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
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.
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.
The cloud makes everything easy—maybe too easy. Even with first-hand experience integrating enterprise data for analytics, it’s easy to forget the lessons of the past. For instance, freshly minted developers want to code data pipelines in Python and R, but experts know that success depends not on the quality of code, but how often it gets reused, how understandable it is, and how many tests are built around the code. Experts also understand that things like data quality, data governance, data lineage, data privacy, master data, DataOps, data preparation, and curated metadata (i.e., data catalogs) spell the difference between success and failure of an enterprise data project. This webinar will provide a checklist of items that every enterprise developer should consider when designing and implementing a cloud data warehouse. It will also present a maturity model to gauge your capabilities against peers.
You Will Learn:
- The range of cloud data management capabilities.
- The components of a leading data management capability.
- How to gauge your cloud data maturity.
- How to increase your cloud data capabilities.
Unified data and analytic platforms (UDAP) are the culmination of a multi-decade trend toward functional convergence. UDAP products provide a comprehensive and wholly integrated data and analytics experience that accelerates insights, user adoption, and return on investment. For companies afflicted with proliferating data silos and analytical tools, UDAP products are a welcome relief.
UDAP products are all-in-one solutions that support all business and technical users, from executives and managers to data analysts and developers. They speed time to market for new insights, improve return on analytics investments, and simplify governance. Essentially, they make good on the promise of “faster, better, cheaper” when it comes to data and analytics. This session will provide an overview of UDAP, examine its drivers, benefits, and challenges. It will also document the experiences of customers that have deployed UDAP products.
You Will Learn:
- What is UDAP?
- Who should implement a UDAP?
- What are the benefits and challenges?
- UDAP Products and Use Cases
Sponsor: Domo, Inc.