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8 data strategy mistakes to avoid

CIO Business Intelligence

Organizations can’t afford to mess up their data strategies, because too much is at stake in the digital economy. How enterprises gather, store, cleanse, access, and secure their data can be a major factor in their ability to meet corporate goals. Here are some data strategy mistakes IT leaders would be wise to avoid.

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Straumann Group is transforming dentistry with data, AI

CIO Business Intelligence

The Basel, Switzerland-based company, which operates in more than 100 countries, has petabytes of data, including highly structured customer data, data about treatments and lab requests, operational data, and a massive, growing volume of unstructured data, particularly imaging data.

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Mastering Data Analysis Report and Dashboard

FineReport

Data Analysis Report (by FineReport ) Note: All the data analysis reports in this article are created using the FineReport reporting tool. Leveraging the advanced enterprise-level web reporting tool capabilities of FineReport , we empower businesses to achieve genuine data transformation. Try FineReport Now 1.

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Building Better Data Models to Unlock Next-Level Intelligence

Sisense

In this article, we’ll dig into what data modeling is, provide some best practices for setting up your data model, and walk through a handy way of thinking about data modeling that you can use when building your own. Building the right data model is an important part of your data strategy. Discover why.

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What is a Data Pipeline?

Jet Global

Data Extraction : The process of gathering data from disparate sources, each of which may have its own schema defining the structure and format of the data and making it available for processing. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.