1871 held its latest quarterly Fintech Forum on September 29th, in partnership with Capco, to showcase Chicago’s strongest data and analytics experts and to help create engagement between 1871 startups and corporate partners in the fintech space. Here are four key takeaways on how to use reporting information to drive insights, automation, and a competitive advantage.
1871's fintech startups joined a panel of experts to discuss the future of data and analytics.
Data and analytics are crucial tools for a business’s decision-making processes, whether it is monitoring customer activity to prevent fraud, analyzing processes to increase efficiency or exploring buying patterns to drive market entry. However, after years of successfully capturing and distributing data, businesses of all sizes have reached a tipping point: how do we transition from reporting information to using it to drive insights, automation and a competitive advantage?
The forum featured an expert panel including Tom Schenk from the City of Chicago, Sandeep Vishnu from Capco, Veda Konduru from VectorScient (1871 Member), Rami Jachi from Nousot, Michelangelo D’Agostino from ShopRunner and moderator Daragh Fitzpatrick from Capco.
Here are a few takeaways from this quarter’s Fintech Forum:
1. Data Automation and Human Intervention
Despite advances in data automation, human intervention is still required. In fact, 80% of data scientists spend their time preparing data for automation through data cleansing processes and checks. Sandeep Vishnu noted that “There has to be human intervention [when it comes to data automation]” because if data scientists do not intervene, the data could come up with the wrong answer to the wrong question. It’s imperative for data scientists to figure out the right questions to ask before they can move up the stack and start to process the data. Business context is important to be able to use and understand numbers - data scientists must be able to use that business context to correctly interpret the results of data automation.
2. Don't Hire a Data Generalist
Before your organization hires a data scientist, the panel recommends that the organization's leadership first ask these key questions:
- Are we collecting enough data?
- Can we report on the data we are collecting?
- Can we automate the reporting of this data?
After answering these questions and determining that you need a data scientist within your organization, look to hire a data scientist who is the best in their particular category, rather than a data generalist. Prioritize those candidates who have the foundational data skills first - other skills can be learned once they are hired. Also, make sure the candidate has strong business acumen and understands business context. This will ensure that he or she can correctly interpret your data.
3. Your Business Should Take A Defensive Posture Against Data Breaches
In general, many organizations tend to be reactive rather than proactive when it comes to securing their data. It becomes dangerous when organizations start to monitor their data processes only after an extreme event has happened. The panel agreed that data scientists need to be aware of threats that aren’t so disruptive and continuously monitor for data breaches.
4. The Pitfalls of AI
There are several benefits to using artificial intelligence and bots to improve business efficiencies. However, you have to set up the bot with the proper constraints to be successful in the long term. Michelangelo D’Agostino stated that “monitoring bots is paramount” and that he’s not sure that all companies are set up to monitor their bots properly, which can lead to negative consequences in the long run.
The Fintech Forum is an ongoing series that promotes a closer relationship between the innovative startups and the 1871 corporate partners within the fintech space. To join the conversation, contact firstname.lastname@example.org.
Want to read more about cybersecurity? Check out our recent guest blog Hacking IRL: What Mr. Robot Teaches Us About Cybersecurity.