The document discusses frameworks for digital transformation and data processing. It provides an overview of BCG's digital transformation framework, which includes digitizing core domains and processes. It also discusses building blocks for digitizing core functions like data and analytics, machine learning/deep learning, and blockchain. The document then covers modern business intelligence blueprints and multimodal data processing blueprints. Finally, it discusses AI/ML blueprints including data sources, labeling, automation, querying, feature stores, experiment tracking, model monitoring, and distributed processing.

1. 1: Bối cảnh Digital Transformation
+ Năng lực (con người) phát triển tuyến tính
+ Công nghệ phát triển theo hàm mũ

2. #BCG Digital Transformation Framework
* Số Hoá Core Domain
* Số Hoá Quy trình Tăng trưởng
* Con người và Tổ chức

3. #BCG Digitize Core Building Blocks
* Data & Analytics
* ML/DL
* Blockchain
Credit

4. #BCG Digitize Core Building Blocks - Domain Driven
* Business Model Canvas
* Customer Journey
* Event Storming
* Aggregate
Credit

5. #BCG Digital Transformation Framework
* Data & Analytics
* ML/DL
* Blockchain

6. #BCG Digitize Core Building Blocks
* Apache Family
* Domain-Driven Data
Architecture
Credit
Technology is ready. Business needs domain experts to drive the implementation.

7. 1. Modern Business Intelligence Blueprint
Query and Processing
Output
Predictive
Historical
Storage
Sources
Ingestion and
Transformation
OLTP Databases
via CDC
Connectors
(Fivetran, Stitch,
Matillion)
Dashboards
(Looker, Superset,
Mode, Tableau)
Embedded
Analytics
(Sisense, Looker,
cube.js)
Augmented
Analytics
(Thoughtspot, Outlier,
Anodot, Sisu)
App Frameworks
(Plotly Dash, Streamlit)
Custom Apps
Ad Hoc Query
Engine
(Presto, Dremio/
Drill, Impala)
Real-time
Analytics
(Imply/Druid, Altinity/
Clickhouse, Rockset)
Applications/ERP
(Oracle, Salesforce,
Netsuite, ...)
Data Modeling
(dbt, LookML)
Spark Platform
(Databricks, EMR)
Databricks/
Delta Lake, Iceberg,
Hudi, Hive Acid
Parquet,
ORC, Avro
Python Libs
(Pandas, Boto,
Dask, Ray, ...)
Event Streaming
(Confluent/Kafka,
Pulsar, AWS Kinesis)
Stream
Processing
(Databricks/Spark,
Confluent/Kafka, Flink)
Metadata
Management
(Collibra, Alation, Hive,
Metastore, DataHub, ...)
Quality and Testing
(Great Expectations)
Entitlements
and Security
(Privacera, Immuta)
Observability
(Unravel, Accel Data,
Fiddler)
Batch Query
Engine
(Hive)
Event Collectors
(Segment, Snowplow)
Workflow
Manager
(Airflow, Dagster,
Prefect)
3rd Party APIs
(e.g., Stripe)
File and Object
Storage
Logs
Data Lake
Data Warehouse
(Snowflake, BigQuery, Redshift)
Data Science Platform
(Databricks, Domino, Sagemaker, Dataiku,
DataRobot, Anaconda, ...)
Data Science and ML Libraries
(Pandas, Numpy, R, Dask, Ray, Spark, ...
Scikit-learn, Pytorch, TensorFlow, Spark ML, XGBoost, ...)
S3, GCS,
ABS, HDFS

