Collect And Conquer: 9 Ways To Get Smarter About The Way You Capture Data

By Christian Floerkemeier, Co-founder and CTO, Scandit

Imagine a shift in retail dynamics, where workers, rather than sitting in a stockroom manually scanning hundreds of barcodes, assume the elevated roles of personal shoppers armed with reams of product and inventory information they can use to advise and checkout a shopper in seconds. Or picture a delivery driver loading their van and receiving precise, intuitive instructions about where each item should be placed for the easiest access, simply by logging the package label with their phone.

Information about physical assets in the form of barcodes, IDs and the like is everywhere, waiting to be collected, digitized and used. But as the Futurum Group’s VP and Practice Leader Steven Dickens puts it, “enterprises have accelerated their digital transformation efforts over the last few years, but one key area is lagging – namely the capture of data in the physical world.”

Workers are saddled with too many manual processes when capturing this data, which are not only tedious and time-consuming but are often error-prone. And once they finally have the data, instead of having immediate insights fed back to them, the information is not properly analyzed.

For as long as this persists, business leaders cannot have confidence in the data underpinning their decisions. The risk of letting these missteps erase their profits is very real – logistical errors alone already cost the pharmaceutical sector a staggering $35 billion every year.

The fix is getting a smarter, more concrete data capture strategy – but that starts with thinking about it as a business essential rather than a ‘nice-to-have’.

What does ‘smart’ really mean

At work, employees need access to the same actionable, real-time insights they enjoy when off the clock. If they can find the weather on their phones with a tap or two, why not the inventory levels at a fulfillment center or nutritional values for grocery items?

The goal of smart data capture is to efficiently capture, combine and analyze multiple data sources like barcodes, text, objects and instantly surface rich insights that people can act upon immediately.

Workers regardless of industry or position stand to benefit, but an effective smart data capture strategy must be underpinned by nine key principles.

(Super)power to the people

  1. Shift the tedious work to technology

Devices that quickly and easily capture data from multiple sources spell the end of tedious, fallible data capture.

In manufacturing quality control, for instance, smart data capture reduces how long skilled workers have to spend on visual inspection tasks by automating the process and taking advantage of computers’ speed and accuracy. Making roles more rewarding can only be a good thing – directly impacting recruitment and retention.

  • Unlock people’s potential

As the pandemic underscored, frontline workers form the backbone of so many of the services we rely on. Now, they need to be empowered with data-based insights that emphasize their unique human qualities like empathy and problem-solving. Workers need the same access to innovative tech that their boardroom counterparts have, which historically hasn’t been the case.

  • Empower customers

Smart data capture doesn’t need to be limited to employees – customers also stand to gain. Giving them in-store access to interactive product information, promotions and offers – much like they see online – elevates their experience tremendously.

Take shoppers at independent UK-based grocers Nisa for example, who can use its app to scan shelves and have personalized deals pop up as augmented reality (AR) overlays. And, as mixed-reality and AR become more widely adopted, this same principle can be applied to wearables, too.

Transforming Processes

  • Emphasize the ‘how’ – not just the who

A user-centric mindset is essential and needs to consider how people work in addition to what they do. For example, rather than making incremental improvements to barcode scanning, consider how the task fits into a worker’s wider to-do list day-to-day. Accelerating code scanning is one thing, but a smarter solution would be to adopt technology that achieves this while also sorting and processing the information automatically and telling users if there’s action they need to take.

Now, rather than simply capturing an isolated piece of information with a button press, that click makes an entire task flow quicker and easier to complete.

  • Feedback value and purpose in an instant

There’s no point making decisions today based on information from weeks or months ago. Strategies must prioritize putting insights at workers’ fingertips at the moment of data collection, enabling them to make decisions in an instant and supporting speedy reporting to the head office.

In practice, it means a delivery driver could point their devices at a stack of parcels in a warehouse and immediately be told their final destinations, how best to load their vans and whether there have been any updates to their route – with just one scan.

  • Ensure versatility and resilience

Solutions must automatically adapt to different and challenging scenarios, instead of putting the burden on users. For instance, when multiple different barcodes are printed on a single item, a smart data capture strategy leverages technology that can use context cues to identify the right one rather than relying on user input. Or, if a code is damaged, the system must be able to automatically switch to text recognition and decode the copy instead.

Pursue technical innovation

  • Integrated, multi-modal platforms

Platforms, whether developed in-house or bought, must be able to integrate data with internal analytics on top of capturing it. For example, the shelf optimization information retail workers gather on a daily basis can be linked with replenishment platforms so stock gaps can be automatically addressed.

  • Use any smart device, anywhere, any time

Be device-agnostic and invest in solutions that don’t just work with dedicated devices, but smartphones, tablets, drones and wearables too. Many companies work incrementally to introduce smart data capture, beginning with the assets they already have or supplying workers with smart data capture-enabled apps that can be run on their own phones.

These smart devices in particular possess multifunctionality and computing power that would be foolish not to take advantage of.

  • Reach beyond human limitations

Technology should build upon peoples’ strengths and augment existing capabilities. Computer vision is a powerful tool, which can help identify things invisible to people, like high-quality fake IDs or forgeries. Through machine learning, platforms can be trained to detect even the smallest discrepancies, improving compliance and minimizing oversights.

The time to get smart is now

The way businesses capture data has not modernized or kept pace with other elements of data management, resulting in scenarios where store associates are stuck in stock rooms, or warehouse workers repeatedly pick and scan items thousands of times a day.

It doesn’t have to be like this. Today’s mobile computing and technological innovations incorporating machine learning and computer vision mean opportunities are now available to unlock the next level of business efficiency and radically overhaul the customer and employee experience.

By adhering to these nine principles, organizations can position themselves ahead of less tech-savvy competitors that continue to insist on archaic data capture techniques. With data such a precious commodity in today’s businesses, it’s essential they shift from simply finding new ways to use it – and realize how critical transforming the collection process truly is.

As CTO and VP Product, Scandit co-founder Christian Floerkemeier is responsible for Scandit’s product strategy and roadmap and is the technical lead behind Scandit’s patented Barcode Scanner technology.

Before founding Scandit, Christian was the Associate Director of the Auto-ID Lab at MIT and a member of the MIT research team that developed the RFID technology which is today in use in major supply chains. Christian also co-founded Fosstrak, the leading open-source RFID software platform that implements the EPC Network specification. He was the technical program chair of the Internet of Things Conference in 2008 and IEEE RFID 2009 and general chair of IEEE RFID 2011.

Christian received a PhD in Computer Science from ETH Zurich and a Bachelor and MEng degree in Electrical Engineering from the University of Cambridge.

For more information, read and download Scandit’s full guide, Capture Value, Not Data, via the link.

error: Content is protected !!