Viviota Blog

The importance of data preparation has grown exponentially with the rise of AI. Data comes in many forms and formats, including homegrown applications, SQL databases, files, sensors, video, and physics-driven analog data. Traditionally, data cleansing is defined as detecting and correcting (or removing) corrupt or inaccurate records from a dataset, table, or database. The data challenge presented is identifying the data's incomplete, incorrect, inaccurate, or irrelevant parts and then replacing, modifying, or deleting the dirty or coarse data.

According to a recent survey, 47% of companies today consider AI/ML as a top priority in 2024. Yet, according to Harvard Business Review “cross-industry studies show, on average, less than half of an organization's structured data is actively used in making decisions—and less than 1% of its unstructured data is analyzed or even used at all.” ¹

This statistic should concern R&D or Engineering decision-makers. Companies are drowning in data, and very few companies can leverage their data because they are stuck trying to find, access, and connect various data sources. So first ask yourself these three questions:

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.

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?

This year, at the Consumer Electronic Show (CES) show in Las Vegas, I had the pleasure of seeing a great presentation from John May, the CEO at John Deere. It was impressive how they have focused on using technology to solve problems for their consumers of heavy equipment.

Harvard Business Review[i] reported that cross-industry studies show, on average, less than half of an organization's structured data is actively used in making decisions—and less than 1% of its unstructured data is analyzed or even used at all. 

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.

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.

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?