Introduction
As we begin a new year, it is clearer than ever that in order to succeed in a globally competitive marketplace, business leaders must develop and nurture strong competencies in all aspects of data integrity. Ensuring confident decision making based on data that is as accurate, consistent, and contextual as possible.
However, ensuring data integrity at scale is an issue that must be solved. If trust and confidence in data-driven insights are to be established. As organizations strive to develop confidence in their data, we anticipate an increase in the following data management trends in 2022.
Top Data Integrity Trend for 2022
Corinium polled 304 worldwide Chief Data Officers or comparable jobs from a variety of industries. Including financial services, insurance, retail, telecommunications, healthcare and life sciences, transportation and logistics, government, education, software technology, and others.
Respondents were questioned about their businesses’ data integrity plans. Including approaches to data quality, data governance, location intelligence, and augmenting corporate data with third-party data.
Let us look at some of the top trends for data integrity for the year 2022.
- Focus on Data Integrity:
According to a recent Corinium survey, just around a third of respondents will believe data-driven insights that contradict their own intuitions. Meanwhile, 22 percent of employees say they don’t believe in data-driven insights in general. And 44 percent say they won’t trust data-driven insights that don’t support their gut feelings. This pervasive lack of faith in data-driven decision-making is a major source of concern across enterprises, as practically every industry becomes more reliant on data.
Looking ahead to 2022, organisations will embrace and emphasize the fundamentals of data integrity in order to feel more confident when making data-driven decisions. Data integration, data quality and governance, location intelligence, and data enrichment are all part of this. Achieving seamlessness and accuracy across datasets, as well as selecting the optimum location for each workload’s requirements, are just a few examples of activities. That businesses may do to earn this confidence. Prioritizing data integrity will be crucial for organizations in the new year. In order to remain competitive, maintain efficiency, and extract the maximum commercial value from their data.
- An Uptick in Data Quality Automation:
According to the Corinium analysis, the typical data team spends 40% of its time cleaning, integrating, and preparing data for use in analytics – with some survey respondents estimating spending up to 80% of their time on these tasks. Despite this, the use of process automation to improve data quality is still quite restricted; in fact, only 51% of industry executives reported that their firms utilize automation very sparingly, while the remaining 14% do not use automation technologies at all.
Automation will be a major emphasis for data executives in 2022, given the constantly expanding volume and velocity of available data. Those that do not proactively address data quality in a scalable manner will undoubtedly see their data integrity deteriorate. As businesses increasingly rely on advanced analytics (including AI/ML) to guide both strategic and tactical decisions, attaining data quality at scale will become increasingly important in the coming years.
- Increase in Real-Time Analytics:
According to a recent survey performed by 451 Research, the increasing desire for streaming and real-time insights is one of the fastest-growing problems for firms attempting to establish a more unified perspective of their data. Concerns revolve around the IT. And logistics required to enable the delivery of these insights. As well as how to maintain data integrity while dealing with real-time data at high speeds.
Because data is dynamic in nature, it must be kept up to date for use in analytics. And analyzed at the speed of business – whether it’s an insurance company assessing claims using insights from dynamic weather data a financial services organization relying on real-time data for fraud detection. Or the hospitality industry where online reservation and booking systems must be kept fully up to date.
However, all of this is altering the way enterprises will approach data management, particularly data quality processes, in the future. As more data is streamed, companies must ensure. That they have the proper tools in place for monitoring changes in data patterns in real-time, identifying abnormalities. And offering trustworthy evaluations and suggestions. Ensuring the authenticity, validity, and completeness of data. And doing so at a fast enough rate to fulfill the need for real-time insights – will become a business requirement.
- Data Integration Teams:
The most significant difficulty that many businesses encounter when it comes to data integration is a lack of workers with the necessary knowledge and competence. However, the rising scale and complexity of corporate IT environments is a factor as well. According to the Corinium poll, 77 percent of respondents stated that processing large amounts of data is at least “very tough,” and 73 percent said that dealing with many data sources and different data formats is at least “pretty challenging.”
Given the personnel constraints, as well as the issues of complexity and change. Many business executives are turning to low-code and no-code integration platforms, which provide them with better flexibility and agility. However, this is not a long-term answer to the difficulties of data integration skills scarcity. In order to effectively adapt to and stand out in the increasingly complicated corporate IT landscape, companies must focus on acquiring enough personnel with the necessary knowledge and competence by 2022.
Conclusion
As the year 2022 approaches, the great majority of businesses should expect increasingly complicated data systems. And a more competitive data market. As a consequence, data leaders should expect to keep setting the groundwork for success as they strive towards a solid data integrity plan. Achieving data integrity at scale is a task. And it is not an easy one, but it must be handled if trust and confidence in data-driven insights are to be established.
