infrastructures around the world begin to think for themselves. Autonomous vehicles will bring massive economic disruption to transportation and logistics thanks to new aggregate efficiencies. Without the cost of having a human at the wheel, autonomous cars could achieve a shared cost per mile below that of owned vehicles by as early as 2030, according to research from financial services company Morgan Stanley.

The transition is getting a push from governments pledging to give up their addiction to cars powered by combustion engines. Great Britain, France, India, and Norway are seeking to go all electric as early as 2025 and by 2040 at the latest.

The Final Piece of the Transition

Considering that automobiles account for 47% of petroleum consumption in the United States alone—more than twice the amount used for generators and heating for homes and businesses, according to the U.S. Energy Information Administration—Rifkin argues that the shift to autonomous electric vehicles could provide the momentum needed to upend the final pillar of the economic platform: energy. Though energy has gone through three major disruptions over the past 150 years, from coal to oil to natural gas—each causing massive teardowns and rebuilds of infrastructure—the underlying economic model has remained constant: highly concentrated and easily accessible fossil fuels and highly centralized, vertically integrated, and enormous (and enormously powerful) energy and utility companies.

Now, according to Rifkin, the “Third Industrial Revolution Internet of Things infrastructure” is on course to disrupt all of it. It’s neither centralized nor vertically integrated; instead, it’s distributed and networked. And that fits perfectly with the commercial evolution of two energy sources that, until the efficiencies of the IoT came along, made no sense for large-scale energy production: the sun and the wind.

But the IoT gives power utilities the means to harness these batches together and to account for variable energy flows. Sensors on solar panels and wind turbines, along with intelligent meters and a smart grid based on the internet, manage a new, two-way flow of energy to and from the grid.

SAP Q417 DigitalDoubles Feature2 Image5 Benefits Of Using A Third Party Logistics VendorToday, fossil fuel–based power plants need to kick in extra energy if insufficient energy is collected from the sun and wind. But industrial-strength batteries and hydrogen fuel cells are beginning to take their place by storing large reservoirs of reserve power for rainy or windless days. In addition, electric vehicles will be able to send some of their stored energy to the digitalized energy internet during peak use. Demand for ever-more efficient cell phone and vehicle batteries is helping push the evolution of batteries along, but batteries will need to get a lot better if renewables are to completely replace fossil fuel energy generation.

Meanwhile, silicon-based solar cells have not yet approached their limits of efficiency. They have their own version of computing’s Moore’s Law called Swanson’s Law. According to data from research company Bloomberg New Energy Finance (BNEF), Swanson’s Law means that for each doubling of global solar panel manufacturing capacity, the price falls by 28%, from $ 76 per watt in 1977 to $ 0.41 in 2016. (Wind power is on a similar plunging exponential cost curve, according to data from the U.S. Department of Energy.)

Thanks to the plummeting solar price, by 2028, the cost of building and operating new sun-based generation capacity will drop below the cost of running existing fossil power plants, according to BNEF. “One of the surprising things in this year’s forecast,” says Seb Henbest, lead author of BNEF’s annual long-term forecast, the New Energy Outlook, “is that the crossover points in the economics of new and old technologies are happening much sooner than we thought last year … and those were all happening a bit sooner than we thought the year before. There’s this sense that it’s not some distant risk or distant opportunity. A lot of these realities are rushing toward us.”

The conclusion, he says, is irrefutable. “We can see the data and when we map that forward with conservative assumptions, these technologies just get cheaper than everything else.”

The smart money, then—72% of total new power generation capacity investment worldwide by 2040—will go to renewable energy, according to BNEF. The firm’s research also suggests that there’s more room in Swanson’s Law along the way, with solar prices expected to drop another 66% by 2040.

Another factor could push the economic shift to renewables even faster. Just as computers transitioned from being strictly corporate infrastructure to becoming consumer products with the invention of the PC in the 1980s, ultimately causing a dramatic increase in corporate IT investments, energy generation has also made the transition to the consumer side.

Thanks to future tech media star Elon Musk, consumers can go to his Tesla Energy company website and order tempered glass solar panels that look like chic, designer versions of old-fashioned roof shingles. Models that look like slate or a curved, terracotta-colored, ceramic-style glass that will make roofs look like those of Tuscan country villas, are promised soon. Consumers can also buy a sleek-looking battery called a Powerwall to store energy from the roof.

SAP Q417 DigitalDoubles Feature2 Image6 Benefits Of Using A Third Party Logistics VendorThe combination of solar panels, batteries, and smart meters transforms homeowners from passive consumers of energy into active producers and traders who can choose to take energy from the grid during off-peak hours, when some utilities offer discounts, and sell energy back to the grid during periods when prices are higher. And new blockchain applications promise to accelerate the shift to an energy market that is laterally integrated rather than vertically integrated as it is now. Consumers like their newfound sense of control, according to Henbest. “Energy’s never been an interesting consumer decision before and suddenly it is,” he says.

