Harnham

Data Analyst

Hayes, Middlesex
£45000 - £50000 per annum + Benefits

Data Analyst
Heathrow
£40,000 - £48,000

One of the most well known aviation brands in the UK are looking for a Senior Data Analyst to join them in Heathrow. This is a fast paced business, with an excellent reputation, now looking to add to their team. You will be joining the revenue team working with internal stakeholders to deliver reporting and insight solutions around tracking and forecasting of revenue. There are excellent opportunities to make actionable impact on the business, if you are curious about data with excellent communication skills this could be a fantastic opportunity for you!

THE ROLE -Data Analyst
As Data Analyst, your responsibilities will include:

  • Liaising with both technical and non-technical stakeholders from C-Level down to gather requirements and deliver Insight and Reporting solutions
  • Management of data life cycle and projects - end to end
  • Process, analyse and manipulate data sets using Excel and SQL
  • Working on several projects simultaneously
  • Generating engaging reports and visualisations and actionable insight where needed

You will need:

  • Proven commercial experience using SQL, Tableau or PowerBI in a commercial environment
  • Degree in relevant subject

HOW TO APPLY
In order to apply for this position, please do so via this site.
For information on this role or other roles in the BI Analytic market please get in touch with Sandra Namatovu at

KEYWORDS

Reporting, Insight, Data, BI, Analytics, Analyst, BI, Business Intelligence, Data, Analyse, Data, Strategy, Analytics

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45468/SN
Hayes, Middlesex
£45000 - £50000 per annum + Benefits
  1. Permanent
  2. Big Data

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3 questions to ask yourself before your next BI hire

Data & Analytics are a vital part of every organisation nowadays, so it is not surprising that the importance of Business Intelligence keeps growing. With increasing demands from executive management, operations, and sales, a stronger and better BI team is essential.  The responsibilities of the BI team include but are not limited to: performing Data validation and Data Analysis, delivering KPI related reports and dashboards, and working with end users to define business requirements and needs. However, as every company is different, every BI department is different as well. This means that from one BI team to another, the needed skills can vary completely. To get the most out of your team, it is important to have a clear understanding of what skills you already have, which skills you need to add with your next hire, and whether this is realistic for your business.  Here are three important questions to ask yourself before your next BI hire:  1) What does your team look like at this moment? To be successful in expanding your team, it is vital to take a closer look at the type of profiles and skillsets you already have. This is a good time to map out where the skills are in your team and see what is lacking, or what can be improved. To do so, you should consider three key elements: how (much) Data is used and made available, how this Data is structured and what is being done with this Data. The following three questions are important here:  Do you get the right Data out of your Datawarehouse/Data Lake? How is the Data structured now, and do you get the reports and dashboards needed?  Are you able to provide stakeholders with the right insights? These questions can function as starting point of deciding what skills you have now, and which areas to focus on with your next BI hire to fill in gaps or improve the areas where needed. 2) What does your Data Roadmap look like?  It is important to have a clear vision of where you want to go with your BI team and how to leverage your Data. At the highest level, your vision will be determined in a Data Strategy. On a more practical, day-to-day level, the steps to take are outlined in a Data Roadmap, with every part of the process requiring a different skillset. 
 What we often see is that companies who are at the start of their Data Roadmap, first hire a Data Analyst. Typically, a Data Analyst knows how to work with the Data and has a strong business sense but is not a specialist in either field. On the other hand, when the Data infrastructure has been set up, the need is higher for someone who can make sense of the Data and present this in reports and dashboards.  Two key points to consider: What is the next step in your Data Roadmap?  What type of skillset is needed to get to that next step? For example, this can be technical skills such as building Data Pipelines or stronger analytical skills to get insights from the Data.  By having a clear understanding what phase of your Data roadmap is next, it will be easier to hire the next member of your team. 3) What is realistic for your business? While you may know what type of profile(s) to hire next, it is important to determine whether this is feasible. The following factors are important to consider: As with every field of expertise, the salary ranges depend on which type of profile you are looking to hire. It is vital here to ask yourself where to invest your money best. For example, it is great to have an Insights Analyst in the team, but is this type of profile the main priority? You might want to first hire a Data Analyst to structure the Data and build useful reports. The candidate market within Data & Analytics is tight, so think about what you can give them in return to attract the best talent. A training program for personal development and the possibility to work flexible hours are two selling points that make your company stand out from the rest.  Location is key for many candidates. Businesses in larger cities are more popular with strong candidates in comparison to more remote businesses.  It is clear, therefore, that multiple factors are involved in determining what your next BI hire should be in terms of skillset and profile.  If you are looking to expand your BI function but not sure where to start, get in touch and I can advise you on the best next steps.  

