With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.
Visit our Blogs & News portal or check out our recent posts below.
Eoin joined the Data and Technology team in 2015 after a former life as a chef. He has immersed himself in the Data and Technology industry and as a Senior Consultant specialises in Senior hires within Business Intelligence. Eoin covers roles across the UK across multiple industries from start-ups to MNC’s.
£75000 - £90000 per annum + benefits and package
Great opportunity for an experienced Data Warehousing lead to come into an environment where they have successfully implemented Snowflake and DBT
With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.
Visit our Blogs & News portal or check out our recent posts below.
Information. Data. The lifeblood of business. Information and data are used interchangeably, gathered, collected, and analysed to create actionable insights for informed business decisions. So, what does that mean exactly? And to that end, how do we know what information or data we need to make those decisions? Enter the Data Architect. The Role of a Data Architect Just like you might hire an architect to sketch out your dreamhouse, the Data Architect is a Data Visionary. They see the full picture and can craft the design and framework creating the blueprint for the Data Engineer, who will ultimately build the digital framework. Data Architects are the puzzle solvers who can take a jumble of puzzle pieces, in this case massive amounts of data, and put everything in order. It’s their job to figure out what’s important and what isn’t based on an organisation's business objectives. Skills for a Data Architect might include: Computer Science degree, or some variation thereof.Plenty of experience working with systems and application development.Extensive knowledge and able to deep dive into Information ManagementIf you’re just starting your Data Architect path, be prepared for years of building your experience in data design, data storage, and Data Management. The Role of a Data Engineer The Data Engineer builds the vision and brings it to life. But they don’t work in a vacuum and are integral to the Data Team working nearly in tandem with the Data Architect. These engineers are building the infrastructure – the pipelines and data lakes. Once exclusive to the software-engineering field, the data engineer’s role has evolved exponentially as data-focused software became an industry standard. Important skills for a Data Engineer might include. Strong developer skills.Understand a host of technologies such as Python, R, Hadoop, and moreCraft projects to show what you can do, not just talk about what you can do – your education isn’t much of a factor when it comes to data engineering. On the job training does it best.Social and communication skills are critical as you map initial designs, and a love of learning keeps everything humming along, even as technology libraries shift, and you have to learn something new. The Major Differences between the Data Architect and Data Engineer RolesAs intertwined as these two roles might seem, there are some crucial differences. Data Architect Crafts concept and visualises frameworkLeads the Data Science teams Data Engineer Builds and maintains the frameworkProvides supporting framework With a focus on Database Management technologies, it can seem as though Data Architect and Data Engineer are interchangeable. And at one time, Data Architects did also take on the Data Engineering role. But the knowledge each has is used differently. Whether you’re looking to enter the field of Data Engineering, want to move up or over with your years of experience to Data Architect, or are just starting out. Harnham may have a role for you. Check out our current opportunities or get in touch with one of our expert consultants to learn more.
26. November 2020
Big Data, once a looming anomaly for many businesses, has transformed. No longer a buzzword, it is essential to enterprises everywhere – from healthcare to hospitality. Whilst it’s taken about a decade to get here, the last two years are truly telling. With the amount of Data flowing through our systems, over 2 quintillion bytes each day. Think Apple Pay at Starbucks, credit card purchase, filling out of forms, GPS, our phone Data, traffic cameras and lights for traffic control. Big data is now big business. Top Industries Using Big Data and Analytics While the most prevalent industries which come to mind are retail, entertainment, and politics. There are two which, until now, have been coming in under the radar and have seen some of the biggest changes using Data & Analytics; healthcare and hospitality. Whilst they don’t seem to go together, they do have one thing in common – the experience. Hospitality As you plan for your next vacation, you may be debating the merits of a hotel reservation versus an AirBnB. Lodging options in the share-economy have forced traditional accommodation options to rethink their strategies. The ease of “mobile first” which allows customers to manage their bookings, stays, and travel experience through their phones is in direction opposition to the client-facing hotel industry. There is a massive shift happening in this industry and a powerful Data Analytics tool can help create visualisations from a company’s Data. Not only can these provide insights for the future, but they also offer suggestions for strategies which can be implemented now to impact future prospects. Healthcare One of the most telling industries being transformed by Big Data is healthcare. Access to care is not only available in-office, in-person, but now with the advent of Telemedicine, patients can get questions even more quickly. No matter the industry today, this is a buyer’s market, or in most cases, a customer’s market. And its customer satisfaction which drives the success of