As the price of solar equipment continues to drop, homes, offices, and factories will become like nodes on a computer network. And if promising new solar cell technologies, such as organic polymers, small molecules, and inorganic compounds, supplant silicon, which is not nearly as efficient with sunlight as it is with ones and zeroes, solar receivers could become embedded into windows and building compounds. Solar production could move off the roof and become integrated into the external facades of homes and office buildings, making nearly every edifice in town a node.

The big question, of course, is how quickly those nodes will become linked together—if, say doubters, they become linked at all. As we learned from Metcalfe’s Law, the value of a network is proportional to its number of connected users.

The Will Determines the Way

Right now, the network is limited. Wind and solar account for just 5% of global energy production today, according to Bloomberg.

But, says Rifkin, technology exists that could enable the network to grow exponentially. We are seeing the beginnings of a digital energy network, which uses a combination of the IoT, Big Data, analytics, and artificial intelligence to manage distributed energy sources, such as solar and wind power from homes and businesses.

As nodes on this network, consumers and businesses could take a more active role in energy production, management, and efficiency, according to Rifkin. Utilities, in turn, could transition from simply transmitting power and maintaining power plants and lines to managing the flow to and from many different energy nodes; selling and maintaining smart home energy management products; and monitoring and maintaining solar panels and wind turbines. By analyzing energy use in the network, utilities could create algorithms that automatically smooth the flow of renewables. Consumers and businesses, meanwhile, would not have to worry about connecting their wind and solar assets to the grid and keeping them up and running; utilities could take on those tasks more efficiently.

Already in Germany, two utility companies, E.ON and RWE, have each split their businesses into legacy fossil and nuclear fuel companies and new services companies based on distributed generation from renewables, new technologies, and digitalization.

The reason is simple: it’s about survival. As fossil fuel generation winds down, the utilities need a new business model to make up for lost revenue. Due to Germany’s population density, “the utilities realize that they won’t ever have access to enough land to scale renewables themselves,” says Rifkin. “So they are starting service companies to link together all the different communities that are building solar and wind and are managing energy flows for them and for their customers, doing their analytics, and managing their Big Data. That’s how they will make more money while selling less energy in the future.”

SAP Q417 DigitalDoubles Feature2 Image7 1024x572 Benefits Of Using A Third Party Logistics Vendor

The digital energy internet is already starting out in pockets and at different levels of intensity around the world, depending on a combination of citizen support, utility company investments, governmental power, and economic incentives.

China and some countries within the EU, such as Germany and France, are the most likely leaders in the transition toward a renewable, energy-based infrastructure because they have been able to align the government and private sectors in long-term energy planning. In the EU for example, wind has already overtaken coal as the second largest form of power capacity behind natural gas, according to an article in TheGuardian newspaper. Indeed, Rifkin has been working with China, the EU, and governments, communities, and utilities in Northern France, the Netherlands, and Luxembourg to begin building these new internets.

Hauts-de-France, a region that borders the English Channel and Belgium and has one of the highest poverty rates in France, enlisted Rifkin to develop a plan to lift it out of its downward spiral of shuttered factories and abandoned coal mines. In collaboration with a diverse group of CEOs, politicians, teachers, scientists, and others, it developed Rev3, a plan to put people to work building a renewable energy network, according to an article in Vice.

Today, more than 1,000 Rev3 projects are underway, encompassing everything from residential windmills made from local linen to a fully electric car–sharing system. Rev3 has received financial support from the European Investment Bank and a handful of private investment funds, and startups have benefited from crowdfunding mechanisms sponsored by Rev3. Today, 90% of new energy in the region is renewable and 1,500 new jobs have been created in the wind energy sector alone.

Meanwhile, thanks in part to generous government financial support, Germany is already producing 35% of its energy from renewables, according to an article in TheIndependent, and there is near unanimous citizen support (95%, according to a recent government poll) for its expansion.

If renewable energy is to move forward in other areas of the world that don’t enjoy such strong economic and political support, however, it must come from the ability to make green, not act green.

Not everyone agrees that renewables will produce cost savings sufficient to cause widespread cost disruption anytime soon. A recent forecast by the U.S. Energy Information Administration predicts that in 2040, oil, natural gas, and coal will still be the planet’s major electricity producers, powering 77% of worldwide production, while renewables such as wind, solar, and biofuels will account for just 15%.

