The Latest Data and Analytics Trends

The Latest Data and Analytics Trends

In a data-driven world, organizations increasingly rely on data and analytics to drive decision making and gain a competitive edge. However, keeping up with key developments and new technologies in this rapidly evolving field can be challenging. By staying informed about these trends, technology leaders and organizations can better leverage their data assets to drive growth and innovation in the months ahead.

To help tech leaders and their organizations stay on top of the latest trends, global IT research and advisory firm Info-Tech Research Group has released its Data and Analytics Trends 2023 report. The report covers a range of topics, including artificial intelligence and machine learning, data marketplaces and monetization, and identity authenticity.

"As we enter a new era of data and analytics, a data-driven culture and embracing emerging technologies are essential for organizations to remain competitive in the digital age," says Chris Dyck, advisory lead for the Data, Analytics, and Enterprise Architecture practice at Info-Tech Research Group. "The changing landscape can be overwhelming, but technology leaders that leverage these trends will be able to identify opportunities to optimize their data strategies, unlock the full potential of organization's data assets, and drive better business outcomes."

The report helps teams tackle the challenges of identifying trends in data and analytics while modernizing their current capabilities. By examining nine data use cases for emerging technologies, the report aims to inform and educate data leaders, CXOs, and IT professionals on key trends and provide guidance on which ones to adopt, empowering them to enhance their organizational capabilities to remain competitive in a data-driven economy.

Nine trends for the coming months

1. Data gravity

Data gravity is the tendency of data to attract applications, services, and other data. A growing number of cloud migration decisions will be made based on the data gravity concept. It will become increasingly important in data strategies, with failure potentially resulting in costly cloud repatriations.

2. Democratizing real-time data

Data democratization means data is widely accessible to all stakeholders without bottlenecks or barriers. Success in data democratization comes with ubiquitous real-time analytics.

3. Augmented data management

Augmented data management will enhance or automate data management capabilities by leveraging AI and related advanced techniques. It's possible to leverage existing data management tools and techniques, but most experts have recognized that more work and advanced patterns are needed to solve many complex data problems.

4. Identity authenticity

Veracity is a concept deeply linked to identity. As the value of the data increases, a greater degree of veracity is required. For example, more proof is provided to open a bank account than to make friends on an online social platform. As a result, there is more trust in bank data than in social media data.

5. Data monetization

Data monetization is the transformation of data into financial value and demands an organization-wide strategy for value development. However, this does not imply selling data alone. Monetary value is produced by using data to improve and upgrade existing and new products and services.

6. Adaptive data governance

Adaptive data governance encourages a flexible approach that allows an organization to employ multiple data governance strategies depending on changing business situations. The other aspect of adaptive data governance is moving away from manual and often slow data governance and toward aggressive automation.

7. AI-driven storytelling and augmented analytics

AI and natural language processing will drive future visualization and data storytelling. These tools and techniques are improving rapidly and are now designed in a streamlined way to guide people in understanding what their data means and how to act on it instead of expecting them to do self-service analysis with dashboards and charts. Ultimately, understanding how to translate emotion, tropes, personal interpretation, and experience, as well as how to tell what's most relevant to each user, is the next frontier for augmented and automated analytics.

8. Data marketplace

The data marketplace can be defined as a dynamic marketplace where users decide what has the most value. Companies can gauge which data is most popular based on usage and choose where to invest. Users can shop for data products within the marketplace and then join these products with others they've created to launch truly powerful data-driven projects.

9. DevOps > DataOps > XOps

The merger of development (Dev) and IT Operations (Ops) started in software development with the concept of DevOps. Since then, new Ops terms have formed rapidly, such as AIOps, SalesOps, SecOps, and more. All these methodologies come from Lean manufacturing principles, which seek to identify waste by focusing on eliminating errors, cycle time, collaboration, and measurement.

Download and read the full Data and Analytics Trends 2023 report to learn more about each of the trends for the year ahead.

Article published by icrunchdata
Image credit by Getty Images, DigitalVision Vectors, sesame
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