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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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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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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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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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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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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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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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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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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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 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.
Learn how a cloud-based data warehouse architecture is designed to address the limitations of traditional databases.
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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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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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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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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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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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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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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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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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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 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 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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A data mart is a subject-oriented database that meets the demands of a specific group of users. Data marts accelerate business processes by allowing access to information in a data warehouse or operational data store within days as opposed to months or longer.
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Data preparation is the process of cleaning and transforming raw data prior to processing and analysis. It is a time consuming process, but the business intelligence benefits demand it. And today, savvy self-service data preparation tools are making it easier and more efficient than ever.
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