Why data matters for hardware engineering companies

As a hardware engineering company, you might be excused for not paying too much attention to the latest trends in data science and artificial intelligence. After all, a lot of your daily work consists of exactly the kind of creative thinking and developing that is making these robots possible. You sell products made from aluminium, silicon and high-grade plastics, not click-per-view advertisements. You might not have a huge interest in the typical artificial intelligence stories about predictive maintenance or managing huge warehouses with robots.

On the other hand, what does matter is how good and how efficient you are at finding out what to create, at designing, manufacturing and selling. And that is where good data management is more and more essential. From evidence-based technology roadmaps to customer relationship management, enterprise resource management and many more. Automating all kinds of manual operations through scripts and macros helps to increase efficiency drastically, and reduces the possibility for errors.

So while as a hardware engineering company, you are maybe not focussed on ‘big data’ and artificial intelligence in itself, still being able to work with sizeable amounts of data and extracting knowledge out of them is quickly becoming an important competitive advantage.

If you went to bed last night as an industrial company, you’re going to wake up today as a software and analytics company.
– Jeffrey Immelt, former CEO of General Electric

Engineering data is typically not of the huge-quantity / low-quality type that gets all the attention. However, there are other reasons why it can still be a big challenge to work with design and manufacturing data. It is spread across many software tools, each performing its calculations, design or analysis with inputs and outputs that are manually copied and pasted. It is contained in documents, spreadsheets, presentations, emails, in different versions and local copies. There is no uniform way of indicating what number represents which parameter, let alone a clear structure in what numbers belong to what actual parts of the design.

At Valispace, we think of data storage, design automation and optimization as parts of a ‘hierarchy of needs’, inspired by ‘The AI Hierarchy of Needs’ as proposed by Monica Rogati from Data Collective. Just as in Maslow’s pyramid of needs, food and shelter are needed before self-actualization, in the engineering data pyramid a basic unified data storage and a certain data structure are needed before it is possible to perform advanced data analytics, apply optimization algorithms and machine learning.

Valispace engineering data pyramid, inspired by ‘The AI Hierarchy of Needs’.

At the bottom of the pyramid is data collection into a single database, available to all engineers collaborating on a certain product or project. Data is exchanged with as many other tools as possible, feeding in and reading it out again. This is the basic set-up to create a single source of ‘truth’ with the latest, up to date values.

Adding structure to that data is one step above in the pyramid. To allow for clear communication and analysis, values are structured using a simple data model. The relations to other values are also stored, for example through formulas. A difference can be made between a physical property (e.g. the mass of an engine blade of 0.3 kg) and other numbers describing such as a design margin or a maximum value. Keeping the data structure simple allows to put in many different kinds of data easily, from back-of-the-envelope spreadsheet calculations to the most elaborate fine-meshed analysis results, from complex state machine logic to large batches of manufacturing, calibration and testing data.

This structure allows for advanced ways of exploring engineering data and its connections, improving the efficiency of daily work and creating new insights. It becomes possible to filter and track data and the sensitivity of one parameter to other values. Interactions with parameters are based on their meaning rather than where they happen to be in a spreadsheet. This opens the door to new user interfaces such as voice, chat or augmented/virtual reality. You could imagine asking Alexa (or J.A.R.V.I.S.) for a breakdown of the latest design changes inside an important subsystem of your product and to detail their impact on the power budget. In fact, we have already set up a prototype Alexa skill for Valispace to do exactly that, get in touch if you would like to be a beta tester.

Building on these layers of data collection, structure and advanced exploration, powerful data analytics can be automated, providing oversight in charts, budgets, timelines or powerful custom scripts.

Finally, having engineering data stored in one place, but also connected, structured and analyzed, many features of a product or project can now be expressed as values inside Valispace. This provides a perfect platform to apply optimization algorithms and automation tools on a real engineering project, the kind of advanced algorithms that have until now been restricted to academic use or small subsets of data. For example, at IAC 2017 in Adelaide, Johannes Norheim from MIT presented a paper on optimization of a satellite design using geometric programming using Valispace and our MATLAB toolbox.


  • Working with engineering data in an efficient way is a key competitive advantage, breaking up data silos and integrating knowledge across your company.
  • Advanced data analytics is based on structured data storage that forms a single source of truth and has automatic data exchanges with other engineering tools.
  • Exciting new user interfaces and methods of advanced optimization and machine learning become feasible with this data structure in place.

Interested? Take the first step today: centralize engineering data from fragmented spreadsheets and emails into a single place where they create value. To learn more about Valispace, have a look at our website and our documentation page, or try out the tutorials in the online demo.

Why you need more than a spreadsheet for your engineering data

When explaining the basic concepts of Valispace – collaboration, technical data exchange, connecting data with formulas – a question that often comes up is how Valispace compares with spreadsheets, and especially with cloud-based spreadsheet such as Excel Online and Google Sheets.

