Business Analytics SOLUTIONS

Discover opportunities using our powerful Ai platform

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

Machine learning success is built by working effectively with people, not just machines.

Our integration framework

Acronym description of newData's technology integration process
Our engagement begins with an understanding of your business challenges and/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 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 data transformation, model development and selection, implementation, and measurement. Our models are easy to interpret and integrate into your business.  While a model can be used for descriptive purposes or to keep a process under control, modeling applications are mainly to predict an outcome.  Understanding the likelihood that an outcome will occur means that it may be influenced by taking a particular action.

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.

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!

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.

Pin It on Pinterest