This document summarizes a data science consulting firm's business analysis of clients and employees. It found that demand for data science roles is growing rapidly in the Philippines but there is a short supply of qualified people. It used clustering analysis to identify ideal client types as those that are analytics-ready, have high maturity, and are returning clients. For employees, it identified versatile consultants from top universities with long tenure across analytics and business intelligence projects as ideal. The firm can focus on retaining ideal clients and employees to address the problems of high client demand but limited skilled people.

1. A CLOSER LOOK:
Data Science Consulting Firm
Steph Roxas | Gladys Yu
FTWCAPSTONEPRESENTATION
Business Analysis
on Clients and Employees

2. DATA SCIENCE IN THE PHILIPPINES
By 2022
340,880
Data Science roles
On 2016
147,420
Data Science roles

3. FTWCAPSTONEPRESENTATION
How can this DS consulting firm reposition itself?
High demand for these roles from CLIENTS
BUT short supply of PEOPLE with necessary skills

4. PROBLEM
FTWCAPSTONEPRESENTATION
What type of CLIENTS should they focus on?
What is the profile of an ideal DS CONSULTANT?

5. PROCESS
DEFINE
THE PROBLEM
DATA
EXPLORATION
METHODOLOGY
SELECTION
ANALYSIS
RECOMMENDATION

6. OVERVIEW
FTWCAPSTONEPRESENTATION
OF THE
BUSINESS

7. Over 15000 total billed hours
consistently from year 2014 to 2019
Year
BillableHours
Employees

8. Year
Projects
Most projects done on 2016
Most clients worked with on 2019
--- Clients

9. Months
BillableHours The most billed work months are from June to November
and least from December to January and April

10. Client Demographics
60 Clients
149 Projects
15 Industries
BI
75.8%
Analytics
24.2%

11. INSIGHTS
Healthcare
has lower
hours but high
resources
Logistics and
Telco have the
highest hours
and resources
Industry
BillableHours
Resources

12. 32 17
49 Employees Programming
38.8%
Math
28.6%
Business
18.4%
Others
14.3%
Employee Demographics
Age: 21-44
17 Current Employees
(Age Joined: 20-41)

13. INSIGHTS
Over time, Average
Productivity Rate for a
consultant = 0.46 - 0.6
Low Productivity Rate
is common among
employees w/ shorter tenure

14. INSIGHTS MAPUA has the
highest
productivity and
improvement rate
but tenure is less
than 2 years
ProductivityRate
Undergrad School
6 months
3 months
UST has the
highest average
tenure of 3.5 years

15. INSIGHTS
Graduates of Business Course
have the lowest productivity rate
but most number of projects
More time is spent
on non-billed hours
as mentors and
lead roles

16. Decision Trees
60% of clients have an
average 405 hrs/proj
(50 mandays)
Analytics-ready clients
before 2017 have
higher average hrs/proj

17. FINDING THE
RIGHT CLIENTS
FTWCAPSTONEPRESENTATION
using K-means clustering

18. CLIENT CLUSTERING RESULTS
Tenure (Months) vs Average Hr/Project
Add a little bit of body textAdd a little bit of body text
Big Data Tech

19. Longest
Client
CLIENT TYPES
DS
Beginners
Returning
Clients
Big Data
Tech
New
Clients
low analytics readiness
low maturity
analytics ready analytics ready
low maturity
analytics ready
low and high maturity
analytics ready
high maturity

20. Client Types
BillableHours
--- Clients
Returning Clients has the highest billable hours
Big Data Tech

21. Returning
Clients
Marketing
Manufacturing
Logistics
Food
IDEAL
CLIENT
Industries:

22. FINDING THE
RIGHT EMPLOYEES
FTWCAPSTONEPRESENTATION
using K-means clustering

23. EMPLOYEE CLUSTERING RESULTS
Tenure (Months) vs Productivity Rate
Leaders

24. Versatile
Consultants
Analytics
Consultant
Junior
Developers
EMPLOYEE TYPES
Leaders
Programming Course
BI Projects
Level 1 role
From Top 3 Univs
Analytics and BI Projects
Level 2 - 4 role
Math Course
UP & ADMU
Analytics Practice
Business and
Other Course
From Top 3 Univs
Level 2 -4 role

25. Versatile Consultants and Junior Developers have more BI projects
Analytics Consultants have Analytics and BI projects

26. Versatile
Consultants
From Top 3 Univs
Analytics and BI Projects
Promoted
Long Tenure
IDEAL
EMPLOYEE

27. RECOMMENDATIONS
Productivity Rate Monitoring for each employee
Predict Employee Attrition based on Billable Hours
(Logistic Regression: 72% accuracy)

29. FOR THE WINE
Steph Gladys

30. Thank you!
FTWCAPSTONEPRESENTATION