Articles about Data Science

Companies can now gather more information about their customers than ever before. But according to a new study data science is not benefiting marketers, with 84 percent of marketing executives saying their ability to predict customer behavior is guesswork.

The report from predictive analytics company Pecan AI, based on surveys carried out by Wakefield Research, finds four out of five marketing execs report difficulty in making data-driven decisions despite all of the consumer data at their disposal.

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Although 81 percent of respondents to a new survey say that their data team's overall productivity has improved in the last 12 months, 95 percent of teams are still at or over their capacity.

The study of over 500 US-based data scientists, data engineers, data analysts, enterprise architects and chief data officers by Ascend.io finds automation is emerging as the most promising path to increase data team capacity and productivity.

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Data, as we know, has become a valuable commodity and that has thrown the privacy and transparency aspects of what information organizations hold about us into sharp relief.

Mozilla believes that we should have more control over our data and is launching a new platform to give people more choice over what data they share and with who, and allowing them to help with research projects.

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According to new research 96 percent of data teams are operating at or over capacity, thanks to a surge in demand for data pipelines.

The study by data engineering company Ascend.io shows 93 percent of respondents anticipate the number of data pipelines in their organization increasing between now and the end of the year, with 56 percent predicting the number to increase by more than 50 percent.

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A new survey carried out by Researchscape for data management company Ataccama reveals that 79 percent of executives and 75 percent of line of business users face problems with data quality.

With 78 percent of organizations relying on data more when compared to the year before the pandemic this is a major problem, as using poor quality data in analytics and AI initiatives can lead to misinformed business decisions.

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Only 23 percent of knowledge workers say their organization is ahead of the curve in digital capabilities to support knowledge work according to a new survey.

The study from iManage shows 68 percent of knowledge workers believe 'information contained in digital documents and files' is vital to their business. Respondents rate contracts, emails, and spreadsheets as the three most important sources of digital information.

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New research from Ascend.io finds that since the onset of the COVID-19 pandemic 78 percent of data professionals have been asked to take on responsibilities outside of their core job function, with 97 percent now signaling their teams are at or over capacity.

The study of over 300 data scientists, data engineers and enterprise architects in the US also reveals that to increase capacity 89 percent of data professionals are turning to automation, low-code, or no-code technologies, with 73 percent citing automation as an opportunity for career advancement.

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While data is essential to businesses it increasingly seems that there is a gap between creating data science and actually using the information in production.

Open source analytics company KNIME is aiming to eliminate this gap with the launch of Integrated Deployment.

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If you are like me, there is a good chance that you are confused as well about the most recent terminology to use in the field of data science … pardon, artificial intelligence … no, I mean data science. No, I mean artificial intelligence. Please, somebody tell me what I should call it and what the difference is!

Isn’t artificial intelligence just a new cool name to label the old traditional data science? Don't both concepts cover the same algorithms? And isn’t it all machine learning anyway? This is what I used to think until I took a pause to write this post. During this breather, I went back in time and tried to remember all the names that used to be used to label this field of what essentially is data analytics. Let’s see …

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