Why Do You Need Cloud Data?

Could there be a silver lining on your data cloud?

These days, data is integral to successful companies and accessing, tracking, reporting on and storing large amounts of data gives the forward-thinking business a distinct advantage. While a lot of firms are still storing their data on their own servers, accessible via traditional methods, modern ones recognise the logistical benefits of cloud data.

With astronomical amounts of data being created each and every day, you can choose to embrace it now, or risk being left behind as your competitors move ahead of you. What is your strategy to manage your data? Well, first, you must recognise what data you currently possess. You must have a structure in place to deal with it.

Firstly, you must deal with the availability of your data. Your data scientists must be on top of collating, controlling and shaping the data into something that is actually useable within your business. Data can fly around at different speeds and in different forms that must be processed into something comparable which equals real value for your company.

We are really talking about speed here. Your company should be able to access the data quickly, it must be accessible through one channel, so people aren’t having to waste time going to multiple sources and this can drive real-time decisions.

As you take steps to move your data to the cloud, your current data structure should be updated and refined to make the move smoother. Security is definitely a priority and if yours is lacking in any way, it must be evaluated and reinforced. Possibly engage a managed service partner and automate backups to reduce overall costs. Each solution will be slightly different, depending on the type of business, the types of data that needs to be managed and the level of security needed.

It is easier to survey and identify problems and gaps in your solution if you operate with data at the forefront of your cloud plans. With this overview you can see what data is going to waste and can rectify that problem. As cloud is the future, when new opportunities present themselves, you will be at the leading edge when it comes to data exploitation.

When introducing AI to the mix, the cloud aspect makes even more sense. With the compartmentalisation and availability of the data and the speedy availability and deliverability, analysis happens quickly and accurately, leading AI to utilise the data to achieve your goals, which saves time and money in the long-run. As we stated before, it is about making that data as easily available as possible so that your company can truly benefit from the data it owns to build models and predict behaviours, to drive sales and keep costs low.

Collaboration, and ultimately monetisation, is also possible due to the cloud. Offering controlled access to partner companies can benefit both parties of the project matches mutual needs. Prior to the cloud, this type of sharing was fraught with difficulties and could become dangerous due to the lack of security protocols. With the measures in place with the cloud, this is much safer now. Companies within a sector like retail have data that can be shared and monetised, as long as this is done ethically. Product performance is one such example of how this shared data can benefit both parties, helping the supplier to improve pricing or promotion. While this won’t work for every company, it certainly has its proponents.

To create as seamless a transition as possible, from traditional data storage and usage, you must employ the right team, a team with experience that knows the easiest route through. With that team in place, the future of your business will be secured.

Curious about how Zenshin Talent can help your organisation hire the best candidates? Contact us today for a no-strings conversation about your needs and our experience.

Are Companies Wasting Their Job Candidates’ Time?

For job candidates, time can feel like it is ebbing away

As a hiring manager, does it feel like you never stop interviewing? As a candidate, does it feel like interviewing never ends? What is the normal amount of candidates to take through to interview stage and how many interviews does it take to know which candidate is the right one?

Those in recruiting circles rarely talk about these topics as they are considered a standard part of the business of recruitment. Typically, 6-10 people are brought through to interview and those who are, should expect to face 2-3 rounds of interview but it can lead to 5 or more.

If can be disheartening when, as a candidate, you find out about another round of interviews, after you aced the first and second. It is even worse when, after jumping through all of those hoops, it all ends in rejection, especially when you felt that you dazzled them and answered every question.

The major confusion comes when a candidate imagines the path to a job. Their imagined path will probably contain seven steps from searching for the job vacancy to sending resume and cover letter to the invite to the interview to accepting the offer to starting work. In reality, there are many more steps in the process and when facing ten or more steps, the candidate can start to lose hope. This is amplified exponentially if the process goes on for over a month.

Thanks to a glut of CVs being sent in for jobs on online recruiting platforms and an explosion in the number or generalist recruiters, there now requires more of a ‘whittling down’ of the prospects. So, weirdly, it is no walk in the park for the hiring manager either.

Recently, with the Covid-19 crisis, more managers have been exposed to video interviewing, so now, in addition to the first phone interview, there are now multiple rounds of video interviews, made easier by services like Zoom and Microsoft Teams. In the past, there were people who were difficult to wrangle into the same location, but now they can join from anywhere with an internet connection. It feels though, that because the interviews are not face-to-face, that sometimes hiring managers might feel more hesitant to say yes to a candidate, which leads to more rounds, just to make sure. In large firms, the decision may not just be up to one individual so the hiring pipeline is blocked further.

