Last year, the global impact of the coronavirus helped fuel the adoption of artificial intelligence (AI) and machine learning (ML) in almost every industry as organizations accelerated their digital transformation journeys. The pandemic has brought considerable changes in many areas, like faster drug delivery, remote working, efficient supply chain management, virtual meetings, online learning for students, and more. Technology stocks have seen massive growth in their value as they provide the digital platform for this new change.
But there are significant challenges that are holding back companies from achieving their digital transformation goals. According to the World Economic Forum, over 80% of organizations across industries plan to accelerate their digital transformation efforts, yet 70% of digital transformations have failed to achieve their objectives.
Artificial Intelligence is at the heart of every organization's digital transformation strategy. It has become pervasive across the enterprise and beyond – from the movies we watch on OTT platforms, things we buy online, social media activities, places we like to travel, improving patient care, predicting machine failures in manufacturing, targeted advertising, and pro-active customer care in Telco. Companies need to have a robust AI strategy to be successful in their digital transformation journey.
Everyone is talking about AI and its benefits. Still, most companies struggle to operationalize their AI/ML models and put them into use for their own business. Successful implementation of AI is a step-by-step process. It starts with the understanding of the business problem you are looking to solve. Then you must curate, cleanse, enrich, and prepare the data for AI consumption. It also requires acquiring data from other sources relevant to your business problem and using it to develop AI/ML models that can respond to the data in the real world. Plus, you also need to train your developers and businesspeople on new skills and a culture change to adapt to a new way of working.
Data is the foundation and fuel for AI. You need high-quality, trusted data for training machine learning models and you need to infuse AI into business processes to drive results. Companies need to start by collecting their data, acquiring additional data from third parties, and making data accessible throughout their organization to drive successful AI. For example, a retail company needs access to all types of data, like historical, clickstream and location data to build an effective AI/ML model for driving targeted upsell cross-sell marketing programs.
Once the data is collected, infusing AI-powered intelligence and automation in the data management solution can help organizations build and manage workloads in the cloud. Organizations can improve data transparency, connect to diverse data sources, and manage increasingly complex multi-cloud environments. This approach enables people across the company—from business analysts to data scientists and data engineers—to access high-quality data quickly and easily for their analytics initiatives, driving innovation and providing organizations with a competitive edge.
Now, let’s consider the use case of a financial services firm that wants to consolidate and modernize its many on-premises data warehouses and data lake into a cloud data warehouse and cloud data lake as part of an ongoing customer experience initiative.
Their business initiative targets customers who have shown interest in products from different categories online but never purchased a product. However, digital signals (for example, web clickstream, social, transactions, etc.) may help the firm influence or better engage with its customers to increase wallet share.
The company wants the IT team to move all data from on-premises databases, files, or CRM and ERP data to the cloud and be ready to support various advanced analytics and AI projects. They cannot use a custom home-grown integration solution as it doesn't scale to meet their complex data management challenges.
Plus, new data types (such as social, machine sensor and log files, unstructured text, third-party data, and so on) have led them to adopt new technologies such as open-source data processing frameworks like Apache Spark. And they process and store data in cloud environments such as Amazon Web Services, Microsoft Azure, or Google Cloud Platform.
Informatica's Intelligent Cloud Services can help this company integrate on-premises systems and cloud applications to leverage their data effectively and efficiently.
Using Informatica’s cloud services, the company is able to:
Customers choose Informatica because we are the only vendor to offer the industry's first Intelligent Data Management Cloud, designed to help businesses innovate with their data on any platform, any cloud, multi-cloud, and multi-hybrid. This complete cloud-native and AI-powered platform is the critical missing piece for companies to move from simply modernizing to truly transforming for a digital world.
To learn more, watch this new demo video on building an automated data pipeline with Informatica’s Cloud Data Management Solution.