Services | ||
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Vision & Strategy (Enabling) | ||
Data Warehouse Modernization - Vision
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The Business Case supporting Data Lake and AI initiatives
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Data Strategy
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Data Governance & Architecture (Guiding) | ||
Data Governance Strategy
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Data Management
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Data Warehouse Modernization - Architecture & Governance
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Master Data Management - Architecture & Governance
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Data Lake Architecture Definition of Data Lake Zones to cover the various types of analytics with bimodal usage, governance, data management, exploration and operationalization
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Conceptual Enterprise Data Model (CEDM)
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Dimensional Enterprise Data Model (DEDM)
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Data Quality Subset of Data Management addressing Integrity/accuracy, availability, accessibility, coverage, compliance, timeliness/currency
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Data Warehouse & Data Mart ArchitectureData Architecture and data modeling + governance covering the Data Warehouse and the derived Datamarts in standard Inmon/Kimball fashion or Hybrid.
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Data Value Chain (Executing) | ||
Data Capture & Management for Operations | ||
Master Data Management - Execution
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Collect & Organize for Analytics | ||
IoT and other real-time Ingestion
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ETL migration to Hadoop/Spark
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Pattern base ETL
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Design & Implement a Data Lake with proper level of Governance
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ETL Is a type of data integration that refers to the three steps (extract, transform, load) used to blend data from multiple sources. Extract, Load, Transform (ELT) is an alternate but related approach designed to push processing down to the database for improved performance
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Analyze (incl. ML/DL/NLP & AI) | ||
Descriptive Is the interpretation of historical data to better understand changes that have happened in a business (the what). Descriptive analytics describes the past using a range of data to draw comparisons. Most commonly reported financial metrics are a product of descriptive analytics, e.g., year-over-year pricing changes, month-over-month sales growth, the number of users, or the total revenue per subscriber.
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Diagnostic Is the interpretation of historical data to understand why it happened and rely usually on OLAP capabilities allowing slicing, drilling and pivoting. Additional capabilities such as but not limited to market basket analysis and forecasting have also been embedded to OLAP tools.
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Predictive
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Prescriptive Recommending the best path forward, based on multiple business constraints, objectives and metrics (what should I do) by applying mathematical and computational algorithms)
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AI & Big Data – Executive advise This is a consulting mandate. The objective of this offering is to guide and educate CIOs and CDOs in their AI and Big Data endeavors. The target audience is organizations that have challenges in the positioning the variety of concepts, technologies, model frameworks, Hadoop distributions, cloud offerings and varying applications and specialization of AI, cognitive services, NLP, deep learning, and so on.
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Rent a Data Scientist
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Deploy | ||
Dashboards & Reports Visualizing information in the form of Dashboards and/or reports, including multi-dimensional
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Models & Algorithms - deployment in process/application
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Callable AI
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Monitor | ||
Monitoring of Data Ingestion and Integration Via dashboards and alerting, it monitors the ingestion and integration aspect of the Data &
Analytics Value Chain to inform of the state of the analytics data
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Monitoring of Models Performance Reusing recommendations and predictions produced thru various models, this monitoring intends to
detect model degradation in order to retire or replace them with another version or
brand new model
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Data & Analytics Platforms (Providing Capabilities) | ||
Data & Analytics Platforms technical architecture (includes multi clouds and/or premise)
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Data & Analytics Platforms recommendations
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Hadoop/Spark distribution Install
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Data & Analytics Platforms sizing
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Big Data – Architecture (including Lamda, Kappa, …) Adopting Kappa versus Lambda or other variations, has immediate effects in the overall Big Data Architecture. For example, Lambda approach uses a 3 layers mindset: a speed layer, a batch layer and a serving layer using the batch and speed layer to support queries with low latency
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Securing a Hadoop/Spark platform Securing a Hadoop cluster includes: Kerberos, encryption at rest/in memory/in motion, access profiles, authentication, zones, remote VM, pentest, key management, metadata management, logging, policies…
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Training KPI currently offers training in Cognos and will expand to cover additional areas of the KPI Analytics & Big Data Offering
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