a business. In healthcare, it’s patient satisfaction. Patient satisfaction scores underlies everything from hospital funding to the return visits in the private sector. Like any business, the patient experience in the healthcare industry, begins with initial contact, staff responsiveness, communication by doctors and nurses, wait times, even equipment and cleanliness of facilities to name a few examples. Once all the Data and information is gathered, collected, and analysed, these healthcare professionals are able to make any necessary adjustments. As quickly as Data has grown in the last couple of years, the projections for healthcare can expect to see a high volume in the next seven years. One of the highest benefits which can add to patient experience is the database of patient’s information can be shared across healthcare organisations saving time, money, and patient stress which all leads to better treatment for the patient’s needs. In fact, according to the International Data Corporation (IDC), healthcare Data is expected to grow faster than industries such as the media, manufacturing, or financial services. Advancements such as chatbots, virtual assistants, Big Data Analytic tools, and medical imaging have all added to the transformation. As strong and as prevalent as many of these advances are, many organisations still struggle to find the right candidates with the right Data skill sets. Many have neither a blockchain strategy nor have plans to implement one and are falling behind. There is a next generation opportunity here to more fully transform digitally, but the right people need to be in place to make it happen. Digital transformation isn’t slowing down and is becoming more critical at a rapid rate. By making investments in your health IT, analytics tools, and people, you’ll be ready to close the digital transformation gap. If you’re interested in Big Data and Analytics with an eye toward the Life Sciences field, we may have a role for you. Check out our current vacancies or get in touch with one of our expert consultants to learn more.
19. September 2019
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.
13. February 2019
Data. It’s what we do. But, before the data is read and analysed, before the engineers lay the foundation of infrastructure, it is the programmers who create the code – the building blocks upon which our tomorrow is built. And once a year, we celebrate the wizards behind the curtain. In a nod to 8-bit systems, on the 256th day of the year, we celebrate Programmers’ Day. Innovators from around the world gather to share knowledge with leading experts from a variety of disciplines, such as privacy and trust, artificial intelligence, and discovery and identification. Together they will discuss the internet of tomorrow. The Next Generation of Internet At the Next Generation Internet (NGI), users are empowered to make choices in the control and use of their data. Each field from artificial intelligent agents to distributed ledger technologies support highly secure, transparent, and resilient internet infrastructures. A variety of businesses are able to decide how best to evaluate their data through the use of social models, high accessibility, and language transparency. Seamless interaction of an individual’s environment regardless of age or physical condition will drive the next generation of the internet. But, like all things which progress, practically at the speed of light, there is an element of ‘buyer beware’, or in this case, from ‘coder to user beware’. Caveat Emptor or rather, Caveat Coder The understanding, creation, and use of algorithms has revolutionised technology in ways we couldn’t possibly have imagined a few decades ago. Digital and Quantitative Analysts aim to, with enough data, be able to predict some action or outcome. However, as algorithms learn, there can be severe consequences of unpredictable code. We create technology to improve our quality of life and to make our tasks more efficient. Through our efforts, we’ve made great strides in medicine, transportation, the sciences, and communication. But, what happens when the algorithms on which the technology is run surpasses the human at the helm? What happens when it builds upon itself faster than we can teach it? Or predict the infinite variable outcomes? Predictive analytics can become useless, or worse dangerous. Balance is Key Electro-mechanical systems we could test and verify before implementation are a thing of the past, and the role of Machine Learning takes front and centre. Unfortunately, without the ability to test algorithms exhaustively, we must walk a tightrope of test and hope. Faith in systems is a fine balance of Machine Learning and the idea that it is possible to update or rewrite a host of programs, essentially ‘teaching’ the machine how to correct itself. But, who is ultimately responsible? These, and other questions, may balance out in the long run, but until then, basic laws regarding intention or negligence will need to be rethought. Searching for a solution In every evolution there are growing pains. But, there are also solutions. In the world of tech, it’s important to put the health of society first and profit second, a fine balancing act in itself. Though solutions remain elusive, there are precautions technology companies can employ. One such precaution is to make tech companies responsible for the actions of their products, whether it is lines of rogue code or keeping a close eye on avoiding the tangled mass of ‘spaghetti’ code which can endanger us or our environment. Want to weigh in on the debate and learn how you can help shape the internet of tomorrow? If you’re interested in Big Data and Analytics, we may have a role for you. Check out our current vacancies. To learn more, contact our UK team at +44 20 8408 6070 or email us at email@example.com.
