Data Visualization Management and Warehousing
The analysis of Big Data is a wonderful tool that provides new opportunities for the development of retail business. DataArt is an expert at the existing products and services based on Big Data.
Consulting on Big Data Processing And Analysis
Turnkey Big Data Software Development
Big Data Integration With Your Software
Big Data Analysis
Big Data Management and Support
Big Data Visualization
Data Integration
- Automate data ingestion with open source tools and systems
- Improve data ingestion performance and scalability
- ETL (Extract-Transform-Load)
Data Warehousing and Storages
- Lean transformation using open source data management systems
- Migration to cloud and hybrid data storage systems
- Data storage scalability improvement
- Transition from scale-up to scale-out
- Data migration
Data Engineering
- Building modern data pipelines
- Improving performance and scalability with open source solutions
- Transforming batch data processing into real-time
Data Mining / Machine Learning
- Recommendation engines
- Deep learning
- Predictive analytics
- Unstructured data digitalization
Data Warehousing and Processing
- Migration to cloud and hybrid data storage systems
- Unstructured data storage
- Data access performance optimization
- Data storage scalability improvement
- Lambda/Kappa architectures
Analytics and BI
- Unstructured data processing
- Fast search
- Self-service BI
- Extending analytical reports with DS capabilities
Data Management
- Master data management
- ETL (Extract-Transform-Load)
- Reference data management
- Improving data ingestion performance and scalability
Data Visualization
- Large graphs visualization
- Open-source frameworks
- Interactive data documents
- HTML5 & 3D
Our Approach
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Building Modern Data Analytics Architectures
The evolution of big data and cloud technologies, as well as an increase in business engagement with data and analytics, has brought a significant shift in the design principles of modern data analytics architectures. DataArt helps build architectures that have a flexible, modular structure, enabling components to evolve at the speed of business. Such platforms enable real-time and batch processing, support secure data management and governance, scale on demand, and bill only for the resources used.
Deliverables for clients:
Through the design and construction of modern data analytics architecture solutions, DataArt delivers:
- A modular and flexible data analytics foundation that evolves with business needs
- Future-proof architecture that will accommodate new data sources and downstream applications and uses
- A move from historical reporting to predictive and prescriptive analytics
- The capabilities for digital transformation and business agility
- A move to self-service and the Citizen X (integrator, data scientist, etc.)
- Reduced costs and accelerated change in data architecture and analytics using next-generation cloud technologies
- Democratized data, business intelligence, and advanced analytics through business-driven data sharing, lineage, quality, security, and governance
- Greater responsiveness to line-of-business (LOB) users
Services
- Modern Data Architecture Design and Delivery
- Data Analytics Implementation
Skills and Expertise
To plan and construct high-quality data analytics solutions, DataArt harnesses specialist skills like:
- Data Integration and Data Engineering
- Data Warehousing, Data Lakes and EDW Automation
- Big Data Analytics, Real-time, Graph Data Analytics
- Data Governance, Lineage, Metadata Management
- Data Visualization, BI, Reporting
- Data Science and Machine Learning
- Data Distribution, API Data Delivery
- Master and Reference Data Management, Semantic Data
- AI-Infused Applications
Partnerships
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Enabling an Insights-driven Organization
To make actionable intelligence possible, companies have to design data architectures and processes that ensure transparency, reliability, and accessibility, while also enabling direct real-time interaction with data by business users. DataArt helps to establish a modern data management foundation and Agile self-service BI by building business-focused POCs and gradually evolving the data analytics platform and processes.
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Data Management
DataArt helps organizations reach the following objectives:
- Achieve high data quality by establishing robust data quality management and integration practices
- Define a practical value-driven and business-led data governance framework to support data quality, security, modeling, and integration
- Improve transparency and trust in data and increase data management efficiency with data lineage and metadata management
- Make data and analytics easily accessible to a wide range of business users and drive user engagement with those data systems
- Ensure successful delivery of high-priority business-use cases while simultaneously evolving the data architecture and operating model
The Journey to Modern Data Management Services
To achieve these goals, DataArt provides the following consulting services:
- Data strategy consulting
- Data landscape profiling
- Analysis and prioritization of data and analytics pain points and business scenarios
- Solution roadmap definition-architecture and operating model evolution, POC funnel, business case delivery
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Capability assessment, target state envisioning, solution design, technology and tools selection and implementation in:
- Data governance and quality management
- Metadata management and data lineage
- Data modeling and integration
- Data architecture and storage
- Data security
- Data warehousing and reporting
- Business Intelligence and Advanced Analytics
- Master and reference data management
- Graph and semantic data
- Data distribution and data products
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Agile Business Intelligence
Augment traditional standardized Data Warehouse (DW) and Business Intelligence (BI) workloads with self-service BIThis allows business users to efficiently source data and explore its value by building analytical models or visualizations in a matter of minutes or hours.
