November 12, 2021 • 44 mins
In this new podcast in English from Data Stand-Up! Jesus talks to Francesca Gabetti, CEO at Team EQ. Francesca is co-founder & CEO of TeamEQ, the first Human SaaS. A Predictive Team Analytic solution that drives Team Intelligence and real time decision-making . TeamEQ, powered by Machine Learning, increases team's alignment, engagement & performance at all levels of the organization. The use of analytics in HR is growing, with organizations aggressively building people analytics teams, buying analytics offerings, and developing analytics solutions. Companies are hiring people analytics staff, cleaning up their data, and developing models that help transform.
TeamEQ makes team management smart, data-driven & more effective, through real time management and continuous feedback, transforming companies into high-performance & intelligent living systems. It accelerate digital, agile & cultural transformation, developing new leadership needs with just few minutes of time investment per week.
We hope you like this episode.
TeamEQ makes team management smart, data-driven & more effective, through real time management and continuous feedback, transforming companies into high-performance & intelligent living systems. It accelerate digital, agile & cultural transformation, developing new leadership needs with just few minutes of time investment per week.
We hope you like this episode.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:06):
Bedrop is an independent data science and AI firm specialized
in data driven business change. In this podcast, our guests
help us spread knowledge and experience with our listeners.
Speaker 2 (00:28):
Hello, good morning, Francisca. How are you doing today?
Speaker 3 (00:32):
How are you fine? Thanks? Great?
Speaker 4 (00:34):
Day to day, It's sunny, it's sunny, and well, today's
a very special day because as a Bedrock we are
speaking with Francesca CEO of Timmy Q, a very special
company with whom we collaborate, with whom we barnered very recently.
(00:56):
And I think that everyone listening to this conversation will
be very keen to hear what you have to say
and what timmyqu is about and how beat Rogues is
helping you. But before we go into those details, would
you mind briefly introducing yourself and tell the listeners briefly
what's tmy you as well?
Speaker 3 (01:17):
Yes, of course, thank you. Jack. So, so I'm like
quite a typical file.
Speaker 5 (01:23):
My mother is physicians, so I come from a from
a family where data and analytics, so let's say, it
was a was a main issue. And after that I
started studying economics and during my life work like in
organizational uh, in the organizational world.
Speaker 3 (01:45):
I also my family comes from.
Speaker 5 (01:47):
Entrepreneurials experiences, so somehow I had this in my DNA
and I played basketball, which is something UH that plays
a key role in in the team qu development. And
always had also this humanistic soul since since the beginning
and mixed the UH since my first my first teasis
(02:11):
and UH and studies work what was more like analytics,
so mathematics and physics and and the other part for
me that it's the humanistic part of human beings and emotion,
emotional development and conscious development. So for me, these two
souls are at the origin of tm Q and started
(02:35):
with my with my first experiences in organization and also
UH somehow breaking through technologies because I was I was
lucky enough to be called as country manager of a
tech attach company back in two two thousand and eight,
and actually that was I user generated company, so what
(03:00):
we call the today user generation user generation videos, and
that was UH television programs coming from contribution from users.
So that was quite pioneers using Facebook and Twitter that
were at their early beginnings.
Speaker 3 (03:18):
UH. So let's say that are three souls.
Speaker 5 (03:21):
The first is technology and data and analysts analytics with
business and the secondly human human development is really a
passion about about people. And the third is is a
little bit transversal, but it's a it's of course about pioneerism.
Speaker 2 (03:42):
That makes sense.
Speaker 4 (03:43):
I mean, I think you've explained really well where Timmy
Q comes from.
Speaker 2 (03:47):
So when you read about tim.
Speaker 4 (03:49):
Q, and now you can go into the details of
what platform does, how it works, the technology and the
proposition value it says, people analytics, right, and the posts
to make sure that those clients of yours understand how
teams are performing and why, and that you understand the
reasons behind how the users of the platform, which are
(04:14):
employees of your clients collaborate, work together and trying to
find the relationships between them to see how can you
nurture them or contribute to improve them.
Speaker 2 (04:27):
Right, But I like to hear from you what timmy
qu is all about.
Speaker 5 (04:34):
Yeah, well, I think that back back to twenty thirteen,
I think I remember one day I was in Silicon Valley.
I actually I spent there almost two months, and I
was working as an organizational development in the organizational development field,
so basically with teams and leaders to yeah, to support
(04:59):
change man and UH and leadership processes. And at some
point you uh you really think about the impact of
technology uh into human the human resource fields, And that
was for me something very uh very It was a
key moment because that was some kind of of questioning.
Speaker 3 (05:23):
About the impact of technology.
Speaker 5 (05:25):
So for me, that was a little bit worrying to
think how technology could impact human resources and how it
was a good a good way to somehow reflect on
this dispossibility of impacting and and actually bring value to
people and know that technology not being technology only a
(05:47):
way of getting data and getting data and more data
and big data without a meaning. So what was the
purpose of technology while talking about human resources? Because of
course I was working in marketing previously in other companies,
and I remember being passionate about finding insights through data.
(06:08):
And of course when we talk about products or logistics
or let's say planted optimization, the reasons are completely different
than when we talk about people, ecosystems and teams. So
that was my concern and my challenge at that moment,
how can we bring a technology that makes sense into
(06:30):
the human resource development At that moment, that was not
the people analytics world was not even used.
Speaker 4 (06:39):
So it's kind of the beginning of that term, right,
people analytics now you start to see more and more
of a competition sort of say coming up. But before
we go into that, I like to briefly cover how
timicky works in terms of how data UH from those users,
(07:01):
interactions from users will being from users, whatever the characteristic
is is collected, and also how users and teams are
onboarded into the platform, because I think that if we
go through this, it will be easier to then go
on on the on the subsequent questions.
Speaker 3 (07:21):
Yeah, so there are two two very key moments.
Speaker 5 (07:24):
So the first is the first question is what do
we ask uh so, which are the relevant data and
the minimum quantity of data and we can uh we
can have from people, so we can ask people, we
can we can ask their time to answer and uh
and which are the relevant variables from a team that
(07:48):
we need to somehow understand to to.
Speaker 3 (07:53):
To be able to take action.
Speaker 5 (07:54):
And this is a very first reflection that it's so
key since we are we are coming from our world
when we asked our employees sometimes ours to answers to
UH yearly or or climate service where results were in
the ends of human resources people and then that were
(08:18):
not shared with other with the the.
Speaker 3 (08:22):
Levels of the company.
Speaker 5 (08:23):
So people were disengaged, and that was the history of
human resource data somehow. And the second question is how
do we work with this uh, with this uh possible
attriction that it's uh, it's objectly present when we ask
(08:46):
people about how they feel and uh, and when we
think about our organization, we come from control systems most
of the time.
Speaker 3 (08:57):
So people are.
Speaker 5 (08:58):
Accustomed to think about their personal life and their professional
lives as two different spheres of or dimension of themselves.
And this is of course in the past, and the
pandemic the Pandemic show showed us clearly that was already.
Speaker 3 (09:16):
In the past.
Speaker 5 (09:17):
But somehow people could have this fear of what happened
if I answer the truth. And so it's so important
to make people and teams understand that yes, it's possible
together data and these data are in fiber and actually
are for teams and for people, not for the company,
(09:40):
not for human resources. Somehow the sames that.