Due to the internet, and the ease in which people can apply for jobs or can be headhunted, the prospects can be snapped up for other roles before HR can say ‘second interview’, which throws more uncertainty into the mix.

Some hiring managers are now moving away from the rigidity of the 6-10 rule and are arranging to interview candidates they are interested in as soon as they see a resume that catches their eye. This is down to both the fact that video interviews make things easier to arrange, and also, due to the fact that they are desperate to fill positions, especially when it comes to Cloud, Data and AI. They will then likely park the candidate they like until other candidates crop up, then they compare. This can be excruciating for the candidate who genuinely made a great impression. The problem is that they may fade in the memory of the interviewer over that time.

With an average of 250 applicants for every job, managers need to thin the herd. First they either look through, or use software to sift through, the CVs. Then come the phone interviews to thin it even further, with managers looking for tell-tale signs in a not-so-formal setting. Perhaps the candidate trips up over what is on their resume. If you are in the top 2-3% who actually make it through to the next interview, that means you have a good chance of getting the job.

Other factors can affect whether you actually get that job, of course, including cultural fit, primary traits the managers think will work within the role, salary etc.

To answer the question whether companies are wasting candidates’ time, there is no answer other than how the candidate feels. There are reasons behind the long-drawn-out process, yet some recruiters are changing with the old ways and the future is looking brighter for both candidates and hiring managers.

Curious about how Zenshin Talent can help your organisation? Contact us today for a no-strings conversation about your needs and our experience.

Hiring The Wrong Data Scientist Will Damage Your Business

Hiring the wrong type of Data Scientist can take you down the wrong avenue

Whilst the sector can be complicated and bewildering, one of the main problems facing businesses is the fact that they are often hiring the wrong data scientist for their needs. As crazy as it may seem, there is deep confusion within firms that require data professionals but what can be done?

The blame does not just rest on the shoulders of those who are hiring. Prospective candidates may be averse to highlighting the mistakes made by employers if it means that they do not get the job.

Some who might be referred to as data scientists, are closer to statisticians and data analysts. They may work with Excel and they may create business insight visualisations. They may be nicknamed ‘clickers’ and they have a positive role to play in businesses, but they are not coders and cannot make or replicate the breakthroughs that companies desperately need during these times but may be earning the salary of the more qualified coding data scientist due to the confusion.

The Senior Economist for Glassdoor, Daniel Zhao, states, “This muddling of job titles is changing the composition of the data scientist workforce and holding down wages.”

This is having an effect in two ways. ‘Clicker’ data scientists may be receiving higher salaries meant for ‘coder’ data scientists, or they may be receiving the normal salary for someone in that role. This gives the employers the false impression that data scientists fit into this wage bracket and any data scientists with higher expectations are being unrealistic and thus, will never be hired.

This all starts at the job spec stage. A lot of these descriptions do not really specify what the role requires. This can turn away seasoned data professionals and leave the door open for those who are less skilled. Companies accepting candidates who have 50% of the requirements can be hiring the wrong data scientist. You must not be afraid to hire someone who has 100% of the requirements due to suspicion or fear of over-qualification. There are many facets of this problem because businesses can, conversely, scare away good prospects by demanding too many years’ experience in a certain field or too many qualifications that are not relevant. Perhaps the company has decided to cover all bases by advertising the role as a senior role but research has shown that this scares away perfectly-suited potential candidates. Specificity is the watchword here, as it will guarantee that only the right scientists are interviewed and hired, and it also means that collectively, data as a whole will move forward into a bright future.

Data wrangling and cleaning big datasets require programming and coding skills. This is the meat and potatoes of the data scientist and it cannot be done by someone lacking coding skills. Point-and-click has its uses but that software does not have the complexity to do the heavy work. Data scientists need to be able to code in languages like Python and R.

This kind of mix-up was bound to happen when you have companies launching data projects for the first time or seeking to exploit their data without a full understanding of how these goals will be achieved. Each discipline uses the same or similar terms so it is understandable that management who are just learning the terms would assume that these roles are the same. This naivety can be countered by employing or partnering with experts who can recognise higher skills, who know how to build data teams and what the structure of those teams should be. This isn’t just a problem that can we waved away as ‘geek stuff’. Your company’s bad data analysis could lead to lawsuits and heavy fines, so it is worth bearing in mind that nipping this problem in the bud before it gets out of hand will pay off well in the future.

