Data Mining Services
Data mining is the process of retrieving valuable insights from huge, diverse, and constantly renewable datasets with pattern recognition technologies, statistical and mathematical methods.
Since 2000, ScienceSoft provides data mining services to help companies uncover data patterns and make accurate data-driven decisions without investing in in-house data mining competencies.
Why Outsource Data Mining to ScienceSoft
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How Our Data Mining Services Unfold
1
Business analysis
- Defining business objectives to reach with data mining.
- Outlining exact questions, hypotheses, tasks to resolve with data mining.
- Determining issues with the existing data mining solution.
- Deciding on data mining KPIs.
2
Source data preparation
- Determining data mining sources and requirements for a data set.
- Data collection and transformation.
- Data quality management.
3
Data mining models creation
- Selecting optimal data mining methods to use.
- Defining the criteria for the future model/models evaluation (output quality KPIs, KPIs related to business outcomes).
- Creating data mining models.
- Running the developed data mining models on the prepared data sets.
4
Data mining models maintenance and tuning
- Continuous monitoring and tuning of data mining models for greater accuracy.
- Developing new data mining models (also, on new data sets) to address arising business needs.
5
Reporting and alerting
- Reports and dashboards with data mining results ready for business use.
- Alerting according to the predefined rules (e.g., pricing change detection).
6
Custom data mining applications (optional)
- Developing software with data mining capabilities for self-service use.
7
Quality assurance
- Evaluating data mining quality, including the quality of insights.
- Data mining quality evaluation reports.
- Testing software with data mining capabilities.
DATA SOURCES WE COVER WITH DATA MINING SERVICES
Websites
Online marketplaces
Emails
Social media
Enterprise systems (CRM, ERP, HRMS, etc.)
Business reports
IoT sensors
Medical devices
Research papers
Open-source databases
Data Mining Use Cases
Manufacturing
- Stock planning.
- Equipment failure prediction.
- Prediction of defects or reduced yield.
- Operations cost optimization.
Retail (including ecommerce)
- Merchandising strategy optimization.
- Online store UX design, conversion rate optimization.
- Shopping channel analysis.
- Cross-selling and upselling.
- Price monitoring and optimization.
Healthcare
- Treatment effectiveness assessment.
- Recommendations on lifestyle changes.
- Personalized care planning.
- Proactive care (definition of trends and patterns in patient condition requiring a doctor’s attention).
- Fraud detection in healthcare insurance.
Marketing
- Customer behavior and buying pattern analysis.
- Customer segmentation.
- Marketing campaign optimization.
- Sentiment analysis.
- Market trends evaluation.
- Competitor analysis and tracking.
- Pricing strategies and promotion tactics optimization.
Finance
- Fraud detection (cash, transactional, insurance, etc.).
- Financial risks analysis and management.
- Regulatory compliance.
- Credit rating evaluation.
HR
- Employee behavior and performance analysis.
- Employee retention prediction.
- Employee performance management.
- Recruitment management.
- HR policies evaluation.
- Employee engagement strategy optimization.
Benefits of Data Mining Outsourcing with ScienceSoft
Transparent service delivery based on KPIs
We deliver data mining services based on the agreed quality KPIs, which may include:
- Output quality KPIs:
- Interestingness metrics (e.g., how data mining model/pattern can become beneficial).
- Accuracy metrics.
- Reliability metrics (e.g., how a data mining model performs on different data sets).
- Missing alerts.
- KPIs related to business outcomes (e.g., conversion rate, sales, accounts receivable, etc.)
- Your satisfaction score defined with interviews and questioners.
Focus on data security
To ensure data security on multiple levels, we employ:
- Highly secure cloud facilities for data storage and processing (Azure, AWS, Google Cloud).
- Secure data transfer methods (FTP and VPN) controlled via regular health checks.
- 24/7 in-house data security monitoring.
Technologies and Methods We Use for Data Mining
Programming languages
Click on the programming language to learn about our capabilities in it.
Databases and data storages
Click on the technology to learn about our capabilities in it.
Big data
Click on the technology to learn about our capabilities in it.
Data science libraries, frameworks, and services
Data mining techniques
Data cleaning and preparation
Pattern tracking
Classification
Association
Regression
Clustering
Anomaly detection / outlier detection
Sequential pattern mining
Prediction
Decision trees / random forests
Time-series data mining
Natural language processing
Visualization ( reports and dashboards)
Statistics methods
Descriptive statistics
Logistic regression
Boosted regression
KNN-regression
Penalized regression models (Lasso, elastic net regularization, least-angle regression, support-vector machines, etc.)
Tweedie distribution
Time series methods (ARIMA, ARMA, SSA)
Markov chains
Structural equation modeling
Exploratory and confirmatory factor analysis
Statistical power analysis
K-means clustering
Bayesian methods (Naïve Bayes classifier, Bayesian model averaging, Bayesian adaptive designs, etc.)
Spline-based models (multivariate adaptive regression splines, etc.)
Survival analysis
Neural networks
Convolutional and recurrent neural networks (LSTM, GRU, etc.)
Autoencoders (VAE,DAE, SAE, etc.)
Generative adversarial networks (GANs)
Deep Q-Network (DQN)
Feedforward Neural Network
Radial basis function network
Modular neural network
Data visualization
Our Hallmark Data Science and Big Data Analytics Projects
Data Science Consulting for Electric Energy Consumption Analysis and Forecasting
ScienceSoft suggested high-level software architecture and provided detailed recommendations on creating machine learning models for electric energy consumption analysis and forecasting software, which would allow electric power companies to optimize their load management and price determination procedures.
Big Data Implementation for Advertising Channel Analysis in 10+ Countries
ScienceSoft implemented a big data analytics system, which allowed one of the top market research companies to carry out comprehensive advertising channel analysis for different markets.
Development of a Big Data Solution for IoT Pet Trackers
ScienceSoft delivered a scalable IoT data management solution that enables the processing of 30,000+ events per second from 1 million devices.
Development of a Data Science Solution for Clay Pigeon Shooting Scoring
ScienceSoft helped develop image analysis software to determine clay pigeon shooting results automatically. The solution is based on convolutional neural networks and the background subtraction algorithm.
Data Science Implementation for Sales Analysis and Forecasting
ScienceSoft supported a leading FMCG manufacturer by delivering science-based sales forecasting and attainable sales targets.
Development of an Advanced Data Analysis for a Regulatory Authority
ScienceSoft developed a BI solution with a robust analytical module to conduct financial market analysis and determine development and capital raising strategies, market participant analysis to discover hidden relationships between entities, trigger fraud alerts, etc.
Data Mining Service Options We Offer
Regular data mining outsourcing
- Get valuable business insights out of large, heterogeneous and constantly changing data sets without hiring an in-house data mining team.
- Suitable for 6+ month- cooperation
* If you don’t want to manage large databases in-house, consider adding our DWH-as-a-service.
One-time data mining outsourcing
- Gain quick insight into pressing problems.
- Try how data mining solves a particular business problem.
Embrace Data Mining Capabilities
Outsourcing data mining services, you benefit from:
- Increased ROI from marketing campaigns due to accurate prediction of campaigns’ outcomes.
- Optimized operational performance due to defining reasons behind operational bottlenecks.
- Increased customer acquisition rate and decreased customer churn due to accurate targeting of well-defined customer segments.
- Quick fraud detection due to outlier analysis.