Viviota Blog

Existing testing approaches are time consuming taking several months to run 1000s of tests on 100s of samples. The tests are destructive meaning the batteries tested are not usable when testing is complete. Data collected from testing is only valid for the batch of batteries testes (i.e., they are tied to battery chemistry). As battery consumption increases, the length of time to test batteries becomes critical. If supplies are depleted before testing is completed, the testing results are worthless.

For several years now, I have had the privilege to work with numerous companies that strive to improve people, processes, and technologies related to engineering data. Through this continuous learning, I have realized that one of the biggest challenges for companies is "cognitive inertia," not processes and technologies. Allow me to explain.

Are You “Digital Transformation-Ready”?

So, your customers are committed to a Digital Transformation (DT) journey. How does this affect the engineering organization? Engineering executives expect integrating digital technology into all aspects of the business will deliver great value. They expect DT to fundamentally change and improve business operations. As part of the DT journey, engineering teams want to unify and update systems, organizations, and processes to support next-generation product development cycles. Their engineering tools requirements, infrastructure ,and processes will be viewed through this lens. Are your offerings ready to support your customers’ DT projects with scalable, digital enterprise-ready architecture and capabilities?

Many of us that follow EV trends know that the US is substantially behind both the EU and China in terms of both the number of EVs on the road and the infrastructure that is in place to support growth of the EV footprint in the US. For months we’ve been hearing about ambitious goals from the current administration that Congress has been working towards legislating with new bills that would improve all types of infrastructure, including the push to expand sales of zero-emissions vehicles. Nothing has been signed into law yet, but with the Senate passage of the bipartisan infrastructure bill (also called the Infrastructure Investment and Jobs Act), we know what that bill currently looks like.  Then there is the separate bill referred to by several names: the American Families Plan, the Build Back Better bill, the human infrastructure bill, and the $3.5T social spending bill (although it probably won’t be $3.5T when negotiations run their course). Even the latter bill will likely have some EV-centric content based on what we’ve heard from lawmakers, especially those from Michigan. So, with everything subject to change—where are we?

Customers turn to Viviota and to NI technology to better measure and analyze machine data from sensors. It turns out, sensor data does not always work well with traditional IT software, and there is a gap of software tools for this purpose.

The complexity of the electric power industry creates enormous opportunity for Fortune 500 companies and promising technology companies. Increased energy demands, capacity limitations, environmental constraints, varying load shapes, distributed generation and the deployment of new smart technologies all come into play.

The epic storm in February in our home state, Texas, exemplified the fagile nature of our electric grid and the catastrophic consequences of it failure. While new technologies will be developed to build a robust and resilient 21st Century grid, this new grid is still in its infancy and will take years, most likely decades, to reach that utopia of which we dream.

Traditionally, the data cleansing is defined as the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data.

This is post 2 in the series on 'The 7 Data Habits of Highly Effective Product Companies'. Tune in to our LIVE WEBINAR '7 Data Habits of Successful Product Companies' - MARCH 31, 11 a.m. CDT

Traditionally, the data cleansing is defined as the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data.