How do we help to deliver on Gartner’s 12 analytics trends for 2022

Early in April, research group Gartner shared their 12 data and analytics trends that deserve investigation this year.

Gartner sees three imperatives driving data and analytics in 2022

  1. Activate diversity and dynamism. Use adaptive AI systems to drive growth and innovation while coping with fluctuations in global markets.
  2. Augment people and decisions to deliver enriched, context-driven analytics created from modular components by the business.
  3. Institutionalize trust to achieve value from D&A at scale. Manage AI risk and enact connected governance across distributed systems, edge environments and emerging ecosystems.
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It’s great to see how our capabilities to deliver data transparency, data integrity, data privacy and data literacy align to delivering on these trends.

Data transparency

We define data transparency as the ability to easily access and work with data no matter where they are located or what application created them, and, as the assurance that data being reported are accurate and are coming from the official source.

The ability to share data is a key analytics trend for digital transformation, but, typically the ability to share data at scale and with trust is lacking. We have the knowledge and capability to support sharing of data, without compromising data privacy.

Gartner also highlights the need for effective collaboration and governance across functions to support a more agile business. Collaboration is a key focus of our data transparency capability.

Data transparency also enables what Gartner refers to as business-composed D&A – enabling business users and technologists to collaboratively craft business-driven analytics capabilities.

Data integrity

Data integrity is the quality, reliability, trustworthiness, and completeness of a data set – providing accuracy, consistency and context.

Unsurprisingly, artificial intelligence is a core theme in this year’s trends. Data integrity underpins many of these trends.

“Without the right data, building AI is risky and possibly dangerous,”  says Rita Sallam, Distinguished VP Analyst at  Gartner. 

Gartner talks about capabilities such as data-centric AI, a metadata-driven data fabric, and adaptive AI systems all of which are dependent on data integrity capabilities such as data engineering, data quality and data enrichment.

AI risk management is another key trend mentioned. A lack of trust is a key challenge that must be overcome if AI is to achieve mainstream adoption.

Data privacy

Of course, trusted data must also be appropriately secured. Our focus on data privacy enables data to be shared for analytics without compromising privacy – whether on-premise or in the cloud.

Information officers can quickly and easily define data access policies, apply these consistently across multi cloud environments, and audit access.

Data Literacy

Through 2025, Gartner estimates that the majority of CDOs will fail to foster the necessary data literacy within the workforce to achieve their stated strategic data-driven business goals.

Our data management courses, in partnership with eLearningCurve, provide both beginners and experts with sound data management fundamentals and expert tips and tricks. We help you to build capability from within, providing a common foundation and allowing each team member to add their own areas of specialisation.

Where to start?

Gartner’s recommendation is that you should be exploring these trends and making decisions on where to invest to add value to your business.

Would it be ridiculous to explore the gaps in your data management foundation as part of this process?