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Top 10 Data & Analytics Trends Business Leaders Should Follow (2021)


The article’s opening argues that traditional analytics techniques rely heavily on large amounts of historical data hence could become obsolete due to the impact of the COVID-19 pandemic. Forward-looking data and analytics teams are moving away from traditional AI techniques. However, moving away from traditional AI techniques that rely on “big” data to a class of analytics that requires less data can be pretty challenging. Still, Gartner says that these 10 data and analytics trends will surely make that transition smoother. So, let’s look at these trends and see what else is on the horizon in the coming years.

1. Smarter, More Responsible, Scalable AI

As COVID-19 changed the business landscape, historical data has been rendered almost useless. That’s why AI needs to be scalable and smarter than ever and be able to operate with ‘small data.’ Furthermore, scalable AI is highly adaptive and enables faster ROI, and it’s the only way for companies to reap the real benefits.

2. Composable Data and Analytics

Composable data and analytics foster cooperation while also expanding the organization’s analytic capabilities. Business leaders can connect data insights to business actions and deliver a flexible, user-friendly, and usable experience by combining components from diverse data, analytics, and AI systems.

3. Data Fabric as the Foundation

As data becomes more complex, the data fabric framework will enable composable data and analytics. Furthermore, by combining different data integration types, data fabric reduces design, deployment, and maintenance time by 30%, 30%, and 70%, respectively. Last but not least, data fabrics can combine existing data hub skills and technology with future-proof methods and tools.

4. From Big to Small and Wide Data


Wide data allows for the analysis and synergy of a wide range of small and varied (wide), unstructured and structured data sources, whereas small data models can provide useful insights with fewer data. This will assist enterprises in dealing with increasingly complex AI queries and information scarcity, as well as improving contextual awareness and decision-making.

5. XOps

XOps (data, machine learning, model, platform) will enable prototype scaling and deliver a flexible design and agile orchestration of governed decision-making systems by ensuring reliability, reusability, and repeatability while reducing technology and process duplication and enabling automation.


6. Engineered Decision Intelligence

Engineered decision intelligence enables organizations to gain the insights needed to drive business actions more quickly by grouping decisions into business processes and even networks of emergent decision making, which opens up new opportunities to rethink or re engineer how organizations optimize decisions and make them more accurate, repeatable, and traceable.

 

7. Data and Analytics as a Core Business Function

Businesses frequently undervalue data and analytics, missing out on numerous opportunities, as they can greatly accelerate digital business initiatives. By moving data and analytics to a core function and involving chief data officers (CDOs) in setting goals and strategies, business value can be improved by a factor of 2.6x.

8. Graph Relates Everything

A new wave of graph technologies is causing a paradigm shift in data and analytics. As a foundation of modern data and analytics, the graph has the ability to improve and enhance user collaboration, machine learning models, and explainable AI.

9. The Rise of the Augmented Consumer

Predefined dashboards, manual data exploration, and insights reserved for a handful of data experts are a thing of the past. They will be replaced by automated, conversational, mobile, and dynamically generated insights customized to a user’s needs that will give insight knowledge to anyone in the organization.

10. Data and Analytics at the Edge

Moving data analytics technologies closer to physical assets from traditional data centers and cloud environments will reduce or eliminate latency for data-centric solutions. Furthermore, this enables more real-time value and allows data teams to scale capabilities and extend impact across the business.

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