Speaker 3 (09:44):
We use use I use this.
Speaker 5 (09:47):
I like this, uh, this idea of team of sports analytics.
So I think sports analytics is you for teams and
for coaches to better understand the teams dynamics and improve
in the locker room. Okay, so we can talk, we
can share, we can be truthful with ourselves because our
(10:08):
object is to win the game. And in that moment,
there's no there's no fear, okay, because we need to
take the best decision for the team, and our data
are helping us toward those decisions. And this is a
little bit of dynamics that we reply and with technology,
we're recreating Timmy Q. So this locker room moment where
(10:29):
our data are for us as a team, for us
as a leader, and that really can empower us to
take better decisions.
Speaker 4 (10:38):
Also, the benefits of using the technology and the data
retrieval process by Timmy Q goes well beyond HR because
I think it can positively affect other layers of the organization.
But what's the typical scenario that you may encounter when
(11:01):
you're approaching organizations that you've never worked with, meaning you
may be in a commercial or pre sales stage. You
are explaining how timicky works, what's the purpose of it,
and that on how you're trying to promote the value
of people analytics. But my question is is this in
(11:24):
the priority least for leaders beyond the HR environment? And
do you reckon That's hard to translate the value of
investing in people analytics at this very moment.
Speaker 5 (11:39):
I think the pandemic changed everything. And I really think
that we feel that we know and we see the
people analytics market growing like fifteen percent yearly. Now, so
we have we're past and we were in a like
(12:00):
discovery moment for people analytics. Where as you said, uh,
there was almost no budget assigned for people analytics. In
our case, we are a team analytics, so it's it's
even a it's even a different like like value. But yes,
there was no budget assigned, no clarity on responsibilities. So
(12:22):
who was in charge of this budget that was hr
typically sometimes innovation people, uh, sometimes it or whatever. So
after the pandemic changed, was there leader leaders needed to
take the paths of their teams and and there was
(12:44):
a suffering uh across the company for not having the
human data that could somehow allow different conversation and to
really understand, uh, where were our people in terms of motivation, trust,
well being in a moment where we were.
Speaker 3 (13:04):
All locked down.
Speaker 5 (13:05):
So there the conversation completely changed and are now clearly
on CEO's table and uh, and this is a this
is a new, a new moment because we understand clearly
that our business results, our right our bottom line is
(13:27):
completely uh correlated with how our people are performing and
how our people are are feeling. And also the behaviors
are completely correlated to two perceptions. So we need to
be able to measure and to understand in real time
(13:47):
in the present what's going on in the company because
the context is so complex that you need real time decision.
Speaker 4 (13:55):
I believe that bringing up the the pandemic is highly relevant.
I think that for many businesses and for large corporations
that were used to be in present at their main office,
at their corporate office or her headquarters, it really changed
(14:17):
to be at home working remotely, and managers at different
levels within the organization needed to have that understanding of,
you know, how people were interacting with each other and
what was their feelings. So I think that the timing
is great. I think that, as you said, even though
(14:39):
I'm not involved in people analytics as you are, it's
in the agendas of the sea level executives, as you're saying,
I think it's a priority for them now now trying
to look at things differently and trying to set example well,
trying to think of examples where things didn't go so well.
(15:03):
What have you learned from previous engagements with clients of
different industries, of different sorts, Because I guess that sometimes
the value of teamyq is well received by a nature
leader or a team leader. But then it's a matter
of making sure that the technology is well adopted within
(15:25):
the organization and that the different employees, the different people
begin to use the technology and begin to use the platform.
So probably those early engagements or the kickoff of those
breeds sometimes go really well, and sometimes they don't. So
if there is someone out there that is thinking of
(15:45):
TIMMYQ and they think it's good for their business, what
are their learnings of difficult engagements or the engagements that
were particularly challenging for you, And what have you learned
that you would do differently to make sure that the
adoption of teamyquse a success.
Speaker 5 (16:03):
Oh my god, this is a great question. Actually, we
learned a lot from our mistakes. I think we started
maybe a little bit too early. Sometimes I say that
because that was twenty sixteen when we launched it the
very first proud So we were really pioneered and we
had the time to learn. And this is important in
(16:25):
a moment where probably the market was not in expansion
with the people analytics tools, so you really needed to
convince the client and to learn by doing together with
the clients. And I think what we learned the first
is what you just said. So the key role of
a good onboarding, a good kickoff meeting, and a very
(16:52):
successful communication at the beginning two teams, so that the
data since the first measurement are reliable because people are
telling the truths and they understand the power of transparency
and the power of having a voice, a new voice
inside the company. And when we when we didn't succeed
(17:12):
at the beginning, was especially when the company tried to
get like small pilots in UH in areas that were
critical areas and UH and like engage very few people.
Speaker 3 (17:27):
So what I think that the power.
Speaker 5 (17:29):
Of people analytics in general and team analytics is really
to UH support a cultural transformation. Digitalization could be a
Gile transformation, could be a key change management that's inside
the company, and somehow use it like a boost or
an accelerator of this transformation. And when you are trying
(17:52):
to keep it like very small in a critical area,
actually are not taking the best out of the tool,
and then you're not engaging key people in the organization,
like like directors or sea levels as you said before,
that are hard the ones that somehow are the first
pillar of the change. So you ask your people at
(18:13):
the like blue Colors or somebody to answer to some questions,
but they see that you're not engaged as a company
to really really provide an action answer. So I think
that we learn UH to to engage the right people
inside the company, the right decision maker to create an
experience since the beginning UH and and build this experience
(18:37):
from a sometimes also a pilot of course, a pilot
with a with a number of people that could be
between five hundred two thousands depending on the on the
on the company, and then also small companies. It doesn't matter,
but that the changes come from the willing of the
company to transform, to UH, to create an evolution and
(18:59):
to really somehow empowered teams and empowered leaders to work
with real time human data.
Speaker 4 (19:07):
And I don't know if it's a quick question for now,
but I think I can ask this. So now that
we start to take a look at how data is
being utilized to learn how users interact with the platform.
Speaker 2 (19:21):
And this is something that Petrok is doing with Timmy Q.
Speaker 4 (19:25):
How would you ambition the future to be when it
comes to Okay, the platform is learning how much a
user is sharing in terms of thoughts and feedback with
the technology, how would you think the tool itself could
learn from those interactions to increase the engagement from these users.
(19:46):
Is it related to actions being suggested to improve the
well being? Is it related to I don't know, creating
tailored responses based on what the user is sharing. I
know we are doing work in the realm of n LP.
We will have to develop some machine learning models that
(20:08):
learn from those interactions. But if you were to think
of the ideal development roadmap, where would it take us
when it comes to the platform itself having the mechanisms
to promote.
Speaker 2 (20:24):
Adoption on how it interacts with users.
Speaker 4 (20:28):
I don't know if if I if I was clear enough.
Speaker 3 (20:32):
Yes, yes, of course.
Speaker 5 (20:33):
And I was thinking, because I always hear your great podcast,
so really congratulational.
Speaker 3 (20:38):
I love it.
Speaker 5 (20:39):
And I was hearing Mikhaiela like a few weeks ago,
and she was saying something interesting that researcher. The difficulty
of research is to choose to prioritize what to for
where to focus on and really understand which is the
best researcher for the moment.
Speaker 3 (20:57):
So it's a little bit the same for us.
