Microsoft Data Analytics Solutions You Shouldn’t Live Without

Much of an enterprise’s data resides in Microsoft applications and services such as Microsoft 365, SQL Server, Microsoft data warehouses, etc., so it makes sense to apply Microsoft Data Analytics solutions. But that’s not the only reason to take Microsoft analytics so seriously.

Despite Microsoft’s ubiquity, most enterprise data is spread across a forest of silos that have nothing to do with the Redmond software giant.  No, the real reason to think about Microsoft Data Analytics is that these solutions are really good. Gartner is a big fan of Microsoft’s Data Analytics and Business Intelligence solution set. Microsoft is far and away the leader in Gartner’s Magic Quadrant for Analytics and Business Intelligence Platforms. The second one behind them is Tableau. Everybody else is just so far far off the mark. Microsoft not only leads in the category; the Microsoft Data Analytics solutions are used far more broadly than anybody else.

Magic Quadrant for Analytics and Business Intelligence Platforms

Magic quandrant for analytics and business intelligence platform diagram

While Microsoft is the leader now, with its vision it is likely to stay that way. “Microsoft is a Leader in this Magic Quadrant. It has massive market reach through Microsoft Office and a comprehensive and visionary product roadmap,” the Gartner report said. “Microsoft offers data preparation, visual-based data discovery, interactive dashboards and augmented analytics in Power BI. This is available as a SaaS option running in the Azure cloud or as an on-premises option in Power BI Report Server. Power BI Desktop can be used as a stand-alone, free personal analysis tool. Installation of Power BI Desktop is required when power users are authoring complex data mashups involving on-premises data sources.”

Microsoft’s approach to Data Analytics is so valuable because of the dozen or so solid analytics tools that together constitute a family. If one solution is limited, the other one picks up and fills in the gaps. The Microsoft Data Analytics Platform is a game changer for how enterprises do Data Analytics, drive the business, make decisions, and invent new products and services.

In the past, enterprises took a lot of time developing analytics that were not integrated, that were siloed out themselves, that had complications between non-integrated analytics technologies. That led to even more silos draining the value of data long-term. With Microsoft, you have everything streamlined, put together in consistent process – everything is integrated and secure.

Not only that, Microsoft is great at integrating their solutions, bringing them together across the various technologies, streamlining those processes, and providing connectors that get your advance Data Analytics applied much faster.

Having everything fully integrated across the Microsoft platform, encrypted and secure and advanced from a compliance standpoint, all help streamline delivery and take the worry off the customer that they might be exposing data.

Microsoft is also a single source for information as to how to use all your analytics tools from this portfolio, and a single source for getting questions answered and support issues handled. This versus trying to manage a hodgepodge of ‘best of breed’ tools. Instead, Microsoft offers a superior integrated Modern Analytics Platform approach.

The ACTS Connection

ACTS has a deep partnership with Microsoft in all these areas, including the data platform. For instance, ACTS understands Azure and its relationship to developing and deploying a modern data warehouse.

As a Microsoft partner, ACTS helps enterprise achieve the optimum value from the Microsoft Data Analytics Platform. It is one thing to buy pieces from the Microsoft Data Analytics portfolio, it is another thing to make them all work, which is where ACTS comes in. I call it the shiny tool that companies fail to leverage because they don’t have deep knowledge of implementing it, or don’t have the knowledge of their data to build it up, or aren’t equipped with all the organizational supporting factors to do value engineering. When implemented right, the Microsoft Data Analytics Platform, along with their data services, will accelerate you analytics journey – fast.

ACTS Data Analytics Assessments

Choosing the right Data Analytics Platform solutions and exploiting them fully requires a deep understanding of your current state. ACTS offers in-depth Data Analytics Assessments that drill down into the details.

ACTS analyzes Use Case Opportunities, examines current solutions and tools and how they are executed, and also assesses:

  • Data Preparation and Manipulation
  • Analytic Tradecraft
  • State of Planning and Development
  • Infrastructure
  • Data Management
  • Analytics Services and Interactions
  • Human Insight and Actions
  • Data Operations
  • Data Quality
  • Engagement
  • Organizational Enablers, and
  • Persona Definition, Identification, Management and Development


One awesome solution, though not part of the Microsoft Data Analytics Platform, is CRISP-DM, short for CRoss-Industry Standard Process for Data Mining. CRISP-DM includes a methodology that defines stages of your data mining project, and what has to be done for each stage. Once the project is defined, CRISP-DM can help create and manage your process model, a model that applies to the entire life cycle for your data mining effort. You can evaluate your data strategy with our modern data maturity assessment.

CRISP DM diagram

This framework is a great way to define your processes that the Microsoft family of analytic solutions will specifically address. “The life cycle model consists of six phases with arrows indicating the most important and frequent dependencies between phases. The sequence of the phases is not strict. In fact, most projects move back and forth between phases as necessary,” IBM explained. “The CRISP-DM model is flexible and can be customized easily. For example, if your organization aims to detect money laundering, it is likely that you will sift through large amounts of data without a specific modeling goal. Instead of modeling, your work will focus on data exploration and visualization to uncover suspicious patterns in financial data. CRISP-DM allows you to create a data mining model that fits your particular needs.”

Another useful approach is the Machine Learning Canvas, a template to apply structure to your Machine Learning implementation and make sure ML is meeting your use case objectives.

