Business Resilience

Automation is a hot topic across industries today, and incorporating automation into your business’s best practices can be tempting. But before you dive head-first into the world of automation, generative AI, and the like, there are significant potential risks to be aware of. In this blog, we will explore 3 important risks to consider before implementing automation into your business. 1. Data protection This one might seem like an obvious risk, but there's more to it than you think. Automation systems often handle large volumes of sensitive information, private personal information (PPI), protected personal information (PII), consumer data, financial records, etc. If this data is not properly managed and controlled, these automated systems can become highly coveted targets for cyber-attacks or data breaches. When using automation to improve your processes, it's important to ensure that your systems are equipped with strong security measures to protect your data from unauthorized access. Some examples of robust data protection include: Implementing encryption Regular security updates Robust access controls And more Be sure to have a crystal-clear data protection policy in practice and train your employees on best practices for handling highly sensitive data. There can be severe consequences at stake if this is not properly executed, including legal penalties, loss of data, reputational damage, and more. It's imperative to take the proper precautions to safeguard your data before adding automation to your repertoire. 2. Ethical and legal implications Automation in the business world often raises ethical and legal concerns. As generative AI and other forms of automation are evolving to take on more decision-making roles, it's essential to consider how these decisions align with your company's legal requirements and ethical standards. From a legal perspective, you must ensure that your automation practices comply with the relevant regulations. This includes data protection laws like GDPR or CCPA, which mandate specific requirements for handling personal data. Failing to comply with these regulations can result in significant fines and legal repercussions. Ethically, your business should be transparent about how adding automation into the mix may impact its stakeholders. This includes informing employees about potential job expectations and ensuring that automated decisions are fair and just. Balancing efficiency with ethical considerations is the key to maintaining trust and upholding your business's integrity. 3. Operational changes Integrating automation into your business can also present several operational challenges. First, there's the risk of system integration issues. Automation often involves connecting various systems and software, which can sometimes lead to incompatibility, or problems with technical glitches. These issues can disrupt your operations and potentially affect your company's productivity. With the added challenge of increased complexity in your internal processes, there is more room for error. While automation attempts to simplify tasks, the technology itself can pose different, more complex technical challenges. You'll need to be prepared to spend a significant amount of time and allocate the proper resources to set up and maintain your automation software. Ensure your employees receive proper training and documentation to help them operate as efficiently as possible with your new processes. Moreover, the over-reliance on automation technologies can sometimes result in a loss of human oversight. It's essential to strike a balance between automated processes and human intervention to ensure that systems function correctly and adapt to any unforeseen challenges. Mitigating risks of automation with data governance While the risks of automation are real, they are not completely insurmountable. By proactively addressing data protection, ethical and legal concerns, and operation challenges, your business can navigate the complexities of automation more effectively. One way to mitigate these risks is by investing in robust data quality and data governance products. These tools can help ensure the accuracy and security of the data processed by your automation systems, providing an added layer of protection and reliability. Have questions? Our team is here to help!

Explore the key drivers of business resilience and learn strategies to navigate market shifts, cyber threats, and economic uncertainty.

Retailers aren’t short on personalization tools. If anything, the complete opposite is true. With the rise of AI-driven segmentation, everything needed for sophisticated and data-driven personalization is seemingly already in place. And yet, the results often fall short. Campaigns miss the mark. Product recommendations feel irrelevant. Customer experiences lack cohesion across channels. The technology is working, but the outcomes simply are not. So, what’s actually going wrong? More often than not, the issue isn’t the strategy. It’s the data supporting it. The promise of personalization (and why it falls short) Personalization in the retail industry is built on one simple premise: the more you understand your customer, the better you can serve themThat understanding is built on data. It flows in from ecommerce platforms, CRM systems, loyalty programs, mobile apps, and in-store interactions. When that data is accurate, complete, and connected, it enables experiences that feel intuitive and almost effortless from the customer’s perspective.This is the vision retailers invest in: higher conversion rates, stronger loyalty, and more efficient marketing. But that promise depends on something often overlooked and less visible to the consumer: data quality.Because when the underlying data is flawed, even the most advanced personalization strategies begin to unravel. How the problem develops Data problems rarely start as obvious failures. On the contrary, they build up quietly over time. A customer creates multiple profiles across different channels. Address details are entered inconsistently. Records go stale. Duplicate identities begin to stack up. Over time, what once looked like a rich dataset becomes fragmented and unreliable. This is a familiar pattern in modern retail environments, where data is constantly moving across systems that weren’t necessarily designed to work seamlessly together. On the surface, everything still appears functional. But beneath that surface, inconsistencies grow and eventually surface in the customer experience. When data issues reach the customer Personalization doesn’t fail all at once. It erodes gradually. An email promotes something the customer already purchased. A recommendation feels slightly off. A promotion arrives just a little too late to matter. Each interaction seems minor on its own, but together they create a disconnected experience. Outdated profiles lead to mistimed messaging. Fragmented data creates disjointed journeys. Instead of feeling understood, customers are left navigating experiences that don’t quite add up. Why AI alone isn't enough AI is everywhere right now, so it makes sense that companies have woven it into their general business operations. But the truth is, AI doesn’t solve data problems. It scales them. Take, for instance, a customer that exists within your CRM as multiple profiles. AI doesn’t realize this is the same person and instead treats every version separately. Essentially, when the underlying data is flawed, you’re not just making mistakes— you’re actually making them faster and across more channels at once. So, AI alone is not enough to scale retail personalization. If the data is strong, AI can unlock real value. But if the data is weak, it amplifies the gaps and makes the impact harder to ignore. The hidden cost of getting it wrong The impact of poor data quality on personalization isn’t always immediate. There is rarely a single point of failure. Instead, the impact builds over time. Engagement declines. Campaign performance weakens. Teams spend more time correcting data than using it. Opportunities are missed. Eventually, confidence in personalization efforts starts to drop. Not because the strategy is flawed, but because the data cannot support it. Fixing personalization starts with fixing data Improving personalization outcomes doesn’t always require another platform or a more advanced model. In many cases, it requires revisiting the fundamentals. It starts at the point of entry, validating customer data as it’s captured. Ensuring accuracy early prevents errors from spreading across systems. Tools like address validation and email verification help maintain clean, usable data from the start. From there, consistency becomes critical. As data moves across systems, it must remain standardized and enriched to stay reliable. Finally, there’s unification. Retailers need a single, trusted view of the customer. That requires resolving duplicates and connecting fragmented records across channels. Capabilities like identity resolution and data matching make this possible. And importantly, this isn’t a one-time fix. Data quality requires ongoing monitoring and governance. When these elements are in place, personalization becomes easier to execute and far more effective. Customer profiles become more reliable, messaging becomes more relevant, and the experience starts to feel connected instead of fragmented.

