Just as a plant needs certain things to flourish (water,
good soil, sunlight), so does a team. Likening a team within a company to a
plant may seem ridiculous, but both need optimal conditions in order to thrive
and, when a team fails to achieve, it is because one or more of these are
missing.
A change of mindset is required within organisations that
labour under the misapprehension that all you need to do is find the candidates
with the right resumes, put them together and wait for the magic to happen.
Specific goals are required in order to measure performance.
What is measurable and realistic? Without goals set, some team members can feel
lost, or slack off unnoticed. Without goals set, the team can feel
directionless, shifting from one priority to the other, unsure which takes
precedent. These goals do not have to be set in stone, unyielding in time, but
should be clearly defined.
Similar, but not the same, is the idea of a common purpose.
Whilst goals are measurable, a purpose is more abstract, coming directly from
the philosophy of the business. Sometimes companies are scared or intimidated
by teams working within something misunderstood, such as data. Understanding
how the team fits into the business as a whole is important for the business
and the team. These things must be understood before demoralisation sets in.
In addition to the ‘why’ things are done there must also be
a focus on the ‘how’ too. Without this understanding, the team can find itself
going down blind alleys. The expertise of the team must be listened to and
taken onboard by management. A path must be agreed upon. Autonomy can be a
sticking point and if that conversation is had at the start, it can solve a lot
of problems before they have developed.
This leads to accountability. People tend to believe that
the majority of people like to avoid accountability, but this is not true. What
employees like to avoid is punitive measures that are visited upon them because
they made a mistake. If the organisation is one that understand that mistakes
happen and it is all about how the mistake is fixed and what lessons are
learned, that creates a working environment where members of a team are happy
to be accountable.
You will be interviewing candidates who may have been
promised the earth before and let down. Going into new roles they will want to
be reassured that the resources they expect will be provided. These need to be
delivered in a timely fashion in order for them to deliver the agreed-upon
results.
Reassuring the candidates that the team will be prioritised
and the resources, be it hardware, software or personnel, goes a long way.
Keeping promised means you will keep your staff.
Empowerment is paramount if the team works across many
departments and is very much in demand. Teams can become inundated with
requests to work on numerous different projects and they must have the power to
turn those projects down if they have competing priorities. Being a great team
brings with it expectations that you can solve everyone’s problems and with
that comes the issue of being spread too thin, which leads to disappointment
and reputational damage.
This all comes down to one word: trust. You must trust yourself to build a great team. You must trust the team. The team must trust you. The business must trust that the team knows what it is doing. Without trust, things fall apart quickly. If you are unsure about how to proceed after reading this, you should know that there are specialist recruitment partners you can trust to help build that team and power your company into the future.
Curious about how Zenshin Talent can help your organisation? Contact us today for a no-strings conversation about your needs and our experience.
It really adds up to treat your team like human beings
A business which conducts itself with purpose and
professionalism naturally elevates itself above the competition. A relentless
drive to increase in size in order to satisfy shareholders can serve to reduce
the people who work there to figures, robots even.
Whether a company chooses to take this route or not, will
define its future. Thankfully, more companies are adopting the path towards a
mission that is not just based on money, but on treating employees with
respect, acknowledging that they hold the keys to the future of the business.
Seeking meaning through actions is what humans do, and work
is no different. Yes, we all want to be sufficiently recompensed for our time,
and perks are nice to have too, but the realising of potential and a sense of
contributing meaningfully are major factors in the long-term mental well-being
of employees. Allowing workers to learn and grow means the good of the company
goes beyond fulfilling the goals of the business but can emerge out into the
society around it, benefitting those who may be outside of the immediate area
of influence.
When it comes to recruitment, it is easy to spot employers
who are engaged. It can be as simple as hiring managers replying to emails or
responding to candidate resumes in a timely fashion. For all the sound and fury
surrounding the search for new prospects to fill job roles, if what follows the
flurry of activity is the metaphorical sound of crickets, a specialist
recruitment partner starts to begin to understand why the hiring has been a long
drawn out process so far.
But just because an employer isn’t engaged with these issues
doesn’t mean they don’t want to be. It can be difficult asking for help when
you don’t really know what kind of help you need or how to go about starting
the process.
