Giving Your Team What They Need

All hands on deck is the desired outcome

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.

Responsive Employers = Respected Employees

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.

New Approaches To Data Team Building

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.

You Need Clarity In Your Data Plan

At the centre of your plan must be clarity

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.

Why Is There Increased Demand For Data Science Candidates?

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.

What Is Shaping Business Data Analytics?

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.