Hidden Insights

Data Management

Blend, cleanse and prepare data for analytics, reporting, or data modernization efforts.

Gerhard Svolba2 April 2019 0

7 ways analytical methods improve data quality

Data scientists spend a lot of their time using data. Data quality is essential for applying machine learning models to solve business questions and training AI models. However, analytics and data science do not just make demands on data quality. They can also contribute a lot to improving the quality

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Data Management

Rein Mertens19 March 2019 0

Privacy legislation need not inhibit AI-driven innovation

Artificial intelligence (AI) offers many opportunities for innovation. It already allows us to improve traffic flows, safely manage large crowds of people at events, analyze automated MRI scans for particular diseases and disorders, and check the effectiveness of treatment. However, new privacy legislation – such as the European General Data

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Analytics | Data Management | Internet of Things

Reece Clifford12 March 2019 0

Unlocking analytics for multi-sport events

The reach of sports analytics is growing by the day. Increasingly, fans expect access to additional information when viewing their favourite sport. The Premier League has started to provide information about distance run, completed passes and penalty placement history. The Australian Football League shows—on live TV—the heart rate of a

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Sports & Entertainment

Data Management

Mark Frankish4 February 2019 0

The National Health Service's data challenge

New technologies promise to achieve what no politician has yet managed in the 70-year history of the UK’s National Health Service (NHS): improving patient care while simultaneously saving money. In the government’s 10-year-plan announcement this month, far-reaching new technological measures were announced. They demonstrate just how key digital transformation will

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Health Care

Data Management

Ivor G. Moan22 January 2019 0

Data Preparation in the Analytics Life Cycle – Quality, integration and governance

Recently, as a result of the EU’s General Data Protection Regulation (GDPR) and other regulations, new governance requirements for data management have emerged. These have had some interesting effects on the data preparation process. This post is the third in a series on data preparation (Data preparation in the Analytics

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Data Management

Data Preparation in the Analytical Life Cycle

Ivor G. Moan18 January 2019 0

Data Preparation in the Analytical Life Cycle- Preparation Trends

This post is the second in a series on data preparation based on a webinar about its role in the analytical life cycle. The first discussed how data preparation fit into the analytical life cycle. This post considers some trends in data preparation and some of the structures and processes

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Data Management

Rainer Sternecker17 January 2019 0

Self-service data preparation: The self stands for value!

Data preparation is often seen by companies as a difficult and dangerous job, one best left to IT. However, business departments often do not want to wait for their data, so thick SQL books and spreadsheet applications are booming in most offices. This does not really make sense, however you

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Customer Intelligence | Data Management

Ilanite Keijsers16 January 2019 0

Can your customer data platform facilitate the right decisions?

Data may be expanding exponentially, but this expansion in itself is not the be-all and end-all. Data is very important, but only because it enables organisations to learn more about their customers and offer them a better service. Therefore – and this is crucial – data allows organisations to make

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Data Management

Business Analytics Portugal26 November 2018 0

Estudo sobre a utilização e necessidade de uma Plataforma Analítica

As analíticas vão ser imprescindíveis no futuro. Está é a opinião generalizada da maioria dos gestores. No entanto um estudo levado a cabo pelo SAS sobre Plataforma Analítica - “Here and Now: the need for an analytics platform”, revelou que apenas 35% está efectivamente com projectos de implementação ao nível

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Data Management