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Recently, I attended a Cortana Intelligence Suite (CIS) class at Microsoft in Charlotte. Inspired by possibilities with Microsoft’s Azure and Machine Learning, I decided to write my first blog. I intend to write a series around each component within the Azure suite, which will include some examples of the functional application within the Manufacturing industry.
Cortana’s Intelligence Suite is Microsoft’s big data and Machine learning platform. Whether you work with a company that buys “best of breed” solutions, and you manually manage your data, or you only have one system, but you lack the ability or wherewithal to analyze it, CIS can help.
CIS is broken up into several components. Each component has an intended purpose, though you may be able to do similar functions across several elements, and you may only use a few of them for your business. Ryan Swanstrom, with Microsoft, provided our class with an intuitive way to remember the purpose of the components within CIS. I pulled the below image from their class handout, and I applied his nifty mnemonic associations in red. Starting from the bottom up…
Now, I’d like to provide you with a brief introduction to each component, and my future blogs in this series will take a deeper dive into them individually.
As many of you know, Azure’s intent is to provide cloud-hosted apps and services to Microsoft’s customers. Azure offers developers both Microsoft and Third Party tools to operate in the cloud and takes full advantage of the IoT. Additionally, you can still host Virtual Machines.
Many IT organizations may scoff at Azure because they think they understand its full functionality. Well, my friends, Microsoft is playing for the win. Their expansion and improvements of its platform are sure to wow the audience. I admit I’m becoming a bit of a fangirl after seeing some of its capabilities.
Azure Data Catalog
Isn’t it frustrating when you try to tie different types of data together from all over the place, and all the fields or tables are labeled differently? How much time do you waste slogging through tables to find your relevant data? I know my previous job was full of people with tribal knowledge on where to find data and how it relates to the way we did business.
The future of data management will tie together those end users with business knowledge and allow them to identify data relevant to your IT team.
Azure Data Factory
The data factory is a tool to move your data. Pull together all your data into one place, transform it, and distribute it! It’s your kitchen. Bring in the ingredients, cook something fun, and push it out to the dining room for consumption.
Azure Event Hubs
The event hub is used for truly Big Data and the IoT. It allows for a ton of data to be brought in for analysis. Think of it as the front door to funnel in large amounts of data, like temperature monitoring on all your reactors or sensitive product shipments.
You can even consume the data for behavior analysis on a mobile device. What does that mean exactly? Check back for more details.
Azure Data Lake
If the event hub is the front door, the lake is the storage closet for large amounts of data. You use the lake to store all of your data in its original non-relatable format, and it can integrate with your data warehouse you may have right now.
It differs from the Blob storage solely in the amount of data you’re collecting.
Azure SQL Database
The SQL database likely needs no brief introduction, but for those of you unfamiliar with what exactly IS an SQL Database and how does it relate to Azure, I shall attempt an explanation.
An SQL Database is where your data resides in tables with key identifiers, which makes it relatable to other sets of data. If you are familiar with Microsoft Access or Microsoft Excel, you already know about data storage in tables with fields and columns. If you’ve ever created a VLOOKUP in Excel, you understand how to create relationships between two different data sets in an effort to analyze your data. SQL is simply the coding language for this particular database.
The Azure SQL database takes all of the above and puts it in the cloud. Now, many of you may say, “What a minute… I don’t want my data in the cloud! That’s not secure.” I challenge you to rethink your philosophy and do a little research on the matter. Storage in the cloud can be more secure than an on-premise solution nowadays (depending on several factors).
I’m going to talk about Document DB when we do the deep dive into this section because I think it’s important.
Azure Machine Learning
AML is where you go to create your machine learning models and API’s. What is Machine Learning exactly? Well if you read about it in textbooks, your eyes are likely to cross, and you might need to be a Computer Science major to understand it. Microsoft’s Swanstrom laid it before us as “the machine looks at the data and suggests something. If that something doesn’t make it better, it will try something else. If it still doesn’t make it better, it will try something else. It will keep trying until it finds something that works.” I know that’s a broad statement, and I’m going to provide a practical application that makes sense when I write about this component in detail. In laymen’s terms, its predictive analysis based on data you feed the machine. One example is when you go to Amazon, and you see the recommended items… Based on your past purchases, we recommend these items. That’s Machine Learning! How might one use machine learning in Manufacturing? You could increase the yields of your Finished Goods if you have data based on a specific reactor, what was done prior to the production (cleaning, maintenance, etc), which operator does the best job on a particular product, and even which raw materials to use (based on how much data you put in on your raw materials, such as quality data, ML could help with product engineering. The possibilities are nearly endless.
Azure HD Insight
HDInsight is Hadoop as a service. Hadoop is an open source tool used to store files larger than what is allowed on a server, which is why our mnemonic is “Scale it.” Apparently, it’s faster than moving those large files across your network. My knowledge of Hadoop is rather lacking, so I’m looking forward to researching its function and real-world application.
Azure Steam Analytics
Like the name insinuates, Stream Analytics is used to stream data from the cloud and use it for near real-time analysis. ASA allows for comparison of new data coming in (by using the Data Factory “Move it!” to periodically move data copies from SQL DB to blobs for consumption) against historical data in an effort to simplify analysis and identify outliers.
Robert Hanson, a Data Scientist with Microsoft, did a brief demo on Stream Analytics in class. You could tell he was super excited about how easy it was to use Stream Analytics and its possibilities. It took him less than 15 minutes to set up a data connection for temperature and throw together a graph for visual consumption, and half of that time, he was discussing the site or functionality.
Power BI is a pretty way to prove your worth to the executives or to your fellow team members. It brings visualization to your analysis through interactive reports within a browser page. If you are a fan of PowerPivot or the eye-pleasing graphs out of Excel, you’ll enjoy the capabilities of Power BI. Show the CFO your company KPIs, Top customers, and Gross Margins by product all on one page.
As a frequent user of “Okay Google,” I eagerly listened to the Microsoft team discuss Cortana. I look forward to the day where I can say, “Cortana show me sales by product by region for 2016,” and she spits out a report with the information I asked for. Or how about a real-world example? “Cortana remind me to ask Kim for her address next time she calls.” Seeing as I have an Android device, I can download Cortana from the Play Store to test her out.
Speech is not the only way that you can interact with Cortana. Cortana can analyze visually as well audibly. How about an app that can predict your age based on a picture of your face using Cortana?
One myth I’d like to dispel when I do a deep dive into Cortana is what exactly is shared with Microsoft when you use Cortana. When I brought up the question of ownership of data and machine learning results in class, I was told Microsoft collects data on what functions you use in their software and how you use it. It does not collect data on personal use. I look forward to finding some good literature on this topic because I fear many companies are disabling Cortana due to security concerns.
That’s all, Folks!
Well, if you’ve stuck with me for this long, I hoped I sparked your interest in CIS and its potential business application. Please check back to hear more about each component of CIS!