IEBIStrat: Immersion Event on Developing a BI and Analytics Strategy

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.

Ready to register? Please see our Immersion Events Schedule for class dates and our comprehensive Immersion Events F.A.Q. for class costs and other frequently asked questions.


Curriculum

Module 1: Understanding Traditional vs. Modern BI

To lay the foundation for this class, we’ll introduce architectural concepts and terminology related to BI and analytics platforms, old and new. Topics covered include:

  • 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

In this module, we’ll explore tools for assessing your organization’s BI/analytics maturity and discuss common problems and challenges with improving analytical capabilities. We’ll also explore potential future state scenarios and begin developing a personalized roadmap for evolving your organization’s data platform. Topics covered include:

  • 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

The ability to ingest data from a variety of sources is vital to an analytics platform. In this module, we review the technologies available in the Microsoft stack that support ingestion and introduce other capabilities these tools support. Topics covered include:

  • Integration Services
  • Azure Data Factory
  • Azure Event Hub and Azure IoT Hub
  • Apache Kafka for HDInsight

Module 4: Cataloging Data

An important component of a modern data platform is the data catalog. The data catalog is a resource for users who not only need to find and use data, but also to understand its intended usage and limitations. It is also a useful way to track for IT professionals to track and document organizational data assets. Topics covered in this module include:

  • Azure Data Catalog use case scenarios
  • Data asset registration
  • Metadata management
  • Data discovery process
  • Catalog security

Module 5: Preparing Data for Analysis

Typically, ingested data must be transformed in some way before it can be used for analytical purposes. Many of the ingestion tools introduced in an earlier module also support various types of transformations. In this module, we revisit the ingestion tools and introduce Azure data preparation tools to compare and contrast their capabilities for performing cleansing, reshaping, and other types of transformation activities. Topics covered include:

  • 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

Whether you need to prepare your data for analysis or prefer to store raw, unprocessed data, you need to understand your options. This module explores your storage options for structured, semi-structured, and unstructured data. Topics covered include:

  • 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

The Microsoft data platform provides a variety of technologies for data analysis. In this module, we review use cases for each of these tools and discuss implications for adding any or all of them to your technical environment. Topics covered include:

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

Module 8: Publishing Data

The results of transformed data or data generated by analytical tools can be stored for consumption. This module covers the options available in the Microsoft data platform for analytical data. Topics covered include:

  • 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

The tools supporting data consumption allow users to access data in a variety of ways to answer day-to-day questions or to derive new insights into trends and outliers affecting the business. In this module, we explore how these tools support standardized report delivery and self-service reporting and analysis. Topics covered include:

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

Module 10: Preparing Your Roadmap

Now that you have a better understanding of how the various tools in the Microsoft data platform fit together, you’re ready to think about your next steps. Because it’s easy to feel overwhelmed by all the possibilities, we end this class by helping you put together a high-level roadmap and reviewing best practices for taking your data platform to the next level. Topics covered include:

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

Ready to register? Please see our Immersion Events Schedule for class dates and our comprehensive Immersion Events F.A.Q. for class costs and other frequently asked questions.


Questions?

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