Every quarter, Gartner interview talent analytics leaders to gain their perspectives on issues facing them and their teams. This quarter, we spoke with Diogo Tavares Antunes about his team's work setting up NOS' talent analytics capabilities.

1. Q2 2018 13
About Diogo Tavares Antunes
Diogo is the people analytics
project leader at NOS, responsible
for establishing a people analytics
strategy, developing analytics
tools and building capabilities
across HR to support a culture
of data-driven decision making
within people management. Before NOS, he was
involved in several human capital and management
consulting projects, mainly at Deloitte.
Building a
Foundation
for Analytics
Success at NOS
An Interview With
Diogo Tavares Antunes
About NOS
NOS is the largest communications and entertainment
group in Portugal. It was born from the merger of two
of the major communications companies in the country:
ZON and Optimus.
Its services include the latest-generation fixed and
mobile phone, television, internet, voice and data
solutions for all market segments. NOS is a leader in
Pay TV, new generation broadband services and cinema
exhibition and distribution in Portugal. It offers a broad
portfolio of products and services with tailor-made
solutions for each sector and for businesses of different
sizes, as well as ICT and cloud services.
NOS is part of the Euronext Lisbon stock exchange
index and reported an annual revenue of €1.56 billion
in 2017.
Q2 2018 13

2. 14 Talent Analytics Quarterly © 2018 Gartner, Inc. and/or its affiliates. All rights reserved. CLC180121
E
very quarter, we interview talent analytics
leaders to gain their perspectives on
issues facing them and their teams. This
quarter, we spoke with Diogo Tavares Antunes
about his team's work setting up NOS' talent
analytics capabilities.
We know people analytics has been an increasing focus
for NOS over the past year. To start, could you describe
what was behind the decision to focus on people
analytics?
DIOGO: Companies are starting to recognize that they need
data and analytics to understand what makes people join,
perform well and stay at an organization. This data can also
provide insights on who will most likely be successful and
make the best leaders. We also know data can improve our
understanding about what is required to deliver the highest-
quality customer service and innovation.
Although a people analytics mindset is not yet common
at Portuguese companies, we were surrounded by a set of
circumstances that were compelling enough for us to move
forward with creating a people analytics team, organizing
our data, developing new models and helping the company
transform its business:
• Our HR function desired to assume a more
transformational role in human capital management as a
strategic partner to the business.
• We understood that one of the best ways to gain a
competitive advantage in an increasingly complex and
competitive global market was to take advantage of the
large amount of human capital data available to gain a
better understanding of our workforce.
• We had an opportunity to start from scratch because
our previous analytics function was relatively immature,
and we had recently undergone a transformation of
our HR systems, policies and models due to our recent
merger process.
• Our teams had an entrepreneurial mindset built on a
shared vision, and we had commitment and enthusiasm
from our key project sponsors to face technical, political
and organizational obstacles that could arise.
You mention a fairly blank slate to start from. Could
you give us some more detail on where you began
and where you hope to go?
DIOGO: Our starting point in HR was from a relatively
immature functional capability, which in the past focused
mainly on operational reporting of HR metrics and
elementary analysis. However, our ambition to move forward
from this starting point was big. The main goal was to create
a stronger analytics function that allowed us to understand
patterns and results and act in advance. We knew this type of
capability would require advanced analytics and predictive
modeling to support our goal. Ultimately, we wanted to use
analytics to improve every step of the HR value chain and
to empower employees.
What steps are you taking to make your vision for
people analytics a reality?
DIOGO: Our mission is to embed a culture of analytics
and data-driven decision making in human capital
management at NOS.
To make it a reality, we have been creating an analytical
ecosystem through a four-phase approach (see Figure 1):
1. Defining Strategy — We started this journey by
establishing our vision and mission for people analytics
and by defining levers to our functional strategy.
For example, establish an analytics mindset across
HR, promote synergies with strategic teams, build a
multidisciplinary team and identify key stakeholders.
2. Designing the Functional Model — We framed our
new function in the HR division. We then clarified our
scope and future interactions within the business and
HR division and identified a critical set of technical and
analytics skills we would need.
3. Establishing the Operational Model — To create
actionable insights, we explicitly defined the
step-by-step methodology of our analytics value chain.
During this period, we did a lot of data cleansing to
guarantee solid and reliable metrics and KPIs. We
focused on four critical categories of metrics: talent
development, HR service, financial performance and
operational efficiency.
4. Implementation — We defined our technology strategy
and identified the proper technologies to support our
desired analytical evolution. To ensure the success of
the people analytics function, we also designed and
developed an analytical tool to promote quicker findings
and faster answers.
You mentioned that part of strategy setting was
identifying key stakeholders. What other groups do
you work with, and what are their responsibilities
when it comes to conducting work in the people
analytics value chain?
DIOGO: One critical success factor of people analytics
implementation is getting the buy-in, and therefore
synergies, of strategic areas like IT or business intelligence.
We got our support and quality IT teams on board at a very
early stage. Working closely together on the technology
strategy and identifying the most appropriate technologies
and implementation partners were crucial to assuring reliable
data governance and providing operational support for our
business intelligence software.

