Cahyo is a data geek, gamer and comic nerd.
Excel and Database are his favorite since his middle school.
Having graduated from a Vocational High School of Informatics and Technology
made him able to start his career early and led many DWH BI projects at his early 20.
He currently leading a data team in bizzy.co.id as the Head of Data Analytics.
Previously he worked for Microsoft Indonesia as Data Platform Technology Specialist where he provides strategic technical leadership supporting Microsoft customers and partners to adopt, deploy, and support solutions based on SQL Server and Data Platform related technologies.
***
This slide was shared at Tech in Asia Product Development Conference 2017 (PDC'17) on 9-10 August 2017.
Get more insightful updates from TIA by subscribing techin.asia/updateselalu
3. Business OverviewBuilding Data Team
Ask yourself:
• What data will your teams be working with?
• What do you want to do with this data?
• Who is going to benefit from this data’s generated value? Directly (ie. the user or consumer of the generated value);
Indirectly (ie. the organization’s bottom line)
• How much time and money can you invest in this project?
• What ROI do you expect?
Hiring Intelligently
• Identify your team’s existing skill sets and what you might be missing
• Prioritize according to immediate needs
• Include analysts in your data science team
Technology ,Tools & Resources to Achieve the Goals
• Think about scalability
• Think about accessibility & stability
• Think about the tools
• Buy / Rent / Build
4. Business Overview
The Importance of Soft Skills
Building Data Team
Get To Know The Problem
• Understanding what actually must be solved
• Never be analyst to whom problems are “throw over the fence”
• The problem you’re asked to solve is often not the problem that needs solving
• Solve the correct, yet often misrepresented problem
Learn How to Communicate
• Without the ability to communicate, it becomes difficult to understand others challenges, articulate what’s possible, and
explain the work you’re doing
• Practice makes perfect.
Key Attribute for Data Personnel:
• Independent
• Questioning
• Innovative
• Adaptive
• Intuitive
• Mentoring
8. Business Overview
Team Components & Specialization
Building Data Team
Data Engineer:
• Data Collection
• Data Warehouse Modelling
• Data Transformation
Data Analyst:
• Data Exploration
• Data Visualization & Presentation
• Business Knowledge
Data Scientist
• Machine Learning
• Statistics!
• Insight Seeker
Data Ops
• Big Data Infrastructure
• Data Architect
• Data Security
• Data Performance
Data Steward
• Master Data Management
• Data Cleansing
• Data Catalog
9. Business Overview
Solution Components
Data Infrastructure
Analysis Services
Tabular
Web Scrapper
Data Source
Information Management
Master Data Services
(MDS)
Data Quality Services
(DQS)
Integration Services
(SSIS)
Cleanse
Manage
Integrate
Data Storage
Data Modelling
Data Warehouse
Data Visualization
Not Developed Yet. Area for future/planned improvements
Note :
Identity Management
Azure AD Connect / Sync
Not Planned Yet
Analysis Services
Multidimensional
Excel
Ad-Hoc Report
Internal Dashboard & Mobile BI
Internal Reporting Portal
Reporting Services
(SSRS)
Data Science
Azure Machine Learning
Back Office Apps
Deprecated. Migrated to SQL Server Master Data Services (MDS)
Data Staging
SQL Database
External/Customer Dashboard
Power BI Embedded
For Bizzy SELECT
External Interaction
Azure Function
Data Lake
Azure
Cognitive
Services
10. Business Overview
Solution Architecture - Data Warehouse
Data Infrastructure
Back Office Apps
LOB Apps
Web Scrapper
External Data Scrapper
Master Data Services
(MDS)
Data Input & Management
Data Preparation
Integration Services
(SSIS)
Data Quality Services
(DQS)
Data Staging
SQL Database
Data Warehouse
Analysis Services
Tabular
Data Warehouse
OLAP / Cube
Consume
Excel
Reporting Services
(SSRS)
Executive
Dashboard
11. Business Overview
Infrastructure Cheat Sheet
Data Infrastructure
Size Big Small
Data Type Uptime
Growth
Slow Fast Fast Slow
Structured
High On Prem DB/DWH
Cloud DB/DWH
On Prem DB/DWH
Low Cloud DB/DWH Cloud DB/DWH
Unstructured
High On Prem HDP Cluster
Low Ad-Hoc Cloud HDP Cluster
ex. Products
On Prem DB/DWH SQL Server, Oracle, Postgre, MySQL
On Prem HDP Cluster Cloudera, Hortonworks, MapR
Cloud DB/DWH Azure SQL DB, Azure SQL DW, AWS RedShift, GCP BigQuery
Ad-Hoc Cloud HDP Cluster Azure HDInsight, AWS EMR
12. Business Overview
Data Jujitsu by Dr. DJ Patil - Turning data into product
Data Product
When in doubt, use humans
Start Simple
embraces the notion of the minimum viable product
and the simplest thing that could possibly work
Ask yourself:
1. What do you want the user to
take away from this product?
2. What action do you want the user
to take because of the product?
3. How should the user feel during
and after using your product?
13. Business Overview
Drive Action with Data in Bizzy
Data Product TIPS!
Customer
SO
Finance
Approval
Buyer
Create PO
GR
Warehouse
Shipment
Cust
Received
DO
Collection
Finance
Invoice
Customer
Payment /
AR
Vendor
Payment /
AP
B2B e-CommerceDaily Notification of end to end process
Internal Notification
• Finance Pending Approval
• Buyer Pending PO(Back Order)
• Vendor Pending GR
• Pending Shipment
• Pending DO Collection
• Finance Pending Invoice
• Vendor Pending Payment
• Customer Pending Payment
• Etc
External Notification
• Customer AR Statement
• Vendor Payment Information
14. Business Overview
Drive Action with Data in Bizzy
Data Product TIPS!
Daily Notification of end to end
process
Sample Report Layout
99 AVG $
Summary/Statistics
Category 1
Category 2
Category 3
Category 4
Series 1 Series 2 Series 3 Series 1 Series 2 Series 3
99 MAX $
a b c d e f g h i f2 g3
Trend of work performance
Detail Pending Item need follow up
15. Business Overview
Bizzy is Hiring!!!
Join Winning Tech Team
- Software Engineer - Front End
- Software Engineer - Back End/Full Stack
- QA Engineer
- UI/UX Designer
- Tech Writer
Some of our stack:
PHP, Laravel, Javascript, vue.js, node.js,
MySql/MariaDB, MongoDB,
AWS (Lambda, EC2, RDS, S3, SNS, etc)
Selenium, Postman, JIRA, Bicbucket, Confluence, Pipeline, etc