From Bad Data to Bad Outcomes: How Flawed Collection Hurts Businesses
Smart Communications’ Sally Schulte offers insights on how flawed collection hurts businesses. This article originally appeared on Solutions Review’s Insight Jam, an enterprise IT community enabling the human conversation on AI.
As businesses become increasingly dependent on data to drive innovation and guide automation, data quality has never been more crucial. High-quality data gives organizations confidence in their decision-making, data management, and compliance processes – and offers a huge advantage when looking to leverage AI and ML within the business. The impact of its low-quality counterpart is extensive and costly, both financially and in terms of the missed opportunity they create.
Despite its importance, collecting high-quality data presents a significant challenge for many organizations. A 2022 study revealed that 77 percent of companies face data quality issues, and 91% of respondents said these issues directly impact their company’s performance. When approaching the improvement of data collection, companies need to reconsider whether the technologies they are using can deliver the digital experiences people expect and the data quality they need.
The Important Connection Between Data Collection and Data Quality
Even now, in the digital age to date, most organizations rely on a combination of highly manual, outdated data collection processes made up of paper forms, fillable PDFs, Word documents, and confusing spreadsheets. These processes are not only inefficient for companies to manage – and almost impossible for them to secure – but they are incredibly frustrating for customers.
Data shows the acute impact of data collection interactions on customer experience (CX) and customer retention. According to the 2024 Smart Communications Benchmark Report, 67 percent of customers will abandon interactions due to complex data collection processes – and 66 percent of customers are likely to change companies if their communications expectations are unmet.
And these processes jeopardize more than just customer experience. They impact data quality because of:
- an inability to structure or format information as it is collected
- missing integrations needed to perform real-time data validations
- poorly worded questions that result in incorrect or incomplete information
- overly complex forms asking for repetitive or unnecessary information
- inconsistent experiences across devices feeding different information to internal processes
Today’s customers expect a seamless digital experience 24×7. From mobile-friendly, guided experiences to prefilled information and the ability to upload photos or images when needed, interactions must be convenient, relevant, and fast. And when that happens, data quality skyrockets.
The Consequences of Bad Data
Poor data collection experiences don’t just frustrate customers; they lead to a host of operational issues caused by incorrect or missing information.
The impacts of poor data quality include:
- Missed Opportunities: Incomplete or incorrect data can result in delayed or unfulfilled requests, leading to missed revenue, additional servicing costs, and internal inefficiency.
- Increased Operational Costs: Low data quality requires manual cleanup and results in misinformed decisions downstream, increasing operational workloads for IT and lines of business.
- Limited Utilization of Other Technologies: Enterprise technologies – especially automation and AI technologies – cannot reach their full potential if they are fed low-quality data.
Organizations must address the gaps in data collection to the same extent they have addressed gaps in other parts of the business – and ensure that processes are being driven by accurate, reliable information.
Cloud Solutions and AI: A Perfect Match
Data is the lifeblood of AI. AI and ML tools use information to train algorithms and enhance performance. However, the quality of their output is only as good as the data feeding them. Reliable, timely, accurate data ensures that AI systems deliver relevant and precise results.
Collecting information digitally and correctly is a critical first step in any AI or ML communication initiative. A cloud-based data collection solution ensures accuracy on the front end – and then uses robust integrations to pass data to backend systems without needing to store the data. Good data in. Good data out.
And, when it comes to data – where it’s going, how it’s getting there, and how it will be stored – companies need options. Which makes versatility an essential part of any digital data collection solution. Companies need a solution that gives multiple options for data storage and that can connect to a variety of technologies – bypassing ones that aren’t necessary and only integrating with the ones that are.
This is the type of flexibility only cloud solutions can offer.
Introducing “Smart Forms”
One step companies can take towards improving data quality is to incorporate enterprise-grade “smart forms.” A “smart form” is a digital form that uses business rules and workflows to guide users through a process, avoiding unnecessary questions, prefilling information, and securing data throughout. They can be highly customized – from branding and layout to helper fields and status updates.
Using various authentication methods and integrations, they can auto-fill customer information already on file, reducing customer effort and expediting the experience. “Smart forms” don’t require data extraction, manual data entry, or any of the other steps typical of a data-centric business process – reducing human error, structuring data intake, and making the experience more efficient for everyone.
In today’s data-driven world, businesses must prioritize high-quality data collection to ensure smooth operations and satisfied customers. Poor data collection frustrates customers and limits a business’s ability to fully leverage AI technologies and deliver superior experiences. By adopting cloud solutions and refining data collection processes, companies can position themselves to thrive in an increasingly competitive and technology-focused marketplace.