Blog

Maximizing value in small scale data science projects

In our latest blog post, my colleague Arash Toyser wrote about data and its relation to value using a very intuitive and informative analogy of gold and goldmines, making the point that raw data is the goldmine and the extracted insights are the actual gold. Continuing the analogy, one can argue that gold in itself

Data is the new gold… or is it?

Many industrial companies have been collecting data for several years if not several decades with varying degree of success in terms of the quality of the data collected. We asked ourselves, if data is the new gold, why are companies struggling with converting it to real tangible revenue? Afterall, this was the reason we started

Infographic: 5 steps for productive work with process data

The new MultiViz is here!

When we founded Viking Analytics in 2017, our mission since day one has been to put manufacturing data – and all its possibilities – in the hands of professionals that know the most about the production lines, the processes, the machines. Our self-service data analytics software, MultiViz, became the embodiment of this mission. Three years

Viking Analytics joins the IBM Accelerator Program

Viking Analytics is very proud to join the IBM Accelerator Program Sweden. In the next months, we will work together to integrate and establish a sales partner program to MultiViz through IBM Ecosystem and RedHat marketplace. “This is a major step for Viking Analytics, as it relates directly to our scalability and attests to the

The science behind Industry 4.0: theoretical foundations of Deep Learning via Sparse Representations

Each day, it increases the frequency we hear terms such as artificial intelligence (AI), machine learning (ML) and deep learning (DP). A lot of time these terms are used interchangeably and the relationship between them is not always clear. Artificial intelligence is the intelligence demonstrated by machines to creatively solve problems. Machine learning is a

In the media: Three Challenges in Using Machine Learning in Industrial Applications

Machine learning (ML) is present in many aspects of our lives, to the point that is difficult to get through a day without having contact with it. The most common example is doing a simple Google search, trained to show you the most relevant results. But ML can also be found in our smartphones, through

Fast & Cautious: how factory leaders are trying to win the productivity race

Recently, we have been in a series of digital events promoted by Business Sweden in Poland, Austria, and Hungary. It has been a very enriching experience, not only from the deal-making side, but also because it gave us perspective into how leaders are approaching the use of data analytics to improve overall plant performance. For

Closing the knowledge gap between retiring and newcomer engineers

It is not an exaggeration that knowledge is one of the most valuable assets a company has. Even with the growing trend of automation, technical knowledge can not be just directly bought off a shelf, as it heavily relies on the experience of more seasoned professionals. Not managing it properly can lead to loss of

Challenges in the journey of digital manufacturing

Digital manufacturing is forecasted to bring out massive, disruptive changes to the industrial world. Digital manufacturing is not about technology alone – it involves a complex braid of technology and business strategies across people, machines, and processes from the different departments within the organization. It is expected that digital manufacturing will result in a paradigm