Finding support throughout this can be hard, if you are
starting from scratch, as an organisation has either made steps towards being
responsive and responsible, or it hasn’t. If it hasn’t, then it can feel like
an uphill struggle. All companies make noises about listening to their
employees but the difference between intending to do something and actually
going through with it, is huge.
Processes and systems need to be in place, and putting them
there takes time. Companies can and do change but change is often slow-paced.
Relationships can be complex and there may be a lot to unpack.
Having an experienced specialist recruitment expert onside
can help greatly. They will have experience of sorting the strategy for
onboarding new candidates within your sector, will possess knowledge of the
values shared by all of the different generations you may be recruiting and
they can promote your new way of doing things to the candidates as if they are
an external observer.
Within Data, AI and Cloud, it is important to view new
technology as a way to enhance the potential of your workforce rather than a
way of enslaving them to an omnipresent company presence. There will always be
doubters who misunderstand the aims, or those who are old-school who attempt to
mould what is happening to fit their standard worldview. Specialist recruitment
partners are outside and have a better overview of how things are going and
where things may be going wrong.
Responsive businesses will treat all the people in and
around the business with dignity, pays fairly, challenges and reassesses its
values in the face of criticism, develops an environment of accountability and nurturing
where people can thrive and will help the company thrive, in return.
Companies must adapt during these times. Showing
appreciation, encouragement and consideration is not a weakness. Getting fully
involved in the recruitment should not draw you away from your current
responsibilities. If you have the right help on your side, these problems will
not even be a concern anymore.
Curious about how Zenshin Talent can help your organisation? Contact us today for a no-strings conversation about your needs and our experience.
Open up to a bright new way of doing things this year
In this new year, it is important to remember that we can
only move forward if we are willing to think differently, question and even
reject the old ways of doing things. Change is a hard challenge but if the past
two years have taught us anything, it is that things left to chance will not
get you to the point you need to be at quickly enough.
The need to separate past, present and future is paramount
if a company has any ambition for the future. As the old L.P. Hartley quote
goes: the past is a foreign country, they do things differently there. When you
look at how you used to do business or you used to do recruitment maybe five,
ten, twenty years ago, you wouldn’t recognise the organisation.
Things move on, sometimes on purpose, sometimes accidentally
and sometimes forced by external pressure, but always necessarily. If your
company is striving to be in control of its destiny, everything it should be
doing should be on purpose, or at least in anticipation of market forces.
Whilst it is not always possible to tell what shape your
team will be in the future based on where it is in the present, strategy is
key. It has been known for some organisations to hire double the amount of
workers within a role, under the misbegotten expectation that the role’s
workload will double in the near future.
This is usually based on the misunderstanding of what is
required and how these data teams work. This can be based on panic from
management who wish to make sure they have enough support and not wishing to
show their lack of knowledge of that specific field. It can also happen due to
a misguided idea that resources will be saved if there is a double hire during
interviewing. Either way, it usually isn’t necessary and necessitates a need
for deeper understanding of teams. Resources may be better spent on an external
expert or a specialist recruitment consultant used to building data teams.
Starting a team small makes it more manoeuvrable.With
ever-shifting priorities, more agile teams are required within businesses,
especially start-ups. The hiring should also be agile. Scaling-up, one expert
team member at a time, is the way to go for smaller teams.
However you feel about data teams, or whatever your beliefs,
one thing is crystal clear: the old ways don’t work for data teams. The idea,
which can be prevalent amongst management, that a one-size-fits-all
multi-disciplinarian in data science will solve all your problems, is wrong.
Firstly, someone like that who is a genius is as rare as
hen’s teeth and, thus, totally in-demand and earning the big bucks. Secondly,
if they are not a genius, they are a jack-of-all-trades and a master of none.
Doing the opposite and just hiring a large team can result
in an unwieldy process. The natural instinct to cover all bases is
understandable but can waste time and money, and create bigger problems further
on down the road. There can be no more burying of heads in the sand.
The prime plan should involve a younger, diverse and, most
importantly to start off with, small team. The team’s skills should all
complement each other and there can always be external help brought in. Skill
development should be baked into the plan as these young prospects will be
looking to stay with a company that values them, and the company will reap the
value in the long term.
Information on their oft skills and passion should be sought after during their interviews and it all boils down to slotting them into the team you are building. There are experts out there who are adept at building data teams and if you need help, just ask for it.