Inside the Microsoft Data Analytics Platform

Not all pieces of the Microsoft Data Analytics are created equal. Azure Synapse is a cornerstone, supporting Azure Data Factory and machine learning. Beyond this are cognitive services, streaming capabilities, and Power BI, all of which we will dive into throughout the rest of this blog. Here is how Microsoft positions the platform. “Make the most of your big data with Azure

Connect and analyze your entire data estate by combining Power BI with Azure analytics services—from Azure Synapse Analytics to Azure Data Lake Storage. Analyze petabytes of data, use advanced AI capabilities, apply additional data protection, and more easily share insights across your organization,” Microsoft said. “There are a large handful of foundational Microsoft-focused Data Analytics products: Learn more here Azure Analytics Solutions.”

With that background, let’s look at the Microsoft Data Analytics tools that should be part of your analytics and data insights arsenal.

Azure Synapse Analytics

Absolutely core to the Microsoft Data Analytics Platform is Azure Synapse which includes data integration, enterprise-class data warehousing and big data analytics. “Azure Synapse Analytics gives you the freedom to query data on your terms, using either serverless or dedicated resources—at scale. Azure Synapse brings these worlds together with a unified experience to ingest, explore, prepare, manage, and serve data for immediate BI and machine learning needs,” explained Microsoft.

Azure Data Factory

Chances are your data is in myriad silos, many in the cloud and others on premises. All this data has to be integrated to build a proper data repository. Azure data factory is the data integration service that automates the movement of data in hybrid environments. “Integrate all your data with Azure Data Factory—a fully managed, serverless data integration service. Visually integrate data sources with more than 90 built-in, maintenance-free connectors at no added cost. Easily construct ETL and ELT processes code-free in an intuitive environment or write your own code. Then deliver integrated data to Azure Synapse Analytics to unlock business insights,” Microsoft’s Azure Data Factory web site explained.

Azure Databricks

Closely related to Azure Data Factory, Azure Databricks offers big data analytics – with a big splash of AI. Apache Spark helps discover formerly unseen insights hidden in your data. At the same time, you can “build artificial intelligence (AI) solutions with Azure Databricks, set up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn,” the Azure Databricks web site said.

Azure Data Lake Storage

As you build your data repositories and data hubs storage, metadata becomes critical. Azure Data Lake storage is designed for data experts to store an array of data. Once the data is in a Data Lake, Data Analytics professionals can easily work with it. “It removes the complexities of ingesting and storing all of your data while making it faster to get up and running with batch, streaming, and interactive analytics,” Microsoft argued on its Azure Data Lake web page. “Azure Data Lake works with existing IT investments for identity, management, and security for simplified data management and governance. It also integrates seamlessly with operational stores and data warehouses so you can extend current data applications.”

Azure Blob

Data Lake isn’t the only storage game in town. Azure Blob storage is an object oriented system built for the cloud. “Blob storage is optimized for storing massive amounts of unstructured data. Unstructured data is data that doesn’t adhere to a particular data model or definition, such as text or binary data,” Microsoft explained on its Azure Storage Blobs web page.

Power BI

Many Microsoft power users already know power BI. And for IT pros, power BI is a great way to generate data from Microsoft 365. But it is also an awesome tool for Data analytics professionals which can “unify data from many sources to create interactive, immersive dashboards and reports that provide actionable insights and drive business results,” Microsoft explained on its Power BI web site.

Azure Machine Learning

Machine learning is a vital component of a mature approach to Data Analytics. Machine learning moves your organization from diagnostic and predictive, to fully prescriptive – meaning your analytics will tell you how you can make something happen. But Azure Machine Learning functions are broader than that. “The Azure Machine Learning service empowers developers and data scientists with a wide range of productive experiences for building, training, and deploying machine learning models faster. Accelerate time to market and foster team collaboration with industry-leading MLOps—DevOps for machine learning. Innovate on a secure, trusted platform, designed for responsible machine learning,” explained the Microsoft Azure Machine Learning web page.

Azure Stream Analytics

Many enterprises are content to look backwards, analyzing data that’s been sitting in a system for a while. Advanced mature analytics include real time capabilities. “Azure Stream Analytics, the easy-to-use, real-time analytics service that is designed for mission-critical workloads. Build an end-to-end serverless streaming pipeline with just a few clicks. Go from zero to production in minutes using SQL—easily extensible with custom code and built-in machine learning capabilities for more advanced scenarios. Run your most demanding workloads with the confidence of a financially backed SLA,” the Azure Stream Analytics Web site explained.

Azure Data Explorer

Creating a data repository and categorizing your data through tagging is wonderful. Unlocking the value of that organized data involves making it easy for end users and your data professionals to get at the data they need. “Azure Data Explorer is a fast, fully managed data analytics service for real-time analysis on large volumes of data streaming from applications, websites, IoT devices, and more. Ask questions and iteratively explore data on the fly to improve products, enhance customer experiences, monitor devices, and boost operations. Quickly identify patterns, anomalies, and trends in your data. Explore new questions and get answers in minutes.”

Azure Data Explorer

Azure Time Series Insights

The Internet of Things (IoT) is taking over everything from factory floors, to farms to everyday offices. These devices collect massive amounts of data at the point where things are actually happening – data vital to your Data Analytics insights. Very little of this IoT data today is harnessed. That’s where Azure Time Series Insights comes in – by turning this IoT data into insights that drive action. “Improve operations and decision-making with decades of IoT data delivered with rich visualization and a turnkey experience. Use real-time data insights and interactive analytics to accelerate IoT data use throughout your organization,” Microsoft said.

If you’re looking to monetize, drive innovation and radically boost ROI with your data, ACTS empowers you to unleash that value. We deliver solutions for the entire data journey map from data collection to data management to downstream analytics, to cognitive AI and Machine learning.  Get started with our Modern Data Maturity Assessment  or contact us directly at [email protected] for a personalized consultation.

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David Baldwin

Practice Manager – Data & Insights, ACTS, Inc.

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