If, as they say, ‘knowledge is power,’ then so is access to real-time data about tangible assets and real-world operations. Most of the data in our personal lives is fully digital: but almost all companies need to collect, analyze and act on data related to physical assets. Fully understanding an organization’s footprint of physical assets serves as the guiding light for important decisions, unlocking valuable customer insights and providing leaders with unparalleled oversight into how their businesses are performing.
If they can get their hands on it, that is.
The issue is that while organizations can achieve great things when the right people can access the right information at the right time, few have a cohesive strategy for how to best capture this same information from the tangible assets within their organizations. Which means that these businesses are falling at the very first hurdle. If important decisions – whether in the boardroom or on the frontlines – are based on imperfectly collected information, businesses run the risk of building their strategy on a house of cards.
Despite so much information about tangible assets being available, manual processes and disjointed techniques mean much of it is missed or tainted. It’s like insisting on blowing up a bouncy castle without an electric pump – doable, but painfully slow and inefficient. It’s high time businesses wake up and realize they’ve been doing just that when trying to gather data – they need to embrace the new reality of smart data capture.
Innovation isn’t always comfortable
It wasn’t so long ago that the cloud was a new, unknown quantity. Questions abounded over whether it was safe to entrust Big Tech with so much information. Businesses fretted about whether it would jeopardize security, business continuity and confidentiality, and consumers feared that storing their data somewhere other than their personal device would compromise their privacy.
It didn’t take long, however, for the cloud to leave an indelible mark on the way we live and work. So much so that we often forget how often we interact with the cloud. Most smartphones have cloud storage baked into them and businesses of all sizes have realized how much it streamlines data and document sharing.
Every technological revolution has to start somewhere. There’s inevitably trepidation and uncertainty, but that is soon replaced by acceptance and eventually reliance, to the point where a time without it is almost unrecognizable. In a decade’s time, hopefully we’ll all look back at the way we gather data now and wonder why it took so long to get smart about it.
Bring data capture out of the stone age
But what does it mean to get ‘smarter’ about data capture?
Unlike a lot of digital transformation initiatives, many stakeholders don’t understand the need to have a dedicated strategy for data capture, which for years has been fragmented and piecemeal across different processes and business units. The retail industry is a good example here: full of dull, linear, sequential processes like scanning hundreds of barcodes, one after the other. It’s been going on so long and is so much part of the process that enterprises have become blind to the problem – antiquated data capture is simply accepted as part of doing business.
It’s also a responsibility that falls disproportionately on frontline workers, who have historically been left behind in digital initiatives. 73{29fe85292aceb8cf4c6c5bf484e3bcf0e26120073821381a5855b08e43d3ac09} of retail decision-makers, for example, say digital transformation initiatives have not yet reached the frontline.
The ‘old way’ of capturing data means:
- Collecting data from tangible items and operations is tedious, inefficient, inaccurate, and incomplete
- Instantly accessing real-time, easy-to-use, data-based insights is difficult or impossible for frontline workers and in-store customers.
These result in lower efficiency, poorer customer and employee experiences, slow decision-making, and reduced resilience and flexibility. We believe that the time for change is now. We must get rid of the tired and tedious processes that plague so many jobs but are perhaps most prevalent among frontline roles. Technology is, at its heart, about solving problems. For too long, frontline logistics workers have had to trudge through tasks like searching for packages in vans, when there’s already technology out there that can help them do it faster and better. Individually scanning items, or worse, keeping records with a pad and pen, simply cannot be the way forward.
Similarly, embracing more streamlined and efficient data capture techniques gives workers more time to be ‘human.’ They can spend longer providing bespoke service to customers, the kind that builds lasting loyalty. And, by arming them with devices that have the flexibility to function as information hubs, communication tools and data capture machines, they can be upskilled beyond their typical responsibilities. Imagine a warehouse worker being able to scan an entire pallet of goods at once, and have all the relevant information automatically put into a spreadsheet. The worker saves time and improves his or her efficiency, allowing for more or different activity during the day with reduced repetitive effort, and the business gains a fuller picture of the tangible assets, faster.
A smart approach instead involves an integrated hardware and software strategy that modernizes how employees, customers and IT systems interact with tangible assets and processes, allowing for the efficient capture and analysis of multiple data sources and adding actionable insights at the point of collection. Workers become more productive and customers are kept happier, but the biggest winners are businesses’ bottom lines.
Capturing data should also be possible regardless of conditions. When data capture tools are equipped with computer vision technologies and machine learning capabilities they can adapt – and excel – in any scenario, be it low-light conditions or damaged barcodes and labels, saving workers from finding their own workarounds and solutions. Machines can even help to detect what’s invisible to the human eye, helping workers to identify things like fake IDs that can be difficult to spot.
But workers aren’t the only ones who stand to benefit. Giving them better tools speeds up the supply chains so customers get what they want faster. And, in that critical last mile, overworked delivery drivers can see their mounting workloads eased with tools that trim the fat from their days. That means no more clipboards, no more juggling scanning and communication devices, and most importantly, fewer late packages.
A rallying cry
The reality was – and still is – that data from tangible assets is everywhere just waiting to be collected. This information is often and easily captured from barcodes of course, but these are just one part of an ecosystem that includes QR codes, text, IDs, and the objects themselves – anything that can be seen by a camera-enabled device.
Anybody who works with data will have seen the bevy of ways it’s improperly collected. Just the other day, a ticket I’d bought to an event was scanned at the door with a mobile phone, only for the worker to then tick my name off a physical list. There are so many examples of inefficient, unnecessary, or useless data collection, but sadly, situations like this are far too common.
Real-time data about real-world operations has never been more important to businesses, yet that all-important first step – capturing it – is still painfully imperfect. With outdated processes rife in the collection process, how can anybody be sure that the information they have is worth building critical decisions on? How can you even be sure?
Getting smarter about the way data is captured makes workers’ jobs easier and enhances customers’ experiences, improving business outcomes in kind.
In other words: let 2023 be the year businesses arm themselves with the data capture equivalent of the electric pump and deliver the results they need.
About the Author
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.
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