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When ML and AI IT-Driven project teams look like this, maybe Agile is not the right solution

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You need a Data-Driven Cross-Team Collaborative Platform

"Data science is the easy part. Getting the right data, and getting the data ready for analysis, is much more difficult. The sea of data is vast and growing exponentially. To avoid drowning, executives must connect the data strategy to the analytics strategy."

McKinsey Research 2018

The trust of the business users in the data foundation is vital for users to make use of the derived insights.

The need to protect data integrity during the life cycle of a data element is especially important during data movement activities. It’s easy to accidentally corrupt a dataset through a human error when processing data, causing the data set to be useless for analysis in the next step. The best way to protect data integrity is to automate as many steps as possible in data movement and validation activities leading up to the point of data analysis.

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A better way to build and manage your data pipelines

DATA - The DNA of your Business

Align data and analytics with business outcomes

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Data Science Project Management

Huge failure rates for analytics, AI, and big data projects

Researches

  1. Jan 2019: Gartner says 80% of analytics insights will not deliver business outcomes through 2022 and 80% of AI projects will “remain alchemy, run by wizards” through 2020.

  2. Jan 2019: NewVantage survey reports 77% of businesses to report that "business adoption" of big data and AI initiatives continues to represent a big challenge for business.

  3. July 2019: VentureBeat AI reports 87% of data science projects never make it into production

  4. 2019: Gartner predicts that through 2022, 85% of AI projects will deliver erroneous outcomes due to bias in data, algorithms or the teams responsible for managing them

Insights

  1. Most of the managers believe that new technology will solve the problem

  2. Data access and understanding.

  3. Analytic models don’t meet the business requirements or fail changes in the data.

  4. Lack of collaboration.

 
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6744320, Tel Aviv,   Israel