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Catalyst Makes You Look Smart: Big Data Analytics for Non-Data Citizens

by Svetlana Sicular  |  July 24, 2015  |  Comments Off on Catalyst Makes You Look Smart: Big Data Analytics for Non-Data Citizens

Analytics is exploding — it is becoming everybody’s domain, rather than an expert’s domain. The digital economy requires to expand your existing expertise with new knowledge. The digital economy is begetting citizen-data scientists along with citizen-developers and citizen-not-my-job people. Things are getting mixed up in the digital whirlwind: Amazon is now worth more than Walmart, Uber is not a car company but a data company, Fujitsu grows lettuce at its former semiconductor fab using big data in the IoT!

Source: Fujitsu Japan

Source: Fujitsu Japan

The Internet of Things is crossing borders of a single domain: each domain amplifies and creates demand for other technologies — cloud, security, mobility, storage and many more. Big data analytics is a long-term focus of these converging technologies. It is the digital economy’s philosopher stone (commonly known as a sorcerer’s stone in the American “translation” of book one of Harry Potter), it turns data into gold of insights. (Sometimes, it can turn data into something very different from gold too.)

But here is a problem! Technology vendors ceaselessly pursue business buyers and business users, saying, We can do sophisticated analysis.Ok, what do I do with it? A business buyer asks. Technology is not enough, domain expertise is what turns data into gold.

Along with the business users, technical professionals comprise a rapidly growing group of citizen-data scientists — they are non-data technology experts who get to a realization that the future of their technologies is in analytics. Compared to the well-known population of business citizen-analysts, they represent an often overlooked analytics audience — savvy technologists who have tons of necessary skills for analysis although they are not familiar with analytics. These users’ mindset is ready for analytics, and they can very quickly grasp technologies other than their own. Gartner predicts, by 2020, data science, especially machine learning, will be so critical to the software engineering process that at least 50% of master-level software engineers will be required to exhibit a basic understanding of it.

I’d prefer not to contrast technical and business citizen-data scientists, because the key to their success with analytics is their domain expertise. Both grasp analytics for their needs, just technical people are still way better with technologies (for example, they can program).

In two weeks from now, at the Catalyst conference in San Diego, I will conduct a workshop on big data and analytics for non-data professionals. We will jointly find big data in their own domain — those data sources that might look useless today, but combined with other data sources, they could suddenly turn into gold. For example, combined warranty information, call center notes, sensor logs, geolocation, claims data and weather details can reveal reasons for previously inexplicable malfunctions, and minimize a recall impact. We will also work on identifying big data opportunities in their domains. Can’t wait to learn what they are!

Domain expertise combined with analytics is a recipe to look smart in the big data world: it is the ability to unearth your big data and think out what you can do with it in the field you know well. Tracking the latest Apache projects makes you look competent, but it is not gold. Knowing how to find your big data and how to use it to transform your business — that’s what make you look smart, that’s gold. Watch Twitter for #GartnerCAT — I promise lots of gold there.

Follow Svetlana on Twitter @Sve_Sic

Category: data-scientist  analytics  big-data  data  data-paprazzi  information-everywhere  innovation  inquire-within  

Tags: big-data  catalyst  data-paprazzi  gartner-predicts  information-everywhere  

Svetlana Sicular
Research VP
6 years at Gartner
23 years IT industry

Svetlana Sicular is passionate about bringing analytics to domain experts and helping organizations successfully compete by applying their business acumen in analytics and data science. She is convinced that domain expertise and high-value data are the greatest assets that companies should monetize in new analytics applications. Read Full Bio




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