Data analytics can drive many business benefits, including faster time to market, lower costs, and fewer risks. However, businesses need to define what results they are looking for from data analytics to reap the benefits.
For most technology-intensive businesses, one key driver for data analytics projects is getting new products to market quickly and efficiently. Analysing data effectively can improve product design and test processes. It can help to deliver insights around product development, cost-effective production, appropriate target markets, market introduction, and risk management. All of this lets businesses speed up their processes and be confident about making the right decisions.
You can follow these three steps to start applying data analytics to design and test processes:
1. Build a scalable system for your data
The more data an organisation possesses, the harder it is to effectively manage and analyse. Spreadsheets can work well for small, localised datasets, but won’t scale up because data is isolated among individual users or test stations, or spread across multiple sites. Working like that, organisations can’t control datasets or guarantee that they have the most recent data.
Teams should look at working with the IT department to create a more robust, scalable, supportable system. However, this approach comes with internal costs so it’s important to ensure other ongoing IT projects are fully supported and staffed as well. Once the right data analytics solution is in place, it will soon start paying for itself through the value it can deliver in terms of transformative insights that can be actioned to optimise business performance.
2. Take small steps toward better data for better results
Often, the data analytics required by businesses can be basic, such as looking at product performance in a consistent way to tell if a design is performing correctly. This is especially true in the prototype phase, when there are fewer samples to evaluate and the actual specification is still in flux. Later, in the manufacturing phase, there can be plenty of data but it may still be siloed and stored at another site.
The better the quality, accuracy, and timeliness of data, the more trustworthy and impactful the insights will be. Businesses that can improve the data they work with can thus improve the results of the decisions and actions they take based on that data.
3. Clarify the target problem
Without a clear target problem to address, businesses can get lost in analysing vast amounts of data. While the results may be interesting, without direction, these projects rarely deliver particular value.
Businesses can define the business problem they want to solve using design and test data. The objective could be improved time-to-market, lower R&D cost, better production yield, or something more specific to the operation. The next step is then to create or acquire scalable tools that let IT teams get data under control.
Many Businesses are drowning in data. There are so many data sources that it’s easy to accumulate files that are poorly organised and difficult to manage. With the right technology, infrastructure, and analytics in place, organisations can unlock the full potential of data for beneficial business outcomes.
Bob Witte, VP Technology Solutions, Keysight Technologies
