Viviota Blog

Listen to our blog:

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:

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.

Please make sure to sign up for this exclusive summit in Novi, MI!

Since our company's inception, we have had the privilege of engaging with a diverse range of customers, all grappling with a common challenge-the management of their data. Whether a small business or a large enterprise, the issue of technical debt, a result of short-term thinking about data, is a consistent theme. Technical debt, in this context, refers to the cost that accumulates when short-term solutions are implemented to address immediate needs, leading to a complex, inefficient, and duplicate data infrastructure over time.

This technical debt spawns from a patchwork of applying technologies one by one, reacting to a current need. A prime example is when companies opt for a do-it-yourself plan because they don't have to engage procurement or IT and currently have resources available. Aside from the expanding technical debt, this strategy ignores the ongoing service support issues that cost 10x what an off-the-shelf product when looking at the total cost of ownership.  I have observed extreme examples of this behavior at companies that have been around for many years. The decision to patch things and solve a short-term pain is very tempting. Leaders convince themselves they can do it cheaper and better because it is custom-built for them versus the pain of trying to convince stakeholders to procure a product.

It's clear that addressing and reducing technical debt is not a simple task. It requires a structured methodology for identifying, justifying, and funding new projects. This funding is not just about acquiring technology; it's about generating the emotional momentum needed to overcome the inertia that has built up over many years in replacing outdated technology.

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:

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. 

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?

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.

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