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Data Analytics. AI. ML. What’s the Difference?


There are transformative technologies in the world today with consistent effect and reliability in their promise to alter or change the ecosystem. Industries have transformed, and early adopters with it, while others race to understand how best to adapt or integrate said emerging technologies into their organizations in an effective and seamless manner.

Among those, artificial intelligence is far from being a new concept. As a technology, it’s been with us for a while now, but things have changed. We look at cloud-based service options, the applicability of AI on several critical organizational functions, and the power of computing, among many others.

In fact, AI’s impact on several industries is predicted to grow quite rapidly and is expected to be in the high billions by 2025. AI or artificial intelligence is a buzzword, but organizations continue to struggle with their digital transformation to become data-driven. What’s the challenge, and how can it be solved?

The thing is, businesses are embedding AI solutions into their business portfolio, but face issues in the form of cost, privacy, security, integration, and even regulatory forms. But could analytics play a role in the acceleration of the onboarding of AI in enterprises. After all, enterprises that have deployed analytics are two times as likely to receive senior management buy-in for AI adoption.

While many believe AI to be part of a big digital revolution, analytics rank as a part of the evolution that could lead to successful AI implementation. For example, machine learning models are most effectively trained on huge datasets. Similarly, in an organization that is analytically aware, more specifically those that deal with data integration and preparation, data wrangling, and more, AI is a natural progression.

Artificial intelligence, in a way, is a straightforward transition for those organizations with a mature analytics system. Research even suggests that global technology leaders that are most successful with adopting AI-based technologies often incorporate a data strategy into their core business functionalities – APIs, interfaces, and more.

An enterprise-wide policy on data standards is one method to streamline analytics and the machine learning practice. Furthermore, maintaining said data policy could help identify stakeholders and in monitoring enterprise-wide access and strategy, resulting also in the reduction of employee confusion.

AI Matures Over Time with Analytics

Artificial intelligence and machine learning function towards maturity over a period depending on the data and quality of said data. This speaks to specific organizations’ investment in data warehouses or data storage, as a part of the process of aligning assets for AI implementation. After all, data quality is a direct measure for the quality of data predictions.

In time, we are likely to witness companies focus on solving the challenge of acquiring and maintaining accurate data for AI to live up to its promise of a data and business revolution. At the same time, it is also important to understand that penetration and maturity aren’t always associated with a positive correlation. For instance, even with the deepest analytics penetration of all sectors, e-Commerce is known to hold the lowest maturity.

Analytics to Pave Way for AI Adoption

In today’s era, organizations must possess a solid understanding of business intelligence (BI) stack, including capabilities for analytics storage, governance, and the ability to manage unstructured and structured data. These tools and techniques are the building blocks of an effective AI strategy. Let’s take a look at more ways in which analytics positively underpin an AI-based future:

  1. An investment in big data analytics is critical to the success of combining unstructured and structured data that sits alongside legacy data sources such as ERP and CRM systems.
  2. Investing in big data architecture or strategy strengthens the BI stack of technology from storage, ingestion, modelling, discovery, visualization, machine learning, and analytics.
  3. Atop of this, organizations must venture in to explore tools required to enable data visualization and exploration by the end-users and the business itself.
  4. Building an enterprise-wide business management system enables companies to create robust platforms for big data for more than just descriptive analytics. It could include reporting and implementation methodologies around machine learning, artificial intelligence, predictive and prescriptive analytics at scale.
  5. An enterprise-wide BI platform could also accelerate AI adoption via algorithms, deployment of best practices, and solutions. In fact, an organization’s deep analytics expertise can help in leveraging AI and ML more effectively.

Organizations are now in an ecosystem that increasingly require decision-making that involves significant technology implications. But understanding the difference between AI, ML and analytics, and the existence of the latter in the augmentation of the former is important and key to business-critical success. In the end, it’s always been about choosing the right tools for the right job.


Jay Nair – Chief Operating Officer, Marlabs Inc.

As the COO, Jay has played an important role in accelerating the transformation of Marlabs into a digital services and solutions provider. He spearheaded the Digital360 initiative, which offers a complete suite of digital services across industries. Jay’s broad and varied business experience and skills helped Marlabs incubate NextGen technologies that provide outstanding business value. He also played an important role in transforming the company from a small group of 15 to more than 2,300 employees globally, growing into a $100 million company.

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