Presented by Squark
“No code” is a generational shift in how software is created, used, and consumed. SaaS vendors in the space package technical functionality into simplified user experiences. Computer programming is replaced by a user experience where graphical drag-and-drop interfaces or step-by-step flows guide users to complete work that previously required code. No-code tools may cross the line into “low code” and offer APIs. Thus, the personae for “no-code” software varies with vendors offering broad platforms, verticalized, and/or industry solutions to buyers and users in business, IT, or both.
In the world of analytics and data science, the “no-code/low-code” concept manifests itself in phrases that you may have heard: No-Code Data Science. No-Code AI Cloud Analytics. No-Code Predictive Analytics. These SaaS or software systems automate the data, data science, and analytics work required to create and operationalize predictive models and integrate the resulting predictions into production.
Features and functions of no-code AI software include:
- Data discovery and data connectors for accessing, profiling, and exploring source data
- Automated data preparation, enrichment, and feature engineering for improving and enhancing data and data quality
- Automated machine learning for creating models, predictions, and forecasts
- Explainable AI for understanding and exploring why models learn or predict how they do
- MlOps and ModelOps for pushing results into production and then maintaining and updating them over time as data and model performance changes
While a newer software category, no-code/low-code AI is already saving many businesses time, reducing cost, and increasing revenue. Take for example UPMC — a $23-billion nonprofit health care provider in the U.S. with over 92,000 employees and more than 4 million insured members. They use the no-code AI SaaS from Squark for identifying people across the United States who are likely to participate in living liver donations. As cited in Forbes, Squark’s no-code AI SaaS helped UPMC reduce the time to do this work from 6 months to 1 day. Response rates went from 1 in 10,000 to 1 in 75. The 50% lift over baseline led to not only profit impact but also real human impact helping improve lives.
Succeeding with AI in any B2B and B2C organization can follow UPMC’s success using no-code data science to maximize customer acquisition, retention, upsell, and more. At Squark’s No-Code/Low-Code VentureBeat panel, Jonathan Corbin, the VP of Global Strategy and Success at HubSpot, said this about no-code AI and data science:
“We’ve seen positive movement from the models we’ve put in place and out the output from them. I think the important thing to remember with ML and self-learning models is that they get better over time. And so, as you’re thinking about implementing a solution using AI, ML, and no code, I would say you commit to a goal, and you invest in the technology that helps you get closer to that endpoint.”
Adoption of no-code technologies, including AI, is rapidly growing. Whether you are just starting to comprehend what’s possible or deep in evaluation, consider the following guidance when evaluating no-code AI technology:
1. Define an achievable and valuable use case
Identification of the business use-case for AI is critical. It frames the business impact and identifies what will be done with results. Select use cases where the predictions guide an immediate action, frame a decision, or inform/feed a downstream business process.
2. Understand your available data and data maturity
What data will be used? Who owns it? How do you connect to it? No-code tools can offer a range of data integration methods from file uploads to connectors, pre-built partner integrations, to APIs and more.
3. Create a justifiable financial model
Valuable AI use-cases reduce cost and/or increase revenue. Use a pro-forma financial model with capital budgeting metrics like NPV and IRR or similar, to justify investment and prove your solution provides economic value over time.
4. Identify a specific owner to deliver the work
AI initiatives cross functions and require coordination with other teams. While no code offers the opportunity for business teams to DIY, it is important to gain support and alignment with data owners and supporting teams. Appoint a primary business owner who “drives the bus” across groups. This person will run point for use-case identification, alignment, delivery, and managing change across people, data, and process to deliver results.
5. Focus on end-to-end operationalization
Getting models into production and keeping them predicting as accurately as possible matters. More capable no-code AI tools deliver models, predictions, and explanations to other systems. Model code and other results can be exported in various formats and made available through APIs, microservices, and containers. Ops are further enabled via automated relearning/scoring when data and models drift.
No-code solutions work for organizations that do not have teams of data scientists and for those with teams of data scientists. Consider what Jon Francis, the Global Head of Analytics at PayPal and former Chief Digital and Chief Analytics Officer at Starbucks said about no-code data science:
“I’ll never find enough data scientists or have enough budget to hire enough people to quell the demand that we have and so thinking more and more about opportunities to create, through technology augmentation, which would allow other folks in the organization to do more inferential and data science work on their own.”
Success with no-code AI and data science is literally right at your fingertips, only a click away. The technologies in it are more capable, affordable, and proven than ever before. Apply no-code AI by first identifying your use case and the data required to deliver it. Appoint a champion to execute and then increase your team’s efficiency by augmenting your staff with the right AI tool. Then operationalize to increase revenue, reduce costs, while using fewer resources to do so. Act quickly to evaluate and deploy the products in this innovative and newer space because your competitors already are.
Judah Phillips is Co-Founder/Co-Ceo, Squark.
Learn more about how Squark is helping companies predict future outcomes that drive revenue with their no-code data science platform here.
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