5 Common Barriers to Tableau Adoption

5 Common Barriers to Tableau Adoption

Getting full value for your investment in Tableau requires much more than completing a PO, getting a new server spun up, and installing the software. While Tableau’s mission is “to help people see and understand their data”, all too often, implementation efforts fail to fully account for the “people” side of the equation.

The fact is that any change in technology requires a change in behavior – if you want to get the full intended value for your investment. As anyone with a dusty exercise bike in the garage can tell you, even the most top-of-the-line technology is worthless unless people actually use it.

In our experience, when an organization is disappointed by lackluster Tableau utilization, the source of the issue is rarely technical. Instead, it’s usually the human aspects of implementation that contribute to less-than-optimal results. We need to apply the same discipline and rigor to managing the human elements of a Tableau deployment as we do to the technical objectives for the project. Below we discuss 5 common barriers to Tableau success that we’ve experienced, as well as a few tips on how to avoid them. READ MORE

Arrows Get to the Point

Arrows Get to the Point

Sometimes the best visual is an arrow.

With Tableau, it’s easy to include arrows to make a point about business performance. While colorful bar charts, scatter plots, and line graphs can visually highlight key information, a set of tiles or small table of numbers can succinctly indicate the health of a business. By adding up and down arrows with color next to a number, you can show the value of that number relative to another time period or a forecast. In this way, you provide context and meaning – is $1 million of sales good or bad, for example? If your forecast was for $2 million, then a red downward arrow provides the visual indicator for the meaning of the number. READ MORE.

A Marquis Moment! Achieving Flexible, Self Service Analytics with Adaptive Biotechnologies

A Marquis Moment! Achieving Flexible, Self Service Analytics with Adaptive Biotechnologies

Marquis Moments are moments worth celebrating. They're not about us, they are about you. When we hit a monumental milestone, make a transformation, or just plain win, we like to take a moment to recognize it. 

Today, we celebrate one of our North American BioTech Clients: Adaptive Biotechnologies

Helping clients achieve flexible, self-service analytics in the cloud or on-prem is what we do daily.  We are continually inspired by our clients' work. And with Adaptive Biotechnologies, we can't help but feel like the work they are doing is extra important.

We'd be surprised if you haven't read or heard about this awesome company. They're making quite the buzz in the Seattle area as a startup pioneering the use of immunosequencing to revolutionize patient care. READ MORE

Combining Data from Different Sources

Combining Data from Different Sources

I think we can all agree, Tableau looks great in a demo with the sample data stored in Excel. But the true test of a self-service analytics tool is working with real data that may reside in more than a single Excel workbook. Thankfully, Tableau has two different methods of combining data to give the user flexibility: data joining and data blending. I want to address a few potential misunderstandings and highlight a few caveats to keep in mind when using either method. READ MORE.

Data Shaping: The Must-Do Initiative Topping CIO Roadmaps

Data Shaping: The Must-Do Initiative Topping CIO Roadmaps

Data Shaping is Important.

Read on to learn why:

We all have tons of data, but do we use it to the fullest? We don’t need to convince anyone of the need to use data to make decisions, it’s 2018. When you use your data, you see clearer. You see the road behind, you predict the road ahead, and you can make the right decisions navigating your path.

But, if you’re like most organizations, you have data collected, but making sense of it is overwhelming. We all have employees who have too much on their plate, and even knowing where to start with data shaping, let alone having the time to do it seems impossible. READ MORE.