To make better business decisions, there needs to be a comprehensive view of customers. Therefore, data standardization is crucial for businesses to help drive these decisions.
With the advent of modern communication channels, social media, and mobile applications, businesses have experienced a dramatic increase in the quantity of data available to them. But, just collecting and processing all this data does not lead to better business outcomes. As per a Google survey conducted in 2017, 61% of marketing decision-makers said they had difficulty accessing and integrating the data they needed.
As per a study done by Google and Econsultancy, 95% of leading marketers say “to truly matter, marketing analytics’ KPIs must be tied to broader business goals.” But to do that, businesses need accurate analytics, and this can be driven only by quality data.
Data standardization is a common approach that can help businesses deal with large amounts of data by integrating it from different sources and transforming it in a consistent format. This makes it easier for businesses to identify errors and anomalies in their data.
Standardizing Marketing Data
Here are a few reasons why data standardization is important to improve marketing analytics and operations.
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Marketing Channels
Marketing channels are increasing considerably, and there is a huge amount of data coming from several sources like social media channels and marketing email campaigns. Businesses need this data to be in the same format and labels to assess user engagement. For instance, Twitter tracks user engagement in terms of favorites and retweets, while Facebook does it via likes and shares. Intelligent insight extraction requires all this data to be in a standardized manner so that it can easily be used for analytics.
Conversion Attribution
Customers usually visit several channels before making a buying decision. It’s highly likely that they visited the website first and then went through their Facebook and Twitter feed of the brand and then decided to make a purchase. In such cases, all the data generated from various sources get integrated, and marketers end up with multiple records that belong to the same customer.
But for conversion attribution, businesses need the accurate path the user followed to reach the conversion. So, marketers need to uniquely identify each data entry and then track down the user’s path. But with data standardization, all of this data is transformed in a consistent format, enabling the marketing team to identify which data entries belong to the same entity or user.
Validation Controls
Data standardization is not just about data storage and transformation. Another essential aspect that it focuses on is data capture. Here, data fields are checked against standard rules before being stored in the database. This level of uniform validation enables marketers to input data in a valid format and pattern.
Best practices to maintain data standardization
With organizations becoming more data-centric, they are now at increased risk of spending their time and resources on analytics initiatives that are being driven by poor quality data. Therefore investing in a data standardization tool that automates most of the work is important so that brands can reach the right audience with the right content at the right time.
Also Read: Content Marketing- Three Data Quality Practices to Follow
Here are a few practical tips that can help organizations keep their data standardized.
- Organizations can put data validation controls on all data entry sites for maximum data accuracy and cleanliness.
- Automated data quality management tools can be used to eliminate manual effort and optimize data cleansing and deduplication processes.
- Getting buy-in from management can ensure that not only data but other processes are kept standardized during the analysis process.
- Organizations can create and manage central data for the employees to understand what data is being stored and why.
- There needs to be constant monitoring of data quality indicators like data validity and consistency. Businesses should make sure that there are no anomalies in their dataset that can cause poor data quality metrics.
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