By Dharmendra Chouhan
Data-driven business models help enterprises operate with conviction rather than intuition to draw actionable insights and make informed decisions. No wonder, data and analytics leaders worldwide are looking for newer, more innovative ways to conduct advanced analytics at a limitless scale. As a result, major paradigm shifts are occurring in this space. New technologies optimize value propositions and offer competitive advantages for early adopters. Here are the three biggest trends reshaping data and analytics for 2023 and beyond.
#1 Focus Analytics on Driving Business Growth
Amid constant technological innovations, organizations are moving away from a purely technical perspective for business intelligence. Aligning analytics functions with overarching organizational goals—be it better revenues, higher customer satisfaction or streamlined operations—brings better results. The shift involves a strategic alignment, where data is more than a byproduct of operations. As such, companies are investing more in data collection, management and storage to find a perfect balance between the business benefits of data and the technological debt it gathers.
However, with artificial intelligence inundating almost every industry, there’s a fear of missing out on this front for organizations still grappling with too much data and too few resources. AI’s capacity to confer a competitive edge through real-time insights, cost-efficient analysis and enhanced predictive results is undeniable. At the same time, there are risks of data breaches, biased information created by large language models (LLMs), inappropriate content and security loopholes marring its adoption.
To combat these risks and still focus on driving business value from analytics, organizations will need an open-eye approach backed by strong guardrails for their cloud-scale data. Human feedback-based ML training will ensure data accuracy, while focusing on KPIs to underline analytics will bring more provable value. Decision-makers will need to align results with pre-set metrics, especially for mission-critical prioritized functions.
#2 Data Will Take the Centerstage
Keeping data at the center, the popular data mesh principles decentralize data architectures for cross-department collaborations. The approach of treating data as a product identifies users as customers and helps create data products they need for better decision-making and high productivity.
The concept is relatively different from data assets. Organizations tend to hoard the assets, but products are shared across the board for enabling delightful user experiences with ready-to-use datasets. In addition, it allows easy data access to all users in the company, offering a 360-degree view of employees, customers, product lines, etc.
Advanced data modeling helps implement data mesh architectures by combining department-level data into a single source of truth and sharing it with multiple users to unify fragmented reporting structures within an organization.
The data mesh concept has been around for a long time, but in 2023, it is expected to take center stage as teams strive to build customizable data pipelines to reduce data governance issues. Treating data as a reusable product empowers non-technical users with self-serve analytics for gaining insights faster without any reliance on IT teams. Implementing a secure and controlled access mechanism will further enable an ecosystem for data monetization across various marketplaces.
#3 Growing Dependence on Machine-Generated Data
Machine data comes from every potential source that powers an organization, be it data centers, connected devices or the Internet of Things (IoT). Some examples may include computer clusters, RFID tags, automated logs, machine learning systems, GPS, sensors, etc. `Once the format of machine-generated data is fixed by humans, zero manual effort is involved in further processes.
Evidently, machine data will keep infiltrating organizations of all sizes, depicting behavioral patterns of users and other systems interacting with a specific device. For example, take an average e-commerce website where users not only buy a product but also browse several categories and leave the site without a purchase. In this case, data generated on their app becomes valuable for the website to gauge their actions and find better ways (discounts, special offers, etc.) to indulge them for longer durations next time, converting window shoppers into buyers.
Leveraging all these latest trends with powerful tools to extract and harness data will help build a robust analytics architecture for organizations in 2023 and beyond.
The author is director of engineering,Kyvos Insights
