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How to Overcome Obstacles in Data Lake and Warehouse Strategies: 3 Best Practices for Enterprise Architects

In this special guest feature, Kimberly Read, the enterprise architect at Faction, suggests that to support the business case for multi-cloud, enterprise architects can benefit by addressing three primary considerations. Multi-cloud initiatives—drawing on services from public and private clouds—can help organizations stay ahead of the curve.

Data Evolution in the Cloud: The lynchpin of competitive advantage

This report from our friends over at Snowflake reveals the extent to which the data sharing economy is powering business growth and how organizations are leveraging data from a range of sources to drive innovation, create better customer experiences, and meet regulatory requirements.

Infographic: The tools and technology of Data SEO

Data SEO uses many tools and technologies drawn from different branches of data science. To use data science in order to automate, predict or visualize an SEO strategy, marketers will need some or all of the tools featured in this infographic developed by our friends over at Oncrawl.

Why CRM and Data Warehouses Fail with Customer 360

This whitepaper, “Why CRM and Data Warehouses Fail with Customer 360,” from our friends over at Profisee explains why achieving a complete view of the customer is so difficult and how customer relationship management (CRM) systems and data warehouses, especially in the insurance industry, do not manage customer-related data well. The core elements of this type of data fall into a discipline called Master Data Management (MDM).

Data Platforms – A journey. The Yesteryears, Today, and What Lies Ahead

In this contributed article, Darshan Rawal, Founder and CEO of Isima, explains how the data ecosystem has exploded in the last decade to deal with multi-structured data sources. But the fundamental architecture of using queues, caches, and batches to support Enterprise Data Warehousing and BI hasn’t. This article looks at the architectural styles of the three eras of data management – pre-big data, the open-source revolution, and the cloud-native version. It will be a dive into trade-offs of each and what lies ahead. You’ll get a techno-strategic best practices of architecting data platforms as they pave the path to recovery for your organization.

Why CRM and Data Warehouses Fail with Customer 360

This whitepaper from our friends over at Profisee explains why achieving a complete view of the customer is so difficult and how customer relationship management (CRM) systems and data warehouses, especially in the  insurance industry, do not manage customer-related data well. The core elements of this type  of data fall into a discipline called Master Data Management (MDM).

Survey: 97% of Enterprises Seek to Accelerate Data Transformation, with Time Spent on Data Preparation A Barrier to Insights-Driven Decision-Making

Matillion, a leading provider of data transformation for cloud data warehouses (CDWs), and IDG Research have released findings of an IDG Research MarketPulse survey, “Gaining Time, Savings, and Insights via Cloud-Powered Data Transformation.” The research exposes the challenges companies face in leveraging enterprise data for analytics and identifies data portability, time-to-value, and self-service for business users as top requirements to address these challenges.

Databricks Launches SQL Analytics to Enable Cloud Data Warehousing on Data Lakes

Databricks, the data and AI company, announced the launch of SQL Analytics, which for the first time enables data analysts to perform workloads previously meant only for a data warehouse on a data lake. This expands the traditional scope of the data lake from data science and machine learning to include all data workloads including Business Intelligence (BI) and SQL.

How Automation Helps You Exploit the Value in Big Data

In this sponsored post, Simon Shah spearheads marketing at Redwood Software to support continued market growth and innovation for their cloud-based IT and business process automation solutions. He believes that by using automation to collect and manage your big data processes, you will truly exploit its value for the business.

Video Highlight: A Vision of Analytics — Challenge the Data Warehouse Status Quo

The pandemic and related disruptions have caused companies to think hard about their data and analytics strategies and how they get real-time answers. In the keynote presentation below, Yellowbrick CEO, Neil Carson, discusses why the single most important set of technologies a company should be investing in today is an analytics infrastructure. Carson explains why these are the technologies that will determine whether your business survives.