Curious about how Zenshin Talent can help your organisation? Contact us today for a no-strings conversation about your needs and our experience.
The concepts of Data Science, AI and Cloud have gone from
buzz words to becoming fundamental components of how businesses now operate.
Data now sits at the core of operations and the Covid pandemic has significantly
sped this up.
Transforming data into insight has revolutionised business.
The people involved in creating the team need a foundational understanding of
the key components and ideas behind data science and big data analytics.
The urgency to apply Data, AI and Machine Learning
principles to improve, for example, efficiency of remote working means that
many companies may not fully understand what they are trying to achieve. It’s
one thing to say, “We need to implement AI, because it will make us more
efficient and our competitors are doing it” but what are the specific
objectives and how do you get that result?
The speed at which the data is required to pass through the
system differs from project to project but largely large data projects require
faster processing to make sure the data is sorted in a timely manner. The data
will also likely be entering the process from different directions and sources
and must be cleaned, sorted and ordered in order for it to make sense compared
to the data already in the system and for insights to be found.
For a big data process to run smoothly, these days it is
common for it to utilise a streaming system which will work in a close to real
time capacity. Data pipelines must be solid and strong to cope with so much
data passing through.
Big data is really no different than any other kind of
dataset apart from the size and speed of the data being processed, plus the
fact that the data will likely require new solutions to challenges it presents.
Forming insights from huge amounts of data is the end goal and the key is
usually finding innovative ways of making this possible.
The storage and processing of largescale amounts of data is
what will define a big data system. This is, more often than not, happening on
more than one server, which is where Cloud computing comes in, which brings
with it other challenges such as security and allocation. Tasks must be broke
up into smaller parts in a variety of ways in order to make the most of the
resources needed.
What is the data and where is it coming from? Text, images,
logs? APIs, Servers, sensors, social media? There are so many directions that
your data can come from and it must be configured somehow, eventually, into one
process. The perfect scenario is for the data to be transformed so that it is
organised and formatted at the point of entry but that is not always possible
and the work must be done at the backend by talent data scientists.
Quality is the watchword when it comes to data, and the
system must be able to sort and separate good data from bad, making sure the
processing power of the servers, be they physically on the premises or
Cloud-based, is best used and not wasted on fool’s errands. Using resources to
clean the data first can save time and money in the long run. At all stages it
must be ascertained whether the data is providing real value.
When you are clear about what you are looking to get out of
data, it becomes apparent which type of individual you need to hire to achieve
those goals. Companies will have specific needs relating to the type of data
specialist they need and for which job.
The major problem at this stage is that, if the goal is not clear, candidates will try and probably fail at interview as they attempt to mould their skill set to the generalist job requirements set out on the job spec. Dedicated recruitment partners will guide you through this minefield.
Curious about how Zenshin Talent can help your organisation? Contact us today for a no-strings conversation about your needs and our experience.
No need to throw your hands up because we can help
There is undoubtedly an increase in the demand for data
scientists so we thought we would take a look at the factors that are affecting
the demand and also those factors impacting upon the lack of suitable
candidates.
Not only have companies been using data science to handle
their own data pre-Covid-19, but during the pandemic they have scaled up their
data usage to measure the effects of the pandemic on their businesses in order
to find solutions to newly-emerging problems.
The explosion within the sheer amount and complexity of data
that each and every person produces thanks to our interactions with technology,
has led to an urgent need for companies to mine, clean, sort and model that
information in order to make sense of it in relation to their business.
The actual global value of data science has increased from
around $4bn in 2019 to $64bn in 2021, which goes some way to demonstrate why
everyone is requiring data scientists, and that is not counting other
disciplines such as data analytics.
Before the pandemic, the role of data scientist was still
very much in demand, being considered cool and displaying huge growth. The
reason behind the heralding of data science to such a crazy height back then is
possibly down to the fact that there is much overlap and misunderstanding
between data science and the other newly emerging roles. When a hiring manager
does not understand the differences between these seemingly similar roles, the
job specs and hiring can draw in candidates who are then given more
responsibilities, some of which are outside their skillset. A lot of the time
the candidates just get on with it, tired of having to explain themselves and
their role to management who are not listening. This can ultimately lead to
projects falling behind because certain roles are stretching the data
scientists or because there is just too much work to do in the allotted time.