Data is one of the most valuable assets a business has. Organizations rely on accurate, trustworthy information to understand customers, drive operational efficiency, support compliance efforts, and make informed decisions. When data is incomplete, outdated, duplicated, or inaccurate, it can create obstacles across the business—from missed customer opportunities to inefficient processes and flawed decision-making. By understanding the causes and consequences of poor data quality, organizations can take proactive steps to strengthen their data foundations and support long-term growth. What does it mean to have poor data quality? Data may be defined differently across organizations and industries. One source defines bad data first, saying, “We define bad data as those acquired through erroneous or sufficiently low-quality collection methods, study designs, or sampling techniques, such that their use to address a particular scientific question is scientifically unjustifiable”. Examples of poor data quality in business include outdated customer contact information, improperly formatted address data, and customer data with typos. The impact of poor data quality extends to all departments within a business. For example, if you have bad customer data such as duplicate records or inaccurate records, it can affect the finance team for billing purposes, the renewals team for identifying who their customers are, and operations for processing and reporting on accurate products that the business has sold into. What causes poor data quality? Unfortunately, no business is immune from poor data quality. In fact, without a consistent data strategy, it is virtually inevitable. By knowing what to look for, you can target the precursors for poor data quality before they become deeper issues. 1. Inconsistent data collection methods If your business has not spent ample time ensuring that the inputs coming into your CRM or invoicing system are uniform, then you risk inaccurate information through these non-uniformed inputs. Establishing standard processes across all data-entry points is a great place to start; this will ensure that the data coming in is trusted, consistently formatted, and accurate, saving time and resources for your employees. 2. Ineffective data management The lack of best practices and policies could negatively impact your data consumption and management. If there are various ways to define, view, and manage data across your business—with no standard in place—you could be creating a roadblock when it comes to sharing insights across departments and overall decision-making. When it comes time for processes like data migration and integration, these standards only become foggier, making data less reliable with inconsistent formatting and quality. Your team should have a clear understanding of what data management looks like and how to work with data to meet business standards and goals. Sixty-two percent of businesses believe that a lack of basic data literacy skills impacts the value they get from their investment and technology, according to our latest study. After you standardize your records, we highly recommend that your business is trained across all departments on how to read, write, and argue with data. 3. Outdated data Understanding how to use data does not matter if the data is no longer useful. Data should be refreshed and viewed on a regular cadence to aptly take actions or make pivots as a business as needed. If your business is only collecting data and not reviewing it, then emails and addresses are becoming outdated and useless, leading to missed communication with customers and flawed decision-making down the line. Consequences of poor quality data Collecting data is not just for your convenience. Data collection should have a direct impact on business decisions and facilitate customer interaction. As a result, data quality issues can have debilitating effects, like the following consequences. Reduced efficiency Poor data quality can negatively impact the timeliness of your data consumption and decision-making. In fact, poor data quality may cost the US economy as much as $3 trillion in GDP. The best way to leverage data in a timely manner is to utilize tools alongside your process to create efficiencies with your time and resources, which will allow the expansion of timely strategy and tactics throughout your fiscal year. Without them, you are wasting time and energy that could be spent making decisions that support business growth on managing data quality issues and correcting avoidable mistakes. Missed opportunities Data quality issues affect communication between your business and the customer. If you do not have accurate contact information for your customers, then you cannot reach them to facilitate conversions. Poor data quality also causes you to miss opportunities to gain customer trust. Improper data leads to inefficiency in customer service interactions, impersonal emails, and ultimately unsatisfied customers. Reduced revenue Poor data quality can lead directly to flawed analysis and lost revenue, which is not uncommon. For example, marketing campaigns or analysis based on faulty data means not reaching potential customers and missing out on conversions. If you are investing money in failed mailing campaigns, you are also directly losing revenue. Not to mention, ineffective data management processes can also lead to a failure to follow important regulatory requirements and result in direct fines. How to improve your data quality The benefits of good-quality data can be felt across all departments. Standardized data, processes, and tools give your people the confidence and trust in the data and insight they need to make the best timely decisions for the business. To improve your data quality, you need to invest in and maintain systems that support these procedures. Follow consistent collection methods The worst way to maintain high-quality data is to start off with poor-quality data. Therefore, you need systems in place at the point of collection to prevent faulty data from ever entering your database to begin with. Resources like real-time email verification ensure that customer data is valid and accurate as soon as you receive it, preventing you from having to correct mistakes down the line. Use effective data quality tools Many tools on the market can help establish efficient processes to ensure high-quality data is captured and managed. Data validation and data management solutions are two options to ensure that both sides of the coin, data collection and ongoing maintenance, are reflected within your business. Whether you are looking for data migration, data cleansing and standardization, or enrichment with additional consumer attributes, Experian’s data management solutions could provide the tools you need to maintain high data quality and reach your goals. Take charge of your data quality with Experian The impact of poor data quality can be seen throughout multiple daily business operations, from unsatisfied customers to poor decision-making. In a data-driven world where returns matter, these are risks your business cannot afford to take. Fortunately, with an entire suite of data quality management tools, Experian can help you determine which ones can support your individualized business needs. For more information on Experian’s data management solutions, contact a consultant today!

