What is Wisdom?
Last week, our Data + Analytics team headed down to New Orleans for Wisdom 2018, the RapidMiner user conference. For those unaware, RapidMiner is an easy-to-use data science platform used by a variety of people with varying levels of analytics expertise. At the conference, we were able to network with analytics teams, learn about the origins of analytics within their own businesses, and how they are applying advanced analytics in creative and innovative ways. We also heard about new techniques and the future product roadmap for RapidMiner – all while geeking out over the power of data to drive value.
We were fortunate enough to have the opportunity to share our data and analytics perspectives with the conference. Clarkston Data + Analytics service leads and experts Maggie Seeds and Elise Watson were able to participate in the event as speaker and panelist respectively. Maggie gave a timely presentation on how companies can achieve real results from data science projects and Elise participated in a discussion on the rise of the citizen data scientists. Discussions, panels, and speakers seemed to gravitate around a single question – “How can companies gain competitive advantage through analytics with such high demand of data scientists in the market?”
Citizen Data Scientists
Companies can pursue two routes to bring data scientists within the organization – hire them or develop them from the existing pool of resources. Though some might gravitate to the seemingly easier path of the first option, the latter might be easier to achieve than you think. In fact, it could even be happening right under your nose already.
We talked to conference participants about how they got started with data science and many said they simply started with a business problem or question. Without any formal analytics training, they began to research tools or resources that could help them address their challenge. Tools like RapidMiner, with a lower barrier to usability, allowed these individuals to rapidly ramp up their initiatives and enable the potential of data science within their business.
This is somewhat of a common situation for many of our clients. They’ll have employees across the organization trying to learn R and Python or downloading data science tools in their spare time. This occurrence has become so prevalent that there is a term for these employees – meet the Citizen Data Scientist.
That’s right: people in your organization who are highly curious and use data today can be trained to use data science, machine learning, and AI – no Ph.D. in statistics or mathematics required. In fact, leading organizations are taking advantage of this and developing formal programs to help organize and train citizen data scientists across the business. It is, however, important to understand what expertise these citizen data scientists might lack.
RapidMiner co-founder Dr. Ingo Mierswa offered caution during one of the keynotes regarding some common mistakes that most data scientists have made at some point. For instance, not knowing how to correctly normalize data or using a helpful attribute in modeling that wouldn’t be available at the time of prediction can lead to falsely positive results during testing that fall apart in real life. If decisions were automated using the information from these scenarios, these minor mistakes would be especially costly.
Citizen data scientists may be familiar with every piece of data you collect, but they also need to know how to treat it with best practices. This can be overcome with experience and research. Citizen data scientists should be constantly learning to develop and refine their skills; finding the right tool for your business can help with this process.
RapidMiner Chief Marketing Officer, Tom Wentworth, kicked off the conference with survey results showing that “ease of use” was the most important criteria for analysts in choosing a data science platform. Usability can accelerate the adoption of advanced analytics in your organization and democratize data science capabilities across the organization. With the right tools and guidance, you can find the insights hiding in your data and activate analytics throughout the business.
Champion of Analytics
The benefits of an organizational champion of analytics was another theme throughout the conference. This champion knows the value that can be realized from data and can help define and demonstrate quantifiable ROI from data science projects to the rest of the business.
Broad organizational adoption of advanced analytics is still fairly new. There is a lack of clarity on prioritization and how to determine which projects will show the greatest value. The decision-making process needs to evolve alongside the adoption of advanced analytics to enable data-driven decision-making. Often, analytics can drift into a curiosity support function. Having the right decision makers and analytics communicators on the team can avoid this drift and guide efforts toward higher impact projects. As a decision maker, consider if you’re looking for data to support a predetermined position or if you’re truly ready to let the data guide your direction.
Activating analytics in your organization is not as simple as buying an analytical tool. Years of BI tools have further honed that lesson. Actualizing analytics within your business is about finding the right people, ramping them up, and demonstrating the value of projects effectively throughout the organization. With this approach, you can create an advocacy network for analytics and grow the capabilities to achieve even greater insights in more areas of your business.
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Coauthor and contributions by Elise Watson