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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