FAQ

The senior executive team of Sextant each have many years of exposure to all aspects of the application of data analytics and the potential implementation barriers faced by OEMs. They are much more engaged throughout each phase of project implementation, adding a continuous senior level strategic perspective.

Our clients avoid the adoption obstacles commonly experienced with off-the-shelf solutions. Frankly, this means we’re faster to completion and faster to market.

We embrace the concept of making data visually appealing and providing access to everyone who needs it, however there are some dangers. One of the biggest hindrances to moving the business forward is organizing data in ways that leads to inconsistent interpretations of business conditions and ignores data rules. For example, sales trends can be easily viewed, but if the mechanisms aren’t in place to account for returns, promotional units, fleet sales and government purchases, it’s possible to come to inaccurate conclusions. Data science is more than input, it’s about the discipline to associate business circumstances with knowledge to create meaningful insights. The potential hazard is that simplifying software to make it easy for anyone to use often means data rules get ignored.

Our projects always begin with a discovery discussion, and our first job is to understand the business situation. From there we evaluate the three components of data analytics: data, technology and human expertise. That begins to reveal what value we can provide and what obstacles need to be overcome. We can be a change agent or accelerator. The sophistication of the solution is dependent on the client’s needs and situation.

Big data viewed from a macro perspective can be distracting for many organizations, consequently causing them to delay applications, or even worse misapply. Data analytics needs a discriminating eye to harvest, maintain and grow to make it valuable. For example, with the right disciplines for data collection and automation we’ve uncovered opportunities beyond traditional applications in areas like: telematics, profiling, understanding the customer’s customer and event marketing. Big data is really about finding “valuable” data.

The most important things to look for are:

• Software should mirror business process and not require business process to mirror the software
• Have a process to customize relatively quickly or without having to wrestle with vendor
• An open data model that has the extensibility to add attributes and data sources with reasonable ease so you can leverage changing business conditions and opportunities exposed by new data
• Multiple interfaces that allow novice users to interact with a basic level front-end and experienced users to interact with a more complex front-end
• A method of incorporating business rules as well as geographic and temporal considerations

Of course! We never grow tired of the view and find it inspiring and energizing. Some of our most innovative discussions with clients happen in the mountains.