IEBIStrat: Immersion Event on Developing a BI and Analytics Strategy

(Retired – no longer offered as a public class)


For decades, traditional structured business intelligence solutions have enabled users to repeatedly ask and answer the questions that are well-known to the organization. In recent years, new technologies have emerged—predictive analytics, big data analytics, machine learning, among others. These technologies allow users to explore new sources of data in new ways and answer questions in ways that were never before possible.

Have these new options for using data sounded the death knell for the Enterprise Data Warehouse? How can you build a BI strategy that preserves the best of your existing investments and lays the groundwork for a future state of your data platform?

In this 3-day Level 200 training class, we discuss the process for assessing your organization’s current level of BI maturity and identifying the future level of BI maturity that aligns technologies and best practices with your users’ business needs. We explore the difference between traditional BI and data analytics solutions and review scenarios for expanding your BI capabilities to include analytics. We also evaluate how various tools and capabilities in the Microsoft stack can support your data analytics requirements.

As we evaluate these tools, we compare and contrast them in regards to their suitability for specific BI & analytics scenarios. We also consider skill levels required to use these tools, the environments required to develop ingestion pipelines and to put them into production, and security options.

This class does not teach you everything you need to know about each of the discussed technologies, but it does teach you the following:

  • How the pieces fit together to create a modern analytics architecture
  • How to select the tools that best suit your organization’s analytical requirements
  • How to prepare yourself and/or your staff to implement these tools

By the end of the class, you’ll have some new ideas and inspiration to get started with your own BI and analytics roadmap and understand which technologies and skills are needed to build a foundation for your organization’s next-generation BI.

Target audience:

  • DBAs who need to support a technical infrastructure for BI, analytics, and data science
  • Managers who need a better understanding of how these technologies fit together to manage data as a strategic asset
  • IT professionals with a data background who want to learn how the BI space is evolving and want to prepare for the future

Instructor: Stacia Varga

Need Help Justifying Training? Here’s a letter to your boss explaining why SQLskills training is worthwhile and a list of community blog posts about our classes.


Module 1: Understanding Traditional vs Modern BI

  • Common problems with accessing data for analysis
  • Definition and goals of traditional BI
  • Problems with traditional BI
  • Benefits and challenges of self-service BI
  • Data governance
  • Definition and goals of modern BI

Module 2: Establishing a Maturity Baseline

  • Maturity assessment tools and benchmarks
  • Phases of platform maturity
  • Dimensions of a maturity model
  • Best practices and recommendations for moving to the next phase

Module 3: Ingesting Data

  • Getting Started
  • Integration Services
  • Azure Data Factory
  • Azure Event Hubs
  • Azure IoT Hub
  • Apache Kafka for HDInsight

Module 4: Cataloging Data

  • Azure Data Catalog
  • Data Asset Registration
  • Metadata Management
  • Data Discovery
  • Azure Data Catalog Security

Module 5: Preparing Data for Analysis

  • Ingestion tools that also prepare data for analysis
  • Data Quality Services
  • Azure Stream Analytics
  • Apache Storm for HDInsight
  • Apache Spark for HDInsight

Module 6: Storing Data for Analysis

  • SQL Server for XML, JSON, or graph data
  • Master Data Services
  • Azure Blob Storage for all types of data
  • Azure Data Lake Store for big data
  • Azure Cosmos DB for relational, NoSQL, or graph data

Module 7: Analyzing Data

  • PolyBase in SQL Server
  • SQL Server Machine Learning Services (R, Python)
  • Azure HDInsight
  • Azure Machine Learning
  • Azure Data Lake Analytics

Module 8: Publishing Data

  • Relational data mart options in SQL Server, Azure SQL Database, Analytics Platform System, and SQL Data Warehouse
  • Analysis Services (Multidimensional, Tabular, Azure)
  • Apache HBase in HDInsight

Module 9: Consuming Data

  • Paginated and mobile reports in Reporting Services
  • Excel: Power Query, Power Pivot, Power View
  • Power BI

Module 10: Preparing Your Roadmap

  • Recommendations for managing the process
  • Comparison of approaches: Store and Analyze versus Analyze and Store
  • Roadmap creation


If you have any questions not answered by our F.A.Q., please contact us.