3. Q2 2018 15
Figure 1: NOS' Analytics Ecosystem
Your functional model is a place where a lot of our
members are still struggling, especially around skill
needs. How did you determine the skills you needed
on your team?
DIOGO: Because we correlated success in people analytics
with the ability to recommend and deploy actionable insights,
we divided the skills we needed into two sets. The first set
included data and statistical modeling — essentially, data
science skills. The second is what I would term consultancy
abilities: business and HR acumen and communication skills
to allow us to identify and face upcoming challenges. The
former is important to carrying out analysis with data from
various sources and getting to reliable analysis built on
statistical models. The latter allows our function to apply
HR and business acumen to ask the right questions and
ensure the insights we generate are easy to interpret and
execute on. This acumen should be complemented with
communication skills (e.g., storytelling) for an effective buy-
in of the recommended actions to ultimately drive adoption.
To execute more quickly, we decided to start the technology
stage with an external partner. We then brought in staff
with statistical skills and have been upgrading their skills
simultaneously as we build out the technology. We focused
on analytics training to upgrade those skills. The consultancy
abilities have been addressed not only with our people
analytics training sessions but also with on-the-job training
of our team. Our training plan will vary according to the
skills and knowledge our team needs to accomplish our
projects goals (e.g., to rethink employee experience we
have participated in design thinking workshops and service
experience design).
Thinking about your operational model, what KPIs
are you tracking, and how did you decide to track the
specific categories of metrics you mentioned before?
DIOGO: To ensure people analytics was operational, and
to provide answers to key talent questions, we used four
categories of outcomes — talent development, HR service,
financial performance and operational efficiency — during
interviews with key stakeholders to identify the most
important people management topics at the company. Based
on their responses, we created a set of KPIs that we could
use to track over time and understand actions to take based
on how these KPIs tracked.
Since then, our set of KPIs has evolved with additions
to answer new business and human capital management
needs. Tracking all of them on a daily basis was not feasible,
so we have been improving and adjusting a human capital
balance scorecard across the year to guide our annual people
strategy plan. For example, we evaluate the recruitment
quality, the meritocracy of career opportunities and salary
promotions, and the financial impact of our turnover.
From an implementation perspective, where have
you seen success using the metrics you track to
inform people management discussions, decisions
and investments?
DIOGO: As I mentioned, one of our biggest steps so far was
the creation of an HR analytics tool (see Figure 2), which
is a great illustration of our success through iteration. It’s
a quick way for HR staff, and eventually business leaders,
to access and interpret HR data. The KPI gathering we did
was fundamental when designing the HR analytics tool.
We organized the KPIs in 12 blocks to get full coverage
of our human capital management practices: workforce,
organizational structure, attraction, retention, reward,
benefits, performance, talent, training, development, budget
and control, and productivity.
We’ve used the tool in a number of ways to inform decisions.
Last year, for example, we used our HR analytics tool at
a strategic level to discuss our human capital results. This
provided insights on what challenges our HR function should
plan to focus on in 2018. It also led to the development of
new analysis and greater evolution of our tool based on
needs raised during those discussions. At an operational
level, our platform has been used, for example, to analyze
NOS salary competitiveness in the market.
Source: NOS.

