DvSum blog

4 steps to speed up Data Discovery and expand Self-Service Analytics

ata Discovery is a necessary step in Self-Service Analytics to find, prepare and analyze data to generate insights. However, it is slow and complex,  which results in analysts not being able to deliver timely insights or for business teams to be able to actually do self-service analytics.  In a previous article, I discussed this as a key challenge identified by Analytics leaders. You can read that post here.  In this article we talk about 4 steps to add Data Intelligence to your data using a centralized Data Catalog that will make Data Discovery faster and smarter.  

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Playing offense with Data Catalog to score on self-service analytics goal

Business Teams have access to Data?, self-service analytics platform and tools?, and have hired data analysts and scientists?‍?. But decision-makers are drowning in data and starving for insights. The biggest hurdle is the speed of Data Discovery by Analysts and Scientists.  Data Catalogs can help address this challenge by adding definition, context, profile, quality of data. It helps make data easy to find, understand, and trust. In turn, allow Analysts to build and ship analytics faster and deliver timely insights to the business.  

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Data Quality and Anomaly Detection in the Era of Big Data

Our world has become increasingly digitized. Data is the new currency. Business leaders who successfully tap into the insights provided by big data can establish a clear advantage over their competitors, which has the potential to persist over the long-term.

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Big Data Quality, a landmine or spring-board for your Digital Supply Chain initiatives?

Global Pandemic has become a catalyst for companies to revisit their global supply chain strategy and accelerate adoption of Digital Supply Networks (DSNs). Effective DSNs rely on data to be accessible, of high quality, and relevant. Read how Data Catalogs and Data Quality drive success for Digital Supply Network initiatives.

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Is there 20% excess inventory hidden in your data?

Data quality is the secret ingredient that makes inventory optimization efforts truly effective. Want to reduce inventory levels? Start with DQ.

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