Our dependence on data shows no signs of slowing down. We’re collecting data directly from customers, appending it from third-party resources, social media, etc. but as many companies have learned, it isn’t the quantity of data that matters as much as the quality. To be useful, data must be accurate, complete, valid, unique and structured according to a standardized format.
According to the 2022 State of Data Quality report, almost 70% of all enterprises surveyed have started their Data Quality Management (DQM) journeys. It’s quickly becoming evident that organizations that don’t keep pace with DQM trends and technology will be left behind. Here are the most important trends you should know about.
1. Building a data-literate work culture
Though data is recognized as a critical element for success and can be used by everyone in an organization, very few actually understand the value of the data they work with. 38% of respondents to a survey accepted a lack of skilled resources for managing and analyzing data was holding them back. 34% did not understand the data they worked with.
When you don’t know the potential of the data before you, you cannot put it to work. This has led to the realization that businesses need to prioritize building a strong data-literate culture. Top executives as well as front-line employees need to have the ability to understand data, analyze it and engage with it.
2. Moving online with cloud data technology
From looking at the cloud as a way to break silos and store data centrally, it has now become the go-to place for digital solutions, services and automation tools. The number of cloud service providers for data quality-related tasks is steadily increasing. Some of the key reasons for this are the scalability they offer, the low upfront costs and ease of use.
New cloud data solutions focus on a range of services from automated data quality checks, automatic translations and security to rapid migration, AI-driven data operations and governance integration. Data hubs are also becoming more popular as the demand for structured data warehousing and management grows.
3. Deploying AI/ML models for efficient DQM
In a bid to make DQM processes more efficient and reliable, data teams are moving away from manual processes towards developing and deploying AI/ML models. According to a McKinsey report, the investment in AI has crossed the $165 billion mark as of 2021.
This is being used to solve common data quality issues and automate tasks such as data classification, tagging, predictive analysis and more. With AI, data quality teams can go beyond structure data management needs and text needs to automate functions related to natural language processing, computer vision, knowledge graphs, etc.
4. Automating processes for higher efficiency
Automation is on the rise across the entire data management industry. The main reason for this is its ability to save time. Some of the processes being automated by metadata and AI include data discovery and onboarding, data quality monitoring, gold record creation and data matching. Given that even good data can decay over time makes automation for processes like regular quality checks is all the more important.
Companies are also working on developing out-of-the-box solutions to automate other related tasks. As more data management processes are automated, the system becomes more accessible and business users can have more control over their data.
5. Increased focus on trust and security
Though organizations have started working on data governance and trust, it is yet to reach its full potential. As awareness grows, DQN solutions are focusing more attention on fine-tuning governance opportunities and trust architecture. Over $34 billion has been invested into the digital identity and trust architecture sector.
The use of intelligent data warehouses to automate and integrate trust requirements and drive data encryption is increasing. Tools that automate data checks for security vulnerabilities and governance are being seen as a way to gain a competitive advantage.
6. Data quality as a compliance tool
Government regulations on social, governance and environmental aspects are becoming more stringent. As a result, organizations are having to spend more time and effort on compliance and contractual obligations through data quality checks.
For example, brands promoting themselves as sustainable brands now have to work harder to prove themselves worthy of the tag. Many are being accused of greenwashing or skewing their reports. Even if the accusation is unwarranted, the only way out of this is to insist on high-quality reports from vendors and meet government-regulated mandates with timely reports of their own.
7. Low-code/No-code data apps
As organizations work towards democratizing data and making everyone responsible for data quality, there’s been an increased demand for low-code/no-code data apps. These are tools that anyone can learn how to use even if they do not have any prior coding knowledge or extensive technical skills. The use of containerized applications that can be deployed on any hardware without having to change the base code is also rising. From 2021 to 2027, the number of organizations using containerized apps
The idea is to empower people to onboard data independently, take steps to improve data quality and automate checks to save time for data engineers. They can then use this time towards prioritizing other tasks required for DQM.
Keeping up with DQM Trends
Organizations at the forefront of DQM are working on 3 main categories; data governance, technology and innovation and building trust in their data to stay ahead of competitors in today’s evolving digital world.
For this it is important to have the right tools at your disposal. Innovative tools available today and lowering the time and costs associated with data quality management and allows organizations to profile and clean up data with a fraction of the resources that were once needed.
There are many plug-and-play AI applications and Machine Learning models you can use through the data quality lifecycle to keep pace with these trends. Most importantly, recognize that data quality is not the responsibility of a few people on your team but shared by everyone.
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