TechBytes with Lauren Maffeo, Associate Principal Analyst at GetApp (a Gartner company)

TechBytes with Lauren Maffeo, Associate Principal Analyst at GetApp

Tell us about your role and the industry/technology you cover at Gartner.

I’m an Associate Principal Analyst at Gartner. I work in Gartner’s Digital Markets unit, where I write research for GDM’s GetApp (a Gartner company) brand. I cover trends in cloud Business Intelligence (BI) for small and midsize businesses (SMBs), which includes Data Mining, Analytics, Big Data, and Artificial Intelligence techniques like Machine Learning (ML).

How big is your Research and Analytics team?

We currently have close to 30 analysts covering various topics within GDM.

We hear a lot about the exciting opportunities in AI and Data Science. What AI-related challenges do you deal with on a daily basis?

You can trace almost any challenge with AI back to the dataset used to train it. GetApp recently surveyed close to 500 leaders in US-based SMBs to learn how they use data to make decisions. Across the five verticals we surveyed – Accounting/Finance, IT/Tech, Healthcare, Sales, and Marketing – all of them cited data quality as a key concern. If the quality of data used to train AI technology is poor, that technology’s results will be less than stellar.

Tell us about various research and analyses on the role of AI and Automation in Marketing, Sales, and Customer Support.

GetApp’s survey showed that Sales and Marketing leaders were most likely to say they need any BI software they use to have collaboration features. Leaders in both industries also shared a strong need for Data Visualization. (68% of marketers and 57% of sales leaders, to be precise). However, marketers were most likely to say they have the right data and insights to make business decisions, whereas sales leaders were least likely to say so.

Which industries have been the quickest and most flexible in their adoption of AI and Automation technologies?

Perhaps unsurprisingly, the IT/Tech sector is relatively advanced. GetApp’s survey found that respondents in this sector are most likely to have two or more employees whose role it is to collect and analyze data. (58%, to be specific.) Respondents working in IT/Tech were also most likely to find Data Visualizations helpful in their work.

Which have been lagging in these areas?

GetApp’s survey found that sales teams were least likely to report having data scientists. This is concerning, as our research found a positive correlation between how confident respondents are in their ability to use data to make decisions, and how impactful they feel data is on their businesses. (i.e. the more confident they are, the more impactful they feel data is.) We also found that leaders at companies with data scientists are much more confident they have the right data and insights to make decisions. Just as the sales engineer role evolved to meet changing needs, I expect data science roles specializing in sales to grow over the next decade.

Salesforce recently announced acquiring Tableau. Google is doing that with Looker. How do you see Data Science and Data Visualization becoming the core of every analytics platform?

These acquisitions show that tech’s biggest brands are attuned to what their users need. CRM is essential for members of the Sales and Marketing teams to manage their customer relationships. Increasingly, both roles have responsibilities that require BI, such as real-time marketing and journey personalization. So, a move like Salesforce acquiring Tableau acknowledges the growing need for roles like sales and marketing to prove their work’s ROI with data.

Tell us more about your roadmap for Data Science and AI in Gartner.

Demand for research about Data Science and AI is growing across businesses of all sizes. Implementing AI in a small business is very different than doing so in a Fortune 500 firm. Whether we work with clients in businesses with 10 or 10,000 employees, Analysts at Gartner cover the challenges and opportunities that AI presents. We also write industry-specific research so that clients in marketing can learn how AI impacts their work vs. those in healthcare.

What is your vision toward bringing AI and Machine Learning sciences closer to basic schooling and graduate research?

Education tends to lag behind tech trends like AI and Machine Learning. The end result is huge gaps in demand for skillsets like Machine Learning to build AI technologies. That said, we are slowly starting to see universities acknowledge this gap and offer an education based on market needs. The University of North Carolina at Greensboro will launch a Master’s program in Big Data this fall, and Northern Virginia Community College will partner with George Mason University to launch a Bachelor of Applied Science Pathway in Cloud Computing.

What is your advice to all AI analysts and Data Scientists who are yet to design their content strategies for better adoption of AI as a science?

Try to balance an equal understanding of AI’s business and technical assets. You don’t need a Ph.D. in Statistics to understand the basics of how AI technologies work under the hood. That said, I see a lot of people publicly discuss AI without understanding how it works. Anyone in a position to bring Data Science and AI into their business should learn the basics first; the Towards Data Science blog on Medium is a strong start.

Lauren Maffeo has reported on and worked within the global technology sector. She started her career as a freelance journalist covering tech trends for The Guardian and The Next Web from London.

Today, she works as an Associate Principal Analyst at GetApp (a Gartner company), where she covers the impact of emerging tech like AI and Blockchain on small and midsize business owners. She is also a community moderator for and a member of the ACM’s Distinguished Speakers Program.

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GetApp (a Gartner company) is the premier online resource for businesses exploring software as a service (SaaS) products. Buyers easily compare software products side-by-side with GetApp’s free interactive tools and detailed product data.

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