Curious about how Zenshin Talent can help your organisation hire the best candidates? Contact us today for a no-strings conversation about your needs and our experience.

Why Is There A Huge Failure Within Data Recruitment?

A 30% failure is huge within any context

It is becoming apparent that many companies are failing to select the correct candidates to hire and it is getting to a stage where those involved must be made aware in order to rectify the mistakes.

A startling figure is that 70% of hires make the first year anniversary at their respective companies. That may seem impressive but one must bear in mind that that means that 30% of new hires don’t even make it to a year working in data at the company.

In what other area of the business would that percentage of failure be acceptable? None. If you failed 30% of the time in any other area of the business, it would be a catastrophe, so why is it okay in data recruitment?

I mean, of course this may be happening in other areas. Only recently we published a very popular blogpost about how only 20% of C-Suite hires are the right fit for their company. We are focusing on data recruitment as it one of our areas of expertise.

Usually companies have adapted their style of hiring for data scientists from their style of hiring for software developers, especially larger companies. This often involves tests such as brainteasers involving coding or statistics, which candidates actually practice for and which, in reality, are a waste of time for everyone involved. These clean-cut problems ignore the fact that data professionals can be working with messy data. While using guidelines from software developers is a step in the right direction from generic or vague hiring practices, it is obviously not going far enough to highlight the best candidates and their skills. If a company is working from an old model, how can it expect to attract cutting-edge candidates.

Outdated techniques are just one side of the problem. Finding prospects with the right skills is of utmost importance but how many businesses know what they are looking for in this area? Each candidate must have a mixture of skills involving business, databases and maths & stats. They should be adept at model building and should be used to using different models to solve various problems.

They must have experience in Machine Learning but if they do not, sometimes their degree may have involved modelling and they just need to brush up on the skills in order to become a valuable member of the team. Team members must be able to, firstly, find the data, and secondly, process the data. These are two separate skills and the candidate must have both. The fact that this is counterintuitive to those who are not experienced in the sector means that hiring managers can be tripped up by this and other similar eccentricities with data. And we have not even touched on the different coding languages a prospect may need to know. For example, data analysts often only work in Excel which means they are ill-suited to other data roles. It is complex and those with some or no experience within it can be challenged by it.

On top of all that, a data expert should be able to understand problems within businesses, recognise how data can be used to solve it, solve it and then translate it back for the layperson to understand. If one part of this solution is incorrect, the problem remains unsolved. They should also be able to handle pressure, learn new things and mix well with other team members.

It is clear now why so many data professionals do not make it to their anniversary in their company and a lot of the time it is not down to flaws within the candidate. Companies should take care when hiring to ensure they are hiring the right fit for their data team, prospective candidates should be encouraged to create their own portfolio to showcase their skills and someone who has expertise in evaluating these should be in charge of progressing those prospective hires to the next stage of recruitment. With more awareness, together we can get those anniversaries up to 100%.

Curious about how Zenshin Talent can help your organisation? Contact us today for a no-strings conversation about your needs and our experience.

Why Are So Many Businesses ‘Cold Starting’?

Many companies are cold starting their data projects

As mentioned in the previous blogpost, Data Professionals are looking at the door when it comes to their current roles due to a few factors, a major one being companies being unprepared and the new hire having to start things from scratch. This situation is often referred to as ‘Cold Starting’ but why is this happening?

When starting to build a team for projects within data, the first line of priorities are collection including user generated content, external data, instrumentation and logging. Useful, useable data is key for taking advantage of AI. Starting with an assessment of whether the data being collected is the correct type of data to satisfy business needs further down the line. Obviously, this varies and depends on the type of business you run, so whether this is categorical or numerical, this should be in place before you expect there to be any momentum when this data can be assessed.

Some companies fail to understand that these foundational blocks must be in place first and that there can be trial and error before this is all up to speed. What we mean by this is that firstly the requisite types of data must be sought out and then interrogated in order to discover whether the data being pulled through is actually correct. Within payment transactions, for example, there are different types and if the incorrect type is being collated, this can have far-reaching repercussions.

This initial stage, as well as ensuring that the right data is being used, requires resources. This means a leap of faith on the part of the company which, in many cases, does not yet comprehend the true value of what is being proposed. This is what we believe is behind the cold starting. Companies adopting this new approach to their information are unsure, until they are sure, and this results in a rush to get all parts of the machine in place and moving at the same time. Misunderstanding the complexity can result in false starts, a high turnover of staff and the aforementioned failures of data.