Speaker 5 (20:59):
We are a king with data uh and uh. First
of all, we are working with humans, so we are
working with teams, which which represent in terms of data
of course complex relations uh and and data gathering from
teams and from human relations are are also subject of
(21:23):
a deeper reflection which involves ethics and AIU data protection
and really questioning what is useful for the company, what
is useful for the team, what is useful for the leader,
and what is not uh useful and which is which
(21:43):
is something that we can gather and actually we are
not allowed to show, okay, And I think this reflection
is at the basis of the question that you just
just ask me. Because of course we can think about
reporting and dashboards, which is the first level of data
kepis and metrics. Okay, we gather the analytics, but then
(22:07):
we have predictive analytics and advanced analytics and AI when
it comes to people, Uh, you are really you're really
asking yourself what is useful for a team, what is
useful for the company. With my client, who is my
client is only the company of course, not as you
said before, the user and the person that is answering
(22:27):
that it somehow experiences the platform every day at all
level of the company. It's somebody that needs to feel
that his time or her time is worth and that
his or her voice is UH is taking you into
account is acknowledged. And this is the main challenge I
think to work in a transversial way within the organization
(22:52):
when you provide value for the for the person that
is somebody UH, that is that can be from a
as I said before, for a blue collar, a white
color somebody that is working in the headquarters, maybe somebody
that is UH in the street and is working for
the company from an external perspective. And at the same
(23:15):
time you need to bring value to human resources, to
CEOs to take strategic decisions. Yeah, these different levels are
inside our future and UH. And while while I would
I will answer you with one word that is collective intelligence.
So I think that what we are doing inside MiQ
is not trying to give answers or exact answers to
(23:42):
to user and bring exact actions. We believe that it's
much more interesting to create a condition to make the
right answers and to and to connect the best uh
the best ideas or the best actions that worked in
(24:02):
another level in a similar experience in a benchmark or
industry and bring them into the platform so that people
can learn one from each other, leaders from leaders, hrs
from hrs. And it's not only the platform providing ideas.
Speaker 4 (24:18):
Plus something else that we've discussed before that I think
it's really valuable and relevant for the conversation is that
when you bring data from other communication and collaboration platforms
into the mix now TIMIIC, you gains or enhances the
results and the and the benefits that can be gained
(24:39):
from using from using the technology. I guess that this
is part of what you have in mind, trying to
get in very valuable data from other sources within the
organization that can complement the data that you collect from
from those individuals.
Speaker 5 (24:57):
Right, Yes, of course, and too twenty was the year
of integrations and digitalization. Yeah, digitalization are now part of
our life in a way that was not even not
even conceivable like just eighteen months ago. So we are
(25:18):
aware that in this connected world, there are a lot
of information out there that can be very useful and
very Let's say that can be key for the next
type of tim Q.
Speaker 2 (25:34):
You.
Speaker 5 (25:34):
Now, the question is how can we connect those data.
They are not only quantitative data but also qualitative qualitative comments.
And this is so so key to be able to
mix both of them, so quantitative and qualitative, and at
(25:55):
the same time to be able to find not evident algorithm. Okay,
uh that that are uh somehow providing our clients and
our teams the possibility to connect not evident patterns and
discover new patterns for them.
Speaker 3 (26:13):
That are that are allowing predictivity. Okay.
Speaker 5 (26:18):
So while we're talking, for instance, concretely about trusting a team,
m hm, how can we measure trust? Okay, we can
measure trust by asking people. We can measure trust by
reading conversations, but understanding how meetings are are developed, understanding
how people are collaborating. Our teams are collaborating some among them,
(26:43):
so not only in the team, but also inter team
and cross team. Our organizational network is structured and learn
from from one industry to the other, one country to
the other, understand dynamics, compare dynamics, and provide new insights.
So I think this is really a moment where once again,
(27:05):
the question is which data are useful, which are not useful,
and which are providing truly insightful information for the new
dimension and for us, the dimension is the team dimension.
So it's not about gathering individual results or individual answers.
It's really understanding how teams are working. Because for a company,
(27:29):
the new cell, the new key cell of an organization.
Speaker 3 (27:33):
Is the team.
Speaker 5 (27:35):
We can have ten messes inside the team or ten
whatever superstar and be a superstar in a team, and
probably the team is not working as we would like to.
So we need to really understand what is the key
performance issues in a team. And sometimes it's a lack
(27:56):
of trust. Sometimes it's a lack of effectiveness. This is
a well being inside the team. Sometimes it's conflict. So
not always the same pattern, but we can learn from
from the organization, from the environment, from the industry, and
this is the future of the team performance is really
(28:16):
understanding what's going on in the team and being able
to predict.
Speaker 4 (28:20):
Really interesting something that I haven't asked, but I think
it's really key to you in how you approach potential
organizations to work with. I don't know if this is
really really remarkable, but based on your experience working with
clients from all around Europe, of course, from Italy, from Spain,
(28:43):
from wherever it is. I guess they're understanding when it
comes to people analytics or the importance of paying attention
to human well being, the regal performance relationship between teams,
it's different, more or less important. But first question is
it really perceived in the same way across geographies and
(29:04):
cultures and do you approach companies differently based on this?
Speaker 5 (29:10):
In general, let's say that the Anglo Saxon word is
much more accustomed to work with the data and in
general to work with the analytics on people compared to
Latin countries. So they get less emotional, so they understand
data are needed to progress, to improve, and they are
(29:34):
less dramatic in the use of human data, and this
is something that helps. On the other side, is it
true that this does attraction that we were discovering before
are also useful because when you reflect at the beginning
of the project and you really ask yourself, why is
(29:55):
it useful for me? Okay, you get another type of engagement,
especially when you have transparency in data, so that you
know that those data that you are gathered are the
displayed into into transparent dashboard for all the teams, so
you can share with other people. So it's not only
on the leader's shoulder, and it's really about opening new
(30:19):
conversation within the team. So I think that this is
something that helps. In the Asian country, we have a
mechanism inside to mike you that actually allows to ponder it. Uh,
the average depending on the on the culture. So that
means that there's no red or green results, absolute green
(30:41):
or red results, but depending on how the company and
the culture is answering, the average shift okay. And this
is this is important why because in Asia we are
not accustomed to evaluate in a negative way, okay, So
a six out of seven also be an alarm, an
(31:02):
alarm signal, while in a German or in an American company,
when you point four or five out of seven, it's
already very high, okay, And it's something that the two
must recognize. You need to work with different culture. You
need to understand the people evaluation pattern and give responses
(31:24):
not only based on the average, but based on the
culture and understanding what's really going on inside the team.
Speaker 4 (31:31):
I guess that that also applies to how we work
as well. With different clients. For us, sometimes it's more
on the heal maturity. Some companies are still trying to
figure out how to produce data across the business, while
others have been producing data for ages, and it's about
(31:55):
how we can make sense of this data at this
very moment, and how can we forecast with the data
that we have in hand, what's going to come. So
it's it's really interesting to hear how these differences across
geographic cultures and maturity when it comes to understanding how
important these people are.
Speaker 5 (32:14):
Yes, and also because HR HR dashboards in general, HR
Data Intelligence is almost the last one in the company,
and this is something that we need to assume UH
and UH and there are people from from HR on
the on the people site are starting now together UH
together data together, data from different sources and to and
(32:38):
to create strategic strategic dashboard, dashboard and way to predict.