If ‘data scientist’ is considered a one-size-fits-all title, then of course
when polls are done, it will seem that the only jobs in that realm are those of
‘data scientist’.
With the ramping up of data projects, the market for data
scientist roles has bounced back from a lull during 2020 due to the pandemic
and lockdowns affecting the economy. There was, even then, a demand for data
scientists, even if the job boards didn’t reflect this, and there is even more
of a demand now for those skilled individuals.
And there is no sign of a slowdown. Data is expected to be
worth $103bn by 2027 and that is without taking into account those sectors
connected to it either, which could lead to an increase of jobs of over 15
percent within the USA alone, probably mirrored globally.
With advances in technology, human beings’ data creation
becomes more complex and increases constantly. As it improves, more businesses
use and harness its uses. Something like the Cloud, which did not exist in its
present form a decade ago, is becoming integral in how companies handle their
computing, storage and data, and with this comes the need for more workers who
can handle and control it.
A major factor in an even more increased demand is that
firms are seeking to improve efficiency and use their data to grow their
business beyond their traditional model. There has been an increase of data
science courses in order to fill the skills gap, which is still a huge problem
for companies filling the vacant or newly created roles.
Right now, the correctly qualified and experienced candidates are hard to find because they are already in roles where they are using those skills already. Only dedicated experts and partners know where to find them. It will take a while before the shortages can be corrected and until then, you either need to know where to look or you need to know someone who does.
Curious about how Zenshin Talent can help your organisation? Contact us today for a no-strings conversation about your needs and our experience.
Let’s look at how Business Data Analytics can slot into your business
Using a set of specific skills like procedures and
techniques to explore and investigate old and new data is what Business Data
Analytics is. The aim is to discover insights through interrogating the data
and arriving at improved business decisions.
Companies looking to transform themselves digitally need to
expect to put in a lot of work and to change how they confront certain business
practices. Making intelligence central to the process is important. The culture
should be inherent within the organisation or else the changes being attempted
will result in poor performance.
We have researched and compiled a few areas that those
businesses should concentrate on in order to create a new forward-facing
culture and in doing so, will help order and secure their business data future.
Businesses should focus on the use of machine learning for
advanced automation of data. Utilising ML for data science and preparation,
data insights and developing models is termed augmented analytics. The
discipline is not yet as matured as other processes but as time goes on, it is
becoming an important part of the future of business data. As bias is removed
from the equation and more automation enters the field, it will lead to more
‘citizen data scientists’.
Relationship analytics is all about answering questions when
you do not know the question yet. It is about finding the connections between
things that are not normally connected using data models. If you have
constantly changing, updating, morphing data, totally unstructured,
relationships are determined and contexts clarified. Graph techniques are being
used to identify the difference between legal and illegal behaviours, between
actions that can help or hinder the organisation. These are especially
important when used with supply chains.
Uncertain behaviours can lead to unpredictable outcomes. This
is also true of incorrectly captured data. What is required is a framework
merging standard and future techniques. Within business, the need to model,
execute and track these decisions is paramount. Without this, the whole system
descends into chaos.
A culture of digital literacy should be advocated for within
the organisation. Something like this needs to be grown and cannot be a solely
‘top-down’ endeavour. Good ethics and an understanding of privacy and data laws
need to be disseminated throughout the company. Too many businesses seek a
‘quick fix’ solutions when the real solution is to build the scheme into the
fabric of the org. Digital literacy should be considered an employee life skill
and will contribute to the extended life of a business too. Companies with a
lot of data, ML or AI workers should be looking into an ethical code of
conduct.
More and more people are accepting the need for data and are
interacting with it in a much more positive way, which means that, on the
positive side, there is much more abundant data, but on the negative side,
there will be too much data to adequately clean and compartmentalise in a
timely fashion. This means that there needs to be automation but also that
there needs to be a much more scalable model available. The Cloud plays a part
in this scalability, allowing companies to alter their IT infrastructure and
also to work with data from a decentralised space.
Algorithms and services are emerging in places and industries they have not been seen in before, due to the sea change. A change in mindset is happening and the more it spreads the more advanced Business Data Analytics will become the norm. If you are interested in exploring data within your business, you need great talent, so don’t hesitate to contact recruitment experts.
Curious about how Zenshin Talent can help your organisation? Contact us today for a no-strings conversation about your needs and our experience.