From that point onwards, data should be analysed. Analytics means the systematic analysis of stats or data and this can involve many different KPIs depending on a business’s needs. AI should then use algorithms to improve KPis and these improvements will feed back into the system ensuring optimisation and higher profits for the company. Of course, we have simplified it here and there are more moving parts in reality but it is surprising how many people don’t cover the basics when starting.

The company will need someone with the appropriate experience on hand to guide the whole process and ensure that the correct seniority of staff are hired and the staffing levels are correct during all stages of the projects. Another problem that can arise if this does not happen is when a junior data scientist is tasked with creating the system and they use algorithms from practice sessions on real world business issues. Getting the right mix is so important.

Indecision can result from a clash of priorities at C-level between wanting the best and wanting the cheapest solution to the problem. If one or more members are afraid that AI is just the newest fad, which will fade some time soon, then that explains why they hold off on it until they can hold off no longer. This is very true of those industries that are not traditionally data-driven.

Start-ups have an easier time of it because they are often engaged with data during the planning process. Older businesses need to reimagine their business from the bottom up and sometimes that can be a daunting possibility. The truth is that the future won’t wait for a business so the sooner they can get onboard with AI and Data, the better. As the old saying goes, “The best time to start something is years ago. The second best time is now.”

Curious about how Zenshin Talent can help your organisation? Contact us today for a no-strings conversation about your needs and our experience.

Projects Can No Longer Be Stalled Due To Covid-19 Uncertainty

Stalled projects are restarting and there is a sense of optimism

A major point we have been made aware of from hiring managers and C-suite executives is the fact that they are beginning to start pushing forward with stalled projects.

After the first lockdown, the majority of companies delayed their upcoming Data & AI programmes until 2021 in order to save the extra effort and extra anguish of trying to set these during a time of extreme uncertainty and instability. They did this in the hopes that the government would guide us out of this period but it is becoming ever more apparent that no one knows when this chaos will end with schools opening and closing again, areas of the country falling into ever stricter tiers of lockdown and a feeling that without full vaccination, this will grind on until at least next year.

Regarding technology, there is a real acceptance that companies can’t stall any longer and need to get on with it. There is a general optimism around AI & Data projects and starting back up with a renewed focus on the goal in hand.

A recent news story highlighted that stalled offshore oil projects could take as long as 3 years to restart. Obviously, data projects shouldn’t take as long as that but they must be prioritised so that they start up sooner rather than later. Here we can highlight the issue of finding leading talent as an integral element for future plans moving forward and quickly building strong teams with complimenting skillsets.

The pandemic caused plans to be shelved and resources were poured into setting up remote working, trying to keep consumers happy by not interrupting the flow of products and services and focusing on marketing to let the customers know that things were ‘business as usual’.

Even before Covid-19 struck, there were a lot of stalled AI and Data projects. This was due to many factors but two of the major ones were a lack of understanding within the organisation of what these projects were meant to achieve and  hiring policies which were woefully inadequate.

Prioritisation of which projects to start, restart, end or automate is extremely important. You cannot run before you walk. There will be an impetus to rush back into these but they actually require a measured response.

When moving their workers to online platforms, a lot of companies discovered flaws in their methodology and began fixing problems. This has set the stage for AI & Data projects to start from a level playing field whereas before they may have been at a disadvantage, not only due to data being misunderstood, but also from systems that were in dire need of updating.

More businesses have actually prioritised automation and data science due to Covid disruption, moving away from opinions to a more data driven strategy. On top of that, cost reduction is also not as much of a priority as it was pre-Covid. These are good times to be candidates experienced in these fields but when it comes down to hiring, companies need to know what they are doing and they need to upskill fast, before all of the best prospects have been snatched up by better-prepared businesses.

PwC predicts that companies will increase spending on cloud-based systems after the pandemic is over. The transformation when the emergency hit was unprecedented and within a few months it was approaching an estimated two-years-worth of growth acceleration. Early investment can actually save money in the long run.

Companies need people who have an overview of the process to step in and lead this revolution. Optimism is growing in the sector and there is no doubt that it can lead to huge successes. If a company has a carefully-selected team with the budget to drive the important projects it is planning, there is no reason why there cannot be something else to celebrate when the Covid crisis is over.

Curious about how Zenshin Talent can help your organisation? Contact us today for a no-strings conversation about your needs and our experience.