Speaker 3 (32:44):
Of course, this is very different whether we talk about.
Speaker 5 (32:48):
Corporates or medium and small companies, and this is this
is different words. While UH huge organizations already started like
three four years ago to work with analytics, companies, maybe
less than ten thousand people are really starting to work
(33:10):
in this field. So they also do the strategic approach
and the consultancy needs from one and the other is different.
Speaker 3 (33:19):
Time.
Speaker 5 (33:19):
We need to we need to understand that while on
boarding the people from the company.
Speaker 4 (33:24):
For interesting okay, probably one of the two last questions
that I had in mind. First one, so based on
your practice field of action, if you want to say, people,
analytics and the different bus business units that you've dealt
with apart from HR.
Speaker 2 (33:46):
If you were to advise.
Speaker 4 (33:49):
Someone who is leaving the technology side of things and
the data analytics side of things within a company, maybe
chief data officer, chief information officer, how would you recommend
them or what would you tell them to focus on
when it comes to governing or making sense of data
(34:11):
that affects the people data analytics side? Am I making sense.
Speaker 3 (34:19):
If I understand? Well, it's it's okay.
Speaker 5 (34:22):
We have an ecosystem of decision maker today. It's like
what I call a board of people analytics, so you
speak with different people at the same time and the
decision is not in one hand. This is the upgrade
that the pandemic allowed in this field. So of course
you were you work with the with the top levels
(34:44):
of the company, we being the its or or legal
for the data protection or the CEO or whatever CFOs.
Of course, you need to provide direct correlation between business
KPIs and people data.
Speaker 3 (35:00):
Okay. This is one of the.
Speaker 5 (35:02):
Main challenges on every I would say sea level desk
at this moment, as we know that it's not evident
to correlate to people data. What there is a lot
of literatures on the correation between the e nps, so
the engagement of people with the UH with the economic results.
Speaker 3 (35:23):
And at the same.
Speaker 5 (35:24):
Time a very a very new literature is starting because
we are starting working with the with the massive data.
Speaker 2 (35:33):
Okay.
Speaker 5 (35:34):
This is the of course the challenge, and it's also
very interesting on how you create different collectives inside the company.
So this is this can work slightly different for different
kind of teams.
Speaker 3 (35:46):
Okay.
Speaker 5 (35:46):
So we are working with some clients that are asking
us Okay, hey ley, I have like six or seven
different type of teams. I have hierarchical teams, I have
project teams, agile teams, I have UH boss less teams. Okay,
can we provide different experiences for them? Can we really
understand their needs, their objectives and somehow create a different
(36:10):
experience toward human data and our answer is yes, okay.
Our approach is like a systemic approach. This is something
unique that means that we see organizations and teams as
living systems. So you need to provide to that system
that you are listening or measuring depending on the need,
(36:34):
a different of frequency, different kind of experience, different kind
of rhythm okay. And this is needed inside a company
because not every team is working the same way and
at the same time. Strategically, and from the top of
the company, we need to aggregate data and to have
(36:54):
a consistent report to take strategy decisions. So there are
always these two these two ways of thinking that needs
to be integrated. Thirt is that sometimes you work with consultants,
with external coaches, with people that are supporting you in
your organizational transformation or change management, and those people must
(37:18):
also be integrated inside the uh UH. The people are
a strategy, so that somehow from the insight that you
are gathering as the company and from the inside that
they are gathering as expert in that field, they can
they can make the change management smoother or support from
(37:39):
from the strategy UH that change and accelerate UH people transformation.
So I think is what is really needed is to
understand the ecosystem of the client in terms of ways
of working teams and also people that are involved and
create the best experience for the client to accelerate the
(38:03):
process and really feel committed to that. And and that's
what I love in my work.
Speaker 2 (38:10):
That's that's very useful for those are very useful insights.
And now if you want.
Speaker 4 (38:15):
To share something with those listening to conversations, what's the
next big challenge for Teamy Q in twenty twenty two
and is it related to the work we do in
regards to data science.
Speaker 5 (38:32):
Yes, I think you've pointed out before our how we
see our future in terms of providing insightful and key
predictive information. This is one of the key challenges on
our tables. So really using digital we are gathering in
a way that it's uh the most uh the most
(38:56):
interesting for the company and for for people. And the
second I would say is always, like every tech tech company,
is how to scale and really bring these values to
more people, more companies. As we said before, creating this
collective intelligence that in our vision is not inside the
(39:22):
territory of the company only. Okay, we can create systems
and we can provide, and I think that the moment
is really now to create some ways of sharing new
sharing information among companies that are facing similar challenges around
the world and providing those data to other companies by
(39:44):
respecting the privacy, of course of people and of companies,
but somehow sharing these information so that it can we
we can increase our collective intelligence on our teams working,
and we can we can take the best strategic action
for the future.
Speaker 4 (40:01):
Awesome, that sounds really good, bright future we have ahead
of us.
Speaker 2 (40:06):
Okay, it's been entertaining forty five minutes.
Speaker 4 (40:12):
I think before we close the call off, I always
ask for a book recommendation, and I know that your
CEO wrote one, but I also read that, so I
would prefer, I don't know, recommendation a different book. We
can't mention that one as well, because I think that
(40:33):
he'll be happy.
Speaker 6 (40:34):
But if you can mention any newsletter, book or knowledge
content that you think it's useful when it comes to
either people analytics or just because you like it, now
it's the time to mention it.
Speaker 5 (40:49):
Yes, Well, I think there is one person that really admired.
He is really a great, great job in people analytics,
which is David Green. Actually he has a he has newsletter,
he has a LinkedIn profile, and it just for a book,
and so that would be my book is The Excellency
People Analytics by Jonathan Ferrar and David Green. And I
(41:12):
think is also very practical approach with the use cases
and challenges that are facing that people and organizations are
facing all around underworld. So I definitely think it's my
book today. And maybe it's not like I heard more
original books. And at the same times, I feel that
(41:33):
sometimes we talk a lot about data and we really
need to understand how to apply.
Speaker 3 (41:38):
Those two people.
Speaker 5 (41:39):
So that would be a basic book and a needed
book for everybody that would like to start working in
people analytics.
Speaker 2 (41:48):
That's right.
Speaker 4 (41:49):
It is not always about the technical bits and pieces
when it comes to applying data science, it's also known
why it's been applied, what's the purpose of it and
for that understanding people and their business is it's key.
Speaker 2 (42:01):
So what was the name again of that book?
Speaker 5 (42:04):
Excellence in People Analyticsan Farr and David Green.
Speaker 2 (42:09):
Okay, you know I'll read it, right, Yes, yes, you
read it? Yeah, no, no, I will, I will, you will?
Speaker 3 (42:16):
Okay.
Speaker 2 (42:16):
I always read the books that I'm recommended.
Speaker 4 (42:20):
Okay, again, Francisca, thank you so much for your time.
I think it's been a very interesting conversation. And again
thanks for taking part in Data stand Up.
Speaker 5 (42:32):
Yeah, thank you, Susan, and we are we are so
glad being able to collaborate with you at bad Rock,
and thank you so much for all the support. Thank
you for this podcast, and thank you for the word
that you are doing with this podcast is really amazing
and every week more interesting.
