How To Master The Quality Of Your Data


Data is becoming increasingly widespread and extensive in today’s digital environment. Companies’ usage of data is also expanding rapidly to match their growing digital presence. Based on forecasts by Statista, the international statistics and market data portal, 74 zettabytes* of data will be created worldwide in 2021, compared to 59 in 2020. To give you an idea of the magnitude, one zettabyte = 1,000,000,000,000,000,000,000 bytes.

Although it seems within the reach of any company to collect data, it is still necessary for this data to be structured, qualitative, secure and easily accessible internally to drive revenue and growth. Let’s look at the criteria for data quality, the methods for evaluating and managing data, the stakeholders with whom to engage and the best tools to use.

What is data quality?

Data quality is a measurement of the state of data based on several criteria: accuracy, completeness, integrity, timeliness, coherence and compliance. These criteria aim to facilitate organisation-wide management and decision-making, in full compliance with current privacy regulations:

  • Accuracy: Does my data reflect reality over time? Are the values returned reliable?
  • Completeness: Is my data being collected in the way in which I want? Do I have all the data I need to make informed decisions?
  • Integrity: Is my data free of errors? Are the values readable and properly formatted?
  • Timeliness: Is all my data available at the right time? Does it allow me to react in real time?
  • Coherence: Is my data consistent across platforms? Is the information centralised in a reliable way? Do all my employees have access to the same data?
  • Compliance: Is my data usage compliant with the GDPR? Does my digital analytics provider put me at risk of financial penalties under European law?

Also Read: Top 3 Big Data Challenges For Enterprises

The importance of quality data

With the accelerating digitalisation of companies and the emergence of new digital pure-players over the last decade, more and more digital actors are adopting data-driven marketing strategies. The implementation of data-driven or data-informed policies aims to respond to the need for ultra-personalisation of messages. This allows businesses to stay one step ahead of competitors, better anticipate market fluctuations, enhance decision-making and continuously improve performance. 

It is no longer necessary to demonstrate the value of collecting, analysing and using data. On the other hand, despite the democratisation of web analytics in all sectors of activity, the stakeholders who handle data still tend to focus on quantity as opposed to quality, despite the rise of data minimisation principles that have been changing our behaviour for the last few years. 

When processing large volumes of data, the quality can be affected by a range of factors, and these mistakes can have far-reaching consequences for a company’s business. This is why it’s essential to be meticulous during the data collection phase, which can be affected by each update or development on the website or mobile application.

How do you manage and improve data quality?

When adopting a data quality approach, organisations are faced with a double challenge: to integrate accurate data into their information systems and to eliminate or correct all the errors identified, such as incomplete data, outdated data, inaccurate data, non-secure data or data that does not comply with current regulations. And these errors, whether from technical or human factors, can occur at any stage of the data lifecycle.

Also Read: Get Your Data Machine Learning Ready

Here is a 5-step method to continuously improve the quality of your data:

  • Define your data quality scope

First, it is essential to define a clear framework for your data quality approach. Depending on your company’s objectives and the information that is useful for driving its sales and marketing strategies, you will be able to map all the data needs of your teams and determine all the relevant contact points in your users’ journeys. The goal is to rationalise and focus your efforts on the important data that will help you manage your digital activity. 

  • Audit your database

This phase consists of profiling your data. You will have to make sure that your databases do not have any anomalies and are complete. Do all your contacts have an email address and/or a telephone number? Are they correct? Are the first and last names correct? Nothing should stand in the way of a successful marketing automation process or email routing. If you carry out this audit thoroughly, you will be able to define an action plan and make recommendations on the rules for creating and maintaining your data. 

  • Clean up your databases

When handling multiple data sources, data sets can become “contaminated” by various types of errors: bad syntax, spelling mistakes, empty fields, faulty tagging, duplicate information, etc. Data cleaning consists of deleting all duplicates, outdated, corrupted or incorrect information and poorly formatted data. This exercise allows you to work on a sound basis in the future, to avoid altering the analysis results and to optimise the enrichment phase. This step can be recorded in order to study the origin of the errors and to better monitor them. 

  • Re-import the dataset, check and validate

Once the data cleaning process is complete, you must ensure that your dataset is actually properly cleaned and standardised. We advise you to provide a nomenclature import file to avoid errors or crashes during the next data changeover in your information system. It is often difficult to achieve a totally efficient cleaning. Micro-errors can always slip through the cracks. So be prepared to perform a second cleanup of your dataset if necessary.

  • Maintain data quality efforts for the long term

It is important to remember that data quality is an approach that must be sustained to guarantee the reliability of your data over time. If a data item contains an error, study it, correct it, record it and then adopt the appropriate rules so that it does not happen again. Also make sure that all your employees who create or handle data on a daily basis are aware of following the rules of hygiene, whether it is when creating the tagging plan or when processing the data. Ideally, you should set up a data governance body, even a small one, to monitor the efficiency of your processes on a permanent basis.

Alex Chapko is General Manager & VP, Eastern Europe, Middle East,Central Asia and India at Piano, a global analytics and activation platform.