Analyze and select self-service BI or analytics toolsets that fit each individual business(e.g., visualization/dashboarding, comprehensive calculation modeling, or supercharged ML-powered analytics).
Integrate BIIntegrate BI with data lineage and metadata management foundations to provide an in-context understanding of the data, along with its flow and semantic and quality characteristics.
Streamline the transitionStreamline the transition from exploration by business users to rapid prototyping/proof-of-concept (PoC) creation by a central BI or data analytics team.
EstablishEstablish BI governance, usage monitoring, and analytics to continuously curate, rationalize, standardize, optimize, and control business-use case implementations that proved value.
Embed and automateEmbed and automate BI and analytics into line-of-business systems and business processes to seamlessly deliver contextual and actionable insights
EmployEmploy usage monitoring and analytics to capture Agile BI value metrics, thereby supporting the understanding, recognition, and continuation of organizational change.
Design a practical data security framework for this era of accessible BI
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Enabling AI, Machine Learning, and Data Science
DataArt helps companies adopt machine learning (ML), predictive analytics, and Data Science to achieve their goals while ensuring data privacy, model explainability, regulatory and compliance requirements, and operational excellence.
A key organizational barrier to enabling ML and Data Science is the lack of internal expertise and talent. DataArt fills this gap by providing short-term data science experts who can make sustainable, long-term changes by:
- Selecting and adopting ML tools and data platforms
- Designing and adopting a data science process that combines learning strategies with practical business case exploration and implementation
Data Science Lifecycle Services
To support the transition to Data Science, DataArt provides a range of consulting, data integration, and custom development services, including:
AI Consulting
- AI Envisioning workshops
- Architecture design sessions, including Data Lakes and AI-infused solutions
- AI PoC implementation
- AI and Data Science project planning, consulting, and assessment
Big Data & Integration
- Data integration pipeline design and implementation
- Text mining and natural language processing (NLP)
- Custom and third-party model integration into an existing system/application
- Data feed integration (data feeds, data APIs)
- Data preparation and processing
- Intelligent agent (chatbot) integration
- Cloud-based AI/ML platform integration
Custom Model Development and Data Science
- Custom ML model creation & training
- Custom DL model creation & training
- Custom image processing
- Custom NLP and text mining
Custom Algorithms and System Development
- Custom algorithm implementation
- Custom data quality management solutions
- Model deployment and operation
- Custom intelligent agent (chatbot) and applications development
Languages and Frameworks
- Hadoop
- Hive
- Pig
- Python
- Scala
- Storm
- Spark
Cloud Platforms
AWS
Google Cloud
Microsoft Azure
Case Studies
FAQ
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How can Big Data help my business?
Big data is a collection of technologies that are designed to perform three operations:
- Process large amounts of data compared to "standard" scenarios.
- To be able to work with rapidly arriving data in huge volumes. There is not just a lot of data, but they are continually becoming more and more.
- Be able to work with structured and loosely structured data in parallel and different aspects.
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What data mining methods are there?
Methods of Data Mining are a set of methods for detecting previously unknown, non-trivial, practically useful knowledge required for decision-making in data.
In particular, these methods include association rule learning, classification, cluster analysis, regression analysis, prediction, clustering, outlier detection and patterns tracking, etc.
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What is big data crowdsourcing?
Crowdsourcing is the classification and enrichment of data by the forces of a vast, indefinite circle of people who perform this work without entering into an employment relationship.
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What is data fusion?
Data fusion and integration are a set of techniques that allow you to integrate heterogeneous data from various sources to conduct in-depth analysis (for example, digital signal processing, natural language processing, including tone analysis, etc.)
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Why does Big Data need machine learning?
Machine learning, including supervised and unsupervised learning, uses models based on statistical analysis or machine learning to generate complex predictions from baseline models.
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What are genetic algorithms?
Artificial neural networks, network analysis, optimization, including genetic algorithms, are heuristic search algorithms used to solve optimization and modeling problems by random selection, combination, and variation of the desired parameters using mechanisms similar to natural selection in nature.
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What is big data simulation?
Simulation modeling (simulation) is a method that allows you to build models that describe the processes as they would in reality. Simulation can be considered as a kind of experimental testing.
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What analyzes can be done using big data?
Spatial analysis is a class of methods that use topological, geometric, and geographic information extracted from data.
Statistical analysis is time series analysis, A / B-testing (A / B testing, split testing - a marketing research method; when using it, a control group of elements is compared with a set of test groups in which one or more indicators have been changed to find out which of the changes improves the target).
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How are big data visualizations created?
Analytical data visualization is the presentation of information in pictures, diagrams, using interactivity and animation, both for obtaining results and for use as input data for further analysis. A very important stage in big data analysis allows you to present the essential analysis results in the most understandable form.