We’ve been on an exponential path with data over the last decade. Data 1.0 was when we used data to drive specific business applications. Data 2.0 was the aggregation of data to support enterprise-wide business processes. And in Data 3.0, we used data to power our digital transformation journeys.
Today, with increasing demands and the growing strategic nature of data, we are now entering the world of Data 4.0. Businesses embarking on Data 4.0 are hyper focused on cloud-native and metadata driven innovations. They benefit from trusted data generating trusted insights at enterprise scale. And they take advantage of artificial intelligence (AI) and machine learning (ML) for intelligent, automated cloud data management.
In short, Data 4.0 is ushering in AI-powered intelligent data management. Are you ready?
Cloud data warehouses, data lakes & lakehouses play a pivotal role
Cloud is foundational to Data 4.0. Cloud enables flexibility, reliability, resiliency, agility and adaptability, among other benefits. And the majority of you are moving to the cloud or are already there. In fact, 83% of enterprise workloads are moving to the cloud (Forbes). The same number – 83% – of organizations are moving their workloads between clouds (Turbonomic).
But this isn’t happening on its own. Businesses are modernizing their analytics in the cloud. And cloud data warehouses, data lakes, and lakehouses play a pivotal role. Challenges abound though. More than six in 10 (64%) of organizations struggle with data management issues (TDWI), and this is impeding their ability to build successful cloud data warehouses, data lakes, and lakehouses. In many ways, it’s deja vu all over again. As with every technological disruption, hand coding was used to prototype and validate innovations. But this is not tenable. The costs of hand coding can offset any sort of prototyping savings by 200%.
To realize the benefits of cloud data warehouses, data lakes, and lakehouses, businesses need cloud-native data management. To be specific, they need metadata management, data integration, and data quality that are cloud native, intelligent, and automated.
The three pillars of successful data management in Data 4.0
These three data management pillars form the foundation to help you realize successful outcomes in cloud data warehouses, data lakes, and lakehouses in a world of Data 4.0.
The first is metadata management, which enables you to effectively catalog, discover, and understand how data moves through your organization.
The second pillar is data integration. Through this, you get the comprehensive support needed for all ingestion and integration patterns, for the mass ingestion of files, databases, and Internet of Things (IoT) streaming data to fill your data lakes. Data integration also allows you to extract, transform, and load (ETL) and do pushdown optimization to process data once it's in the cloud. Better yet, it enables you to do so in a serverless, elastic scaling runtime, with the broadest connectivity across clouds, software-as-a-service (SaaS) applications, and on-premises applications.
And the final pillar is data quality. With this, you can deliver trusted data through comprehensive profiling capabilities, data quality rule generation, a data dictionary, and many other data-quality capabilities.
That’s not all. Informatica just announced that we’re introducing new intelligence and automation capabilities to the industry’s first cloud-native data management solution. We are taking our market-leading capabilities across these three pillars –metadata management, data integration, and data quality – and applying our latest AI and ML techniques to them. Our metadata-driven AI engine, which we call CLAIRE, enables you to build the next generation of cloud self-integrating systems.
CLAIRE: The cornerstone of Data 4.0
Let me share a little more about CLAIRE.
CLAIRE can scan all data sources within your enterprise. It can code and script your business intelligence platforms and your big data platforms. And that’s whether you’re using our portfolio of data management tools, or tools from other vendors. CLAIRE works with on-premises applications, cloud applications, on-premises databases, and cloud databases – and spans all data formats from complex, to hierarchical, to unstructured. We take all of this metadata and we combine it with our portfolio of AI and ML models that span a plethora of advanced algorithms.
But it isn’t the algorithms per se that make CLAIRE so powerful. It is the sophisticated and highly curated training of these algorithms against comprehensive metadata. That’s what enables you to get the intelligent and automated productivity benefits of business rule translation, of self-healing processes, of intelligent search ranking through auto-curation, of metadata tagging. CLAIRE also gives you the advantage of intelligently inferring and mapping schemas and of identifying dataset similarities. You also gain operational benefits like schedule optimization or anomaly detection in datasets.
