Tag Archive for Digital Transformation

How Non-Developers Can Use Microsoft Flow to Create Automated Workflows

In my previous article, Develop Workflows and Business Processes without Developers, I mentioned how there are numerous options for creating workflows without the need to write actual code. In my second article on workflows, I’m going to go a little deeper into one of the options I discussed: Microsoft Flow. As I mentioned, Flow is a drag and drop service solution used to create automated workflows within Office 365. These workflows can connect different applications and services; including enterprise apps (Office 365, SharePoint Online, Salesforce, CRM) and social apps (Twitter, DropBox, MailChimp), to name a few. The drag and drop interface allows for a relatively simple solution for creating an automated workflow.

Getting Started with Microsoft Flow

To get started with Flow, it’s as simple as going to the Microsoft Flow site and then starting to build new applications within the browser. Other options and software require separate applications for you to install to get going. Flow is completely browser-based, so no need to download anything else! Once you are logged into the site, you have the option of using any of the hundreds of templates already built, create a new process from a template or even start out with a blank canvas to create your new workflow. Some templates available with Flow can help with tasks such as:

  • Sending yourself a reminder in 10 minutes
  • Sending a “Working from home today” email to your manager
  • Blocking out your Office 365 calendar for an hour

Note: If you do not have an O365 plan that includes Flow, you can sign up for a free 90 day trial.

Automated Workflow Integrations and Templates

In addition to the many triggers and steps Flow has to offer, there are a lot of different services that integrate with Flow. There are too many to list, and it keeps growing, so follow the URL to see the latest services that integrate with Flow.

Some of the services and templates available can truly make your day to day office tasks easier. For instance, of the out-of-the-box templates, there is a flow to send a “working from home today” email to your manager. You can even take that template and tweak it so that it’s also a “don’t forget to submit your timesheet” button that you press once a week to your direct reports. Anything that is repeatable can easily be made into a Flow process.

Some more advanced templates integrate with other third party services. For instance, there is a template which automatically creates a Dynamics CRM entry from a SharePoint list item. Imagine having a SharePoint list which allows users from across the company to add in potential sales contacts. Not all users in your organization may have access to CRM, so when they create an entry using the SharePoint list, Microsoft Flow will automatically trigger and enter the sales contact entry into CRM without any action. You can easily configure this template to have an approval step if the process feels too automated!

Microsoft Flow on the Go

For remote workers, there is a mobile app for iOS and Android that allows you to trigger the workflows or get notifications by the press of a button. So you can open the Flow app, and trigger your email without the use of your computer. You even have the ability to send SMS (text messages) from the flow itself. So, if you are out-of-the-office, enable the Flow which texts you anytime your favorite client emails you, and you will be ahead of the game.

By using Microsoft Flow, your users can create workflows into back-end systems to help run business processes – all without having to call IT! Flow has provided users with the empowerment to work and create their own automated workflows. Still need help implementing automated workflows for your organization?  Looking for help with a workflow that is too advanced for Flow or need to integrate with systems not yet available with Flow? Contact us and see how BlumShapiro Consulting can help get you started on your way and assist.

About Brent:

Brent

Brent Harvey has over 10 years of software development experience with a specific focus on SharePoint, Project Server, and C #and web development. Brent is an Architect at BlumShapiro Consulting. Brent is a Microsoft Certified Solutions Expert in SharePoint 2013, Solutions Associate in Windows Server 2012, Specialist in Developing Azure Solutions and Professional Developer in SharePoint 2010.

Technology Talks Episode 3: Cloud Computing

Listen to our new podcast, Technology Talks, hosted by Hector Luciano, Consulting Manager at BlumShapiro Consulting. Each month, Hector will talk about the latest news and trends in technology with different leaders in the field.

In this episode, Hector speaks with Michael Pelletier, Chief Innovation Officer and Partner at BlumShapiro Consulting about cloud computing and the role it can play in your organization and your digital transformation journey.

Listen to our previous episodes on our SoundCloud page >>

 

Technology Talks Podcast

Listen to our new podcast, Technology Talks, hosted by Hector Luciano, Consulting Manager at BlumShapiro Consulting. Each month, Hector will talk about the latest news and trends in technology with different leaders in the field.

Catch up with our first two episodes today:

In this first episode, Hector speaks with Noah Ullman, Director at BlumShapiro Consulting about the 4th Industrial Revolution and Digital Transformation. The two discuss what digital transformation means for your organization and how you can prepare to be a leader in this new digital age.


In episode two, Hector speaks with Brian Berry, Director at BlumShapiro Consulting about big data, the role it can play for your organization and how it connects to Digital Transformation and the 4th Industrial Revolution.

 

 

 

Do Data Scientists Fear for Their Jobs?

What happened in this last election, November 2016? Rather, what happened to the analysts in this last election? Just about every poll and news report prediction had Hillary Clinton leading by a comfortable margin over Donald Trump. In every election I can recall from years past, the number crunchers have been pretty accurate on their predictions—at least on who would win if not the actual numerical results. However, this turned out not to be the case for the 2016 presidential race.

