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Business Challenges of DataThe evolution of AI, ML, IoT technologies is magnifying how data is created and used by businesses.Data-driven decision making has many challenges across business roles from the executive to middle management staff.
ABeam’s BI ApproachABeam BI proprietary approach is based on “Improvement of Analytical Skills” and “Data Analytics System Deployment” to achieve results.Traditional consulting approaches, in most cases, focus on either the improvement of analytic skills or data analytics system deployment, not both.
ABeam's approach supports improvement of data analytics by promoting both sides; improvement of analytic skills and the data analytics system deployment.
Stages of Project ProcessABeam’ s BI approach considers each business problem and takes a phased approach.
Data Analytics Advisory Services
Improvement of Analytical Skills
Data AnalyticsSystem Deployment
ABeam BIApproach
AnalyticalConsultingServices
IT Vendor
TechnicalSupport
Test AnalyticsApproach
Introduction&
Training
Strategy SystemBusinessProcess
Improvement of
Data Analytics
Improvement of
Data Analytics
Strategy ConsultingServices
Human &Organization
As-Is analytics
Define HR
Goal/Strategy Setting Define analytics Roadmap
Data AnalyticsSystem
Development
Improvement of Analytic Skills
Define Organization Maintenance Training/Evaluation System
Define Data Define Analytics System Define Analytics Strategy
Introduction & Training Test Analytics Approach Technical Support
Define Business Process Maintain Analytic Processes and Interaction
Phase 1 Phase 2 Phase 3Find business issues and define To-Be state
Find business issues and define To-Be state
Define goals and determine overall To-Be concept
Define goals and determine overall To-Be concept
Perform independent analytics operations and continuously
improve analytical skills
Perform independent analytics operations and continuously
improve analytical skills
Example of Common Issues
Executive
ProjectManager
Staff
Don’t know where to start ; positioning data analytics, setting goals, identifying themes, etc.
Visibility and access to data across enterprise systems.Limited experience in executing data analytics.
Availability of skills to execute data analytics.Understanding the business impact of data analytics.
Role for Business
Deployment of Organizational Structure based on Data AnalyticsABeam BI improves strategic feasibility and data analytics by creating an organizational structure that centrally manages data analytics executed by each division.
AI / ML Application (not using “Black Box”)Generally with continuous usage of AI(Artificial Intelligence) / ML(Machine Learning) the businesses data will become harder to understand.
ABeam provides “white box” approach that integrates lifecycle of products, services, business and also the intelligence cycle.
It is important to choose and control appropriate AI/ML technology depending on the needs. In our example, we used a self-driving slot car which is controlled by AI and the performance is improved with the use of ML driven from the AI data.
Example of Problem Solving Approach Using AI/ML
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Management Strategy
Analytics Department
Business Strategy
Business Department Analytics for improving profitability
CorporatePlanning HR Accounting
Analytics to support the business
Centrally manage data analytics executed by each divisionPromote improvement of data analytics and strategy feasibility
Enterprise Strategy
Enterprise Department
Data Analytics Level
Management Strategy
Analytical Results(Each department)
Knowledge Management
Issues
Input OutputProcess
Centrally ManageData
SCM Marketing/Sales Services
Strategy
Feasibility
データの構造化
Improve analytics cycle by embedding analytics results into the business operation and system.
Expansion As-Is Analytics / Hypothesis Building
Extract results by combining multiple algorithms and adjust based on experience.
Data Mining / Prediction Analytics
Clean data structure based on international regulations such as ISO and business insight.
Data analytics based on the field workers insight and build hypothesis.
Clean Data Structure
Select Business Problem
Select AI/MLApproach
Continuous Improvementby Analysis
System Deployment
Define possible problems that can be resolved by using AI/ML and efficient approaches.
Select appropriate data (sensors) and technology.Implement AI/ML.
Based on the results from data analysis, verify accuracy and improve AI.
Develop real-time monitoring system to make continuous analysis.
• Assessment of self-driving cars behavior as expected
• To avoid the risk of slipping from the track, analyze data to make improvements
Develop system for• Visualization of driving status• Investigation on the best practice of “Good” driving
Define considered approaches:• What is needed to fulfill “self-driving”?• What is the necessary technology combination?
• Gyro sensor/Ultrasonic Sensor/Accelerometer/Angle Sensor, collect data useful for AI
• Design the method of decision making & controls