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