Skeptics also say that renewables’ complex management needs, combined with the need to store reserve power, will make them less economical than fossil fuels through at least 2035. “All advanced economies demand full-time electricity,” Benjamin Sporton, chief executive officer of the World Coal Association told Bloomberg. “Wind and solar can only generate part-time, intermittent electricity. While some renewable technologies have achieved significant cost reductions in recent years, it’s important to look at total system costs.”

On the other hand, there are many areas of the world where distributed, decentralized, renewable power generation already makes more sense than a centralized fossil fuel–powered grid. More than 20% of Indians in far flung areas of the country have no access to power today, according to an article in TheGuardian. Locally owned and managed solar and wind farms are the most economical way forward. The same is true in other developing countries, such as Afghanistan, where rugged terrain, war, and tribal territorialism make a centralized grid an easy target, and mountainous Costa Rica, where strong winds and rivers have pushed the country to near 100% renewable energy, according to TheGuardian.

The Light and the Darknet

Even if all the different IoT-enabled economic platforms become financially advantageous, there is another concern that could disrupt progress and potentially cause widespread disaster once the new platforms are up and running: hacking. Poorly secured IoT sensors have allowed hackers to take over everything from Wi-Fi enabled Barbie dolls to Jeep Cherokees, according to an article in Wired magazine.

Humans may be lousy drivers, but at least we can’t be hacked (yet). And while the grid may be prone to outages, it is tightly controlled, has few access points for hackers, and is physically separated from the Wild West of the internet.

If our transportation and energy networks join the fray, however, every sensor, from those in the steering system on vehicles to grid-connected toasters, becomes as vulnerable as a credit card number. Fake news and election hacking are bad enough, but what about fake drivers or fake energy? Now we’re talking dangerous disruptions and putting millions of people in harm’s way.

SAP Q417 DigitalDoubles Feature2 Image8 Benefits Of Using A Third Party Logistics VendorThe only answer, according to Rifkin, is for businesses and governments to start taking the hacking threat much more seriously than they do today and to begin pouring money into research and technologies for making the internet less vulnerable. That means establishing “a fully distributed, redundant, and resilient digital infrastructure less vulnerable to the kind of disruptions experienced by Second Industrial Revolution–centralized communication systems and power grids that are increasingly subject to climate change, disasters, cybercrime, and cyberterrorism,” he says. “The ability of neighborhoods and communities to go off centralized grids during crises and re-aggregate in locally decentralized networks is the key to advancing societal security in the digital era,” he adds.

Start Looking Ahead

Until today, digital transformation has come mainly through the networking and communications efficiencies made possible by the internet. Airbnb thrives because web communications make it possible to create virtual trust markets that allow people to feel safe about swapping their most private spaces with one another.

But now these same efficiencies are coming to two other areas that have never been considered core to business strategy. That’s why businesses need to begin managing energy and transportation as key elements of their digital transformation portfolios.

Microsoft, for example, formed a senior energy team to develop an energy strategy to mitigate risk from fluctuating energy prices and increasing demands from customers to reduce carbon emissions, according to an article in Harvard Business Review. “Energy has become a C-suite issue,” Rob Bernard, Microsoft’s top environmental and sustainability executive told the magazine. “The CFO and president are now actively involved in our energy road map.”

As Daimler’s experience shows, driverless vehicles will push autonomous transportation and automated logistics up the strategic agenda within the next few years. Boston Consulting Group predicts that the driverless vehicle market will hit $ 42 billion by 2025. If that happens, it could have a lateral impact across many industries, from insurance to healthcare to the military.

Businesses must start planning now. “There’s always a period when businesses have to live in the new and the old worlds at the same time,” says Rifkin. “So businesses need to be considering new business models and structures now while continuing to operate their existing models.”

He worries that many businesses will be left behind if their communications, energy, and transportation infrastructures don’t evolve. Companies that still rely on fossil fuels for powering traditional transportation and logistics could be at a major competitive disadvantage to those that have moved to the new, IoT-based energy and transportation infrastructures.

Germany, for example, has set a target of 80% renewables for gross power consumption by 2050, according to TheIndependent. If the cost advantages of renewables bear out, German businesses, which are already the world’s third-largest exporters behind China and the United States, could have a major competitive advantage.

“How would a second industrial revolution society or country compete with one that has energy at zero marginal cost and driverless vehicles?” asks Rifkin. “It can’t be done.” D!


About the Authors

Maurizio Cattaneo is Director, Delivery Execution, Energy and Natural Resources, at SAP.

Joerg Ferchow is Senior Utilities Expert and Design Thinking Coach, Digital Transformation, at SAP.

Daniel Wellers is Digital Futures Lead, Global Marketing, at SAP.

Christopher Koch is Editorial Director, SAP Center for Business Insight, at SAP.


Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.

