Infusing Cloud Data Management with Artificial Intelligence – AI Data Management

Last Published: May 10, 2022 |
Preetam Kumar
Preetam Kumar

Product Marketing Manager

The Impact of AI

As a result of the Covid-19 pandemic, enterprises have accelerated their digital transformation initiatives. This is fueling the adoption of cloud computing, artificial intelligence (AI) and machine learning (ML) for handling big data. But businesses face challenges to achieving their digital transformation goals. According to the World Economic Forum, over 80% of organizations across industries plan to fast-track their digital transformation efforts. Yet 70% of digital transformations have not met their objectives.1

AI is at the heart of every organization's digital transformation strategy. It is impacting our everyday lives. AI-driven platforms are behind everything from our health care to our customer care. Companies need a robust strategy including AI data management for a successful digital transformation.

Implementing AI Is a Challenge

Everyone is talking about AI and its benefits. Still, most companies struggle to operationalize their AI/ML models in their data centers. They find it challenging to apply them in their own business. Successful implementation of AI is a step-by-step process. It starts with the understanding of the business problem you need to solve. Then you must curate, cleanse, enrich and prepare the data and data sets for AI consumption. You also must get your data from other sources relevant to your business problem. You have to develop AI/ML models that can respond to real-world data. You also need to train your developers and businesspeople alike. To adapt to a new way of working, they need new skills and change in the culture.

Data Needs AI and AI Needs Data

Data is the foundation and fuel for AI. High-quality, trusted data is needed for success. To drive business results, ML models are needed. These models infuse AI into business processes. To drive successful AI, start with three basic steps:

  1. Collect your company data and acquire additional third-party data
  2. Focus on your strategy to manage data and data preparation
  3. Make your data available in real time across your organization

Here’s an example from the retail industry. To build an effective AI/ML model, a retail company accesses a variety of data types. These may include historical data, clickstream data and location data. The company can then build an effective AI/ML model. This will drive their targeted upsell cross-sell marketing programs.

AI-powered intelligence and data automation help build and manage workloads in the cloud. They improve data transparency and governance. They also let you easily connect to diverse data sources. As a result, you can manage complex multi-cloud environments. Your teams of business analysts, data scientists and engineers can access actionable analytics. The result? Business growth and a competitive edge.

How a Financial Services Firm Uses AI-Powered Automation for Cloud Data Management

As part of its customer experience initiative, a financial services firm is leveraging digital data and cloud. Their goals are data consolidation and modernization. The company is moving its many on-premises data warehouses and data lakes into a cloud data warehouse. A cloud data lake is also part of their ongoing customer experience initiative.

Their focus is on customers who have never purchased a product but have shown interest in their products across different online categories. Digital signals may help better engage the customer and influence them to increase wallet share. The IT team moved all data into a cloud data warehouse. Data sources included on-premises databases, files, CRM and ERP. A custom, home-grown integration solution wouldn’t scale and couldn’t meet complex cloud data management challenges. With their data in the cloud, AI data management is enabling the team to support various advanced analytics and AI projects.

New data types including social, machine sensor and log files, unstructured text and third-party data, have led them to adopt new technologies. These include open-source data processing frameworks, like Apache Spark. And they process and store data in cloud environments such as Amazon Web Services, Microsoft Azure and Google Cloud Platform.

Informatica's Intelligent Data Management Cloud (IDMC) is helping this company integrate on-premises systems and cloud applications to leverage their data efficiently and effectively.

Using IDMC, they can do the following quickly and seamlessly:

  • CONNECT - Connect to data sources in many different systems and storage formats, on-premises or in cloud storage
  • INGEST - Automate cloud mass ingestion including drift functionality
  • GOVERN - Parse, transform, curate and enrich data for high-quality governed data
  • AI - Build intelligent data pipelines powered by Informatica’s AI engine, CLAIRE
  • SEARCH - Gain end-to-end data visibility and lineage with semantic-like search capabilities and AI-powered dataset recommendations
  • SELECT - Auto-select the processing engine like advanced pushdown optimization and serverless or normal ETL processing based on workloads

Why Informatica?

Customers choose IDMC, Informatica’s industry-first end-to-end AI data management cloud solution. IDMC helps companies innovate with their data and ensure that they have data protected. It works on any platform, any cloud, multi-cloud and multi-hybrid. IDMC serves as the critical missing piece to transform a business for a digital world.

To learn more, read this blog on master data management or watch this video on how to build an automated data pipeline with IDMC.



First Published: May 06, 2021