The Undeniable Need for Data Validation

By Daniel Hussem - Director of Marketing for Troparé Inc.


Click here to read the original article on Business2Community.com


EVTL data process flow

Introduction

Data validation is a must for companies working with data collection, data analyses, reporting, or preparing data for client companies. Accurate data from the start leads to accurate results.

A key task is to validate data before it gets used for marketing/sales purposes in order to guarantee high-quality outcomes. Thus, data validation becomes a critical step in any workflow. This process is often appeared as time-consuming or unnecessary for companies working within the B2B industry. Yet, mistakes and broken data are even more expensive.

There is a huge risk of losing time and money due to invalid data. File validation services can help bring more clarity and efficiency to everyday data workflows.

Why Validate?

Data validation should be conducted prior to importing and processing the data. This process aims to check the accuracy, formatting, and quality of the data.

Given a traditional ETL workflow, the extract-transform-load process holds a considerable responsibility, as it is tasked to extract, transform, and load data into the target source. However, data validation is a criterial step in that process as well which is why EVTL (Extract, Validate, Transfer, Load) might be a more suitable format.

In recent years, file and data validation has acquired new roles. Marketing and sales lists are often filled with incorrect data or data which will most likely be out of date in the next 3-6 months. Application of insufficient, broken or outdated data proves to be inefficient and not cost-effective. File validation now serves as a preventive measure as invalid or wrongly formatted data often results in missed opportunities, wasted time, and lost revenue.

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How does it work?

There are numerous ways to validate data. All of them are based on preconstructed validation rules. These rules are to help detect anomalies, errors, or broken records according to a set of expected parameters. The most common validation rules are discussed below.

Cross-reference validation

In this case, incoming data is compared to a reliable dataset. If the recent data does not match the available data, it will be rejected. Cross-reference validation proves to be very efficient for the validation of lead lists.

Data type validation

This type of validation is used to recognize the inconsistency of the data type associated to the value, triggering a request to check the entered data.

Range checking

There are often predefined constraints on numeric data for particular fields. The application rejects the answer if the given numeric meaning does not correspond to the range.

Complex data validation

Many companies have an extensive set of custom requirements for their validation process. The data will be verified according to a number of custom parameters at the same time.

Steps to data validation

The mission of data validation is to ensure your data is complete and valid. Judging by the expected results it may seem that data validation requires tenacious efforts and complex actions. However, the situation is not as complicated as it seems. Data validation usually encompasses 3 simple steps:

1. Planning

Before bursting into action, it is advisable to draw a roadmap of the whole process. A set of decisions, common data flows and file parameters, benchmarks, and methodologies should be set up in advance.

2. Validation of the database

Make sure that all the required data is present and properly formatted in your source dataset so you have a good foundation for the future results.

3. Validation of the data format

Remember, compliance is crucial. The overall structure and formatting of the data are checked at this stage to make sure you are comparing apples to apples.

Benefits for B2B sales and marketing

Sales and marketing departments nurture new leads not just through their own data collection practices. Third-party data is commonly used to enhance, enrich, and prove new data insights as well. With a regular stream of new data coming in, there are 3 key benefits of systematic data validation:

1. Preventing wasted time

Applying systematic data validation upon file arrival will enable you to reject it prior to letting inaccurate data infiltrate your campaigns.

2. More accurate results

Repeating the same mistake twice is unconventional. It often takes more than one touch to reach a lead. Inaccurate data leads to skewed results.

3. Better use of your marketing budget

ABM campaigns have a higher upfront cost which comes with a bigger possible chance of engagement. Allocate your budget on resource you are confident in succeeding.

Conclusion

Companies across all business spheres use data for their daily operations. Unfortunately, databases often appear to be filled with invalid or irrelevant data.

Whatever the size of your business, data validation is relevant for you. With data coming from so many different sources, in so many different formats and types, making sure your data is accurate, complete, and properly formatted at the very beginning of your marketing funnel will result in more efficient and effective campaigns.

About Troparé Inc.

Troparé is a leading provider of self-service marketing data management and field sales prospecting solutions. Built to overcome the challenges of working with disparate data, Troparé’s Marketing Data Management Studio (MDMS) and Field Sales Technology streamline and empower marketing and sales professionals to operate more effectively and efficiently.

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