From Data Science to Data Stories: Bridging the Gap to Digital Transformation (Highlights)
Katya Vladislavleva, DataStories (Evolved Analytics)
Predictive analytics and data science are gaining importance and proven impact despite the hype. Surprisingly, the data science universe and the business universe keep coexisting without too much overlap. We claim that data-driven solutions will see a greater success in business and industry only when they are understood and internalized by domain experts (not just data scientists), and when domain experts take ownership of the solutions. This can only happen if predictive analytics outcomes are communicated to domain experts in human language with a narrative. Otherwise, they have little chance to be sustainably deployed.
Digital transformation and data-driven strategy are proven to increase EBITs, but are assumed to require epic efforts in terms of upfront investment and unique talent acquisition. Budgets are almost always spent on collecting the data with little to no plans on what to do with it later, which makes the transformation incomplete. Interestingly, the technology exists to turn all of this data into immediate actions without epic efforts and with existing human capital. Datastories’ claim is that turning data science into data stories is the missing ingredient in completing the data-driven transformation and making it an enjoyable and natural next step to make.
This video is a short version of the presentation given at MATLAB EXPO. To watch the full-length video, see the link in the "Other Resources" section below.
Recorded: 28 Jun 2016
Related Products
Learn More
Featured Product
MATLAB
您也可以从以下列表中选择网站:
如何获得最佳网站性能
选择中国网站(中文或英文)以获得最佳网站性能。其他 MathWorks 国家/地区网站并未针对您所在位置的访问进行优化。
美洲
- América Latina (Español)
- Canada (English)
- United States (English)
欧洲
- Belgium (English)
- Denmark (English)
- Deutschland (Deutsch)
- España (Español)
- Finland (English)
- France (Français)
- Ireland (English)
- Italia (Italiano)
- Luxembourg (English)
- Netherlands (English)
- Norway (English)
- Österreich (Deutsch)
- Portugal (English)
- Sweden (English)
- Switzerland
- United Kingdom (English)
亚太
- Australia (English)
- India (English)
- New Zealand (English)
- 中国
- 日本Japanese (日本語)
- 한국Korean (한국어)