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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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Making OT-IT integration a reality with new data architectures and generative AI

CIO Business Intelligence

Data remains siloed in facilities, departments, and systems –and between IT and OT networks (according to a report by The Manufacturer , just 23% of businesses have achieved more than a basic level of IT and OT convergence). Denso uses AI to verify the structuring of unstructured data from across its organisation.

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SAP Datasphere Powers Business at the Speed of Data

Rocket-Powered Data Science

We could further refine our opening statement to say that our business users are too often in a state of being data-rich, but insights-poor, and content-hungry. This is where we dispel an old “big data” notion (heard a decade ago) that was expressed like this: “we need our data to run at the speed of business.”

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Discover Efficient Data Extraction Through Replication With Angles Enterprise for Oracle

Jet Global

When extracting your financial and operational reporting data from a cloud ERP, your enterprise organization needs accurate, cost-efficient, user-friendly insights into that data. Enterprise-level organizations like yours often have multiple data sources and systems. The alternative to BICC is BI Publisher (BIP)​.

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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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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. What is an ETL pipeline?

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Talk Data to Me: Why Employee Data Literacy Matters  

erwin

Or the product line manager who wants to understand enterprise impact of pricing changes. David Loshin explores this concept in an erwin-sponsored whitepaper, Data Intelligence: Empowering the Citizen Analyst with Democratized Data. Reducing the IT bottleneck that creates barriers to data accessibility.