Dorien Marijs

13. February 2019

News

Thank You, Next: How Machine Learning Is Revolutionising The Way We Make Music

From Vinyl to Tidal; we all know that the way we consume music has changed. Technological advances have made Steve Job’s claim that he would put “1,000 songs in our pockets” seem antiquated, whilst Spotify’s algorithms serve us tracks that we’ll love before we’ve discovered them ourselves.  But can the technologies that have brought us these advancements change the way we make music? Whether it’s leading to new instruments or creating a song without our input, Artificial Intelligence is a game changer.  Make Some Noise Until recently, the best way to imitate a sound was by experimenting with the different settings on a keyboard. However, this is no longer the case, thanks to Google’s research arm Magenta. They’ve created the NSynth Super, an instrument that generates sounds based upon Deep Neural Network techniques.  These algorithms allow the NSynth to not only imitate a sound, but consistently learn more and more about the specificities of that pitch, creating something closer to reality. Users can then combine those individual sounds to create something unique and entirely original. This is potentially just the beginning of a new wave of music, and in a decade’s time the NSynth could end up having as big an impact as autotune.  Talking About AI Generation Whilst we’re still waiting to see the impact of instruments akin to the NSynth, machine-led compositions are becoming more and more commonplace. Using a Recurrent Neural Network (RNN), one can feed a model existing music and ask it to generate something new. By learning the patterns and rhythms of notes from a variety of compositions, the model should be able to output an original and melodical sequence. Although these may not be the most amazing tracks in the world, they do serve a purpose. Music production platform Jukedeck allows users to input their requirements for a piece of music (genre, temp, mood, length, instruments etc.) that can then be automatically generated using AI. Obviously these aren’t designed to be chart hits, but production music that can be purchased cost-efficiently for YouTubers, Short Films and other backing-tracks.   Despite the fact that this remains the most common use of AI in music, some artists are looking to push this even further. Musician Taryn Southern, for example, has created an EP based purely on AI compositions generated using Amper Score. The platform generated a beat, melody and basic structure before Southern then rearranged and added lyrics too. Could this form of collaboration become the future of mainstream music? Rage Against the Machine Learning As with any change, AI’s interruption of the music industry is not without controversy, and there are those who believe that the human contribution is what makes music what it is.  Indeed, there are still several limitations to what AI can achieve creatively. Despite a neural network’s success with creating original compositions, another’s ability to write lyrics was somewhat lacklustre. Despite being trained on a combination of lyrics (for structure), and literature (for vocabulary), its output was largely nonsense and included lines such as “I got monk that wear you good”.   Perhaps, like Southern’s compositions, AI is best used as an accompanying tool. London-based start-up AI Music offer technology that ‘shape-shifts’ songs to adapt to the context in which they’re played. This could be anything from tempo changes to match a listener’s speed to remastering tracks to appeal to different moods and situations. IBM’s Watson Beat, on the other hand, creates compositions that naturally fit to the visuals of a video. In this context, as within many other industries, AI looks set to support our existing skillsets rather than replace jobs.  Whether you’re looking to create collaborative technologies or revolutionise an industry, we may have a role for you. Take a look at our latest opportunities or get in touch with one of our specialist consultants to find out more.

Luke Frost

07. February 2019

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