It’s no secret that businesses have become more data-driven in recent years, with powerful big data analytics increasingly delivering on the promise of adding business value. As a result, the quality and enrichment of data have become top priorities for business leaders on a global scale. In fact, according to a recent survey of chief data officers, 88% have started building automation into their data management processes to help manage data quality.
With that in mind, and as data ecosystems are expected to grow in complexity and size, here are four top trends that are shaping how data is utilized by businesses in 2022.
- Data integration and data integrity will continue to be key initiatives.
Over the last 18–24 months, we have seen accelerated cloud adoption. Not only has the center of gravity for data shifted to the cloud, but more analytics are happening, and as a result, the whole data ecosystem has become increasingly complex. Most enterprises have traditional data warehouses, cloud data warehouses, and a combination of on-premise, hybrid cloud, and public cloud platforms.
In 2022, organizations will continue to focus on data integration and data integrity to eliminate data silos and deliver a unified view of data and trusted business insights. With the digital transformation and increasing complexity of corporate IT landscapes, businesses will be challenged to recruit employees with the right skills and expertise.
- Streaming data and real-time insights will grow in importance.
Because data is dynamic, it needs to be kept fresh for analytics and analyzed at the speed the business requires. The changes in data need to be fed into business applications and analytics applications in real time.
According to IDC, IoT data is the fastest growing segment of the Global Datasphere, and about 20% of that data was created in real time during 2020. Connected devices certainly constitute a large portion of this. For example, we are seeing real-time data streamed via cars, smartphones, medical devices, smart watches, manufacturing lines, and more. A growing number of use cases are leveraging population patterns with human mobility and traffic data.
With continued work-from-home policies and avoidance of crowded environments, more data will be streamed, and the consumption of real-time content will continue to increase and drive the requirements for real-time analytics of consumer behavior, trends, and endpoint data.
- The focus with AI and ML will shift to use cases and value.
Process automation, information security, and predictive analytics will take center stage as we shift the conversation from machine learning (ML) algorithms to the use cases that benefit from AI.
The top use cases will be around creating more business agility and minimizing risk. The need for timely insights will be driving the need for real-time access to data and automation. As we stream data, we need to also understand any changes in data patterns, start alerting about anomalies or any changes in data trends in real time, and make recommendations on how to address those data drifts or anomalies.