Deploying the Modern BI Stack

Data

Deploying the Modern BI Stack

This post is the companion to our recent webinar on the modern BI stack. I chose the word “deploying” here intentionally because doing it well means you never finish. You will be “deploying” permanently.

It would be convenient for our crew and advantageous for all of our clients if we were able to participate in the earliest stages of every project. However, that’s rarely the case. We work with many fine companies, many have excellent technical staff, and some have senior leadership that already “gets it.” Most often, we come in after a project has already started, and sometimes we’re engaged when something has gone wrong.

Strategically Building in Value

Ideally, we love opportunities when we can help clients shape their plans during the early stages, before they have made expensive decisions on toolsets, timing and process. Why? Because we have more leeway to shape important decisions early. We can help clients assess readiness, make recommendations to their core team, help them shape enablement plans and teach best practices. In general, we can reduce risk and cost and ensure that the client has an excellent outcome.

But even if we aren’t engaged early, one of the first services we quote is called a strategy, vision and roadmap (SVR) session. In this way, we can help clients avoid common pitfalls, which may include:

  1. Too much focus on technology
  2. Not knowing enough about your people (skills, needs, wants)
  3. Not understanding your processes

I don’t mean to imply that all companies have no understanding of these three areas. Clearly, that is not the case. But it’s important to understand the necessary details about your people and processes before you start making technology stack decisions. You have to pick the tools that fit your team’s needs, considering their skills and knowledge.

The deliverable clients receive when we do an SVR is a detailed roadmap for the next six months to two years, depending on the scope and complexity of your needs while understanding the ambitions your leadership has. A well-defined plan will include details on the training and ongoing enablement of your people. It considers your business processes, and finally we can then make informed recommendations on the technology stack that best fits your needs and objectives.

Best-of-Breed Is Better

Most clients don’t completely sunset existing systems. There are parts of the old that can continue into the future for quite some time in parallel with the next technology components. Sometimes, permanently.

I’m a strong advocate of avoiding the single-vendor stack solution because typically they are not as good a value for our clients than the best-of-breed solution. The new tools that have been created over the past decade, the maturation of the cloud, and the interoperability that are built into modern data tools means the old perceived advantages of the single-vendor solution are now disadvantages.

Data Flow for BI

Once you’ve defined your rollout team, created an enablement plan and selected the technologies you need to acquire, you can focus on the data flow that you have to develop, improve and enhance. Every data flow includes:

  1. The Acquisition of Data
  2. The Preparation of Data
  3. The Visualization of Data

Typically, it’s step 3 that gets leadership excited. Interactive dashboards (and we do love Tableau) are one of the most important components of your technology staff. It’s how data is turned into actionable information. In order to be adaptable to the rapidly changing business climate we face today, it is important that your non-technical staff, analysts, managers and leaders can all access the information securely but can also make modifications, or create new dashboards, while minimizing the need for technical staff input. Tableau has proven over the past decade to offer the most value for the majority of our clients.

But Tableau isn’t the only tool you’ll need. You have to consider data preparation, data storage, and how your different user constituencies need to access the information.

The Biggest Challenge Is Data Munging

It would be nice if all data came complete, structured properly and all from the same source. That has never been true, and in the future, it will be less true.

However, our ability to ingest, clean and reformat data has dramatically improved over the past few years. Tools like Alteryx, Matillion, FiveTran, Snowflake and Tableau are making it easier to obtain, clean, reformat, store, create and share information quickly and affordably, if you plan well.

Tableau’s New Data Model

Zooming in on Tableau’s new release v2020.2, I demonstrate how Tableau’s new “Relationships” data model simplifies the process of combining data tables by enabling you to build defined data structure dynamically in the logical layer of your workbook rather than rely on more ridged table joins in the physical layer of your workbook.

Tableau now provides three fundamental ways to combine source data—via joins in the physical layer (as always); by using data blending (in the worksheet); or by the “noodle” in the logical layer (via “relationships”). Relationships will make it easier for non-technical users to bring together different tables that may include multiple facts and dimensions in a way that considers the level of detail needed in the view you must articulate.

During my webinar, I demonstrate the use of relationships, build some views and calculations and highlight some of the differences between relationships, blends and joins. There is a recording of my webinar is available here.

Please feel free to reach out to our team. We look forward to helping you achieve your goals!

 

More About the Author

Dan Murray

Director of Strategic Innovation
The Analytics Revolution: Old Stack, New Stack and Best-of-Breed Solutions Over the last 40 years, we’ve seen a revolution in data analytics, starting with the invention of SQL and the Relational Database by ...
Deploying the Modern BI Stack This post is the companion to our recent webinar on the modern BI stack. I chose the word “deploying” here intentionally because doing ...

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