But this is not the first time this has happened. In 1936, Franklin Delano Roosevelt defeated Alfred Landon, much to the chagrin of The Literary Digest, a magazine that collected two and a half million mail-in surveys—roughly five percent of the voting population at the time. George Gallup, on the other hand, predicted a Roosevelt victory with a mere 3,000 interviews. The difference, according to the article’s author, was that Literary Digest’s mailing lists were sourced from vehicle registration records. How did this impact the results? In 1936 not everyone could afford a car, therefore, the Literary Digest sample was not a truly representative sample of the population. This is known as a sampling bias, where the very method used to collect the data points introduces its own force on the numbers collected. On the other hand, Gallup’s interviews were more in-line with the voting public.

The article cited above also mentions Boston’s ‘Street Bump’ smartphone app “that uses the phone’s accelerometer to detect potholes… as citizen’s of Boston … drive around, their phones automatically notify City Hall of the need to repair the road surface.” What a great idea! Or was it? The app was only collecting data from people who a) owned a smart phone, b) were willing to download the app, and c) drove regularly. Poorer neighborhoods were pretty much left out of the equation. Again, an example of sample bias.

The final case, and not to pick on Boston, but I recently heard that data scientists analyzing Twitter feeds for positive and negative sentiment, had to factor in the term “wicked,” as a positive sentiment force, but only for greater Boston. Apparently, that adjective doesn’t mean what the rest of the country assumes is means.

Along with sampling bias, another driving factor in erroneous conclusions from analyzing data is the ‘undocumented confounder.’ Suppose, for example, you wanted to see which coffee people prefer better, that from Starbucks or Dunkin’ Donuts. For this ‘experiment’, we’re interested only in the coffee itself, nothing else. So we have each shop prepare several pots with varying additions like ‘cream only’, ‘light and sweet’, ‘black no sugar’, etc. We then take these to a neutral location and do a side-by-side blind taste comparison. From our taste results we draw some conclusions as to which coffee is more preferred by the sample population. But unbeknownst to us, when the individual shops prepared their various samples of coffee, one shop used brown sugar and one used white sugar, or one used half-and-half while the other used heavy cream. The cream and sugar are now both undocumented confounders of the experiment, possibly driving results one way or the other.

So, back to the elections, how did this year’s political analysts miss the mark? Without knowing their sampling methods, I’m willing to suggest that some form of sample bias or confounder may have played a part. Was it the well known ‘cell-only problem’ again (households with no land-line are less likely to be reached by pollsters)? Did they take into consideration that Trump used Twitter as a means to deliver sound byte like messages to his followers, bypassing the main-stream media’s content filters? Some other factor perhaps as yet unidentified? As technology advances and society trends morph over time, so must political polling and data analysis methods.

Pollsters and data scientists are continually refining their methods of collection, compensation factors and models to eliminate any form of sample bias in order to get closer to the ‘truth.’ My guess is that the election analysts will eventually figure out where they went wrong. After all, they’ve got three years to work it out before the next presidential race starts. Heck, they probably started sloshing through all the data the day after the election!

One needs to realize that data science is just that, a science, and not something that can simply be stepped into without knowledge of the complexities of the discipline. Attempting to do so without the full understanding of sample bias, undocumented confounders and a host of other factors will lead you down the path to a wrong conclusion, aka ‘failure’. History has shown that, for ANY science, there are many failed experiments before a breakthrough. Laboratory scientists need to exercise caution and adhere to strict protocols to keep their work from getting ruined from outside contaminants. The same for data scientists who continually refine collection methods and models for experiments that fail.

So what about the ‘data science’ efforts for YOUR business? Are you trying to predict outcomes based on limited datasets and rudimentary Excel skills, then wondering why you can’t make any sense out of your analysis models? Do you need help identifying and eliminating sample bias, accounting for those pesky ‘undocumented confounders’? Social media sentiment analysis is a big buzz-word these days, with lots of potential for companies to mix this with their own performance metrics. But many just don’t know how to go about it, or are afraid of the cost.

At BlumShapiro Consulting, our team of consultants are constantly looking at the latest trends and technologies associated with data collection and analysis. Some of the same principles associated with election polling can be applied to your organization through predictive analytics and demand planning. Using Microsoft’s Azure framework we can quickly develop a prototype solution that can help take your organization’s data reporting and predicting to the next level.

About Todd: Todd Chittenden started his programming and reporting career with industrial maintenance applications in the late 1990’s. When SQL Server 2005 was introduced, he quickly became certified in Microsoft’s latest RDBMS technology and has added certifications over the years. He currently holds an MCSE in Business Intelligence. He has applied his knowledge of relational databases, data warehouses, business intelligence and analytics to a variety of projects for BlumShapiro since 2011. 

Data scientist