8. 2. Multimodal Data Processing Blueprint
Query and Processing
Output
Predictive
Historical
Storage
Sources
Ingestion and
Transformation
OLTP Databases
via CDC
Connectors
(Fivetran, Stitch,
Matillion)
Dashboards
(Looker, Superset,
Mode, Tableau)
Embedded
Analytics
(Sisense, Looker,
cube.js)
Augmented
Analytics
(Thoughtspot, Outlier,
Anodot, Sisu)
App Frameworks
(Plotly Dash, Streamlit)
Custom Apps
Ad Hoc Query
Engine
(Presto, Dremio/
Drill, Impala)
Real-time
Analytics
(Imply/Druid, Altinity/
Clickhouse, Rockset)
Applications/ERP
(Oracle, Salesforce,
Netsuite, ...)
Data Modeling
(dbt, LookML)
Spark Platform
(Databricks, EMR)
Databricks/
Delta Lake, Iceberg,
Hudi, Hive Acid
Parquet,
ORC, Avro
Python Libs
(Pandas, Boto,
Dask, Ray, ...)
Event Streaming
(Confluent/Kafka,
Pulsar, AWS Kinesis)
Stream
Processing
(Databricks/Spark,
Confluent/Kafka, Flink)
Metadata
Management
(Collibra, Alation, Hive,
Metastore, DataHub, ...)
Quality and Testing
(Great Expectations)
Entitlements
and Security
(Privacera, Immuta)
Observability
(Unravel, Accel Data,
Fiddler)
Batch Query
Engine
(Hive)
Event Collectors
(Segment, Snowplow)
Workflow
Manager
(Airflow, Dagster,
Prefect)
3rd Party APIs
(e.g., Stripe)
File and Object
Storage
Logs
Data Lake
Data Warehouse
(Snowflake, BigQuery, Redshift)
Data Science Platform
(Databricks, Domino, Sagemaker, Dataiku,
DataRobot, Anaconda, ...)
Data Science and ML Libraries
(Pandas, Numpy, R, Dask, Ray, Spark, ...
Scikit-learn, Pytorch, TensorFlow, Spark ML, XGBoost, ...)
S3, GCS,
ABS, HDFS

9. 3. AI and ML Blueprint
Clients
Data Sources
(Data lake +
data warehouse +
streaming engine)
Data Labeling
(Labelbox, Snorkel,
Scale, Sagemaker)
Dataflow Automation
(Airflow, Pachyderm, Elementl, Prefect, Tecton, Kubeflow)
Query Engines
(Presto, Hive)
Feature Server
(Tecton, Cassandra)
Compiler
(TVM)
Feature Store
(Tecton)
Data Science
Libraries
(Spark, Pandas,
NumPy, Dask)
Experiment
Tracking
(Weights and
Biases, Comet,
MLflow)
Model
Registry
(Algorithmia,
MLflow,
Sagemaker)
Visualization
(Tensorboard,
Fiddler)
Model Tuning
(Sigopt, hyperopt,
Ray Tune)
ML
Framework
(Scikit-learn,
XGBoost, MLlib)
DL
Framework
(TensorFlow, Keras,
PyTorch, H2O)
Model
Monitoring
(Fiddler, Arthur,
Arize)
Distributed
Processing
(Spark, Ray, Dask,
Distributed TF,
Kubeflow,
Horovod)
RL Libraries
(Gym, Dopamine,
RLlib, Coach)
Data Science Platform
(Jupyter, Databricks, Domino, Sagemaker, DataRobot,
H2O, Colab, Deepnote, Noteable)
Model Inference
Model Training and Development
Data Transformation
Batch Predictor
(Spark)
Online Model
Server
(TF Serving, Ray
Serve, Seldon)

10. Data Visualization
Data Connector
(Sourcing)
Data Engagement
(Activation/Reporting)
Data Security (Compliance)
Streaming
Processing
Data Modeling
Machine Learning
Data
Product
(SQL)
Query
Provider
Data Lake
Batch Processing
(2)
(1)
(1)
(2)
(2)
(2)
(1) Data Discovery
+ Ingest data from variety data
sources help cut down your
massive data set to a
manageable size where you
can focus your e
ff
orts on
analyzing the most relevant
data
+ Visualize data e
ff
iciency at
scale
+ Agile process for explaining,
exploration and deciding:
segmentation or campaign
orchestration
(2) Data Engagement
+ Plug and Play any kind of Data
Modeling either Statistical or
ML Model
+ Engage Data-driven sta
ff
for
making decision precisely
+ Campaign activation needs
near-realtime data for better
performance
+ Expose hidden-topic asap
(3) Data Governance
+ Secure data
+ Re
f
ine Data Integrity
+ Personalization requires data-
integrity
Data
Governance
(3)
(3)
(2)
Apache Bean Apache Hudi - Apache Atlas
Presto
Dagster - MLFlow
Tensor
f
low/PyTorch
Apache Ka
f
ka
Apache Bean
ReactJS Golang - Apache Karaf
Elastic Search
Clickhouse
Apache Superset Cadence
# Data Engine Block

