Let’s Talk About The Top 10 Data and Analytics Trends

Photo by fauxels from Pexels

From graph and data technology to artificial intelligence, leaders in data and analytics consider leveraging these trends.

With the advent of COVID-19 organizations relying extensively on large amounts of historical data and using traditional analytics techniques, realized that these models are no longer relevant. The pandemic essentially changed everything and rendered a lot of data useless.

On the other hand, forward-looking data and analytics organizations are shifting their focus from traditional AI techniques to a class of analytics that requires less and is more varied. Trends such as these can assist organizations and society tackle radical uncertainties, disruptive changes, and the new avenues that they open. Leaders in the data and analytics field should leverage the top trends into mission-critical investments which will ramp up their capabilities to anticipate, shift, and adapt.

The trends we are about to discuss fit into one the following three themes:

  1. Distributed everything: Requires flexible relation between insights and data to engage a wide audience of objects and people.
  2. Driving business value via effective XOps: Assists in better decision-making and incorporating data and analytics into business models.
  3. Accelerating shift in data and analytics: Leveraging improved composability, innovations in AI, and more agile and efficient integration of multiple diverse data sources.

Trend №1: The rise of the augmented consumer

Photo by Ali Pazani from Pexels

Traditionally, businesses were limited to manual data exploration and predefined dashboards. Usually, this suggests that data and analytics dashboards were restricted to citizen data scientists or data analysts exploring predefined questions.

However, I believe that going ahead, these dashboards will be replaced with conversational, automated, mobile, and real-time insights curated specifically to a customer’s needs and delivered to their point of consumption. This structure would shift the insight knowledge from a handful of experts to everyone in the organization.

Trend №2: Smarter, scalable, more responsible AI

Photo by Kevin Ku from Pexels

Smarter, scalable, more responsible AI will enable better interpretable systems, learning algorithms, and a shorter time to value. Organizations will start realizing the need for AI systems and they’ll need to figure out how to scale up their technologies — something that up until now has been a daunting task.

COVID-19 has changed the business landscape, so much so that it has rendered traditional AI techniques which rely on historical data, irrelevant. This suggests that going forward AI technology must be able to functions with fewer data via “small data” techniques and adaptive machine learning. At the same time, these new AI techniques should ensure privacy protection, compliance with regulation, and minimization of bias to support ethical AI.

Trend №3: Composable data and analytics

Photo by fauxels from Pexels

The aim of composable data and analytics is to utilize components from multiple, analytics, data, and AI solution for a user-friendly, flexible and usable experience, that can enable leaders to derive actionable insights. A majority of the large organizations prefer having multiple “enterprise standard” analytics and business intelligence tools.

Composing new applications via the packaged capabilities of business promotes agility and productivity. The composable data and analytics will not only encourage collaboration and enhance the analytics power of an organization, but it will also increase access to analytics.

Trend №4: Data and analytics as a core business function

Photo by Vinícius Vieira ft from Pexels

Leaders across organizations have begun to understand the importance of utilizing data and analytics to accelerate digital transformation. Data and analytics which were previously considered as a secondary focus for many organizations are now being shifted to a core function. However, many times business leaders underestimate the complexities of data and in turn miss out on a few opportunities. Organizations can consider involving their chief data officers (CDOs) in setting goals and strategies, as they can ensure consistent business value by a significant factor.

Trend №5: Data fabric as the pillar

Photo by Julia Volk from Pexels

As data continues to become increasingly complex and digital business accelerates, data fabric is the architecture that can support composable data and analytics with its numerous components.

Data fabric is known to reduce the time for integration design by 30%, deployment by 30%, and maintenance by 70%. This is mainly because data fabric designs draw on the ability to use, reuse and combine multiple data integration patterns. In addition to this, data fabric can leverage technologies and existing skills from data warehouses, data lakes, and data hubs, while also introducing novel tools and approaches for the future.

Trend №6: XOps

Photo by ThisIsEngineering from Pexels

The aim of XOps — data, machine learning, model, platform — is to attain economies and efficiencies of scale utilizing DevOps best practices. At the same time, XOps needs to ensure reliability, repeatability, and reusability while alleviating the duplication of processes and technology and enabling automation.

Technologies such as these will enable the scaling of prototypes and deliver an agile and flexible orchestration of governed decision-making systems. Overall, XOps will assist companies to operationalize data and analytics to bring about business value.

Trend №7: The shift from big to small and wide data

Photo by Lovefood Art from Pexels

Small and wide data, unlike big data, solves numerous problems for organizations tackling an increasing number of complex questions on AI and challenges with scarce use cases of data. Small data, as suggested by the name can utilize data models that require less data but offer meaningful insights.

Wide data leverages “X Analytics” techniques that enable the analysis and synergy of multiple small and wide, structured and unstructured data sources. This helps to enhance decisions and contextual awareness.

Trend №8: Engineered decision intelligence

Photo by ThisIsEngineering from Pexels

The discipline of decision intelligence includes a wide range of decision-making, including complex adaptive system applications, AI, and conventional analytics. Engineering decision intelligence applies to both individual decisions and sequence of decisions. It groups them into business networks and the process of emergent decision-making.

This functionality enables organizations to swiftly attain insights needed for mission-critical business processes. Engineering decision intelligence when combined with a common data fabric and composability, opens up new avenues to reengineer or rethink how organizations can optimize decisions making them more traceable, repeatable, and accurate.

Trend №9: Graph relates everything

Photo by Burak Kebapci from Pexels

Graph formulates the basis of modern data and analytics with capabilities to improve and enhance machine learning models, collaboration, and explainable AI. Even though graph technologies are not new to the data and analytics world, there has been a shift in perception around them as organizations have begun to identify numerous use cases.

Trend №10: Data and analytics at the edge

Photo by Amine M’Siouri from Pexels

As an increasing number of data analytics technologies start to live outside the traditional cloud environments and data centers, they are moving closer to the physical assets. This eliminates or reduces the latency for data-centric solutions and at the same time enables more real-time value.

Pushing data and analytics to the edge will create new avenues for data teams to scale up their capabilities and extend their impact into different sections of the business. This shift can also make solutions available for situations wherein data cannot be removed from specific geographies for regulatory or legal reasons.

--

--

--

Abhilash, a serendipitous writer, aims to create an impact in this world with his writing. He enjoys espressos, as should all right-thinking people.

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

Starbucks Marketing — DSND Capstone Project

Manipulation is very frequent in chart analysis

Planning for Machine Learning as a Medtech Startup

Introduction to Vectors for Data Science

Deploying a Plotly Dash App on Heroku

Exploring the Monty Hall Problem with Python

5 Differences Between Data Scientists and Machine Learning Engineers

Supply-Demand Gaps in Real-Time using Geospatial Data

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Abhilash Khalkar

Abhilash Khalkar

Abhilash, a serendipitous writer, aims to create an impact in this world with his writing. He enjoys espressos, as should all right-thinking people.

More from Medium

What is the role of data mining in the knowledge management?

How can the National Data Strategy help harness the power of data to support Net Zero?

Everything You Need To Know About Data Fabric

Digital Transformation — Action, Test, Learn, Optimize, Repeat