At Dataiku’s Everyday AI Conferences in New York and in Chicago, an important point of discussion was how organizations can make strides towards a reality that encompasses Everyday AI. To achieve this state of AI that’s so ubiquitous that it becomes a part of everyday lives and business, there are of course technical strategy questions to answer. However, the two sessions that we highlight in this blog specifically reveal a potentially less obvious, but nonetheless crucial, element of the journey to Everyday AI — inspiring and investing in people. For an overview of the main points of these sessions featuring insights from Dataiku customers and partners, continue reading.
To start, why one billion? Nicole Alexander, Global Head of Marketing at Meta, tells us that this number is a starting point, a launchpad of sorts. The idea is that you need a minimum of one billion knowledge workers today in order to scale for tomorrow. It’s a prediction to work towards, but it is worth noting that with a space as rapidly evolving as the AI, machine learning (ML), and data science space, projects and the skills that they require will continue to shift, merge, grow, and morph into the emergence of entirely new talent opportunities.
In the midst of this fairly inevitable metamorphosis, one thing is fairly certain, and that is the need for individuals that possess the ability and expertise to bridge the gap between technical spaces and “real-world” business applications. These individuals that can understand the nuances of the two realms and liaise between the two, are what is referred to in this session as “knowledge workers.” Knowledge workers, in essence, are the people that make tech palpable. To illustrate, like Peter Pan sprinkling Wendy with Tinkerbell’s pixie dust, these knowledge workers lead impactful conversations, facilitate decision making, and distribute a human touch that ultimately makes data accessible and valuable to all, helping AI integrations take flight to the destination of Everyday AI.
Faith, Trust, and ... Democratization
In order to source these knowledge workers, barriers to the field that currently exist have to be broken down. One roadblock is actually the misunderstanding that you have to be a data statistician or other highly technical worker to make an impact with data science. All you really need to start making a difference in your organization is a desire to learn and areas to apply your knowledge, coupled with the support that enables you to do exactly that.
Democratization is key because data science is ready for the masses. And, this doesn’t mean abandoning your expert talent, but rather utilizing them in concentrated, highly impactful areas. Organizations should be focused on opening up the overall project scope and responsibilities to a more diverse group of people so that expert talent can work smarter.
Making Data Science Equitable and Comfortable Pays Off
We are beyond the days of hype laden buzzwords and have transitioned into a time where the largest challenge is mustering the belief that data science, ML, and AI should be equitable. With that, the subsequent support from organizations’ leadership to upskill workers is needed. Leaders must entrust new individuals with access to data to leverage in their day-to-day roles. Finding people who are interested in being a part of the journey and fostering their interest with ample resources is a paramount part of enterprise-wide data transformation.
Zooming Out to the Bigger Picture
With a nudge in the right direction, many people, with a natural desire for growth, will gravitate to learning and certification programs when they have been afforded the comfortability to do so. It is the job of an organization to figure out the ways they will impart this culture of comfort at their organization.
Dataiku partner, InterWorks, for example, is building out a citizen data science upskilling initiative that is a three-month program in which employees are allotted dedicated time to this training. Rachel Kurtz, Analytics Architect at InterWorks, expresses that this program is about creating the space for InterWorks’ talent to grow, knowing that ROI of their skill development makes this investment advantageous for the organization. Upfront investment leads to more efficiency and innovation down the road. At InterWorks, Rachel has witnessed that people are thankful for opportunities to grow and happy to build use cases where they can apply their new knowledge. In turn, these use cases provide immense value back into the organization.
Part of empowering talent is taking time to check in with employees and align career goals with the ways that data is changing operations. Organizations should be asking their people, “What's the difference between today and yesterday for us, and what can we each do to help successfully undergo a data transformation for a better tomorrow?”
Building a Case for Data Science
Data science can make lives easier and explaining how capabilities can be put into action is an important part of building out AI applications. From bank and financial services at Houlihan Lokey to pharmaceutical operations at Astellas Pharma U.S., a major component of scaling AI with Dataiku has been articulating to teams the plethora of opportunities that exist. From identifying brand new revenue streams for clients to achieving optimal code reuse in areas such as forecasting, replicability in a diverse array of business use cases is an area that many organizations have still yet to explore.
But, it’s time to! Data science opens many doors and creates great opportunities for individuals to find efficient ways to contribute massive value within their roles that radiates to organizations and industries at large. Essentially, there are few if not zero reasons that everyone should not be infusing data into their daily business processes.
Visibility and Community
Dataiku specifically enables the proof of value process that is critical in many applications with easy to share visualizations. The flexible product mentality that Dataiku embodies breeds creativity and collaboration across a variety of personas. Being an everyday tool for everyone to leverage is a core characteristic of the platform.
A second part of empowerment is simply being open to share with others. This means not only having clear communication lines with customers and internal stakeholders but also to the AI, ML, and data science community at large. Sharing state-of-the-art work and creating a network that generates ideas for applications is a great way for organizations, as well as individuals, to spark excitement and promote interest in data science.
Be Ready for Change
There will always be new trends influencing industries, but there are also moments where a net new baseline is created and there is a need for fast adaptation. For example, look at the global pandemic and all the ways that it has forced organizations to rapidly rise to the occasion in order maintain marketplace competitiveness. Quick, constant change is the new norm. You never really know what the next evolution down the road will be like, so having a culture of growth already enacted and preemptively charting your maps for mainstream AI will give your organization an upperhand.