MODELING
We build and deploy artificial intelligence models that can help your business reduce costs, increase revenue and mitigate risks
MODELING
We build and deploy artificial intelligence models that can help businesses reduce costs, increase revenue and mitigate risks
Our machine learning platform is powered by Rapidmer, a world leader in Ai technology, and our team of certified newData scientists
MODELING
We build and deploy artificial intelligence models that can help businesses reduce costs, increase revenue and mitigate risks
Our machine learning platform is powered by Rapidmer, a world leader in Ai technology, and our team of certified newData scientists
End-to-end collaboration platform
- Team-oriented – beginners to experts.
- Code-based and visual workflows.
Lightning fast business impact
- One-click deployment and business impact.
- Productivity & performance enablement.
Depth for data analysis
- Native 3rd-party and custom ML assembly.
- Augmented analytics: guided to full automation.
Full transparency and governance
- Secure, easy to trust, fine tune and explain.
- Detect bias and drift.
Our machine learning platform is powered by Rapidminer, a world leader in Ai technology, and our team of certified newData scientists
End-to-end collaboration platform
- Team-oriented – beginners to experts.
- Code-based and visual workflows.
Lightning fast business impact
- One-click deployment and business impact.
- Productivity & performance enablement.
Depth for data analysis
- Native 3rd-party and custom ML assembly.
- Augmented analytics: guided to full automation.
Full transparency and governance
- Secure, easy to trust, fine tune and explain.
- Detect bias and drift.
Our technology, your advantage
Ai Platform
newData’s Ai engine is powered by Rapidminer, a world leader in Data Science and AI platforms. Our talented data science team leverages this platform to build and deploy powerful predictive models, segmentations, and simulations. We customize every solution without the expense.
Accuracy and Precision
The intersection of technology and seasoned data science professionals ensures that our clients get the most accurate and precise modeling solutions.
Accuracy and Precision
The intersection of technology and seasoned data science professionals ensures that our clients get the most accurate and precise modeling solutions.
Automation and Speed
A key benefit of Rapidminer’s Ai platform is the increased speed and reduced costs that comes with process automation. Whether it is reducing the amount of manual data entry and error checking in structured or unstructured text to the efficient deployment of models and the measurement of its impact, newData’s data science team can leverage the full automation capabilities of our Ai platform.
Competitive Prices
Ai is all about efficiency. This means that our solutions perform tasks faster and with fewer errors than without them. This fundamental concept translates to how we work too. So, this means that our solutions cost less to develop. We pass the savings to our clients. Higher quality at lower prices is what we do.
Scale
Although our Ai platform can be deployed at the enterprise level, it can be used just as effectively for a smaller company. That’s what we do, help small to mid-sized businesses with limited data science technological and people resources enable data science. We take the hassle and complexity out of Ai so you can focus on what your business does best.
DREAM is our model integration process
Click on a letter to find out more
Machine learning success is built by working effectively with people, not just machines.
We believe in collaborate engatement
We begin the DREAM process by gaining an understanding of your business challenges or identifying new opportunities. We show how these outcomes can be harnessed with data, technology and analytics. We are enthusiastic about quantifying the impact of closing performance gaps and growing your share of the pie.
Once we get a general understanding of your data, we develop a business intelligence roadmap using our proprietary DREAM process. It includes best practices in design, dashboard development, analytics, implementation and measurement. Our dashboards are easy to navigate, so they get used. They are actionable, so they impact the bottom line.
While we are on point to create the dashboard, our development process is highly collaborative — we call it Iterative Dashboard Development (IDD). This means there are never any surprises. Each dashboard is customized to fit your business like a glove.
After an engagement, we stay connected to ensure your business continues to reap the benefits of increasing revenue, lowering costs or mitigating risks.
DREAM is our model integration process
Click on a letter to find out more
Machine learning success is built by working effectively with people, not just machines.
We believe in collaborate engatement
We begin the DREAM process by gaining an understanding of your business challenges or identifying new opportunities. We show how these outcomes can be harnessed with data, technology and analytics. We are enthusiastic about quantifying the impact of closing performance gaps and growing your share of the pie.
Once we get a general understanding of your data, we develop a business intelligence roadmap using our proprietary DREAM process. It includes best practices in design, dashboard development, analytics, implementation and measurement. Our dashboards are easy to navigate, so they get used. They are actionable, so they impact the bottom line.
While we are on point to create the dashboard, our development process is highly collaborative — we call it Iterative Dashboard Development (IDD). This means there are never any surprises. Each dashboard is customized to fit your business like a glove.
After an engagement, we stay connected to ensure your business continues to reap the benefits of increasing revenue, lowering costs or mitigating risks.
Modeling use cases
Fraud detection
Risk managers decrease losses by predicting fraudulent transactions using powerful anomaly detection algorithms and synthetic data to establish baseline performance.
Customer acquisition
Marketing managers at banks increase revenue by predicting profitable customer segments likely to respond to specific digital and traditional marketing solicitations.
Customer retention
Marketing and operations managers minimize lost revenue by proactively implementing retention strategies for accounts likely to churn.
Customer behavior
Marketing managers increase customer engagement by personalizing messages to behaviorally-based segments while risk managers ensure that the default risk of customers in these segments do not exceed a pre-defined or dynamic threshold.