Speaker 7 (42:48):
So I hope, I hope my podcast has been like
in the middle because I heard so many amazing people
talking about data, so in my case was bored like
a human side and I hope not to be bored.
Speaker 2 (43:02):
No, no, no, no. I think it's it's really relevant.
Speaker 4 (43:05):
We've had people from heavy research companies like the Mind,
from big organizations, from leaders in the space, but I
think that the people analytics component now it's more relevant
than ever, so I think it will be very interesting
for everyone.
Speaker 2 (43:19):
Thank you again, Francisca, have a good one. Bye, Thank
you so much, sir, Bye bye too. The expected them
(44:02):
to the despect Detect
Bedrop is an independent data science and AI firm specialized
in data driven business change. In this podcast, our guests
help us spread knowledge and experience with our listeners.
Speaker 2 (00:28):
Hello, good morning, Francisca. How are you doing today?
Speaker 3 (00:32):
How are you fine? Thanks? Great?
Speaker 4 (00:34):
Day to day, It's sunny, it's sunny, and well, today's
a very special day because as a Bedrock we are
speaking with Francesca CEO of Timmy Q, a very special
company with whom we collaborate, with whom we barnered very recently.
(00:56):
And I think that everyone listening to this conversation will
be very keen to hear what you have to say
and what timmyqu is about and how beat Rogues is
helping you. But before we go into those details, would
you mind briefly introducing yourself and tell the listeners briefly
what's tmy you as well?
Speaker 3 (01:17):
Yes, of course, thank you. Jack. So, so I'm like
quite a typical file.
Speaker 5 (01:23):
My mother is physicians, so I come from a from
a family where data and analytics, so let's say, it
was a was a main issue. And after that I
started studying economics and during my life work like in
organizational uh, in the organizational world.
Speaker 3 (01:45):
I also my family comes from.
Speaker 5 (01:47):
Entrepreneurials experiences, so somehow I had this in my DNA
and I played basketball, which is something UH that plays
a key role in in the team qu development. And
always had also this humanistic soul since since the beginning
and mixed the UH since my first my first teasis
(02:11):
and UH and studies work what was more like analytics,
so mathematics and physics and and the other part for
me that it's the humanistic part of human beings and emotion,
emotional development and conscious development. So for me, these two
souls are at the origin of tm Q and started
(02:35):
with my with my first experiences in organization and also
UH somehow breaking through technologies because I was I was
lucky enough to be called as country manager of a
tech attach company back in two two thousand and eight,
and actually that was I user generated company, so what
(03:00):
we call the today user generation user generation videos, and
that was UH television programs coming from contribution from users.
So that was quite pioneers using Facebook and Twitter that
were at their early beginnings.
Speaker 3 (03:18):
UH. So let's say that are three souls.
Speaker 5 (03:21):
The first is technology and data and analysts analytics with
business and the secondly human human development is really a
passion about about people. And the third is is a
little bit transversal, but it's a it's of course about pioneerism.
Speaker 2 (03:42):
That makes sense.
Speaker 4 (03:43):
I mean, I think you've explained really well where Timmy
Q comes from.
Speaker 2 (03:47):
So when you read about tim.
Speaker 4 (03:49):
Q, and now you can go into the details of
what platform does, how it works, the technology and the
proposition value it says, people analytics, right, and the posts
to make sure that those clients of yours understand how
teams are performing and why, and that you understand the
reasons behind how the users of the platform, which are
(04:14):
employees of your clients collaborate, work together and trying to
find the relationships between them to see how can you
nurture them or contribute to improve them.
Speaker 2 (04:27):
Right, But I like to hear from you what timmy
qu is all about.
Speaker 5 (04:34):
Yeah, well, I think that back back to twenty thirteen,
I think I remember one day I was in Silicon Valley.
I actually I spent there almost two months, and I
was working as an organizational development in the organizational development field,
so basically with teams and leaders to yeah, to support
(04:59):
change man and UH and leadership processes. And at some
point you uh you really think about the impact of
technology uh into human the human resource fields, And that
was for me something very uh very It was a
key moment because that was some kind of of questioning.
Speaker 3 (05:23):
About the impact of technology.
Speaker 5 (05:25):
So for me, that was a little bit worrying to
think how technology could impact human resources and how it
was a good a good way to somehow reflect on
this dispossibility of impacting and and actually bring value to
people and know that technology not being technology only a
(05:47):
way of getting data and getting data and more data
and big data without a meaning. So what was the
purpose of technology while talking about human resources? Because of
course I was working in marketing previously in other companies,
and I remember being passionate about finding insights through data.
(06:08):
And of course when we talk about products or logistics
or let's say planted optimization, the reasons are completely different
than when we talk about people, ecosystems and teams. So
that was my concern and my challenge at that moment,
how can we bring a technology that makes sense into
(06:30):
the human resource development At that moment, that was not
the people analytics world was not even used.
Speaker 4 (06:39):
So it's kind of the beginning of that term, right,
people analytics now you start to see more and more
of a competition sort of say coming up. But before
we go into that, I like to briefly cover how
timicky works in terms of how data UH from those users,
(07:01):
interactions from users will being from users, whatever the characteristic
is is collected, and also how users and teams are
onboarded into the platform, because I think that if we
go through this, it will be easier to then go
on on the on the subsequent questions.
Speaker 3 (07:21):
Yeah, so there are two two very key moments.
Speaker 5 (07:24):
So the first is the first question is what do
we ask uh so, which are the relevant data and
the minimum quantity of data and we can uh we
can have from people, so we can ask people, we
can we can ask their time to answer and uh
and which are the relevant variables from a team that
(07:48):
we need to somehow understand to to.
Speaker 3 (07:53):
To be able to take action.
Speaker 5 (07:54):
And this is a very first reflection that it's so
key since we are we are coming from our world
when we asked our employees sometimes ours to answers to
UH yearly or or climate service where results were in
the ends of human resources people and then that were
(08:18):
not shared with other with the the.
Speaker 3 (08:22):
Levels of the company.
Speaker 5 (08:23):
So people were disengaged, and that was the history of
human resource data somehow. And the second question is how
do we work with this uh, with this uh possible
attriction that it's uh, it's objectly present when we ask
(08:46):
people about how they feel and uh, and when we
think about our organization, we come from control systems most
of the time.
Speaker 3 (08:57):
So people are.
Speaker 5 (08:58):
Accustomed to think about their personal life and their professional
lives as two different spheres of or dimension of themselves.
And this is of course in the past, and the
pandemic the Pandemic show showed us clearly that was already.
Speaker 3 (09:16):
In the past.
Speaker 5 (09:17):
But somehow people could have this fear of what happened
if I answer the truth. And so it's so important
to make people and teams understand that yes, it's possible
together data and these data are in fiber and actually
are for teams and for people, not for the company,
(09:40):
not for human resources. Somehow the sames that.
Speaker 3 (09:44):
We use use I use this.
Speaker 5 (09:47):
I like this, uh, this idea of team of sports analytics.
So I think sports analytics is you for teams and
for coaches to better understand the teams dynamics and improve
in the locker room. Okay, so we can talk, we
can share, we can be truthful with ourselves because our
(10:08):
object is to win the game. And in that moment,
there's no there's no fear, okay, because we need to
take the best decision for the team, and our data
are helping us toward those decisions. And this is a
little bit of dynamics that we reply and with technology,
we're recreating Timmy Q. So this locker room moment where
(10:29):
our data are for us as a team, for us
as a leader, and that really can empower us to
take better decisions.