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Summarizing Data Using the GROUPING SETS Operator

Maybe you have felt overwhelmed when you’re analyzing a dataset because of its size. The best way to handle this situation is by summarizing the data to get a quick review.

In T-SQL, you summarize data by using the GROUP BY clause within an aggregate query. This clause creates groupings which are defined by a set of expressions. One row per unique combination of the expressions in the GROUP BY clause is returned, and aggregate functions such as COUNT or SUM may be used on any columns in the query. However, if you want to group the data by multiple combinations of group by expressions, you may take one of two approaches. The first approach is to create one grouped query per combination of expressions and merge the results using the UNION ALL operator. The other approach is to use the GROUPING SETS operator along with the GROUP BY clause and define each grouping set within a single query.

In this article I’ll demonstrate how to achieve the same results using each method.

Prepare the data set

All queries in this article will run in the AdventureWorks2012 database. If you wish to follow along with this article, download it from here.

Case Study: Data Analyst at Adventure Works

Imagine you’re working as a data analyst at the bike manufacturer Adventure Works, and you’re interested in the company’s income over the last few years. This means you need to group the company’s income per year and run the following query:

Query 1. Income by year

Query 1 returns the following result set:

OrderYear

Income

2005

11331809

2006

30674773.2

2007

42011037.2

2008

25828762.1

Table 1. Company’s income per year.

According to Table 1, the company have been registering income between 2005 and 2008. Assuming that the currency is in US dollars, in 2005 their income was around eleven million dollars. In 2006 it was around thirty million dollars, and so on. This kind of information would be useful for supporting a business decision such as opening a company extension elsewhere.

However, if you still want more details about the company’s income, you must perform a new grouping by adding a column or expression to the GROUP BY clause. Add the order month to the previous set of group by expressions. By doing this, the query will return the company’s income per year and month. Review the GROUP BY clause in the following query.

Query 2. Company’s income per year and month.

The following table contains the result set of Query 2:

OrderYear

OrderMonth

Income

2005

7

962716.742

2005

8

2044600

2005

9

1639840.11

2005

10

1358050.47

2005

11

2868129.2

2005

12

2458472.43

2006

1

1309863.25

2006

2

2451605.62

2006

3

2099415.62

2006

4

1546592.23

2006

5

2942672.91

2006

6

1678567.42

2006

7

2894054.68

2006

8

4147192.18

2006

9

3235826.19

2006

10

2217544.45

2006

11

3388911.41

2006

12

2762527.22

2007

1

1756407.01

2007

2

2873936.93

2007

3

2049529.87

2007

4

2371677.7

2007

5

3443525.25

2007

6

2542671.93

2007

7

3554092.32

2007

8

5068341.51

2007

9

5059473.22

2007

10

3364506.26

2007

11

4683867.05

2007

12

5243008.13

2008

1

3009197.42

2008

2

4167855.43

2008

3

4221323.43

2008

4

3820583.49

2008

5

5194121.52

2008

6

5364840.18

2008

7

50840.63

Table 2. Company’s income per year and month.

This result set is more detailed than the former. In July 2005, their income was around nine hundred sixty thousand dollars. In August 2005, it was around two million dollars, and so on. The more expressions or columns added to the GROUP BY clause, the more detailed the results will be.

If you observe the structure of the two queries, you will see they’re grouped by a single set of grouping expressions. The former is grouped by order year, and the latter is grouped by order year and month.

Suppose the business manager at Adventure Works wants to visualize both results within a single result set. To accomplish this, you may merge the previous queries – Query 1 and Query 2 – by using the UNION ALL operator. First, modify Query 1 by adding a dummy column so it will have the same number of columns as Query 2. All queries merged by the UNION operator must have the same number of columns. This dummy column will return NULL in the OrderMonth column, identifying the OrderYear total rows of this query. The UNION ALL query looks like this:

Query 3. Company’s income per year and per year and month.

Figure 1 shows the result set produced by Query 3. Review the comments in the figure which identify the grouping sets.

 Summarizing Data Using the GROUPING SETS Operator

Figure 1. Company’s income per year and per year and month. Notice the comments added to the figure.

This information doesn’t look new, because you already know that in 2005 the company’s income was around eleven million dollars. In July 2005 the company’s income was around nine hundred sixty thousand dollars, and so on. What’s new to you is that each grouping result –year grouping result and year and month grouping result– is merged.

Maybe you’ve figured out how the NULL values appeared in the result set. Remember you used the NULL as a dummy column to identify the results from the order year grouping. Look carefully at Figure 2 which details the placeholders in the first grouped query.

 Summarizing Data Using the GROUPING SETS Operator

Figure 2. Pointing out the placeholders.

