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Exploding data demand and supply pushes modern data pipelines to the breaking point. Data observability seeks to simplify modern data pipelines by monitoring and correlating data workload events across the application, data, and infrastructure layers of the stack. Observability helps resolve the issues that break production analytics and AI workloads.

This webinar discusses:

• What data observability is.
• How key stakeholders can use it to meet requirements for production analytics and AI workloads.
• How data observability applies to three overlapping segments—on-premises data lakes, hybrid cloud and multi-cloud data warehouses.
• Role of data observability in data modernization and digital transformation.
• Data observability best-practices and use-cases of adoption.

Sponsor: Acceldata.io

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There is a growing divide between organizations that use data effectively and those that don’t. Data laggards don’t prioritize data; consequently, data is fragmented and siloed, and business users spend long hours cleaning and harmonizing it. There are no standard metrics or reports, and no one trusts or shares data. These companies find themselves spending more time arguing about data than acting upon it.

In contrast, the data elite live and breathe data; employees trust the data and take an active role in governing it. Executives use standard metrics and reports when making decisions. Analysts spend more time analyzing data than finding and cleaning it. These organizations use data to work proactively and apply algorithms to optimize or automate business processes.

Attend this webinar, and you will discover:

● What separate data laggards from the data elite

● A formula for crossing the data divide: P4+D=V

● A five-stage maturity model for evaluating progress

● How to assess your organization’s data maturity online

● The three C’s practiced by the data elite

Please register for the webinar today to learn about the journey to becoming data-driven, the technologies, tools, and techniques the data elite use to transform their organizations and disrupt industries. The webinar will highlight an Eckerson Group online assessment that helps organizations assess the degree to which they are data-driven.

Sponsor: Infor

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The data hub enables business managers and BI analysts to take a more hands-on role in data management and data governance. They use the data hub to integrate, refine, and explore data that spans customers, supply chains, employees, and other entities. This helps spread the glue of data governance, master data management (MDM), and data quality.

How can enterprises convert this vision into reality? This webinar will examine the problem of data silos, the proposed solution of a data hub, and its intended benefits. It will assess the inherent tradeoffs of converged platforms, key evaluation criteria and finally guiding principles for successful data hub initiatives.

Join this webinar to learn:

- The causes, challenges, and symptoms of data silos

- The drivers and potential benefits of data management and governance convergence

- Strategies to navigate the inherent tradeoffs of data hub solutions

Sponsor: Semarchy

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Building and operating a data lake requires substantial investment in skills, technologies, and services. However, the activities of putting data into the data lake don’t create value. They only create databases. And ingestion-only data lakes are the failures that have developed into “data swamps” or “data dumps.”

Data lakes create value only when the data stored in them is used to inform or automate actions to drive business decisions. Yet many companies that lament poor data lake ROI are simply storing data into data lakes. Ingesting data into the data lake is certainly important, but only the beginning. Equally critical is a managed approach including landing, storing, provisioning, and cataloging data in order to make it usable and accessible for data consumers.

Join this Eckerson Group webinar featuring a live demo from Qlik to learn the strategies and tactics required to create value from your data lake including:

- Three layers and twelve components of the data lake value chain.

- The role of change data capture to efficiently deliver real-time and low latency data to the data lake.

- The power of data pipeline automation to reduce the complexity of moving data to and through the data lake.

- The role of a data catalog in driving trust in and usability of the data lake.

- The capabilities of data lake automation technologies to create successful, valuable, and agile data lakes.

Sponsor: Qlik

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The dirty little secret of data analytics is that the quality of enterprise data is often quite poor. Data analysts still spend an inordinate amount of time harmonizing data from different systems. Minor changes in source systems and processing anomalies break downstream data pipelines, creating fire drills for data managers and grumpy business users who can’t access reports and data. Until data teams figure out a way to automate the delivery of high-quality data across the enterprise, they will never become a trusted business partner and harness the full power of data.

This webinar will present a framework for modernizing data quality and master data management (MDM). It will show how modern data platforms automate the creation and execution of data quality rules for cleaning data and harmonizing master data across the enterprise. These modern platforms use machine learning to automate manual processes involved in data classification, rules creation, data cleansing, data matching, metadata tagging, and data lineage. They also use scalable data processing engines (i.e., Spark) to validate, clean, and harmonize enterprise data at scale and speed.

You Will Learn:

• The obstacles to modernizing data quality and master data management processes.
• How modern data platforms automate the creation and execution of data quality and master data rules and processes.
• How modern data platforms process any kind of data at scale and speed.

How machine learning automates routing data governance processes, such as data classification, data matching, data cleansing, and data lineage.

Sponsor: Ataccama Corp.