It is not a coincidence that spreadsheets are popular: storing data in tables is such a generic approach that it allows for thousands of different applications. Formulas that calculate the value in any particular cell depending on the other cells form a powerful calculation tool that is still relatively easy to learn (although hard to master). Charts can be created easily. Advanced functions like pivot tables and macros allow to set up more elaborate data analysis and automation scenarios.

A spreadsheet is the digital equivalent of a swiss army knife, a flexible tool to ‘get stuff done’ in our digital lives. Working with spreadsheets has become an important skill to survive and thrive in a digital world, so much so that it forms a mandatory part of secondary school education in many countries.

Swiss army knife
Swiss army knife

It is not a big stretch to imagine using spreadsheets to set up an engineering data exchange, and in fact this is exactly what is in place in many engineering companies: gather all data in one or multiple big ‘master’ tables, including the formulas to recalculate cells if other data changes. Put these on a local shared drive, a document management system or a cloud service such as Excel Online or Google Sheets, to allow for simultaneous editing by different users.

However, large and complex spreadsheets that are in use by multiple people are not a new phenomenon, and the challenges with them have been significant enough to merit quite some academic research and an annual conference. These challenges can include:

  • To keep the spreadsheet from becoming a data jungle and allow to retrieve the essential bits of information, typically some structure needs to be set up initially and carefully maintained through rules, active cleaning and review.
  • The data contains just numbers. For these numbers to be useful in calculations and formulas correction factors are often needed, for example to convert to the correct unit. Data manipulations like this need to be clearly documented to ensure other users work with the data in the correct way.
  • Changes are automatically propagated through the formulas, but their effect remains ‘hidden’ until the correct person happens to looks at the relevant data.

As with any human process, manually maintaining spreadsheets inevitably introduces mistakes. Some of these spreadsheet mistakes have become infamous, but it is increasingly clear that almost every spreadsheet has some bigger or smaller errors. The different types of mistakes in spreadsheets are included in the “Panko-Halverson Taxonomy of Spreadsheet Errors”. For example, ‘execution errors’ can include typos and wrong copies, and ‘planning errors’ can include logic errors inside formulas.

Spreadsheet error taxonomy
Taxonomy of spreadsheet errors, from Revising the Panko-Halverson Taxonomy of Spreadsheet Errors, R. R. Panko and S. Aurigemma, Decision Support Systems 2010.

While the rate of errors in spreadsheets is in itself not much different from human errors in other forms of data storage, reviewing a spreadsheet is notoriously difficult. Have a look at the picture below for a (still relatively straightforward) example. Difficult reviewing is part of the reason why many errors remain hidden and are even copied to other spreadsheets. It is not surprising that these spreadsheet mistakes are often the root cause of expensive last-minute fixes, leading to schedule slips and budget overruns.

Spreadsheet error example
Spreadsheet error example. How much time did it take you to find and fix the mistake?

Adding structure manually, performing data manipulations and taking care of changes all introduce errors in spreadsheets that are difficult to find and fix. How can this be solved? It is essential to set up a clear data structure that is shared between all collaborators, as well as automating as many actions as possible. It is exactly these two solutions that Valispace provides:

  1. Data structure: any data point inside Valispace is more than just a number. It has a name, a unit, a history, margins and many more. It typically is part of a data ‘product tree’ (see picture below). Formulas are constructed based on the name of the input values, rather than referring to an abstract cell number, which makes these formulas much easier to review. In every description or calculation where this value is used, it is always referring to the ‘single source of truth’, ensuring that calculations are consistent among each other.At the same time, very rigid data structures can easily make it complex to set up the data storage system and require significant training from all users. That is why a lot of flexibility remains within the basic Valispace ‘tree’ structure of components, Valis and their properties.

    Component tree
    Example component tree from Saturn V advanced tutorial.
  2. Automation: avoiding manual data manipulations greatly reduces the possibility of human errors. Valispace provides a range of different automation tools:
    • A lot of important but tedious calculations are performed by the Valispace algorithm behind the scenes, for example: automatic unit conversion, automatic propagation of design margins, notifications to the relevant users when something has changed that they need to be aware of.
    • Comparison charts, budget tables, relation charts between values and other data summary visuals are automatically to up to date.
    • Custom automation or scripting can be implemented through the Valispace Python and REST APIs.
    • Values included in other tools can easily be kept up to date with the Valispace add-ons for Microsoft Word, Excel, MATLAB,…

Through data structure and automation, Valispace solves the key issues of engineering data processing. While a ‘swiss army knife’ spreadsheet provides the ultimate flexibility in one tool, more complex data construction work requires dedicated tools to limit the amount of manual labor. Valispace is such a tool, a power drill for engineering data that provides efficient data storage and analysis.

Power drill
Power drill