When there’s more than one group by expression list involved in the query, a NULL is used as a placeholder to identify one of the groupings in the results. Looking at Figure 2 again, a row that has NULL in the OrderMonth column means the row belongs to the order year grouping. When the row has a value in both the OrderYear and OrderMonth columns, it means the row belongs to the order year and month grouping. This situation happens when one of the grouped queries doesn’t have the same number of columns grouped. In this example, the first grouping is by order year and the second grouping is by order year and month.

Although you obtained the desired result, Query 3 would be even larger if you added another grouping set, such as order day. As a data analyst, you decided to search the internet to find a way to achieve the same results but with less work. You find that by using the GROUPING SETS operator you should get the same result set, but with less coding! This really motivates you, and you write the following query using GROUPING SETS:

Query 4. Getting the same result set produced by the Query #3 but using the GROUPING SETS clause.

The result set produced by Query 4 is the same as that displayed in Figure 1. Figure 3 shows the results, but the new technique requires less code. The GROUPING SETS operator is used along with the GROUP BY clause, and allows you to make multi-grouped queries just by specifying the grouping sets separated by comma. However, you need to be careful when specifying the grouping sets. For example, if a grouping contains two columns, say column A and column B, both columns need to be contained within parenthesis: (column A, column B). If there’s not a parenthesis between them, the GROUPING SETS clause will define them as separate groupings, and the query will not return the desired results.

 Summarizing Data Using the GROUPING SETS Operator

Figure 3. Company’s income per year and per year and month using the GROUPING SETS clause.

By the way, if you want to perform the aggregation over the entire result set without grouping but still use the GROUPING SETS operator, just add an empty parenthesis for the grouping set. Look at Query 5 which calculates the company’s income per year, month, and overall total:

Query 5. Company’s income per year, per year and month, and overall.

Notice the placeholders for the third grouping shown in Figure 4. The query calculated the grand total of incomes by just specifying an empty parenthesis as the third grouping set; the third grouping set is the sum of SubTotal for the table itself.

 Summarizing Data Using the GROUPING SETS Operator

Figure 4. Company’s income per year, per year and month and all over the time.

By the way, if you’ve asked yourself “What would happen if the NULL is part of the data and isn’t used as placeholder?” or “How can I tell when NULL is used as placeholder or is just the value?” In this example, I ensured that the grouped columns aren’t nullable, so the NULLs are used as placeholders. In case the grouped columns are nullable, you will need to use the GROUPING or GROUPING_ID function to identify if the NULL came from the GROUPING SETS operator – it can also come with other groupings operators like ROLLUP and CUBE– or is part of the data. Both functions – GROUPING and GROUPING_ID– will be treated in another article.

Conclusion

In this article, you learned how to achieve an aggregate query with more than one grouping expression list by using the GROUPING SETS operator. Unlike other operators such as ROLLUP and CUBE, you must specify each grouping set. These grouping operators are very important for summarizing data and producing grand totals and sub totals. If you want more information about these operators, please read this article.

I suggest that you practice what you’ve learned in this article; this topic is very important for anyone working with SQL Server data. The data volume is increasing very quickly, and it’s vital to summarize it for better knowledge about the business.

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How to Manage Iterative Projects using JIRA

Project delivery using an iterative approach appears to have become the norm over the last couple of years. Using a waterfall methodology, though effective, can be a long process & if your organisation is rapidly changing, you could find that the requirements defined in the beginning is no longer relevant. I had one customer comment that during a waterfall project by the time the solution is delivered, they’ve forgotten what they wanted in the first place!

JIRA is a common tool used to run an agile/iterative project after experiencing it with one of our other customers. I found it a great tool to have line of sight of what needs to be done, in progress, completed, etc. No messy spreadsheets here!

However, if you’re not accustomed to using JIRA it can prove to be ineffective. While managing delivery of a project, I noted that I couldn’t see how we were tracking, user stories were incomplete, statuses hadn’t been defined, etc. The reason I realised was because JIRA hadn’t been configured for any of this. Therefore, I tasked myself with learning how to configure it with the absolute minimum to ensure we were using it efficiently and get back on track.

Below is JIRA Lite – the starting point for running a project iteratively:

Customising a Project

Recommendations

  1. Configure estimation and tracking to hours.
  2. User stories should be robust with clear acceptance criteria.
  3. Add a couple of new statuses; “in UAT” and “in PROD” – Done could mean anything, i.e. work has been completed but not deployed. Unless we all have a consensus of what “Done” means.

It All starts with a User Story…

The key components of a user story should answer the following 3 questions:

  1. Who?
  2. What?
  3. Why?

Who: “As an xyz user”.

What: “I want to do this, that and the other”.

Why: “So that I”.

There also needs to be clear acceptance criteria. Acceptance criteria is not only used to test but will give the developers a clear framework to work within.