Let’s look at how CLAIRE powers the three pillars of data management in Data 4.0.
CLAIRE and intelligent metadata management: Through CLAIRE, Informatica enables organizations to catalog all their data within the enterprise. You can auto tag it and auto curate it, so that it's easily discoverable through semantic searching. You can understand end-to-end lineage explicitly as well – through our AI and ML techniques – inferring primary and foreign keys, and stitching together the lineage of data as it moves through the enterprise. It's through this foundation of intelligent metadata management that we can automate all your integration, quality, governance, risk, and compliance efforts.
CLAIRE and data integration: Now let's talk about the productivity benefits of CLAIRE for data integration. Through our novel mapping designer experience, we enable developers and data engineers to build simple-to-complex data integrations. Our broad portfolio connector capabilities allow us to connect to all sources and all targets. Of course ETL is essential, but in a cloud-native world, it's equally essential to push down to where the data is in the cloud.
We allow you to use change data capture (CDC) to keep the data up-to-date through extensive patterns of mass ingestion across databases. This also applies to files and streaming. CLAIRE can also automatically discover data structures, data types, and recommend the next-best transformation to build out the business logic. Through this intelligent augmentation, you can build out your data pipelines rapidly and bring intelligence and automation, not just to design time, but also to runtime. You get serverless execution, auto tuning, and smart scheduling of your jobs at optimal times – all of which help you realize the economic benefits that you're looking for in a cloud-native data management stack.
CLAIRE and data quality: CLAIRE ensures that your data is trusted. We empower your business through self-service, making it simple to profile, analyze, and identify data issues, and to classify and discover various domains of data, including sensitive data.
Based on all that, we deliver intelligent rule recommendations, so that you can rapidly develop and deploy data-quality rules and standards, and then deploy them and reuse them across various columns and tables within your data lakes, warehouses, and lakehouses. CLAIRE uses NLP to automatically generate these rules, and uses the intelligent discovery capabilities within metadata to automatically deploy these rules.
Informatica Intelligent Cloud Services
Taken altogether, this all adds up to Informatica Intelligent Cloud Services, a next generation iPaaS made up of our ever-growing portfolio of cloud data management products. The productivity of the environment is accelerated by a common user experience across all products, the AI- and ML-driven intelligence of the CLAIRE engine, and a microservices architecture.
We support all cloud ecosystems – Google Cloud Platform, Amazon Web Services, Microsoft Azure, and Snowflake – as well as big data platforms like Cloudera, specific fit-for-purpose data warehouses, data-science platforms like Databricks, and applications and cloud platforms like SAP, Oracle, and IBM.
We do this with a modern, microservices-based architecture that is designed to be independent and neutral. This future-proofs your environment while supporting your multi-cloud, cloud-to-cloud, and hybrid architecture strategies. Data governance and privacy are strict design principles as well as an enterprise-class and enterprise-scale approach.
We currently process over 22 trillion cloud transactions per month.. It's on the back of this comprehensive cloud-native architecture that we're running 95 million continuous cloud security checks, which gives us the honor of having top-level security certifications equivalent to our partner peer group across AWS, Azure, Google Cloud, Salesforce, and others.
Future-proofing your data management, your people, and your business
We’ll continue to see innovation and disruption within the data and analytics stack as we move further into Data 4.0. It is critical to future-proof the skill sets within your organization, your data management investments, and the business logic and the data pipelines you've built. You must evolve them as innovations continue to be made.
To do this, you need a comprehensive set of capabilities across metadata management, data integration, and data quality – the three pillars of cloud-native data management. It goes without saying that you need a partner capable of delivering these industry-leading intelligent and automated cloud-native data management solutions as innovations continue to disrupt.
That’s us. Informatica.
Join us for the Intelligent Data Summit for Cloud Data Warehouses, Data Lakes, and Lakehouses on CLAIREview. When you register for CLAIREview, you have access to live series premieres throughout the summer as well as a host of on-demand sessions featuring analysts, customers and partners, technical experts, and more.