Decision-making is highly reliant on data quality. Without accurate information, you cannot expect to adjust your business processes for predictive outcomes. You will be left relying on pure gut instinct, which any business leader will tell you is about as accurate as a coin flip.
Recognizing that your business may be operating on bad data is crucial to understanding what changes need to be made. The only way to remain competitive in the ever-changing market of today’s world is to work from accurate, reliable, and verified data sources to ensure quality decision making.
Cleansing and harmonizing data requires investment. There are plenty of modern tools and digital solutions to ensure you have quality data flowing from sources to visualization or recommendation engines, but companies deprioritize these investments because it is complex to cleanse and transform data. Spending resources on the flow of data without first ensuring the quality and verifiable source of information you are receiving will only lead to poor data monetization in the future.
Bad data costs your business resources in loss of productivity and with revenue triggers such as client retention. It can also lead to governance, security, and regulatory repercussions, depending on how you handle your clients’ personal information. Even when leaders are aware of the problems with bad data, there is the issue of not knowing how to get clean information or where to start. Most companies don’t know where their data comes from or even whether it’s accurate. That’s not surprising when we consider the fact that many companies are inundated with data from several sources such as transactional systems, unstructured data capturing client sentiment, competitor data and so forth.
Bad data can lead to poor customer acquisition and retention as well as has the potential to increase your expenses and lower your competitive edge. For example, if your target audience is teenagers 14-19 years of age that come from affluent families in the U.S., then your data should reflect that market. This way, you can adjust your products and services to fulfill those customers’ needs.
However, if yourdata extraction process does not have a well-designed and validated algorithm for defining affluency, you are likely to end up with a target audience that does not meet your campaign objectives. Your campaign to to retain or acquire new customers will not be effective.
Your business will spend more money on advertising and fixing product and service issues because your sales may not meet expectations. Now you are stuck playing a game of catch-up to keep your business in operation.
Take that same principle and apply it to a business with a more essential service like electricity companies or waste management. Even better, think about the massive initiatives and data structure needed by complex organizations in education and medical services. These industries require accurate data at all times. With bad data on a patient’s profile, a doctor risks applying the wrong treatment to someone in an emergency.
The most successful brands have a strong sense of their audience. They know what makes them tick, where they live, how old they are, and what types of products and services are important to them. But the only way this information is accessible is through having the right data sets that allow for specific targeting, personalization, and messaging.
Without these parameters in place, a business runs the risk of losing potential customers or clients. If a person feels like you are ignoring them or not hearing their needs as an individual, then chances are high that you’ll lose relevancy in their lives even if your product or service is something they need.
There’s also the issue of trust. When consumers feel like they can trust a brand to deliver tailored experiences based on the information they provide, whether that be through future purchases or filling out surveys, then there will be a greater level of engagement between both parties. The more you can leverage proper data management from customers, the greater your opportunity for increased future revenues.
Data quality management is essential to decision-making. You need data to make insightful corrections to the direction of your business processes and operations. Without accurate and relevant data, you cannot adjust to the needs of your target market. This means that you will not easily recognize current trends in the demands of a consumer-driven marketplace and fall victim to another competitor who happily scoops up these opportunities.
Zillow is a good case of an organization getting impacted with data quality to execute key business transactions. Zillow had to shut down its iBuyingbusiness unit in November 2021 due to the large losses and its inability to provide a return on equity. The business unit was designed to automate the process of flipping homes by providing buyers instant sales options and using data to predict future home prices. However, as the data around costs began to change, this was not adequately captured in the cost basis and hence the pricing models the machine learning models used execute the transactions relied on data that were notaccurately reflecting the business conditions. The outcome as a result was $304M in losses for Zillow andwrite off.
We see examples like this happening daily in business, healthcare, industrial application, and even government service. Without reliable data quality, entire industries could make designs that affect millions of human beings purely on gut instinct that have massive repercussions. If your business operates on data that is false or incomplete, then you could make decisions affecting your customers that may result in lower sales numbers and poorer customer engagement. The tipping point of bad data has the potential to grow exponentially if left unchecked.
A single view of the customer is essential for marketers to create a wonderful experience for their prospects and customers. This can only be achieved if your data is in order, allowing you to find patterns in behavior and target them accordingly. Think of this like a Facebook profile page that only your business has. It is a dossier on a customer that allows you to better serve their needs and is essential to customer acquisition and retention.
A single view of the customer allows marketers to deliver a consistent experience across all channels, which means that customers will get the same content on social media as they do when they visit your website.
The cost of bad data quality extends well beyond just the initial monetary investment to acquire it. Data quality issues can result in higher marketing costs as you try to fix or account for bad data. Your marketing team may be spending valuable time and resources trying to clean up or replace bad data, which prevents them from being able to do the work they were hired to do marketing your product or service. If your company has a high rate of customer acquisition but low retention, that could be another sign that you have poor data quality.
These additional costs are why a proper strategy for investing in good data is so important. Not only will you save money on the front end through lower costs of acquiring better-quality leads, but it will also help reduce wasted time and resources on fixing bad data.
Bad data is a costly, risky problem. However, there are ways to avoid these risks and prevent bad data from happening in the first place. The best way to do this is by working with data management service providers that have the expertise and tools to help improve your overall DataOps and data quality.
It’s also important to look at how they deliver their services including a proven methodology and flexible solution set to help your organization deliver the quality data you require.
Finally, ask them if they can help you improve your own internal processes when it comes to collecting customer information. If they cannot show you how they can help boost your own process, then it might be time to find a new provider that’s invested in your business’ success.
Measuring your data quality as part of a comprehensive data quality management system can help you understand the overall health of your data. It is important to consider the value of each individual data point, but also the value of all data types together, as well as their collective impact on your business while moving through a lifecycle.
If you are not collecting the right type or amount of information, it will be difficult for you to have complete visibility into your business health. If you are unsure where to start, begin by understanding how your current processes are impacting the integrity of your business. Some questions that can help frame this aanalysis include:
Automation is key to avoiding human errors in data. Data monetization occurs when you can fully leverage information for business gain. This is best achieved by removing the human element until data interpretation. Using proper AI/ML tools that can follow the data quality of your information from beginning to reporting is critical. It can be done in the following ways:
Ensure that your data is as reliable as possible and work with only those experienced and proven partners that will help you workaround the common causes of bad data.There are four things you should consider when choosing a data provider:
Reputation: Have they built up a positive reputation among their peers in the industry? Do they have industry certifications or have they made achievements in your niche market?
The expert team at NextPhase will walk you through its data quality systems to put your mind at ease. You will be able to avoid many of the bad data issues that arise because of the transparent nature of NextPhase and its capabilities. Our company has spent years perfecting our systems and works diligently to innovate wherever necessary so we can stay ahead of market demands.
To get started, reach out to our team of experts, and schedule a consultation. We will be more than happy to address your specific needs and help you accelerate customer acquisition and retention with data tools and insights.