Speaker 4 (10:38):
Also, the benefits of using the technology and the data
retrieval process by Timmy Q goes well beyond HR because
I think it can positively affect other layers of the organization.
But what's the typical scenario that you may encounter when
(11:01):
you're approaching organizations that you've never worked with, meaning you
may be in a commercial or pre sales stage. You
are explaining how timicky works, what's the purpose of it,
and that on how you're trying to promote the value
of people analytics. But my question is is this in
(11:24):
the priority least for leaders beyond the HR environment? And
do you reckon That's hard to translate the value of
investing in people analytics at this very moment.
Speaker 5 (11:39):
I think the pandemic changed everything. And I really think
that we feel that we know and we see the
people analytics market growing like fifteen percent yearly. Now, so
we have we're past and we were in a like
(12:00):
discovery moment for people analytics. Where as you said, uh,
there was almost no budget assigned for people analytics. In
our case, we are a team analytics, so it's it's
even a it's even a different like like value. But yes,
there was no budget assigned, no clarity on responsibilities. So
(12:22):
who was in charge of this budget that was hr
typically sometimes innovation people, uh, sometimes it or whatever. So
after the pandemic changed, was there leader leaders needed to
take the paths of their teams and and there was
(12:44):
a suffering uh across the company for not having the
human data that could somehow allow different conversation and to
really understand, uh, where were our people in terms of motivation, trust,
well being in a moment where we were.
Speaker 3 (13:04):
All locked down.
Speaker 5 (13:05):
So there the conversation completely changed and are now clearly
on CEO's table and uh, and this is a this
is a new, a new moment because we understand clearly
that our business results, our right our bottom line is
(13:27):
completely uh correlated with how our people are performing and
how our people are are feeling. And also the behaviors
are completely correlated to two perceptions. So we need to
be able to measure and to understand in real time
(13:47):
in the present what's going on in the company because
the context is so complex that you need real time decision.
Speaker 4 (13:55):
I believe that bringing up the the pandemic is highly relevant.
I think that for many businesses and for large corporations
that were used to be in present at their main office,
at their corporate office or her headquarters, it really changed
(14:17):
to be at home working remotely, and managers at different
levels within the organization needed to have that understanding of,
you know, how people were interacting with each other and
what was their feelings. So I think that the timing
is great. I think that, as you said, even though
(14:39):
I'm not involved in people analytics as you are, it's
in the agendas of the sea level executives, as you're saying,
I think it's a priority for them now now trying
to look at things differently and trying to set example well,
trying to think of examples where things didn't go so well.
(15:03):
What have you learned from previous engagements with clients of
different industries, of different sorts, Because I guess that sometimes
the value of teamyq is well received by a nature
leader or a team leader. But then it's a matter
of making sure that the technology is well adopted within
(15:25):
the organization and that the different employees, the different people
begin to use the technology and begin to use the platform.
So probably those early engagements or the kickoff of those
breeds sometimes go really well, and sometimes they don't. So
if there is someone out there that is thinking of
(15:45):
TIMMYQ and they think it's good for their business, what
are their learnings of difficult engagements or the engagements that
were particularly challenging for you, And what have you learned
that you would do differently to make sure that the
adoption of teamyquse a success.
Speaker 5 (16:03):
Oh my god, this is a great question. Actually, we
learned a lot from our mistakes. I think we started
maybe a little bit too early. Sometimes I say that
because that was twenty sixteen when we launched it the
very first proud So we were really pioneered and we
had the time to learn. And this is important in
(16:25):
a moment where probably the market was not in expansion
with the people analytics tools, so you really needed to
convince the client and to learn by doing together with
the clients. And I think what we learned the first
is what you just said. So the key role of
a good onboarding, a good kickoff meeting, and a very
(16:52):
successful communication at the beginning two teams, so that the
data since the first measurement are reliable because people are
telling the truths and they understand the power of transparency
and the power of having a voice, a new voice
inside the company. And when we when we didn't succeed
(17:12):
at the beginning, was especially when the company tried to
get like small pilots in UH in areas that were
critical areas and UH and like engage very few people.
Speaker 3 (17:27):
So what I think that the power.
Speaker 5 (17:29):
Of people analytics in general and team analytics is really
to UH support a cultural transformation. Digitalization could be a
Gile transformation, could be a key change management that's inside
the company, and somehow use it like a boost or
an accelerator of this transformation. And when you are trying
(17:52):
to keep it like very small in a critical area,
actually are not taking the best out of the tool,
and then you're not engaging key people in the organization,
like like directors or sea levels as you said before,
that are hard the ones that somehow are the first
pillar of the change. So you ask your people at
(18:13):
the like blue Colors or somebody to answer to some questions,
but they see that you're not engaged as a company
to really really provide an action answer. So I think
that we learn UH to to engage the right people
inside the company, the right decision maker to create an
experience since the beginning UH and and build this experience
(18:37):
from a sometimes also a pilot of course, a pilot
with a with a number of people that could be
between five hundred two thousands depending on the on the
on the company, and then also small companies. It doesn't matter,
but that the changes come from the willing of the
company to transform, to UH, to create an evolution and
(18:59):
to really somehow empowered teams and empowered leaders to work
with real time human data.
Speaker 4 (19:07):
And I don't know if it's a quick question for now,
but I think I can ask this. So now that
we start to take a look at how data is
being utilized to learn how users interact with the platform.
Speaker 2 (19:21):
And this is something that Petrok is doing with Timmy Q.
Speaker 4 (19:25):
How would you ambition the future to be when it
comes to Okay, the platform is learning how much a
user is sharing in terms of thoughts and feedback with
the technology, how would you think the tool itself could
learn from those interactions to increase the engagement from these users.
(19:46):
Is it related to actions being suggested to improve the
well being? Is it related to I don't know, creating
tailored responses based on what the user is sharing. I
know we are doing work in the realm of n LP.
We will have to develop some machine learning models that
(20:08):
learn from those interactions. But if you were to think
of the ideal development roadmap, where would it take us
when it comes to the platform itself having the mechanisms
to promote.
Speaker 2 (20:24):
Adoption on how it interacts with users.
Speaker 4 (20:28):
I don't know if if I if I was clear enough.
Speaker 3 (20:32):
Yes, yes, of course.
Speaker 5 (20:33):
And I was thinking, because I always hear your great podcast,
so really congratulational.
Speaker 3 (20:38):
I love it.
Speaker 5 (20:39):
And I was hearing Mikhaiela like a few weeks ago,
and she was saying something interesting that researcher. The difficulty
of research is to choose to prioritize what to for
where to focus on and really understand which is the
best researcher for the moment.
Speaker 3 (20:57):
So it's a little bit the same for us.
