Artificial Intelligence: Barriers to Entry
There’s no question that forward-thinking, data-centric business leaders across most industry sectors are looking to leverage the power of Artificial Intelligence, Machine Learning, and Predictive Analytics. Run any simple Google search on those terms, scroll through Linkedin posts, or check the top job boards and it’s obvious the market is buzzing with discussions and appetites to move forward. However, there have been numerous barriers — not having the right people, not having flexible business processes, not having sufficient applications, etc. — to organizations being able to develop and deploy an affordable and impactful Machine Learning program on their own.
The DataRobot Value: Changing the Equation
Automated Machine Learning platform – and new TESCHGlobal partner – DataRobot changes all that. By packaging data science, user-friendly functionality, and rapid deployment options, DataRobot makes AI far more accessible to organizations of all shapes and sizes and accelerates speed to value. DataRobot shortens the path to AI-driven analytics by building, training and evaluating dozens of predictive models simultaneously from its library of hundreds of algorithms, then making recommendations based on accuracy and speed. Full transparency is provided, as well as advanced functions and customizations for experienced data scientists. Typical users don’t need advanced data science skills to work with and implement the models - what they do need to know is their data, business objectives, and how the model will be deployed.
Your Opportunity: 5 Reasons to Leverage DataRobot in 2019
By targeting, training, and deploying DataRobot predictive models, your organization can quickly benefit from profitable injections of automated AI and machine learning algorithms. Our veteran, forward-thinking team here at TESCHGlobal took a long, hard look at DataRobot’s revolutionary platform; we were impressed by a wide assortment of its functions and capabilities. Here are five that stood out to us as key points for our clients assessing their AI options in 2019:
1. First Priority are Your Objectives. The DataRobot value begins with your targets; this should not be a “science experiment.” As data is brought into the platform, one of the first steps is to identify the field to be modeled – whether that be a classification, forecast, or some other predicted value. It is important for users to have a clear understanding of the target and how it fits within your business. Each business objective quickly becomes the focus of that modeling process, leading to the assessment, engineering, and evaluation of the right mix of dimensions and measures (features) on which DataRobot will base its optimized prediction model for that target.
2. Leverage Your Existing Team & Resources. DataRobot helps bypass the data science talent shortage. Have you tried recruiting, hiring, and budgeting for a veteran data scientist lately? The shortfall of data scientists in the U.S. alone is projected to reach 250,000 within the next five years; those with proven track records are exceedingly difficult to find, hire, and afford. DataRobot overcomes this barrier to machine learning by packaging top-ranked data science and open-source algorithms into a platform that is friendly to your team members who may lack the traditional data science skills of math, stats, and model programming/coding. Given DataRobot’s automated modeling functions, enlightening DataRobot University training, and clear guidance from DataRobot Customer Facing Data Scientists and the TESCHGlobal team, we believe that your knowledgeable business analysts, line-of-business management, and other data-savvy team members will be able to quickly grasp and leverage DataRobot.
3. Speed to Prediction Means Speed to ROI. DataRobot drastically compresses the time required to develop, train, and deploy predictive models. Automated, parallel processes handle aspects of data ingestion and preparation (e.g., missing values, text analytics) and speed through a full array of model approach testing, tuning, blending, and validation in minutes. Resulting models are flagged for highest accuracy, processing speed, or a recommended combination for deployment. All of this has historically represented many days, if not weeks, of manual effort by even veteran data gurus. Speed to model deployment enables speed to smarter business decisions. Furthermore, DataRobot enables organizations with resident data scientists to chew through backlogs of use cases and deliver high-performance models quickly, enabling their team to focus on more complex challenges.
4. Full Transparency - No Black Box. No one likes secrets, especially in the technology driving key business decisions. The TG team was impressed by DataRobot’s level of illustration, documentation, and clarity regarding its modeling approach, testing, and results. With each model stage, step, and output comes a transparent “blueprint,” user-friendly explanations, and model feature analysis. This will be well-received by everyone from your executive suite, legal/regulatory compliance department, and line-of-business management, not to mention your resident data scientists.
5. Integrate with Tableau Analytics. TG is also excited to enable clients to integrate DataRobot predictive models with their Tableau visualizations. As a Tableau partner, we know the power and capabilities of this market-leading analytics platform, and can support your development and deployment of holistic dashboards which encompass historical trends, scenario testing, classification models, optimized forecasts, etc.
TESCHGlobal: Organizational & Data Preparation Services
At TESCHGlobal, we’re excited about the opportunity DataRobot represents for companies in their journey to analytics modernization. As a DataRobot partner, TG’s experienced team is helping joint customers get their data ready for machine learning with cleansing, integration and optimization services. We’re also advising clients on their analytics business strategies, helping organizations answer a variety of questions, including: What are our objectives with AI, machine learning, and predictive analytics? Do we have the right data available to solve this issue? How can we integrate the model results in our business processes? Together, we work through and around barriers to find new opportunities to apply DataRobot in your organization.