4. 16 Talent Analytics Quarterly © 2018 Gartner, Inc. and/or its affiliates. All rights reserved. CLC180121
Can you talk a little about the evolution of the tool to
date? What changed from the first to second version,
and why? How did you determine what kinds of
improvements to make?
DIOGO: Besides creating an analytics mindset in HR, we also
wanted to be acknowledged as an analytical partner by the
business. We believed this would happen more quickly if we
supported the business with an interactive and powerful tool.
However, this would require ample time and resources, which
we knew we did not have in HR at the time. We also weren’t
sure we had the analytical maturity to make a type of jump
to directly helping the business solve complex challenges.
We decided to develop and implement the tool in successive
modules gradually, which allowed us to get some quick wins
but also not move too quickly.
We have been, over time, developing and launching the
following three modules:
1. Workforce analytics focuses mainly on workforce
planning: head count, hires, leaves, compensation and
absenteeism. This first module aimed to increase our
focus on efficiency.
2. HR analytics ensures full coverage of human capital
management practices through a set of KPIs. This
second module aims to answer performance questions
(e.g., Did we reach our goals? How far are we from
achieving our goals?) and track the KPIs over time so
that we can take action based on how these KPIs are
tracking. It aims to increase our focus on performance.
3. People analytics enables us to understand not only the
factors that drive our HR KPIs but also what is about to
happen and what we should do to improve performance
and empower employees. We're working to deploy
this third module to enable, through more advanced
analytics, a deeper understanding of the factors that
drive our critical KPIs and how to improve them.
What about the tool makes it successful in helping
HR use people data?
DIOGO: One of our main concerns was not only to choose
the proper technology from a technical perspective, but also
to develop an intuitive, user-friendly and visually compelling
tool. To do so, we focused on graphics, visualization, design
and brand. From the conceptualization phase until the pilot,
we conducted a sequence of tests with beta users and
stakeholders to ensure we reached the most intuitive tool
experience that we could offer. Feedback was mainly about
user experience and how people use the tool (e.g., where
they expect to see the most important KPIs and reports,
how they read and interact with the dashboards and menus),
graphics visualization (i.e., which best fit the analysis) and
terminology meaning (e.g., turnover is not the same for all
people). We still are, and will be, making updates to get the
best possible experience.
What is your plan for rolling out the tool to your
organization? How will you help your users
understand and take action on the data?
DIOGO: We have been rolling out the tool to the HR division,
and we hope to deploy it in the business to be used by
our organization’s key executives. Each executive will have
limitless access to a critical set of KPIs and analysis in real
time related to their teams.
To promote adequate use of this tool, we have been
deploying training sessions and operational support for users
to provide help, clarify doubts and share new information.
We also plan to perform new and advanced analysis these
executives may need based on their teams’ KPIs evolution
and business demands. This approach aims to enable people
management. We want to empower people and increase the
HR practice’s efficiency, and in turn, increase talent retention
and improve performance.
What are your next steps to continue building
on your success?
DIOGO: We are working to get a deeper understanding of
what causes attrition at NOS so we can act in advance and
promote better talent retention. Then we plan to support
business leaders in identifying and prioritizing their HR
issues. We believe attaining actionable solutions to these
issues will contribute to the people analytics function’s ability
to carry out a consistent approach, and therefore increase
the function’s visibility to the broader organization.
“One of our biggest steps so far was the creation of an HR analytics tool,
which is a great illustration of our success through iteration. It’s a quick way
for HR staff, and eventually business leaders, to access and interpret HR data.”

5. Q2 2018 17
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“Do not forget to tell a
compelling, visually powerful
and meaningful story with
your data.”
How will you know that you have succeeded?
DIOGO: We will know we have succeeded when we are
able to implement a consistent and innovative approach
to people analytics. This end goal should allow us not
only to understand the successes and failures of our HR
practices but also to specifically recommend actionable
insights toward specific problems. Examples include talent
attraction, development, retention practices and employee
experience improvement.
Any last advice for organizations that are just
beginning their people analytics journey?
DIOGO: Here are some lessons our team learned:
• Secure your management team’s sponsorship.
• Establish a people analytics vision and mindset.
• Gather a multidisciplinary team, and upgrade HR’s
analytical skills.
• Promote synergies with strategic teams (e.g., BI, IT).
• Ensure data quality and KPI reliability.
• Implement proper, interactive and user-friendly
technology.
• Ask the right questions, and answer with a focus
on business issues.
• Promote actionable insights (if you are in an early stage,
take baby steps but present results).
• Promote a change management plan in which
communication stands out.
• Do not forget to tell a compelling, visually powerful
and meaningful story with your data.
This interview was conducted by Andrew Bladen
and has been edited for brevity and clarity.
Figure 2: NOS' HR Analytics Tool
Source: NOS.