BI & Data Trends 2022

Interwoven: The End of Competition as We Know It

Discover the top 10 trends in BI and data – and how your business can take advantage of them.

In today’s world, success requires collaboration – not only with your existing partners but with suppliers, customers, and even competitors. What roles do data and analytics play? And how can you get set up for success? Discover the top 10 emerging BI and data trends, and find out how to use them to your advantage.
Collaboration-mining arrives.

Collaboration-mining arrives.

The shift to work-from-home made it critical to embed BI within workstreams and apps. But collaboration after insights are found is just one piece of the puzzle. Working together has to begin earlier, as part of the initial analytics workflow. And going forward, just as you mine data, you’ll have to learn how to “data-collaboration-mine,” too.
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The dashboard is dead. Long live the dashboard.

The dashboard is dead. Long live the dashboard.

You hear a lot about the end of the dashboard – but deep analysis within interactive applications is here to stay. So how is the data dashboard evolving? It’s becoming highly contextualized with AI and alerting. And it’s becoming highly collaborative, maturing into a hub that catalogues insights and distributed data.
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Data lineage provides explainable BI.

Data lineage provides explainable BI.

Analytics users often struggle to explain their data, and fragmentation has made the problem worse. But today, distributed architectures are emerging, with augmented metadata that includes lineage. In an intertwined world, lineage will be mission-critical to providing trust and “explainability.”
3
Insight velocity brings cost into focus.

Insight velocity brings cost into focus.

Live-querying cloud data repositories is a great tool for discovery. But cloud-compute costs can skyrocket. In the long term, insight velocity and cost-per-insight will increase, and you’ll have to figure out how to run the right queries in the right place.
4
Distributed clouds emerge.

Distributed clouds emerge.

Specialized workloads exist for a reason: Processing can be faster at the edge. Compliance is critical. And security is more important than ever. A distributed cloud infrastructure may feel messy, but it strengthens your ability to both access and share interwoven data securely and confidently.
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Embedded insights become pervasive.

Embedded insights become pervasive.

To build a collaborative, outside-in approach to innovation, you need to open up your analytics to your partners, customers, and broader ecosystem – and embed them at every link in the chain. When contextualized micro-insights are more pervasive, it will increase trust in the system.
6
Application automation triggers action.

Application automation triggers action.

The API economy opens up new ways to interweave in joint initiatives with your partners. And app automation is a strongly emerging area that removes the need to code these integrations, making the opportunity more accessible to a wider variety of participants.
7
Data science overlapping with analytics upskills everyone.

Data science overlapping with analytics upskills everyone.

Data science has been seen as something only the few can do. But if common predictive use cases become more accessible for regular users – and if they include explainability and governance – data science, overlapping with analytics, will enable more people to do more.
8
Security becomes a top priority.

Security becomes a top priority.

Regulations are now conflating data management, privacy, security, and identity and access management. And the more you share APIs and data, the more you need to protect against failures. As you interweave with partners, protections shift from nice-to-haves, to musts, to business opportunities.
9
Data fabrics solve for fast access to distributed data.

Data Mesh becomes the new fabric for distributed data.

The need for faster access to data across increasingly distributed landscapes is driving integrated data management that uses metadata, semantics, real-time and event-driven data movement, and orchestration in the pipeline. Putting these capabilities into a distributed architecture is being referred to as a “data fabric.” The discussion on how to handle distributed data has evolved into “data mesh".
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Here to help you on the path to success

  • Active Intelligence Overview

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    Find out what it takes to overcome the barriers to getting value from your data – with the new vision for an end-to-end data-to-analytics pipeline that delivers continuous insights in real time.
  • The Data Activation Summit

    Industry leaders, data innovators, and Qlik execs reveal how to optimize every business moment with Active Intelligence – a state of continuous intelligence where real-time data triggers immediate action.

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