In this week’s roundup, find out how decision intelligence can boost your BI game along with how AI is transforming production. Plus, learn what adventures lie ahead in topic modelling, how data science teams are being compared to Chess vs Checkers. And finally, discover why data is a medium for creativity in this new world.
By Pam Baker, contributing writer for PCMag.com
For most of us, making data-driven business decisions is a four-step process. First, you collect the data. Next, you “mine” it, which just means some combination of tools and data scientists look for patterns and correlations between different kinds of data. Third, those discoveries get pumped into the dashboards and visualizations that managers get to see. From there, it’s up to the manager to interpret what the dashboard is telling them and make their decision.
By A contributing author for AnalyticInsight.net
The world is truly shaped by many advancements in digital technology. There’s no stopping its improvement, given that technology is constantly improving by the day. One of the facets of business that’s positively affected by these improvements is the manufacturing industry. From manual and more primitive processes in the past to automated and advanced processes today, production and manufacturing is evidently now more efficient than it ever has been.
By Lowri Williams, Editorial Associate for TowardsDataScience.com
Computers are great at working with structured data like spreadsheets and database tables. But as humans mostly communicate using language and words, that’s unfortunate for computers. A lot of information in the world is unstructured — for example, raw text in English or another language. How can we get a computer to understand unstructured text and extract information from it?
By Marco Santoni, contributing writer for MarcoSantoni.com
One of the key decisions to take when building a data science team is the mix of roles. This means choosing the right mix of background and of activities that each member of the team should have. I’ll compare two models of teams I’ve experienced so far and define them as chess-team model and checkers-team model.
By Rishi Diwan, contributing writer for twdi.org
The pandemic and subsequent move to remote work has put pressure on data to solve complex organizational and business challenges faster than ever before—from digital transformation and boosting online sales to creating new products and improving operational efficiencies. As data volumes continue to explode, the time window to act on insights continues to shorten. To remain competitive, organizations must act more quickly relying more than ever before on quality data while embracing innovation from applying artificial intelligence (AI) and machine learning (ML).
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