Here’s a simple example:

As a user in the contact centre, I want to start typing an address and as I type, the system should then suggest addresses based on what’s typed and allow me to select one from a list. This is so that I can reduce my data entry time.

Acceptance criteria for the above user story:

  1. A field to start typing the address.
  2. When the first 3 characters are typed in, start giving suggestions in the format of <26 Greenpark Road, Suburb, City, Post code>.
  3. When selected, automatically fill: suburb, city, post code fields.
  4. Suburb, city, post code fields are read only.
  5. If there are no suggestions available, display “…” and make suburb, city and post fields editable.

Estimating and Tracking Time

All stories should have estimates to completed, i.e. 24 hours. This will allow the project team to predict how long it’ll take to deliver specific sprints. As you work on the issues during the sprint, you may need to adjust the Remaining Estimates as necessary.

However, in order for the time tracker to work, you need to configure the Issue Screen by adding time tracking as a field:

  1. JIRA administration.
  2. Issues.
  3. Issue type screen schemes.
  4. Scrum Issue Type Screen Scheme.
  5. Select the Default Issue Screen.
  6. Add the time tracking field.

The issue screen will now include 2 new fields:

image thumb How to Manage Iterative Projects using JIRA

This will feed into a time tracker on the story, e.g.:

image thumb 1 How to Manage Iterative Projects using JIRA

Having the above in place will immediately give you a framework within which to run your iterations and see how you’re tracking!

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Using Materialized Views with Big Data SQL to Accelerate Performance

One of Big Data SQL’s key benefits is that it leverages the great performance capabilities of Oracle Database 12c.  I thought it would be interesting to illustrate an example – and in this case we’ll review a performance optimization that has been around for quite a while and is used at thousands of customers:  Materialized Views (MVs).

For those of you who are unfamiliar with MVs – an MV is a precomputed summary table.  There is a defining query that describes that summary.  Queries that are executed against the detail tables comprising the summary will be automatically rewritten to the MV when appropriate:

In the diagram above, we have a 1B row fact table stored in HDFS that is being accessed thru a Big Data SQL table called STORE_SALES.  Because we know that users want to query the data using a product hierarchy (by Item), a geography hierarchy (by Region) and a mix (by Class & QTR) – we created three summary tables that are aggregated to the appropriate levels. For example, the “by Item” MV has the following defining query:

CREATE MATERIALIZED VIEW mv_store_sales_item
ON PREBUILT TABLE
ENABLE QUERY REWRITE AS (
  select ss_item_sk,
         sum(ss_quantity) as ss_quantity,
         sum(ss_ext_wholesale_cost) as ss_ext_wholesale_cost,
         sum(ss_net_paid) as ss_net_paid,
         sum(ss_net_profit) as ss_net_profit
  from bds.store_sales
  group by ss_item_sk
);

Queries executed against the large STORE_SALES that can be satisfied by the MV will now be automatically rewritten:

SELECT i_category,
       SUM(ss_quantity)
FROM bds.store_sales, bds.item_orcl
WHERE ss_item_sk = i_item_sk
  AND i_size in ('small', 'petite')
  AND i_wholesale_cost > 80
GROUP BY i_category;

Taking a look at the query’s explain plan, you can see that even though store_sales is the table being queried – the table that satisfied the query is actually the MV called mv_store_sales_item.  The query was automatically rewritten by the optimizer.

Explain plan with the MV:

Explain plan without the MV:

Even though Big Data SQL optimized the join and pushed the predicates and filtering down to the Hadoop nodes – the MV dramatically improved query performance:

  • With MV:  0.27s
  • Without MV:  19s

This is to be expected as we’re querying a significantly smaller and partially aggregated data.  What’s nice is that query did not need to change; simply the introduction of the MV sped up the processing.

What is interesting here is that the query selected data at the Category level – yet the MV is defined at the Item level.  How did the optimizer know that there was a product hierarchy?  And that Category level data could be computed from Item level data?  The answer is metadata.  A dimension object was created that defined the relationship between the columns:

CREATE DIMENSION BDS.ITEM_DIM
   LEVEL ITEM IS (ITEM_ORCL.I_ITEM_SK)
   LEVEL CLASS IS (ITEM_ORCL.I_CLASS)
   LEVEL CATEGORY IS (ITEM_ORCL.I_CATEGORY)
   HIERARCHY PROD_ROLLUP (
      ITEM CHILD OF
      CLASS CHILD OF
      CATEGORY  ) 
   ATTRIBUTE ITEM DETERMINES (
      ITEM_ORCL.I_SIZE, ITEM_ORCL.I_COLOR, ITEM_ORCL.I_UNITS,
      ITEM_ORCL.I_CURRENT_PRICE,I_WHOLESALE_COST
    ); 

Here, you can see that Items roll up into Class, and Classes roll up into Category.  The optimizer used this information to allow the query to be redirected to the Item level MV.

