1 Introduction
Intelligent data management, or the ability to identify, understand, and act upon trends in enterprise data and metadata is an essential part of the toolkit of the digitized organization. The range of inputs including databases, messaging systems, applications, legacy systems, cloud datalakes, and sensors generate a huge volume of data, increasing the need for enterprises to have an appropriate strategy and tools to manage it.
There are some concrete challenges that enterprises face when trying to extract useful insights from their enterprise data and metadata. Productivity is often low in data management, because there is simply too much data that needs to be integrated and not enough resources to do it. In the age of user experience, data friction for enterprises can be a hindrance; data located across multiple databases doesn’t create a unified understanding of the enterprise, making it understandable that many enterprises wonder how they should derive insights from their data when they don’t even know what data is available.
Efficiency is another essential category, because manual data management or assisted by legacy data science tools cannot scale to handle the volume of data that enterprises generate today. Data governance is only increasing in importance, and without a comprehensive overview of enterprise data, an organization cannot know how much risk it is being exposed to.
Manual data management is no longer feasible, but there is a natural match between business intelligence and artificial intelligence (AI). AI is adept at ingesting large volumes of data and producing a clear and measurable insight, be it classification and clustering to apply labels to data or applying machine learning to generate predictive decision support. In the age of digital transformation, data management can rely on AI in order to make business intelligence an efficient and optimized activity.
Intelligent data management platforms should support on-premises databases as well as cloud and application-based datasets, because hybrid deployments are the reality for most enterprises. Intelligent data management platforms should provide support for the entire data pipeline, with critical points being ingesting/streaming, cleaning, enriching, cataloguing, protecting, and delivering. AI-supported data management platforms should bring heightened functionality to the pipeline, and even add steps to it, like the ability to relate data elements and entities to each other. Data reporting should be a main output from a data management platfrom that helps the user visualize relevant trends and insights from the data, as well as on the governance of enterprise data. A platform that delivers a fully comprehensive solution will also assist the user and enterprise to implement data remediation when necessary.
Informatica’s Intelligent Data Platform supported by its AI and machine learning engine CLAIRE provides a mature solution to the data management needs of today’s digitized organization.
