Self-Service Data Integration with Autonomous Data Management (AutoDM)

Mar 23, 2022 |
Preetam Kumar

Product Marketing Manager

Automate Data Integration and Data Management Tasks with ML and AI Techniques

Sometimes, my 7-year-old son and I play with LEGO® bricks for hours, building cars, trucks, jets and landscapes. When we need just the right piece to complete a section, we search through large bins, trolling for the right part for the job. Finally, when we find a brick that performs the correct function, the whole structure comes together and…voila!

The same can be said of data integration. Like a LEGO brick, a company's data sits, just waiting to be used in a meaningful way to create something. With LEGO bricks, the “builder” faces challenges when a project gets bigger and more complex. With data, if the volume, velocity and veracity increase, it becomes challenging for those who must manage it, like data engineers, data scientists, developers and data analysts. Whether it's data or interlocking plastic pieces, the problem is similar and can be resolved with proper data integration tools or “bricks.”

When the data is small, it is manageable. But with data proliferation, it becomes quite challenging to harness, control and maintain.

Data Management Tools Must Meet Growing Business Demands

The unprecedented growth in data diversity and volume is staggering: There are a predicted 46 billion connected devices for 2022, while 31 billion devices will be installed worldwide by the end the year1. The ability to drive analytics and data-driven decisions in the data management market is also being disrupted by the emergence of fusion data teams2 and the acceleration of data operationalization.3

Data is increasingly seen as an asset to make more-informed business decisions to help enterprises increase revenue and profits. But a lack of proper data integration tools can saddle organizations with incompatible data silos, inconsistent data sets, and data quality problems. There is constant pressure on data engineers to provide trusted data to support analytics and AI initiatives in the company for making real-time decisions.

But it’s impossible to manage all this data manually with traditional data integration tools with the resources and budgets currently available. The only option? Make the systems do more: automatically manage this data, ensure it's always available, and keep it secure. Autonomous Data Management (AutoDM) can help.

The “What” and “How” Behind Self-Service Data Integration with AutoDM

Autonomous Data Management (AutoDM) software uses machine learning and AI techniques to automate all data management and data integration tasks, which reduces the need for human intervention (and eliminates inadvertent errors), ultimately requiring fewer resources.

So, how does it do this? AutoDM assembles metadata, automates processes and recommends actions to standardize and accelerate data delivery. This reduces the human effort needed to prepare, integrate, consume and govern timely, accurate data for business consumption. You can compare autonomous data management to a self-driving car: You keep your hands on the wheel and let the car guide itself, but you’re ready to intervene if necessary.

AutoDM Use Cases

AutoDM can be applied across infrastructure, data pipelines and business layers to ensure data reliability, prevent data downtime, optimize and scale data pipelines and govern cost. Let's see how.

  • Infrastructure: AutoDM monitors the infrastructure's performance, availability and utilization that supports data management. It can auto-scale the compute by provisioning more CPUs based on the utilization to create data integration jobs and scale down when the utilization decreases. It also recommends ways to eliminate bottlenecks, fix errors or prevent overruns of compute, storage or network resources.
  • Data pipelines: AutoDM enables data engineers and citizen data integrators to support data pipeline use cases such as discovering and preparing data, data integration, building and reusing pipelines and managing ML models.
  • Business: AutoDM sets data processes and policies to observe the health of the business, access data from a wide variety of sources to improve decision making and optimize performance.

The data integration and data management task has evolved: siloed to standardized to augmented and now to autonomous. AutoDM democratizes data integration to all data consumers. In fact, we believe AutoDM will help AI developers to flip the 80/20 rule to their advantage by spending 80% of their time on building models and 20% of their time on data management.

Next Steps

Today, the world is moving from model-centric to data-centric AI, and high quality, trusted data is essential for getting value from your AI initiatives. To see AutoDM in action, sign up for our 30-day trial today.

 

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1 https://techjury.net/blog/how-many-iot-devices-are-there/#gref
2 https://www.gartner.com/en/articles/why-fusion-teams-matter
3 https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/global-survey-the-state-of-ai-in-2020