9 Principles of a Smart Data Capture Strategy

9 Principles of a Smart Data Capture Strategy

Personal mobile devices and digital tools for desk-based workers provide access to useful, real-time data, but frontline workers in businesses worldwide are suffering from a data experience gap, according to Scandit, a smart data capture platform.

According to the the company, most businesses are struggling to accurately capture real-time data from tangible assets, leading to inefficiencies, a negative customer and employee experience, and poor decision making.

Instead, companies should embrace smart data capture to collect, analyze, and act on information from barcodes, text, IDs, and objects, providing rich, actionable insights at the point of data collection and enabling real-time decision making, employee and customer engagement, and workflow automation at scale.

"The way we capture data has not modernized or kept pace with other elements of data management, resulting in crazy scenarios where store associates hide in stock rooms to avoid engaging with customers, or warehouse workers repeatedly pick and scan items thousands of times a day. It doesn't have to be like this; data capture can be smarter," said Christian Floerkemeier, CTO and co-founder of Scandit. "Today's mobile computing and machine learning technology means opportunities are now available to unlock the next level of business efficiency and radically overhaul the customer and employee experience."

In an IDC Infobrief sponsored by Scandit, IDC states that organizations which are effective in building data intelligence experience materially different business outcomes, with improvements in employee retention, customer experience, and up to a 32% increase in revenue growth.

"The ability to accurately capture real-time product information is a transformative force for both customers and organizations. Supply chain applications will greatly benefit from enhanced insights into trailer, cube, and weight utilization rates, leading to optimized trailer loads and improved delivery efficiency. Smart data capture is a technology whose time has arrived," said Jeff Roster, expert advisor at Third Eye Advisory.

Heavy reliance on manual processes holding businesses back

Manual and pen-and-paper data capture, such as ticking off deliveries on a list or counting boxes, still play a large role in all businesses where physical assets must be quantified. Even businesses we think of as "digital" still have a significant physical footprint. For example, from 2010 to 2019, Meta invested more than $16 billion in data center construction and operations in the United States alone.

Tools for frontline workers and customers are often slow to use and do not deliver instant, actionable insights. This means that data returned to the business in order to make decisions is inaccurate, incomplete, and outdated. For example, if errors in retail inventory are corrected, companies can benefit from a 4-8% increase in sales.

"Store associates are a retailer's most significant ongoing investment. By empowering them with smarter data, we can improve employee experience, productivity and efficiency in a single stroke," said Andrea Comi, digital and technology DTC global director at VF Corporation.

9 principles of a smart data capture strategy

Scandit calls for change, encouraging businesses to embrace smart data capture and improve business outcomes. Smart data capture delivers exponential productivity gains, richer business insights, increased employee satisfaction, enhanced customer loyalty, and ultimately profit and revenue growth.

1. Shift tedious work from people to technology

Use technology to restore bandwidth and rehumanize the employee experience.

Smart data capture does not seek to replace jobs, but to improve them. It reduces the amount of time employees have to spend on tedious, error-prone manual data capture processes.

In manufacturing quality control, for instance, smart data capture reduces the amount of time skilled workers have to spend on repetitive visual inspection tasks. Image recognition can be used to identify recurring defects quickly and consistently. It improves recruitment and retention by making roles more rewarding, reduces error and frees up time to spend on more complex and value-adding tasks such as process improvement and customer engagement.

2. Upskill frontline workers

Empower frontline workers with data-based insights that maximize the unique skills they have that machines can’t mimic – such as empathy, judgement and problem-solving.

Mobile computing, machine learning, and augmented reality (AR) all create new opportunities to connect the frontline.

In field service, for example, a smart data capture application running on a smartphone or tablet can identify a part or device. It then shows the engineer an AR overlay with live information about equipment, service histories and maintenance schedules. Customer satisfaction is improved by increasing first-time fix rates and on-the-spot upsell opportunities for parts and service are created.

3. Empower customers everywhere

Make interactive product information, stock levels, promotions and personalized offers as accessible in store as they are online.

