The Many Faces of Metadata Management

tdwi.org

Few things bring down the value of data faster than confusion and uncertainty about what it is, where it came from, and whether it is good quality data. Yet as more users seek to access and interact with data and reports for business intelligence and analytics and as data sources become larger and more varied, confusion and uncertainty spread fast. Executives, managers, regulatory administrators, and other key personnel cannot rely on their reports, KPIs, and dashboards. Users cannot even find reports that the organization is producing. Instead, users spend more of their time trying to locate data and reports and correcting mistakes than they do applying data insights to solve business problems.
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Spotlight

Use Apache Spark, Cloudant, and Watson Tone Analyzer to perform sentiment analysis on a reddit Ask Me Anything web event.Full tutorial at http://developer.ibm.com/clouddataser... by @chetnawarade of the developer advocacy team at IBM Cloud Data Services.

OTHER ON-DEMAND WEBINARS

SMALL DATA, BIG INSIGHTS

Agillic

There is a lot of focus on big data, but sometimes it makes sense to look at the small data at hand and explore how to drive value from that. In this webinar, Mike Weston will present how to iteratively work with the data available, from e.g. purchase patterns, web browsing or location data, and gradually grow big insights.
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Strategies for Fitting a Data Lake into a Modern Data Architecture

McKnight Consulting Group

Whether to take data ingestion cycles off the ETL tool and the Data Warehouse or to facilitate competitive Data Science and building algorithms in the organization, the Data Lake a place for unmodeled and vast data will be provisioned widely in 2019. Though it doesn’t have to be complicated, the Data Lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the Data Swamp, but not the Data Lake! The tool ecosystem is building up around the Data Lake and soon many will have a robust Lake and Data Warehouse. We will discuss policy to keep them straight, send “horses to courses,” and keep up users’ confidence in the Data Platforms.
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Augmenting BI and Analytics in the Age of AI and Big Data

Arcadia Data

Artificial intelligence (AI) and big data technologies are driving major changes in how organizations think about business intelligence and analytics. Rather than be limited to querying and reporting on just what is in traditional BI systems or data warehouses, many business users and analysts want to tap a fuller range of data in systems running Apache Hadoop, Apache Spark, or on cloud data platforms and storage. At the same time, AI practices and technologies (in particular machine learning and natural language processing) are changing how users explore, analyze, and interact with data and the types of insights they can generate.
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Citrix Moves Data to Amazon Redshift Fast with Matillion ETL

Amazon Web Services, Inc

Citrix, a Fortune 1000 multinational software company, needed to migrate massive amounts of data generated by its FileShare platform to Amazon Redshift, where it could perform data analytics to help inform product improvements and drive success. To do this, Citrix needed an Extract, Transform, and Load (ETL) solution. Matillion ETL, easily deployable from Amazon Web Services (AWS) Marketplace, helps Citrix collate and summarize data and augment it with more traditional business data from Microsoft SQL Server for additional context. Join our webinar to learn how organizations of any size can move data to the cloud quickly, accurately, and affordably with Matillion ETL.
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Spotlight

Use Apache Spark, Cloudant, and Watson Tone Analyzer to perform sentiment analysis on a reddit Ask Me Anything web event.Full tutorial at http://developer.ibm.com/clouddataser... by @chetnawarade of the developer advocacy team at IBM Cloud Data Services.

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