Speaker 5 (20:59):
We are a king with data uh and uh. First
of all, we are working with humans, so we are
working with teams, which which represent in terms of data
of course complex relations uh and and data gathering from
teams and from human relations are are also subject of
(21:23):
a deeper reflection which involves ethics and AIU data protection
and really questioning what is useful for the company, what
is useful for the team, what is useful for the leader,
and what is not uh useful and which is which
(21:43):
is something that we can gather and actually we are
not allowed to show, okay, And I think this reflection
is at the basis of the question that you just
just ask me. Because of course we can think about
reporting and dashboards, which is the first level of data
kepis and metrics. Okay, we gather the analytics, but then
(22:07):
we have predictive analytics and advanced analytics and AI when
it comes to people, Uh, you are really you're really
asking yourself what is useful for a team, what is
useful for the company. With my client, who is my
client is only the company of course, not as you
said before, the user and the person that is answering
(22:27):
that it somehow experiences the platform every day at all
level of the company. It's somebody that needs to feel
that his time or her time is worth and that
his or her voice is UH is taking you into
account is acknowledged. And this is the main challenge I
think to work in a transversial way within the organization
(22:52):
when you provide value for the for the person that
is somebody UH, that is that can be from a
as I said before, for a blue collar, a white
color somebody that is working in the headquarters, maybe somebody
that is UH in the street and is working for
the company from an external perspective. And at the same
(23:15):
time you need to bring value to human resources, to
CEOs to take strategic decisions. Yeah, these different levels are
inside our future and UH. And while while I would
I will answer you with one word that is collective intelligence.
So I think that what we are doing inside MiQ
is not trying to give answers or exact answers to
(23:42):
to user and bring exact actions. We believe that it's
much more interesting to create a condition to make the
right answers and to and to connect the best uh
the best ideas or the best actions that worked in
(24:02):
another level in a similar experience in a benchmark or
industry and bring them into the platform so that people
can learn one from each other, leaders from leaders, hrs
from hrs. And it's not only the platform providing ideas.
Speaker 4 (24:18):
Plus something else that we've discussed before that I think
it's really valuable and relevant for the conversation is that
when you bring data from other communication and collaboration platforms
into the mix now TIMIIC, you gains or enhances the
results and the and the benefits that can be gained
(24:39):
from using from using the technology. I guess that this
is part of what you have in mind, trying to
get in very valuable data from other sources within the
organization that can complement the data that you collect from
from those individuals.
Speaker 5 (24:57):
Right, Yes, of course, and too twenty was the year
of integrations and digitalization. Yeah, digitalization are now part of
our life in a way that was not even not
even conceivable like just eighteen months ago. So we are
(25:18):
aware that in this connected world, there are a lot
of information out there that can be very useful and
very Let's say that can be key for the next
type of tim Q.
Speaker 2 (25:34):
You.
Speaker 5 (25:34):
Now, the question is how can we connect those data.
They are not only quantitative data but also qualitative qualitative comments.
And this is so so key to be able to
mix both of them, so quantitative and qualitative, and at
(25:55):
the same time to be able to find not evident algorithm. Okay,
uh that that are uh somehow providing our clients and
our teams the possibility to connect not evident patterns and
discover new patterns for them.
Speaker 3 (26:13):
That are that are allowing predictivity. Okay.
Speaker 5 (26:18):
So while we're talking, for instance, concretely about trusting a team,
m hm, how can we measure trust? Okay, we can
measure trust by asking people. We can measure trust by
reading conversations, but understanding how meetings are are developed, understanding
how people are collaborating. Our teams are collaborating some among them,
(26:43):
so not only in the team, but also inter team
and cross team. Our organizational network is structured and learn
from from one industry to the other, one country to
the other, understand dynamics, compare dynamics, and provide new insights.
So I think this is really a moment where once again,
(27:05):
the question is which data are useful, which are not useful,
and which are providing truly insightful information for the new
dimension and for us, the dimension is the team dimension.
So it's not about gathering individual results or individual answers.
It's really understanding how teams are working. Because for a company,
(27:29):
the new cell, the new key cell of an organization.
Speaker 3 (27:33):
Is the team.
Speaker 5 (27:35):
We can have ten messes inside the team or ten
whatever superstar and be a superstar in a team, and
probably the team is not working as we would like to.
So we need to really understand what is the key
performance issues in a team. And sometimes it's a lack
(27:56):
of trust. Sometimes it's a lack of effectiveness. This is
a well being inside the team. Sometimes it's conflict. So
not always the same pattern, but we can learn from
from the organization, from the environment, from the industry, and
this is the future of the team performance is really
(28:16):
understanding what's going on in the team and being able
to predict.
Speaker 4 (28:20):
Really interesting something that I haven't asked, but I think
it's really key to you in how you approach potential
organizations to work with. I don't know if this is
really really remarkable, but based on your experience working with
clients from all around Europe, of course, from Italy, from Spain,
(28:43):
from wherever it is. I guess they're understanding when it
comes to people analytics or the importance of paying attention
to human well being, the regal performance relationship between teams,
it's different, more or less important. But first question is
it really perceived in the same way across geographies and
(29:04):
cultures and do you approach companies differently based on this?
Speaker 5 (29:10):
In general, let's say that the Anglo Saxon word is
much more accustomed to work with the data and in
general to work with the analytics on people compared to
Latin countries. So they get less emotional, so they understand
data are needed to progress, to improve, and they are
(29:34):
less dramatic in the use of human data, and this
is something that helps. On the other side, is it
true that this does attraction that we were discovering before
are also useful because when you reflect at the beginning
of the project and you really ask yourself, why is
(29:55):
it useful for me? Okay, you get another type of engagement,
especially when you have transparency in data, so that you
know that those data that you are gathered are the
displayed into into transparent dashboard for all the teams, so
you can share with other people. So it's not only
on the leader's shoulder, and it's really about opening new
(30:19):
conversation within the team. So I think that this is
something that helps. In the Asian country, we have a
mechanism inside to mike you that actually allows to ponder it. Uh,
the average depending on the on the culture. So that
means that there's no red or green results, absolute green
(30:41):
or red results, but depending on how the company and
the culture is answering, the average shift okay. And this
is this is important why because in Asia we are
not accustomed to evaluate in a negative way, okay, So
a six out of seven also be an alarm, an
(31:02):
alarm signal, while in a German or in an American company,
when you point four or five out of seven, it's
already very high, okay, And it's something that the two
must recognize. You need to work with different culture. You
need to understand the people evaluation pattern and give responses
(31:24):
not only based on the average, but based on the
culture and understanding what's really going on inside the team.
Speaker 4 (31:31):
I guess that that also applies to how we work
as well. With different clients. For us, sometimes it's more
on the heal maturity. Some companies are still trying to
figure out how to produce data across the business, while
others have been producing data for ages, and it's about
(31:55):
how we can make sense of this data at this
very moment, and how can we forecast with the data
that we have in hand, what's going to come. So
it's it's really interesting to hear how these differences across
geographic cultures and maturity when it comes to understanding how
important these people are.
Speaker 5 (32:14):
Yes, and also because HR HR dashboards in general, HR
Data Intelligence is almost the last one in the company,
and this is something that we need to assume UH
and UH and there are people from from HR on
the on the people site are starting now together UH
together data together, data from different sources and to and
(32:38):
to create strategic strategic dashboard, dashboard and way to predict.
Speaker 3 (32:44):
Of course, this is very different whether we talk about.
Speaker 5 (32:48):
Corporates or medium and small companies, and this is this
is different words. While UH huge organizations already started like
three four years ago to work with analytics, companies, maybe
less than ten thousand people are really starting to work
(33:10):
in this field. So they also do the strategic approach
and the consultancy needs from one and the other is different.
Speaker 3 (33:19):
Time.
Speaker 5 (33:19):
We need to we need to understand that while on
boarding the people from the company.