Today, for example, brick-and-mortar grocery customers trying to compare the nutritional value of products usually have to pick them up one by one and decipher tiny text on printed labels.

While they started off as a way to streamline checkout, smart retailers are now building AR overlays into self-scanning smartphone apps to help consumers locate and compare products and promotions. Rounding out the customer experience in this way increases engagement, conversion rate, and basket size.

4. Design for humans

Be user-centered. Solve for the reality of people’s daily work and lives, particularly where this involves hybrid digital/physical workflows.

Applying user-centered design to the process of receiving goods into a warehouse is a good example. Instead of focusing on isolated, incremental improvements to the instant of scanning, a smart data capture approach would seek to reimagine the entire workflow.

In a smart data capture solution, the user might press a single button on a smart device. An application locates and decodes multiple barcodes simultaneously, counts them automatically, then provides instant feedback on missing packages.

5. Give data instant purpose and value

Design solutions that deliver accurate, comprehensive data and insights instantaneously – rather than hours, days, or weeks later.

Smart data capture delivers accessible, actionable insights to frontline workers at the moment of data collection. It also enables instant, reliable, and more complete reporting back to head office.

For example, a store associate using a smart data capture product markdown solution can scan a shelf of products using a smartphone or tablet and instantly view up-to-date instructions. Real-time reports about what markdowns have been applied are available immediately. It reduces the risk of sales being missed at the critical point of the markdown cycle. The retailer can also respond much faster to changing conditions.

6. Make data capture versatile and resilient

Design solutions that automatically adapt to different and challenging scenarios, instead of putting the burden on users to adapt.

An example is the common situation where multiple different barcodes are printed on a single sheet, label, or price tag. Smart data capture uses contextual, visual cues to identify the right code to scan, rather than the user having to do this. If the code is damaged, it automatically switches to text recognition and decodes the printed digits instead. The user can work fast and accurately on “autopilot” with confidence that the right code will be scanned.

7. Integrated, multi-modal platforms

Develop or use platforms that analyze multiple data sources (e.g. barcodes, text, IDs, objects), integrate these with analytics and can evolve and scale. The real world is unstructured and variable. There’s no single “magic bullet” data capture technology that can solve for every scenario and use case. Flexible, multi-modal approaches are key to capturing data in a more accurate, precise and useful way.

An example is smart data capture shelf management software. Data is constantly captured via associates’ mobile devices or autonomous floor scrubbers. AI-based retail shelf analytics then compare multiple data sources (price labels, barcodes, objects) to system data to detect gaps, low stocks, or prices and promotions to update.

By speeding up the process and reducing reliance on store associates, it makes inventory more accurate, replenishment more efficient and in-store order picking more streamlined.

8. Use any smart device, anywhere, any time

Be device-agnostic. Smart data capture software is powerful, and can utilize any smart device with a camera as an advanced data capture tool. This includes smartphones, tablets, drones, fixed cameras, robots, and wearables as well as dedicated data capture devices such as barcode scanners.

Last mile delivery companies often reduce costs by supplying only a smart data capture enabled app. Drivers run this on their own devices (a bring-your-own-device strategy). Some retailers choose to supply frontline employees with a smartphone they can also use as a personal device and use this as a recruitment and retention incentive.

9. Reach beyond human limitations

Go beyond what unaided humans are capable of, even in optimum conditions and with all the time in the world.

For example, it’s not always possible for a driver delivering age-restricted goods to identify a fake ID just by examining it. However, if you scan the ID and apply machine learning algorithms, this can detect anomalies invisible to the naked eye and highlight a likely fake. It prevents fraud, bakes regulatory compliance into workflows, and creates a clear audit trail.

4-step strategic roadmap

Learn more about Scandit's guidance for businesses on how they can follow a four-step strategic roadmap to implement changes and address the data experience gap in the complimentary resource: Capture Value, Not Data.

Article published by icrunchdata
Image credit by Scandit
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