Data Capture Solutions are Outdated. Let’s Fix Them.

| Products & Solutions

Grocery shopper using automated data capture on a smartphone to compare products on shelf.

Back in 1966, US grocery-store chain Kroger made a plea for better data capture solutions. “Just dreaming a little… could an optical scanner read the price and total the sale?” they wrote. “Faster service, more productive service is needed desperately. We solicit your help.”

Data capture is the process of collecting information from the physical world and turning it into a form a computer can use. And from the earliest days of computing, it’s been a pain point for businesses.

We still haven’t solved data capture

Let’s fast forward to 2022. Coincidentally, Scandit recently talked to Chris Norris, Kroger’s current Senior Manager of Product, In-Store Customer Experience.

We asked him about data capture buying decisions today. “The internal discussions,” he told us, “came down to … driving efficiencies throughout the entire enterprise … as well as creating that world-class customer experience that would differentiate Kroger.”

Kroger store associate delivering world-class customer experience. The right data capture solutions are critical to this.

A lot has changed in data capture in the last 50 years. Nonetheless, Chris’s concerns were almost identical to those of his 1966 predecessors.

The impact of data capture on efficiency, customer experience and employee engagement looms as big today as ever. Over 50% of modern retail leaders point to inventory management and shelf management as tasks that require the greatest support from technology.

Meanwhile, if you talk to frontline workers (who do the bulk of data capture tasks), about a third complain they don’t have the tools to do their jobs effectively.

So what’s the problem with today’s data capture solutions?

While data capture tools have improved since the 1960s, the volume of data we want to collect and access has also increased in parallel. This has never been more true than in today’s era of big data.

At the same time, scanning technology has been held back because it’s failed to address two fundamental challenges.

Data capture solutions haven’t solved end-to-end workflows

Data capture is not as automated as you might think. While there are nuggets of automation, these are still embedded in predominantly manual workflows.

Barcodes, for example, were a major innovation in data capture. But to scan those barcodes, you usually still have to aim carefully at each individual code and press a button. In a receiving goods workflow, frontline workers might have to scan thousands of boxes every day.

Warehouse shelf containing hundreds of shoe boxes. Using current data capture solutions, logistics workers have to scan these one by one.

There are also typically no automated alerts if a package is missing, or if something’s been delivered by mistake. That depends on manual cross-checking – often, still using pen and paper.

It leaves the end-to-end workflow still largely manual, inefficient, prone to error and tedious for staff.

Data capture solutions are all take and no give

Frontline workers spend billions of hours capturing data that’s passed on to somewhere else in a system. But their data capture tools don’t return the favor by giving them access to useful, real-time information at the moment they need it. Data democratization has, largely, not yet reached the frontline.

For example, most store associates can’t scan a shelf of items and see stock levels or required markdowns at a glance. All this information is available somewhere – but it’s not seamlessly connected to the physical products.

In-store customers face the same problem. Comparing nutritional information, for instance, usually means picking up products one by one and deciphering printed labels.

Shoppers examining products on shelf in a grocery store. Current data capture solutions do not allow customers to compare nutritional information easily.

Outdated data capture is costly now… and it’s getting worse

The damage inefficient data capture causes is already incredibly costly. Logistics errors cost the pharma sector alone a staggering $35 billion every year.

Relying on human workers’ willingness to do tedious data capture tasks is also becoming a risky business model. Labor shortages, a millennial and Gen Z workforce, and the rise of the gig economy, are all raising employee expectations.

Customer expectations are rising too, and brand loyalty is on the decline. This makes delivering consistently excellent customer experience more important than ever.

Every hour wasted by inefficient data capture is an hour away from the more valuable job of engaging with customers. Today’s shoppers also want in-store experiences to match the ease of e-commerce. That’s hard to achieve if vital information can’t be accessed instantly on the shop floor.

It’s time to make data capture smarter

It doesn’t have to be this way. Today’s computing capabilities put fixing data capture within our grasp.

– and trends in tech investment are also pivoting towards physical industries. The drive to digitize and modernize physical industries is only increasing.

A new wave of smart data capture solutions are moving beyond labor-intensive processes such as scanning individual barcodes. They can scan many objects simultaneously, and combine multiple inputs (barcodes, text, IDs, objects). And they make data capture work harder by giving workers and customers back immediate, real-time insights.

Smart data capture shifts and upskills

Smart data capture (also known as intelligent data capture) enables better real-time decision-making, automates workflows at scale, and enhances employee and customer engagement.

It does this by shifting to technology the repetitive, mundane tasks that humans dislike and aren’t very good at. At the same time, it upskills and empowers us to do what we do best, better.

What is Smart Data Capture?

Smart data capture empowers businesses to capture and access real-time data using any smart device with a camera. It can be deployed into any ecosystem or application.

Tell me More

No artificial intelligence system can beat humans at empathy, creativity or flexibility. No machine can deliver a package to the second story of an apartment complex, provide bedside care, or give customers the personal assistance that differentiates an experience.

But smart data capture can give us more space to use these skills, and the real-time information we need to act at our fullest potential.

Delivery driver using intelligent data capture on a smartphone to check and update delivery addresses.

4 examples of smart data capture at work

  1. At your desk: Remember how tedious expenses used to be? Writing or typing out the forms, and stapling receipts to the back? Now, we just take a photo. Automated data capture apps analyze it and generate an expense report in seconds.
  2. In a warehouse: Receiving goods using smart data capture software can be ten times faster. It also provides insights such as identifying which products are about to go out of date so these can be prioritized for shipping.
  3. On a shop floor: Using smartphones or tablets, store associates and customers can scan products and get instant access to actionable insights and detailed product data. More intelligent data capture helps us all make quicker, better and more informed decisions in the moment when it matters.
  4. Delivering parcels: Delivery drivers can use smart devices to scan every parcel in the back of a van at once. They can pick out their next parcel fast, or check real-time delivery instructions. The same device can also scan IDs and automate age verification for age-restricted goods.

Data capture automation on a smartphone being used for ID scanning and age verification.

Smart data capture solves the pain point of data capture

For decades, it’s seemed just a part of life that we waste billions of hours capturing data inefficiently – and that it won’t be of any use to us at the point of collection. With smart data capture, we can solve this endemic problem.

Let’s take back our time – and make it count.