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Analytics & Big Data Solution Offerings

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Analytics & Big Data value add

2017 2018
Vision & Strategy (Enabling)
Data Warehouse Modernization - Vision
  • Multi-purpose governed Data lake: Rational behind the migration from a traditional DW
  • Multi-purpose governed Data Lake: Architecture and functionally
  • DW Modernization – Vision Organization’s Adoption
  • High-Level roadmap
No Yes
Cost evaluation for Data Lake and AI initiatives
  • Required data & analytics platforms
  • Data & analytics platforms implementation effort + realization of the Data Value Chain effort
  • Identify cost components & map to business value
  • ROI
No Yes
Data Strategy
  • Data Governance, Data Management, Data Architecture, Security and compliance
  • Processes, stakeholders, technologies, bimodal considerations, Scalability
  • Elasticity, Lineage, Quality, Data Usage, Logging, Access Rights, MDM, Clouds, AI
  • Priorities, target state, Vision & Roadmap
Yes Yes
Data Governance & Architecture (Guiding)
Data Governance Strategy
  • Organization Data Policies covering accesses, availability, validity, integrity, compliance, privacy, coverage, lifecycle and data principles
  • Overall framework of data management, the guidance, with stakeholders, processes and best practices
  • Mapping of how data is used to support the organization’s goals and objectives
No Yes
Data Management
  • Operational data management using Data Policies, Data Principles and best practices
  • Data Quality Targets and its monitoring to support Business goals and objectives
  • Master Data Management
No Yes
Data Warehouse Modernization - Architecture & Governance
  • Data Platform & Data Lake architecture
  • Data & Analytics Value Chain: Data Governance & Management
  • Migration process - Detailed roadmap
No Yes
Master Data Management - Architecture & Governance
  • Processes & technologies: Target level of uniformity & accuracy for core data assets (customers, suppliers, products, etc.)
  • Identification of organization’s specific core data assets to master with contributing data sources
  • Strategy of unique ‘golden records’ or multiple perspectives given usage for each core data assets
  • Definition of Processes & technologies with Stakeholders
No Yes
Data Lake Architecture Definition of Data Lake Zones to cover the various types of analytics with bimodal usage, governance, data management, exploration and operationalization
No Yes
Conceptual Enterprise Data Model
  • Enterprise wide
  • Using business language
  • Organized in Data Domains and Sub Domains with Views
  • Business Dictionary with or without a Data Management framework
Yes Yes
Dimensional Enterprise Data Model
  • Re-organization of the CEDM in Dimensions & Fact Tables
  • Enterprise-wide set of conformed dimensions and clear metrics
  • Used for both traditional DW & Datamarts and/or the OLAP consumption zone of a Data Lake
Yes Yes
Data Quality Subset of Data Management addressing Integrity/accuracy, availability, accessibility, coverage, compliance, timeliness/currency
Yes Yes
Data Warehouse & Data Mart Data Architecture and data modeling + governance covering the Data Warehouse and the derived Datamarts in standard Inmon/Kinmon fashion or Hybrid
Yes Yes
Data Value Chain (Executing)
Data Capture & Management for Operations
Master Data Management
  • Processes & technologies: Target level of uniformity & accuracy for core data assets (customers, suppliers, products, etc.)
  • Design & Implementation of core data assets master (includes matching rules)& the processes/procedures with stakeholders (governor, steward and custodian)
  • QA of core data assets master processes and procedures
  • MDM dashboard
  • Roadmaps for addressing data issues
Yes Yes
Collect & Organize for Analytics
IoT and other real-time Ingestion
  • The process of absorbing data in batch, micro batches and real-time
  • Identification of the tools to use (Kafka, NIFI, Storm, Spark Streaming, Sqoop, Pig, Oozie, ADF, Kinesis, Data Pipeline …)
  • Ingestion code Development & Testing
No Yes
ETL processing migration to Hadoop/Spark
  • ETL code developed in tools that now support code generation towards Hadoop M/R or Spark preventing re-writing
  • ETL/Ingestion exit strategy
  • Parallel testing
  • Staging data thru the various Data Lake zones (best practices)
No Yes
Pattern base ETL
  • ETL/Ingestion is a very significant portion of the costs of the overall Data & Analytics Value Chain
  • Pattern Based ETL/Ingestion aim at reducing these costs by up to 50% using patterns such as Data Vaults with technologies supporting patterns based ETL generation
No Yes
Design & Implement a Data Lake with proper level of Governance A Data Lake could be a throw away, used for one specific purpose using non-sensitive data or it could be the persistent repository of all data of an organization & external data with varying degree of sensitive data in all variations of these two extremes, governance (including security, compliance and privacy) need to be considered in bi-modal modes
No Yes
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
Yes Yes
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. These all describe what has occurred in the business in the time period being measured
Yes Yes
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. Could be completed with Dashboards
Yes Yes
Predictive Uses many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence applied on existing data sets to determine patterns and predict future outcomes and trends (what should happen)
Yes Yes
Prescriptive Applies mathematical and computational sciences to suggest a best path (recommendation) forward based on the constraints and objectives of a business (what should I do)
Yes Yes
AI Education rollout Even with an understanding of the ‘4 main types of analytics’. Organizations have difficulties with the variety of concepts, technologies, model frameworks, distributions, cloud offerings and varying applications and specialization of AI, cognitive services, NLP, deep learning, and so on. The objective of this offering is to guide and educate CIO and CDO in their AI and Big Data endeavor
No Yes
Rent a Data Scientist with or without Governance Renting a Data Scientist without Governance is essentially using the expertise of a Data Scientist in terms of Models and Algorithms Renting a Data Scientist with Governance add Data Governance with Data Management, Architecture and Project Management to the Data Scientist expertise
No Yes
Deploy
Dashboard/Scorecard Visualization in the form of Dashboard and Scorecard services
Yes Yes
OLAP (including OLAP on Hadoop) Visualization in the form of OLAP (cubes implementation) being virtual, MOLAP, ROLAP or Hybrid
Yes Yes
Manage Reporting Solutions Support services of deployed reporting solutions
Yes Yes
Model deployment in process/application Once a model has been trained and its level of accuracy meets the business objectives for predicting, recommending or classifying, it then gets deployed in a process/application. Ultimately, AI is all about optimizing a process (and a process is implemented usually in applications) Includes the management of all these analytics assets (lifecycle, versions, …)
No Yes
Callable Analytics Making a model callable via an API
No Yes
Use our Model (AI) KPI intends to build a series of Model available to our clients thru one or more cloud providers Callable via API Model (and algorithms) under Intellectual Property of KPI Digital Billing usually thru usage consumption
No Yes
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
Yes Yes
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
No Yes
Preventive Model Training Complement the Monitoring of Model Performance offering by retraining existing models using newer or other data sources and determine reliability of models
No Yes
Data & Analytics Platforms (Providing Capabilities)
Design Hybrid (multi clouds and/or on premise) No Yes
Data Platforms sizing No Yes
Hadoop/Spark distribution Install No Yes
Big Data – Architecture (including Lamda, Kappa, …) No Yes
Big Data Platform SDLC No Yes
Securing a Hadoop/Spark platform No Yes
Upgrades and Migrations No Yes
Data Platforms recommendations Yes Yes
Data Platforms technical architecture Yes Yes
Training Yes Yes
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