Successfully Combat Top 3 Bad Data Challenges With 3 Proven Methods

What role do data-driven insights play in your company, and what impact do these types of insights have on your organization’s overall growth? In general, it’s important to be aware of and keep track of various insights, especially data-driven insights in order to stay current with the ever-changing diet of today’s customers. Unfortunately, bad data has become a major pain point for numerous data-driven companies even though a large majority of businesses aren’t aware of the impact it can have on their organization. On the bright side, there are different ways to solve this data crisis.

Before diving deep into this topic, it’s crucial to understand what bad data is. It can be described as any unstructured data with quality issues such as inaccuracy, insufficiency, inconsistency, and duplicity. As an example, data that’s collected from social media is unstructured data that needs to be processed before it’s used for analysis or to make business decisions.

Marketers in particular need to address bad data issues immediately, and fortunately, there are several time-tested methods that can be used to improve data quality. Although there are major bad data problems that can ruin your sales and marketing efforts and overall revenue generation, being aware and informed can make a large difference.

Top 3 Bad Data Problems That Ruin  Marketing Efforts

1. Reduces Customer Engagement

Creating effective engagement with customers becomes extremely difficult if customer data is inaccurate, inconsistent, and outdated. A combination of wrong insights, incomplete buyers personas, and other misleading information can negatively affect business outcomes, customer experience, and ultimately, cause reputational damage.

2. Leads to Sales Loss

According to research by Synthio in 2021, around 25-35 percent of contacts in a company’s CRM become unreachable each year because of changes regarding  jobs, roles, and several other factors.

Furthermore, bad contact data includes invalid email addresses, stale or duplicate information, missing fields, and improper formatting that requires human correction. When inaccurate information is added  into a  company’s database, it often results in flawed lead generation, missed sales opportunities, lost revenue and time, and customer dissatisfaction.

3. Causes Revenue Downtrend

In addition, did you know that on a worldwide scale, bad data costs companies trillions of dollars each year? According to research released by Gartner,  poor data quality costs companies an average of $15 million in losses yearly.

After grasping the concept of bad data being the root cause of many other minor issues regarding data and the use of it, it’s time to break down three hands-on strategies that can clean up raw data and also solve data quality problems.

3 Proven Methods For Fixing Bad Data Problems

It’s safe to say that there are many challenges when it comes to data and its use, but fortunately, there are several effective ways to address and fix various data challenges. This often results in creating data that helps optimize a company’s marketing and sales efforts better.

1. Identify and Fix Duplicate Data

Not only does duplicate data cost money,but it also decreases  sales and even stops  the marketing automation process. To successfully track the flow of duplicate records from multiple sources and to prevent duplicate records from flowing into your existing database, consider doing the following:

  • Set up trigger alerts to automatically be notified about  duplicate data
  • Analyze and refine your database to catch any existing duplicates
  • Investigate sources and processes that generate duplicate records

2. Introduce a Smooth Data Capture Strategy

For those who don’t already know, a data capture process enables a company’s sales and marketing team to collect accurate lead information and discover more about leads. To create a smooth data capture process, you should take action in the following ways: 

  • Track the wrong email addresses or contact information within the customer database
  •  Remove unnecessary fields from web forms, and leverage restricted values, field validation, or field pre-population
  •  Employ automated forms to auto-fill certain fields to make it more convenient for prospects

3. Normalize Your Data

For an organization’s data to be useful, it must be precise, consistent, and recent. Organizations should be able to read, search, and use each segment the same way across all records in their customer database. Data normalization can help in this area by standardizing the formats of fields and records within a database. It also minimizes the cost and time associated with managing a database, locating missing information, and analyzing it for decision-making. To normalize data, you should consider following these steps:

  •  Interpret data and recognize which data fields are predominant
  •  Locate data entry points to determine whether normalization is actually required

Remember that it’s not too late to tackle data challenges you’re facing, and you can start by applying  the aforementioned steps in this article to polish your database and make necessary data improvements. Why stop learning now though? Check out some more educational content about data including data privacy and governance, and let us know your thoughts!

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