A good practice is to compute these summaries and store them in Oracle Database tables.  However, there are alternatives.  For example, you may have already computed summary tables and stored them in HDFS.  You can leverage these summaries by creating an MV over a pre-built Big Data SQL table.  Consider the following example where a summary table was defined in Hive and called csv.mv_store_sales_qtr_class.  There are two steps required to leverage this summary:

  1. Create a Big Data SQL table over the hive source
  2. Create an MV over the prebuilt Big Data SQL table

Let’s look at the details.  First, create the Big Data SQL table over the Hive source (and don’t forget to gather statistics!):

CREATE TABLE MV_STORE_SALES_QTR_CLASS
    (
      I_CLASS VARCHAR2(100)
    , SS_QUANTITY NUMBER
    , SS_WHOLESALE_COST NUMBER
    , SS_EXT_DISCOUNT_AMT NUMBER
    , SS_EXT_TAX NUMBER
    , SS_COUPON_AMT NUMBER
    , D_QUARTER_NAME VARCHAR2(30)
    )
    ORGANIZATION EXTERNAL
    (
      TYPE ORACLE_HIVE
      DEFAULT DIRECTORY DEFAULT_DIR
      ACCESS PARAMETERS
      (
        com.oracle.bigdata.tablename: csv.mv_store_sales_qtr_class
      )
    )
    REJECT LIMIT UNLIMITED;
-- Gather statistics
exec  DBMS_STATS.GATHER_TABLE_STATS ( ownname => '"BDS"', tabname => '"MV_STORE_SALES_QTR_CLASS"', estimate_percent => dbms_stats.auto_sample_size, degree => 32 );

Next, create the MV over the Big Data SQL table:

CREATE MATERIALIZED VIEW mv_store_sales_qtr_class
ON PREBUILT TABLE
WITH REDUCED PRECISION
ENABLE QUERY REWRITE AS (
    select
       i.I_CLASS,
       sum(s.ss_quantity) as ss_quantity,
       sum(s.ss_wholesale_cost) as ss_wholesale_cost,
       sum(s.ss_ext_discount_amt) as ss_ext_discount_amt,
       sum(s.ss_ext_tax) as ss_ext_tax,
       sum(s.ss_coupon_amt) as ss_coupon_amt,
       d.D_QUARTER_NAME
    from DATE_DIM_ORCL d, ITEM_ORCL i, STORE_SALES s
    where s.ss_item_sk = i.i_item_sk
      and s.ss_sold_date_sk = date_dim_orcl.d_date_sk
    group by d.D_QUARTER_NAME,
           i.I_CLASS
    );

Queries against STORE_SALES that can be satisfied by the MV will be rewritten:

Here, the following query used the MV:
- What is the quarterly performance by category with yearly totals?

select  
       i.i_category,
       d.d_year,
       d.d_quarter_name,
       sum(s.ss_quantity) quantity
from bds.DATE_DIM_ORCL d, bds.ITEM_ORCL i, bds.STORE_SALES s
where s.ss_item_sk = i.i_item_sk
  and s.ss_sold_date_sk = d.d_date_sk
  and d.d_quarter_name in ('2005Q1', '2005Q2', '2005Q3', '2005Q4')
group by rollup (i.i_category, d.d_year, d.D_QUARTER_NAME)

And, the query returned in a little more than a second:

Looking at the explain plan, you can see that the query is executed against the MV – and the EXTERNAL TABLE ACCESS (STORAGE FULL) indicates that Big Data SQL Smart Scan kicked in on the Hadoop cluster.

MVs within the database can be automatically updated by using change tracking.  However, in the case of Big Data SQL tables, the data is not resident in the database – so the database does not know that the summaries are changed.  Your ETL processing will need to ensure that the MVs are kept up to date – and you will need to set query_rewrite_integrity=stale_tolerated.

MVs are an old friend.  They have been used for years to accelerate performance for traditional database deployments.  They are a great tool to use for your big data deployments as well!

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Relevance Search – Additional Filtering Using Facets and Filters

Additional Filtering 300x225 Relevance Search – Additional Filtering Using Facets and Filters

Relevance Search distributes a search in a single result list and sorts it by relevance based on a scoring concept. One key thing to know is that the higher the score, the more relevant the item.

Relevance Search can:

• Find matches to any word in the search phrase. Matches include various forms of the search word for example, “service,” will match to “servicing,” or “serviced”

• Search for text in emails and notes

• Search records that you own as well as those that have been shared with you

• Search for text in an Option Set and Lookup field

• Search for text in SharePoint integrated documents (scheduled to be included in the next Dynamics 365 update)

• Search for text within Documents in Dynamics 365. These include documents in a Note, Attachments, Email, and Appointments.