Cross-selling
Marketing managers increase existing account profitability by identifying the “next-best action” based on the customer’s expectations, propensities, and likely behavior.
Collections
Collection managers increase debt collected by predicting which delinquent loans are mostly likely to be recovered.
Better cash/liquidity planning
Credit managers track past usage patterns of customers at ATMs, branches and online locations to predict the relevance of new liquidity instruments that may better serve their needs.
Marketing optimization
The marketing team can utilize past multi-channel campaigns to inform how the overall budget can be allocated and to target customers with right message at the right time.
Customer Lifetime Value (LTV)
A customer’s lifetime value provides information about how long to expect the customer to stay with the bank and how much income the bank is likely to generate. Predictive models can move customers’ profitability and retention by identifying the attributes of the best customers.
Application screening
New account specialists process huge volumes of applications, without excluding important variables, without delays or errors, without growing tired- all of it with regularity and steadiness.
Disease diagnosis and prediction
Medical professionals improve the healthy outcome of patients by decreasing the misdiagnosis of diseases and increase the accuracy of prognoses
Operating room bottlenecks
Clinicians combine real-time data with information about event scheduling to improve workflows, the timeliness and relevance of important notifications, and the handoff of a patient from one area to the next.
Symptoms and health concerns
Chatbots improve health outcomes of patients by listening to symptoms and health concerns and then guides that patient to the correct care based on its diagnosis.
Over prescription of antibiotics to babies
Nurses apply the results of a predictive model using a risk calculator that better targets newborns who are at the highest risk for sepsis without exposing those at low risk to antibiotics.
Earlier cancer detection
Oncologists improve the odds of their patients surviving cancer by improving the quality of screenings, diagnostic tests, and blood work.
Research-enhanced insights
Radiologists improve the odds that their patients survive cancer by augmenting their diagnoses with the latest cancer research by analyzing scanned images and comparing them to findings.
Care transitions after hip and knee replacement surgeries
Hospital administrators reduced length of stay for patients receiving total hip and knee replacements by predicting which patients required inpatient rehabilitation and who would recover at home.
Machine Learning (ML) is no longer a “nice to have” for manufacturers. To remain competitive, manufacturers must use ML to optimize factory workflows, forecast demand for products and components, minimize product defects, reduce production and supply chain disruptions.
Demand forecasting
Forecast demand for manufactured product or component to allocate resources most profitably
Maintenance planning
Proactively anticipate maintenance needs by predicting unscheduled workflow disruptions
Product design
Reduce warranty claims by improving product design based on historical service patterns
Customer insights
Segment customers based on product features, support logs, pricing, and competitive offerings
Environment, health and safety (EHS) risk
Reduce EHS risk by predicting the drivers of non-compliance
We’re excited to add use cases for many industries.
Please be patient as we grow!
We’re excited to add use cases for many industries.
Please be patient as we grow!
Using past data to learn what is likely to happen in the future, identify actionable insights, and intervene to reduce costs, increase revenue and mitigate risks.
Technical Corner
ROC and Lift Curves
ROC curves are frequently used to show in a graphical way the connection/trade-off between sensitivity and specificity for every possible cut-off for a test or a combination of tests. In addition the area under the ROC curve gives an idea about the benefit of using the test(s) in question.
Technical Corner
ROC curves are frequently used to show in a graphical way the connection/trade-off between sensitivity and specificity for every possible cut-off for a test or a combination of tests. In addition the area under the ROC curve gives an idea about the benefit of using the test(s) in question.
D in DREAM represents the DISCOVERY phase
- During this initial phase, we meet with our future customer and ask a lot of questions. We learn about specific challenges and how data science may help close performance gaps.
- A major take-away from the DISCOVERY phase is knowing what we are solving for (TARGET) and what levers (can be used to affect it.
- We also contrast the business’ current “AS IS” state with a future “TO BE” state.
R in DREAM represents the REALIZATION phase
- From the Discovery phase, we have an analytical roadmap. We use this to guide our-building process to realize the solutions: Ai Models or Bi Dashboards.
- To build our solutions, we use a proprietary, collaborative process called Iterative Technology Development (ITD). This agile process accelerates the design of information technology ensuring that you are involved every step of the way and that you get results quickly.
- We also test the technology and ensure it meets the business’ specifications.
- After testing, the solution gets deployed in a production environment.
E in DREAM represents the EDUCATION phase
- During the Education phase, we illustrate to users how insights from the implemented solution can be used to impact decision-making.
- Also, the business conducts User Acceptance Testing (or UAT) and provides newData feedback.
- After making any adjustments from UAT, we conclude the Education phase by training other users in the business.
A in DREAM represents the ACTION phase
- Action is how a person or a tool in the business applies the insights from our solution to an actionable lever – in other words, this is reaping the benefits of your new solution. While the business is responsible for the application of insights, newData is available for questions and general guidance to ensure the process is working smoothly.
M in DREAM represents the MEASUREMENT phase
- During the final phase of the DREAM, the implemented solution is measured using a pre-post and/or a control group methodology. Like the Action phase that precedes measurement, the business is responsible for the measurement of the impact from the implemented solution.