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Data Science as a Service

Data Science as a Service - ScienceSoft

Data science as a service allows companies to get business insights leveraging advanced analytics technologies, including deep learning, without investing in in-house data science competencies.

Since 1989, ScienceSoft provides companies with data science services to enable them to exploit growth and process improvement opportunities.

Get Access to Advanced Analytics Techs and Skills

Optimize your business processes with effective root cause analysis and reliable forecasting delivered by ScienceSoft’s team on a regular or on-demand basis.

What Makes ScienceSoft a Reliable DSaaS Vendor

  • 34 years in data science and data analytics.
  • 10 years in big data.
  • 18 years of experience in rendering data warehouse services.
  • Hands-on experience with all major languages, libraries and cloud services for data science.
  • For the second straight year, ScienceSoft USA Corporation is listed among The Americas’ Fastest-Growing Companies by the Financial Times.

  • ISO 9001 and ISO 27001-certified to assure the quality of the data science services and the security of the customers' data.

  • Domain experience in 30 industries, including manufacturing, energy, retail and wholesale, professional services, healthcare, financial services, transportation and logistics, telecommunications.

Analytics Domains We Cover with DSaaS and Value You Get

Operational intelligence

Operational intelligence

  • Root cause analysis and bottleneck recognition
  • Forecasting of business performance metrics

Improvements in:

Operational decision-making

Working capital management

Process, resource, cost management

Supply chain management

Supply chain management

  • Supply forecasting
  • Demand forecasting
  • Preventive alerting for inventory control

Improvements in:

Stock control

Inventory management

Demand and supply planning

Order fulfillment

Production management

Production management

  • Demand and throughput forecasting
  • Process quality prediction
  • Production loss root cause analysis

Improvements in:

Production process management

Overall equipment effectiveness

Overall resource effectiveness

Predictive maintenance

Predictive maintenance

  • Root cause failure analysis and prediction
  • Remaining useful lifetime prediction
  • Predictive monitoring and preventive maintenance

Improvements in:

Asset lifetime and uptime

Total productive maintenance

Maintenance and repair costs management

Risk management

Risk management

  • Counterparty risk analytics
  • Potential damage prediction

Improvements in:

Risk mitigation

Credit risk management

Liquidity risk management

Fraud detection

Customer analytics

Customer analytics

  • Sentiment analysis
  • Customer behavior prediction
  • Sales forecasting

Improvements in:

Cost per customer/lead

Revenue per customer

Lead conversion rate

Customer acquisition and retention

Quality management

Quality management

  • Defect root cause analysis
  • Production output predictive modeling with varying inputs
  • Image and video analysis, automated visual inspection

Improvements in:

Cost of quality management

Material consumption

Rework efforts

Number of product recalls

Our Featured Data Science and Big Data Analytics Projects

Frequently Asked Questions

Question

What value do we get when choosing data science as a service?

  • Quickly embracing data science capabilities without growing in-house data science competencies.
  • Getting advanced analytics insights for end business users.

Question

How can we be sure of the quality and speed of analytics insights?

DSaaS delivery is based on the agreed service quality KPIs, which may include:

  • Output quality KPIs:
    • Insights by value (high / average / low).
    • Forecast accuracy.
    • Missing alerts.
  • Business result-related KPIs (e.g., improvements in energy consumption or service delivery time).
  • User satisfaction score.

Question

Will our data be secure?

Data safety is ensured through:

  • Storing and processing data on highly secure cloud facilities (Azure, AWS, Google Cloud).
  • Conducting 24/7 in-house data security monitoring.
  • Using secure data transfer methods (FTP and VPN) controlled via regular health checks.

Data Science Technologies and Methods We Use

Pricing Models for Data Science as a Service

Monthly subscription fee

Recommended when the engagement scope is clear, for outsourcing a particular number of data science talents to perform the required activities.

Time and Material

Recommended when the engagement scope is unclear.

Choose Your Service Option

For companies with no data science capabilities

We offer:

  • Analysis of business needs driving the company to apply data science.
  • Source data preparation and cleansing.
  • Development, training, testing and deployment of machine learning models.
  • ML model tuning.
  • Delivering data science output in an agreed format.
  • Integrating ML models into an application for users’ self-service, if required.
Start DSaaS

For companies that need to enhance their data science insights/initiatives

We offer:

  • Analyzing your business needs and the existing data science environment.
  • Evaluating the existing ML models, ML debugging and error analysis.
  • Data cleansing.
  • Continuous tuning and training of ML models for increased ML model accuracy.
  • Adding new data to the ML models for deeper insight.
  • Building new ML models to address new business and data analytics questions.
Hand over your data science initiative

Why Turn to DSaaS Right Now

An expert data science team can help you quickly embrace data science for meeting particular advanced analytics objectives and achieving the following benefits:

  • Up to 30%

    Reduction of equipment maintenance cost due to predictive monitoring and preventive maintenance.

  • Up to 20%

    Increased product throughput and improved on-time delivery due to demand and throughput forecasting and production process optimization.

  • Up to 35%

    Increase of product quality in discrete manufacturing with defect root cause analysis and product quality predictive modeling.

  • 5%

    Reduction of inventory management costs due to the AI-based forecasting of demand-driving factors.

  • 49%

    Consumers willing to shop more frequently due to customer behavior prediction and forecasting.

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