Speaker 4 (33:24):
For interesting okay, probably one of the two last questions
that I had in mind. First one, so based on
your practice field of action, if you want to say, people,
analytics and the different bus business units that you've dealt
with apart from HR.
Speaker 2 (33:46):
If you were to advise.
Speaker 4 (33:49):
Someone who is leaving the technology side of things and
the data analytics side of things within a company, maybe
chief data officer, chief information officer, how would you recommend
them or what would you tell them to focus on
when it comes to governing or making sense of data
(34:11):
that affects the people data analytics side? Am I making sense.
Speaker 3 (34:19):
If I understand? Well, it's it's okay.
Speaker 5 (34:22):
We have an ecosystem of decision maker today. It's like
what I call a board of people analytics, so you
speak with different people at the same time and the
decision is not in one hand. This is the upgrade
that the pandemic allowed in this field. So of course
you were you work with the with the top levels
(34:44):
of the company, we being the its or or legal
for the data protection or the CEO or whatever CFOs.
Of course, you need to provide direct correlation between business
KPIs and people data.
Speaker 3 (35:00):
Okay. This is one of the.
Speaker 5 (35:02):
Main challenges on every I would say sea level desk
at this moment, as we know that it's not evident
to correlate to people data. What there is a lot
of literatures on the correation between the e nps, so
the engagement of people with the UH with the economic results.
Speaker 3 (35:23):
And at the same.
Speaker 5 (35:24):
Time a very a very new literature is starting because
we are starting working with the with the massive data.
Speaker 2 (35:33):
Okay.
Speaker 5 (35:34):
This is the of course the challenge, and it's also
very interesting on how you create different collectives inside the company.
So this is this can work slightly different for different
kind of teams.
Speaker 3 (35:46):
Okay.
Speaker 5 (35:46):
So we are working with some clients that are asking
us Okay, hey ley, I have like six or seven
different type of teams. I have hierarchical teams, I have
project teams, agile teams, I have UH boss less teams. Okay,
can we provide different experiences for them? Can we really
understand their needs, their objectives and somehow create a different
(36:10):
experience toward human data and our answer is yes, okay.
Our approach is like a systemic approach. This is something
unique that means that we see organizations and teams as
living systems. So you need to provide to that system
that you are listening or measuring depending on the need,
(36:34):
a different of frequency, different kind of experience, different kind
of rhythm okay. And this is needed inside a company
because not every team is working the same way and
at the same time. Strategically, and from the top of
the company, we need to aggregate data and to have
(36:54):
a consistent report to take strategy decisions. So there are
always these two these two ways of thinking that needs
to be integrated. Thirt is that sometimes you work with consultants,
with external coaches, with people that are supporting you in
your organizational transformation or change management, and those people must
(37:18):
also be integrated inside the uh UH. The people are
a strategy, so that somehow from the insight that you
are gathering as the company and from the inside that
they are gathering as expert in that field, they can
they can make the change management smoother or support from
(37:39):
from the strategy UH that change and accelerate UH people transformation.
So I think is what is really needed is to
understand the ecosystem of the client in terms of ways
of working teams and also people that are involved and
create the best experience for the client to accelerate the
(38:03):
process and really feel committed to that. And and that's
what I love in my work.
Speaker 2 (38:10):
That's that's very useful for those are very useful insights.
And now if you want.
Speaker 4 (38:15):
To share something with those listening to conversations, what's the
next big challenge for Teamy Q in twenty twenty two
and is it related to the work we do in
regards to data science.
Speaker 5 (38:32):
Yes, I think you've pointed out before our how we
see our future in terms of providing insightful and key
predictive information. This is one of the key challenges on
our tables. So really using digital we are gathering in
a way that it's uh the most uh the most
(38:56):
interesting for the company and for for people. And the
second I would say is always, like every tech tech company,
is how to scale and really bring these values to
more people, more companies. As we said before, creating this
collective intelligence that in our vision is not inside the
(39:22):
territory of the company only. Okay, we can create systems
and we can provide, and I think that the moment
is really now to create some ways of sharing new
sharing information among companies that are facing similar challenges around
the world and providing those data to other companies by
(39:44):
respecting the privacy, of course of people and of companies,
but somehow sharing these information so that it can we
we can increase our collective intelligence on our teams working,
and we can we can take the best strategic action
for the future.
Speaker 4 (40:01):
Awesome, that sounds really good, bright future we have ahead
of us.
Speaker 2 (40:06):
Okay, it's been entertaining forty five minutes.
Speaker 4 (40:12):
I think before we close the call off, I always
ask for a book recommendation, and I know that your
CEO wrote one, but I also read that, so I
would prefer, I don't know, recommendation a different book. We
can't mention that one as well, because I think that
(40:33):
he'll be happy.
Speaker 6 (40:34):
But if you can mention any newsletter, book or knowledge
content that you think it's useful when it comes to
either people analytics or just because you like it, now
it's the time to mention it.
Speaker 5 (40:49):
Yes, Well, I think there is one person that really admired.
He is really a great, great job in people analytics,
which is David Green. Actually he has a he has newsletter,
he has a LinkedIn profile, and it just for a book,
and so that would be my book is The Excellency
People Analytics by Jonathan Ferrar and David Green. And I
(41:12):
think is also very practical approach with the use cases
and challenges that are facing that people and organizations are
facing all around underworld. So I definitely think it's my
book today. And maybe it's not like I heard more
original books. And at the same times, I feel that
(41:33):
sometimes we talk a lot about data and we really
need to understand how to apply.
Speaker 3 (41:38):
Those two people.
Speaker 5 (41:39):
So that would be a basic book and a needed
book for everybody that would like to start working in
people analytics.
Speaker 2 (41:48):
That's right.
Speaker 4 (41:49):
It is not always about the technical bits and pieces
when it comes to applying data science, it's also known
why it's been applied, what's the purpose of it and
for that understanding people and their business is it's key.
Speaker 2 (42:01):
So what was the name again of that book?
Speaker 5 (42:04):
Excellence in People Analyticsan Farr and David Green.
Speaker 2 (42:09):
Okay, you know I'll read it, right, Yes, yes, you
read it? Yeah, no, no, I will, I will, you will?
Speaker 3 (42:16):
Okay.
Speaker 2 (42:16):
I always read the books that I'm recommended.
Speaker 4 (42:20):
Okay, again, Francisca, thank you so much for your time.
I think it's been a very interesting conversation. And again
thanks for taking part in Data stand Up.
Speaker 5 (42:32):
Yeah, thank you, Susan, and we are we are so
glad being able to collaborate with you at bad Rock,
and thank you so much for all the support. Thank
you for this podcast, and thank you for the word
that you are doing with this podcast is really amazing
and every week more interesting.
Speaker 7 (42:48):
So I hope, I hope my podcast has been like
in the middle because I heard so many amazing people
talking about data, so in my case was bored like
a human side and I hope not to be bored.
Speaker 2 (43:02):
No, no, no, no. I think it's it's really relevant.
Speaker 4 (43:05):
We've had people from heavy research companies like the Mind,
from big organizations, from leaders in the space, but I
think that the people analytics component now it's more relevant
than ever, so I think it will be very interesting
for everyone.
Speaker 2 (43:19):
Thank you again, Francisca, have a good one. Bye, Thank
you so much, sir, Bye bye too. The expected them
(44:02):
to the despect Detect
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