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Automatically create process diagrams in Visio from Excel data
Today, we’re excited to announce Data Visualizer, a new Visio feature that automatically converts process map data in Excel into data-driven Visio diagrams. This update, which is available to Visio Pro for Office 365 users, helps reduce manual steps while giving business analysts even more ways to create process diagrams in Visio.
Automatically create process diagrams from Excel data
Diagrams don’t always start in Visio. They often begin as hand-drawn sketches or—in today’s data-driven age—in Excel. Using Data Visualizer, business analysts can represent process steps and associated metadata in a structured Excel table and quickly convert that information into a visualized Visio diagram. You can do this by either using a premade Excel template or an existing spreadsheet of your own design. The premade templates—there’s one for basic and one for cross-functional flowcharts—provide a sample mapping table to populate with diagram metadata. The table includes predefined columns for process step number, description, dependencies, owner, function, phase and more. You can also customize the table with your own columns to meet specific business requirements.
Once the table is populated, Visio’s wizard helps you complete the remaining steps to transform your Excel data into a Visio process diagram. If you customize the premade template or create one of your own, the wizard helps you map certain flowchart parts, like swim lanes and connectors. The resulting diagram is linked to the Excel table, so if the underlying process data is modified, the diagram updates accordingly. Likewise, shape modifications in Visio are preserved if the Excel data changes.
Additionally, analysts can save their Visio diagrams and the underlying Excel mapping table as a single package using the “Export as a Template Package” feature. These packages can be shared and reused by others, eliminating the need to recreate the same diagram from scratch while encouraging process consistency across the organization.
No matter your preference—whether creating diagrams from a template or your own spreadsheet—the underlying Excel data travels with the related Visio Pro for Office 365 file, helping ensure your team always has the latest diagram version.
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August Power BI Desktop Update: Updated Drill experience
In the August update of Power BI Desktop, we released the general availability of inline hierarchies. Inline hierarchies let you explore your data as you drill down in the context of your hierarchies. As an example, imagine you were looking at your Sales Amount by Order Date.
You start out looking at each year, and now you want to see quarters as well. When you expand down using inline hierarchies, we will carry the parent category down so you can differentiate the sales in each quarter for each year.
The previous behavior for drill wouldn’t split out individual quarters by year. It would show sales in each quarter across all years.
In the August release of the Desktop, we took inline hierarchies out of preview, and replaced this previous drill experience with the inline hierarchy experience. Since that update, we have heard feedback from you that while this new experience is very useful, you still have a need for the older experience, especially when using drill with unrelated categories in the hierarchy.
We listened to your feedback, and today we are releasing an update to the August Power BI Desktop release that gives you the ability to use both of these experience in one chart. Once you install this update, you will see two buttons on a visual that has a hierarchy in the axis. The double down arrow icon (
) will do the previous experience of showing the next level of the hierarchy, and the split arrow icon (
) will perform the new inline hierarchy experience explained above. You will see this update in the Power BI web service as well.
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Why Smart Data is Behind Pokémon Go’s Success
It seems like Pokémon decided to accompany me through my whole life. I used to play with the cards when they first came out in the 90’s and now I’m thinking about the huge amounts of data the game is gathering and how they can be analyzed with smart data discovery solutions. Who would have thought that after so many years it would make such a comeback! Even though it is only officially available in three countries, Pokémon Go already beat WhatsApp, Instagram and Snapchat in time spent on the app, and is fighting with Twitter for daily active users. To what does it owe its success?
Most people think that Pokémon Go is so successful because it uses the Augmented Reality camera; but the truth is, it’s successful because it harnessed data into a game. Pokémon Go offers the chance to pretend the world is filled with Pokémon, which we can see through the AR camera of our smartphones. This creates a sense of co-presence. The Pokémon are found in the places that fit them better, for example, a water-type Pokémon will be found near fountains or beaches. But this is not thanks to the AR camera. The real sense of co-presence comes from the perfect placing of Pokémon. The makers of the app did this by taking smart data and turning it into a game.
There is a lot of personalization involved in the game which produces what looks like a seamless integration of the virtual and the real worlds. This makes it look like the game designers custom-placed each Pokémon. But the game can be played worldwide, and there is only one way to generate that level of personalized and localized information: a great usage of smart data.
Pokémon Go’s creators, Niantic, Inc., are Google Earth and Google Maps veterans. In 2012 they created another location-based game called Ingress, which was also a method of data collection and actually provided most of the data for Pokémon Go’s creation. Ingress was based on users capturing “portals”, which were historical sites and public areas that were determined as such by using Google Earth photos. Originally, the sites were chosen by Niantic, but they later encouraged users to submit their own recommendations for sites. About 15 million sites were suggested. The data behind these portals determined the location of “PokéStops” later on. Niantic used data from Google Earth, weather, urban planning, Google Maps, and the data generated by Ingress and its users to determine where each Pokémon should appear. This is why it feels so real.
Smart Data was not just the material for creating the game, it is also the result of it. The app is generating huge amounts of data about its users—mainly their location and movement patterns, but also has access to a lot of information in a person’s phone. All this data is great for business. Forbes wrote an article on the huge business potential generated by Pokémon Go. There are restaurants and shops that ended up with a “PokéStops” status and are taking full advantage of it by attracting app users and potential customers. Others that were not as lucky to be “PokéStops” can buy “lures” and attract Pokémon to their location, therefore attracting users. Since users want to use the Wi-Fi there, businesses can request for them to log in and gather a lot of data on customers. The challenge will be to be able to analyze the data generated and turn it into actionable insights. That’s when Business Intelligence and Smart Data Discovery solutions jump in.
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