11. Mô hình vận hành lõi về tăng trưởng (growth model) dựa trên nền tảng thực nghiệm và data.
2. PROCESS: MÔ HÌNH SCIENCE-BASED GROWTH
”data-driven & experiment-driven” là game changers của ”growth science”

12. Product
Channel
Model
Market
Model/Channel
Fit
Market/Model
Fit
Market/Product
Fit
Product/Channel
Fit
2. PROCESS: MÔ HÌNH SCIENCE-BASED GROWTH

13. Product
Channel
Model
Market
Model/Channel
Fit
Market/Model
Fit
Market/Product
Fit
Product/Channel
Fit
2. PROCESS: MÔ HÌNH SCIENCE-BASED GROWTH

14. Phân phối công nghệ ngày nay
3. Growth Science Views
Objectives
Strategy
Tactic
Performance
Process
Driven
Technology
Oriented
Data
Driven
Business
Oriented
Marketing Manager
Growth Hacker
Innovation Leader
Domain Expert
Marketing Executive
Product Manager
Product Engineer
Designer
Marketing Analyst
Data Scientist
Business Analysis
Domain Specialist
Lean Plum
Exponea
Google Optimizely
CleverTap
Insider
Holistic.io
Looker
Tableur
KNIME
Segment.io
Appsflyer
Adjust
Branch
CaptainGrowth
CleverTap
Insider
Adobe Marketo

15. 4. Hệ sinh thái Công nghệ (gartner.com)
Credit: https://gtnr.it/39hcrkR

16. Credit: Marketing Transit-Map by Gartner.com
https://gtnr.it/39hcrkR
Mobile
Analytics
CX
Marketing
Mgmt.
HIGHLIGHT relevant zones

17. Mobile
Analytics
CX
Marketing
Mgmt.
PROCESS DRIVEN
OBJECTIVES + STRATEGY Credit: Marketing Transit-Map by Gartner.com
https://gtnr.it/39hcrkR

18. Mobile
Analytics
CX
Marketing
Mgmt.
PROCESS DRIVEN
OBJECTIVES + STRATEGY Credit: Marketing Transit-Map by Gartner.com
https://gtnr.it/39hcrkR

19. Mobile
Analytics
CX
Marketing
Mgmt.
PROCESS DRIVEN
OBJECTIVES + STRATEGY Credit: Marketing Transit-Map by Gartner.com
https://gtnr.it/39hcrkR

20. Mobile
Analytics
CX
Marketing
Mgmt.
PROCESS DRIVEN
OBJECTIVES + STRATEGY Credit: Marketing Transit-Map by Gartner.com
https://gtnr.it/39hcrkR

21. Mobile
Analytics
CX
Marketing
Mgmt.
PROCESS DRIVEN
OBJECTIVES + STRATEGY Credit: Marketing Transit-Map by Gartner.com
https://gtnr.it/39hcrkR

22. Mobile
Analytics
CX
Marketing
Mgmt.
PROCESS DRIVEN
OBJECTIVES + STRATEGY Credit: Marketing Transit-Map by Gartner.com
https://gtnr.it/39hcrkR

23. Mobile
Analytics
CX
Marketing
Mgmt.
DATA DRIVEN
PERFORMANCE + TACTIC Credit: Marketing Transit-Map by Gartner.com
https://gtnr.it/39hcrkR

24. Mobile
Analytics
CX
Marketing
Mgmt.
DATA DRIVEN
PERFORMANCE + TACTIC Credit: Marketing Transit-Map by Gartner.com
https://gtnr.it/39hcrkR

25. Mobile
Analytics
CX
Marketing
Mgmt.
DATA DRIVEN
PERFORMANCE + TACTIC Credit: Marketing Transit-Map by Gartner.com
https://gtnr.it/39hcrkR

26. Credit: Marketing Transit-Map by
Gartner.com
Mobile
Analytics
CX
Marketing
Mgmt.
DATA DRIVEN
PERFORMANCE + TACTIC