As you can see, the Relevance Search can do many great things but it can also result in millions of matches depending on the size of your organization. Luckily, for us, Microsoft has thought about that and included a feature called Facets and Filters. We get additional filtering by Record Type, Owner, Modified Date, and Created Date to personalize search experience.

Additional Filtering using Facets and Filters

Global Facets: You can refine your search results to Record Type, Owner, Created On, or Modified On. In this example below, I filtered the search results to only show records for a specific “Owner.”

111617 2201 RelevanceSe1 Relevance Search – Additional Filtering Using Facets and Filters

Entity Specific Facets: When you click on a specific record type, additional facets appear. These facets are specific to the fields of the Record Type/Entity. System Administrators and System Customizers can configure which fields are available for faceting through the entity’s Quick Find view. In the example, clicking on Cases gave me two additional facets: Priority and Origin.

111617 2201 RelevanceSe2 Relevance Search – Additional Filtering Using Facets and Filters

End user configuration: End users can also personalize their search experience by configuring the facet fields that they would like to see for any searchable entity.

111617 2201 RelevanceSe3 Relevance Search – Additional Filtering Using Facets and Filters

111617 2201 RelevanceSe4 Relevance Search – Additional Filtering Using Facets and Filters

Now that you have learned how to narrow your search results you will get results that are more relevant to your needs, making it easier to find what you are looking for.

Keep up with the latest and greatest on Dynamics 365 by subscribing to our blog here!

Happy Dynamics 365’ing!

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Data Management and Integration Using Data Entities – Part 2

Data Management Part 2 300x225 Data Management and Integration Using Data Entities – Part 2

In part two of our Dynamics 365 for Finance and Operations: Data Management and Integration series, we will cover detailed information on data management and integration using OData Services.

This type of integration is real time in nature and mainly conducted in scenarios where business requirements are around office integration and third party mobile apps integration. OData stands for Open Data Protocol, which is an industry standard Representational State Transfer (REST) based protocol for performing CRUD operations (Create, Read, Update, Delete) and integration with Dynamics 365 for Finance and Operations.

OData uses REST application programming interfaces (APIs) and OAuth 2.0 authorization mechanism to receive data to and from integration systems and Finance and Operations.

111317 2255 DataManagem1 Data Management and Integration Using Data Entities – Part 2

With OData Services for Finance and Operations, you can seamlessly integrate with all types of web technologies, such as HTTP and JavaScript Object Notation (JSON) and it lets developers interact with data in a standard yet powerful manner using RESTful web services.

OData endpoint

Data Entities that are marked Yes for the IsPublic property are exposed as an OData endpoint. When the IsPublic property for an updatable view is set to TRUE, that view is exposed as a top-level OData entity. Developers can consume this OData endpoint in their external application such as a .Net application for integration scenarios.

111317 2255 DataManagem2 Data Management and Integration Using Data Entities – Part 2

Integrating Client Application with OData:

OData integration REST API uses the same OAuth 2.0 authentication model as the other service endpoints. Before the integrating client application can consume this endpoint, developers must create and register the application ID in the Microsoft Azure Active Directory (AAD) and give it appropriate permission to Finance and Operations as per the steps below:

Go to Azure Portal > Azure Active directory > AppRegistrations

111317 2255 DataManagem3 Data Management and Integration Using Data Entities – Part 2

Click New Application Registration and select “Web app/API” for application type. Enter your Dynamics 365 URL for sign on.

111317 2255 DataManagem4 Data Management and Integration Using Data Entities – Part 2

Click Create and make sure to note the application ID. Click on the app and go to the “Required Permissions” page.

111317 2255 DataManagem6 Data Management and Integration Using Data Entities – Part 2

111317 2255 DataManagem5 Data Management and Integration Using Data Entities – Part 2

Click add and select “Microsoft Dynamics AX.” Go to the select permissions tab and select all the permissions available.

111317 2255 DataManagem7 Data Management and Integration Using Data Entities – Part 2

Once the Application is created and registered below activities are performed in Dynamics 365 for Finance and Operations. A Data project using the Data Management Framework can be used to create Import Export jobs for loading the data-to-data entities of extraction of the Data.

Click the Dynamics 365 URL > Go to System Administration > Data Management > Click on Import

111317 2255 DataManagem8 Data Management and Integration Using Data Entities – Part 2

In our next part of this series, we’ll look at Asynchronous Integrations. Stay tuned and subscribe to our blog to receive the latest posts in your inbox!

Happy Dynamics 365’ing!

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