Nordic Women and Data Summit is envisioned as an educational, inspirational and networking platform for all women data practitioners in the Nordics.
When it comes to data governance policy, companies are aware they need one, but few of them know where to start. A lot of them are at a crossroads when it comes to creating a policy that works for their organisation. But the first instinct of reaching to a standard policy wouldn’t work because it won’t meet your corporate strategy.
Ismail Elouafiq, a Data Scientist at SVT has drawn a genius association between machine learning systems. (Disclaimer: Ismail’s presentation and this article contain mentions of cats, so people that are allergic or just don’t like cats, you’ve been warned.)
Data gives us the universal truth in every field. And we’re not only talking about here down on Earth but also far above – as far as our eyes can and cannot see. Data science has helped scientists for a long time in the quest for discovering the mysteries of the Universe. Data is how we understand space today, states Elin Eriksson, […]
Data quality is considered as the highest commandment in data management. And it’s with a strong purpose. Only quality data is useful data, and to be quality it must be consistent and unambiguous. All data that is gathered, stored and consumed during business processes directly impacts the success of the business.
Predictive maintenance and maintenance analytics allow machine operators to save on resources unnecessarily spent on frequently scheduled maintenance checkups and part repairments that are ineffective.
Nestlé, the world’s largest food and beverage company with over 2,000 brands, is a great example of how a global company should actively work on providing diversity and inclusion in the workplace through people analytics.
Live games are always connected and allow for a lot of interaction to take place between players – and as a result, are a source of huge amounts of data that opens up untapped capacities of data science and analytics for game creating companies.
The banking industry has been quite resistant to change ever since its beginnings. But financial services are not immune to the biggest technological revolution the word has attested caused by AI. Apart from being under pressure to adapt to the digital economy, banks have started to discover some really valuable AI use cases.
DataOps emerged as a solution for bringing the battle-proven combo of developers and operations into the data processing and eliminating silos between developers, data scientists and operators.
Bad data is the number one enemy for data-driven companies. Although organisations are investing money and effort into eradicating bad data, it still presents a challenge. Decisions made on bad data can turn fatal for the business. But how can companies make sure they are aware of it and find ways to deal with it?