A study by Google and Econsultancy reported that 95% of leading marketers agree that “to truly matter, marketing analytics’ KPIs must be tied to broader business goals.” But to tie your marketing analytics to your business goals, you need informed, accurate analytics, which can only be driven by quality data. According to another Google survey in 2017, 61% of marketing decision makers said they struggled to access or integrate the data they needed last year.
These statistics show how companies now require in-depth analytics and automated marketing tools and operations so that they can increase revenue and reduce costs. As
data generating sources are exponentially increasing over time, it is crucial to perform data standardization before feeding this data to your business intelligence process.
Data standardization accesses and integrates your data from different sources and then performs standardization rules to transform data in a consistent format. This process makes it easier for you to identify errors and outliers in your data, so that you can remove them before moving on with your analysis. Data standardization tools are particularly helpful for building these uniform standardization rules and transformation logic, to achieve a consistent and reliable view of your organizational data.
Why you should consider standardizing your marketing data?
Let’s list some reasons for using data standardization to improve your marketing operations and analytics.
Multiple marketing channels
Marketing channels are exponentially increasing and data comes from multiple sources such as marketing email campaigns as well as social media channels like LinkedIn, Facebook, and Twitter. To assess user engagement, you need this data to be in the same format and labels. Facebook tracks user engagement in terms of likes and shares, while Twitter calls it favorites and retweets. Intelligent insight extraction requires you to have all this data in a standardized manner so that it can readily be used for analytics.
Accurate conversion attribution
A user usually visits multiple channels before making a buying decision. It could be that they went to your website, then browsed through your Facebook and Twitter feed and finally came back to make a purchase. When all this data from various sources is integrated, you end up with multiple records that belong to the same user. But for conversion attribution, you must find out the accurate path a user followed to reach conversion. So, it requires you to uniquely identify each data entry and track down the user’s path. Data standardization transforms your data in a consistent format, allowing you to identify which data entries refer to the same user or entity.
Uniform validation controls
Data standardization goes beyond data storage and transformation. It also focuses on data capture, ensuring that your data fields are being checked against some standard rules before being stored in your database. These uniform validation controls allow you to input your data in a valid format, such that it follows the required pattern.
Error and outlier identification
As you conform your data to standard transformation rules, you can easily identify the anomalies that do not follow the required pattern or format. This way, you can resolve these anomalies before moving on to your next step of insight extraction.
Where to begin?
Before inputting your data into your analysis process, it is important to identify what data to collect, how it will be collected, and the standardization rules it will undergo to ensure a uniform, concise, and consistent dataset.
Following are some of the activities involved in a common data standardization process:
- Assessing value distribution in your dataset by generating histograms against each column of your database.
- Profiling data to identify invalid, unformatted, blank, and incorrect values.
- Replacing invalid and incomplete values with correct data representations, formats, and patterns.
- Identifying common patterns within a dataset such as email addresses and phone numbers, while also defining proprietary patterns using a regex builder, so that you can check data against those patterns as well.
- Making dataset more meaningful by parsing data into multiple attributes or merging different columns into one.
- Running data matching algorithms to identify records belonging to the same entity, and tuning the algorithms for maximum accuracy.
- Building transformational logic to be applied uniformly on all data records to attain the golden/master record.
Best practices to maintain data standardization
It is not enough to standardize your data every time before the analysis process. However, it is beneficial to fix data standardization issues from its core to save more time and cost Following are some practical tips that can help your organization to keep data standardized.
- Putting data validation controls on all data entry sites to enable maximum data accuracy and cleanliness.
- Using automated data quality management tools to eliminate manual effort, and optimize data cleansing, matching, and deduplication processes.
- Getting buy-in from management to ensure that not only data, but other metrics and processes are kept standardized throughout the analysis process.
- Constructing and managing a central data glossary for your golden record to allow your team members to understand what data is being stored and why.
- Monitoring data quality indicators such as data validity, completeness, and consistency, and making sure that there are no anomalies residing in your dataset that can be causing poor data quality metrics.
As companies become more data-centric, each department is now at the risk of spending their time and resources at analytics initiatives that are being driven by poor quality data. Investing in a data standardization tool that automates most of the work for you is the right choice for many marketers, so that they can reach the right audience with the right words at the right time.
ABOUT THE AUTHOR
Javeria Gauhar, Marketing Executive, Data Ladder
Javeria Gauhar, an experienced B2B/SaaS writer specializing in writing for the data management industry. At Data Ladder, she works as Marketing Executive, responsible for implementing inbound marketing strategies. She is also a programmer with 2 years of experience in developing, testing and maintaining enterprise software applications.