Are your customer addresses up-to-date? Have any of your customers moved since the last time you checked their address? Outdated, inaccurate customer address data can cost your business a lot of money and waste a lot of time. From typos when entering an address to a customer moving to a new home, it’s surprisingly easy to have incorrect address information. That’s what makes address verification so important for your business. By validating addresses, you make sure your customer data is clean and accurate. This helps your packages and mail reach their destination on time. What is address verification? Address validation is the process of checking an address for inaccuracies and fixing any problems. Address verification tools from Experian look for inaccuracies in your existing address lists. When an incorrect address is found—whether it’s due to incomplete street data or improper formatting—the system fixes the mistake. Real-time tools can also be used to check customer data as it enters your database. 4 address verification benefits Improve the customer experience: Have you ever ordered a package that arrived long after the expected delivery date? You probably didn’t order from that company again. On-time arrivals make customers happy and ready to purchase from you again. It’s important to create a good customer experience—starting with accurate addresses for on-time deliveries. Increase delivery speed: The right address formatting can potentially increase delivery speed. An address with the wrong formatting may make it to its destination, but it will likely take longer to be sorted and sent to the right distribution facilities. Address verification standardizes address formats, so shipping companies don’t have to spend as much time processing the package. Save money: Address verification saves your business money. For example, a valid address gets delivered to the correct address the first time, saving you the money you would otherwise spend on reshipping a package to the correct address. Save time: You’ll also save time when you verify your address lists. Each time a package or mail is returned to your business, you lose valuable minutes and hours updating the address and reshipping the package. By using address verification, you’ll also save time you’d spend dealing with upset customers who are wondering why they haven’t received their packages. Finally, your employees won’t have to manually update address records or delete duplicate entries. Address verification benefits in action Retail businesses: Whether you offer local deliveries or just want to send marketing mailers to the community, you’ll want to make sure you have the correct addresses for local customers. eCommerce shops: Online retailers often exclusively deliver goods through the mail. That’s why customer addresses need to be correct. You might also have customers around the globe and need international address verification to make sure you have the right formatting. With address verification, you can get packages to customers on time—whether they’re across town or the world. Business professional services: If you’re a business that works with other businesses, like a business bank or lawyer, you can use business address verification to make sure you’re sending important documents and files to the right address. Company address verification works just like residential verification using the latest business address data. Service providers: Businesses like plumbers, landscapers, and on-site computer repair companies rely on accurate addresses to get to customer appointments on time. With address verification, service business owners can feel confident knowing your crews are headed to the right location each time they make a service call. Restaurants: More and more restaurants are exploring delivery as an additional dining option for customers. Investing in real-time address verification helps you collect the correct address from the start, so your deliveries arrive fresh. Keep addresses clean and updated Having a database of clean, up-to-date addresses is important for your business. On-time, accurate deliveries and service not only help you save time and money, but also give your customers a better experience. Happy customers are often repeat customers who will recommend your business to their friends and family. Learn more about validating addresses by working with Experian’s data quality team.

See how trusted, high-quality data empowers organizations to respond to market shifts and gain a lasting competitive advantage.