Predictive Analytics 101: A Beginner’s Guide
Predictive analytics aggregates and analyzes historical data to anticipate future outcomes. It leverages statistical techniques to uncover patterns in big data.
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What are Data Silos?
Learn more about data silos, and how they can have a negative impact on your company.
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What is data reliability?
Definition and assessment guide
Business leaders need data they can rely on to make confident, informed decisions. Learn what reliable data actually is, how to assess your data’s reliability, and how to improve it.
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What is data value?
Across industries and roles, we depend so much on data that it’s hard to imagine working without it. But data, while valuable, isn’t like other corporate assets.
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What is data lifecycle management?
Definition and framework
The data life cycle is no good to anyone as an abstract concept. Its purpose is to help deliver the data health that end-users need to fuel decisions.
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What is Data Integrity and Why Is It Important?
Data integrity is the accuracy, completeness, and reliability of data throughout its lifecycle. Learn how to keep your data healthy with data integrity.
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What is Data Health?
Data health is a measure of the quality of your data. You know that your organization’s data is healthy if you can prove that it’s valid, complete, and of sufficient quality to produce analytics you can feel comfortable basing business decisions on.
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What is API management? Guide to API management tools and platforms
An application programming interface (API) defines and manages the interaction between multiple applications or platforms. API management software is worth investing in because of the value APIs generate.
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What is Data Privacy? Definition and Compliance Guide
Data privacy is about setting different levels of controls to protect customer data information from third parties, and maintaining data integrity. Learn about data privacy, its use case, and how it works in a business setting.
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Data governance framework — guide and examples
A well-planned data governance framework covers strategic, tactical, and operational roles and responsibilities. It ensures data is trusted, well-documented, and easy to find within an organization, and that it’s kept secure, compliant, and confidential.
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What is data trust?
Every organization today is awash with data. Estimates put the amount of data the world creates annually in the zettabytes. That adds up to a big challenge for anyone tasked with making sure that all of their organization’s data is trustworthy. But what does it mean to trust your data?
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Monolithic vs. Microservices: a guide to application architecture
Businesses have two broad choices when it comes to rolling out technology stacks: deploy a single platform that combines many functions, or take a best-of-breed approach that uses microsystems to integrate discrete services from different vendors. What are the pros and cons of each approach?
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What is product intelligence? Your guide to a product intelligence platform
What product attributes make an offering more attractive to potential customers, and which features will make them stick around? What user-level insights can enable your marketing team to deploy more targeted customer campaigns? Which potential new product features should be prioritized to drive the most revenue? Product intelligence is the key to answering these and many other business-critical questions.
Modern Data Warehouse Architecture: Traditional vs Cloud Data Warehouse
Learn how a cloud-based data warehouse architecture is designed to address the limitations of traditional databases.
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Big Data in Finance – Your Guide to Financial Data Analysis
Learn how to apply big data solutions in finance with big data and cloud-based solutions that are transforming the industry.
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Data Lake Architecture – Best Practices Guide
The architecture of a data lake refers to the features that are included within a data lake to make it easier to work with the data.
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What is Data Profiling?
Tools and Examples
Learn how data profiling can help organize and analyze your data produce new insights and give you competitive advantage in the marketplace.
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What is iPaaS? What a Cloud Integration Platform Can Do for You
An integration platform as a service (iPaaS) is a managed solution for hosting, developing, and integrating cloud data and applications. The best iPaaS solutions include easy, graphic tools to help visualize and work with an overall business intelligence picture.
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What is Data Quality?
Definition, Examples, and Tools
Data is your organization’s most valuable asset, and that’s why you must have confidence in your data quality before it is shared.
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What is a Data Warehouse and Why Does It Matter To Your Business?
A data warehouse is a large collection of business data used to help an organization make decisions. It is the foundational component of business intelligence efforts. Learn how data warehouses work, how they are different from databases or data marts, why they are moving to the cloud, and more.
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What are Microservices?
Framework and Architecture Guide
A microservice is used to achieve a high degree of agility and scale for software development. Learn more about microservices and its best practices.
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What is Data Quality Management?
Free Guide & Examples
Learn why organizations should have a data quality management program in place to ensure they are working with the best data possible.
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Big Data in Marketing 101: Why it’s Important, Where it’s Going, and How to Get Started
Whether you are trying to improve customer loyalty and engagement, optimize your marketing performance, or make pricing decisions, big data in marketing has proven to be an indispensable tool.
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Cloud Integration 101
Cloud integration lets businesses connect data hosted on local servers to cloud-native data stores and applications. Cloud integration also provides a path to data analytics platforms, CRM systems, and other applications hosted by third-party providers. These include data warehouses such as Google BigQuery, Snowflake, Salesforce, AWS, and Microsoft Azure.
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Data Lake vs Data Warehouse
Data lakes and data warehouses are both widely used for storing big data, but they are not interchangeable terms. A data lake is a vast pool of raw data, the purpose for which is not yet defined. A data warehouse is a repository for structured, filtered data that has already been processed for a specific purpose.
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