Data powers predictive and prescriptive analytics. As enterprises rush to mine their data, AI and ML adoption is on the upswing. A recent survey by Dataconomy suggests that 75% of C-suite executives have AI and ML initiatives on their agenda. However, over a third of these projects are bound to fail, as companies struggle with poor data quality. Inaccurate and duplicate data leads to miscalculating demand and targeting the wrong prospects. This is confirmed by 59% and 26% respondents respectively. Unifying unstructured third-party data, semi-structured data or data from relational databases remains a challenge. Data silos also reduce accuracy of data driven decisions.
Almost half the data scientists report spending over 10 hours per week on data preparation. The other half spends upwards of 40-hours weekly on data preparation. Given the scarcity of data scientists and the value of their time, this is surely another big drain on a company’s resources.
The challenges for AI and ML as well as analytics are
- data exists in different systems (28%)
- requires merging from different sources (27%)
- needs reformatting (25%).
Do let us know if we can help improve your data models and improve the productivity of your data scientists with better data.Posted on January 24th, 2020
“Here’s to the crazy ones. The misfits. The rebels. The troublemakers. The round pegs in the square holes. The ones who see things differently. They’re not fond of rules. And they have no respect for the status quo. You can quote them, disagree with them, glorify or vilify them. About the only thing you can’t do is ignore them. Because they change things. They push the human race forward. And while some may see them as the crazy ones, we see genius. Because the people who are crazy enough to think they can change the world, are the ones who do.”Posted on December 22nd, 2019
Wish you a Very Happy Diwali 2019. May this Diwali usher in good health, happiness and prosperity to you.Posted on October 27th, 2019