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Tag: Tamr

Spot Supply Chain Risk with Data Mastering

May 25, 2020 |

You don’t have to be an expert to recognize the role data is playing in the fight against COVID-19. From infection rates to the number of ICU beds, vaccine trials, and everything in between. Read more…

Big Data Career Notes: February 2020 Edition

Feb 14, 2020 |

In this monthly feature, we’ll keep you up-to-date on the latest career developments for individuals in the big data community. Whether it’s a promotion, new company hire, or even an accolade, we’ve got the details. Read more…

How ML Helps Solve the Big Data Transform/Mastering Problem

Oct 10, 2019 |

Despite the astounding technological progress in big data analytics, we largely have yet to move past manual techniques for important tasks, such as data transformation and master data management. As data volumes grow, the productivity gap posed by manual methods grows wider, putting the dreams of AI- and machine learning-powered automation further out of reach. Read more…

Tamr Preps for Growth with $18M Round

Jul 11, 2018 |

Tamr today announced that it has raised $18 million in venture capital to help it grow its business, which is focused taking a “machine-driven, human-guided” approach to taming messy data. Read more…

Breaking Down the Seven Tenets of Data Unification

Jun 15, 2017 |

One of the longstanding challenges in analytics is data unification. While federated approaches are gaining some favor, the vast majority of analytic practitioners want the data to be present in one place before analyzing it. Read more…

Big Data and the White House’s Cancer Moonshot

Mar 23, 2016 |

The White House wants to invest $1 billion in a new Cancer Moonshot aimed at accelerating research into ways to detect, treat, and prevent the debilitating series of diseases. It’s not the first time the government has announced a major effort to eradicate cancer by spending big on medical science. Read more…

Five Steps to Fix the Data Feedback Loop and Rescue Analysis from ‘Bad’ Data

Aug 17, 2015 |

Despite enterprises’ best intentions in enforcing top-down standardization of data sets, non-compliant data can easily seep in and, through aggregations, transformations, and standardizations, spread throughout the organization. In a typical enterprise, inventory data from multiple regions and divisions across product lines could easily result in dozens of data sources being used for one analysis. Read more…

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