13. September 2018
There is no denying the ever increasing applications of Data visualisation tools for the modern business. Everyone is aware of them in a business environment, whether they are conscious of it or not. Personally I have been making use of data from a sales perspective long before I was conscious of the titles Business Intelligence and Analytics. Using a simple geo-mapping tool to visualise account locations and statuses, I then shared this with senior management and the sales team. A very simple but useful application, and a far cry from sticking coloured pins in a map! Visualising your options A new breed of data visualisation tools have become prevalent in recent years. Given the number of vendors, varied features and implementation costs, it can be difficult to know which ones to: A) Implement as a Business B) Become familiar with as a BI Analyst/Developer. The purpose of Business Intelligence visualisation tools is something everyone can appreciate. As for centuries people have used pictorial or graphical representations of data for it to be universally understood. The usage and investment in this part of a corporation’s BI platform seems to be ever increasing. According to the trustradius 2015 BI survey: Medium (101-1000) and large (1001+) size companies are most likely to invest in this area of their business in 2015- even more so than big data (Hadoop, Cloudera) and predictive analytics solutions (R, SPSS etc) How to stay in demandWhen talking to clients, particularly regarding new positions being created and growth of BI teams, visualisation experience is increasingly sought after. Sometimes this is to maximise existing tools and platforms, and often to help assimilate their usage into the new Business Intelligence solutions and maximise their use for end users across the business. There are many differences between vendors, but the ones I encounter being used most often are Tableau, Qlikview, Spotfire and the MS BI platform. They all have pros and cons depending on existing systems, data streams, usage plans and of course (and often most pertinent) budgetary issues. From a client perspective, there will be numerous contributory factors to consider when selecting which one to implement, however I think it is important to highlight you will never be able to attract all candidates with any one choice. There will always be analysts and developers with strong subjective preference for certain tools. The positives to take from this is that many BI professionals I speak to are eager to learn a new tool. Furthermore, the general consensus between talented analysts and developers seems to be that established skills are highly transferrable between top vendor’s product offerings. This view seems to be mirrored by hiring managers, not just in the larger corporations. Of course for certain companies, teams and projects, the skills focus will lean towards and include prior experience with a particular vendor’s product as a necessity. However, often, as long as the back end data warehouse and data stream experience is compatible, there is generally a level of flexibility when hiring. Who is using who?This sentiment also rings true with the type of job specifications we are seeing. There is both increased demand for visualisation experience and a degree of flexibility on the particular tools analysts and developers have used in the past. With regard to job type, as a general rule, spotfire seems to be the most favoured for visual analytics, Tableau for visual OLAP, Qlikview for data visualisation drilldown and the MS BI stack for back end Data visualisation which makes sense given its direct compatibility with SQL server databases. Are you on the right track?So the message for hiring managers seems to be to research the type of profiles you’re looking to bring into your BI team and focus your efforts on candidates with that particular skillset. And finally aim to get a handle on which tools are most compatible in terms of transferable skills and usability. With regard to BI professionals it is impossible to have experience with all of the BI visualisation tools out there, but the key is to prioritise the ones you know are most compatible with the industry you are hoping to work in long term. For example if you are keen to progress your career towards a BI manager role within a big 4 consultancy, and you’re finding the job specs focus on particular tools you have not used. Keep up to date through courses, webinars, conferences and media events. From all the BI and analytics professionals I have worked with (including Developers, Analysts, SAS programmers, Programmers, DBA’s and consultants) over 49% have used one of the top four vendors for visualisation and reporting. This is only set to increase in 2016 as the market consolidates, and conversely the proliferation of broader skills and experience won’t go to waste with the advent of new tools coming to market. What is your experience as a BI professional or someone trying to build a team? How do you view the suitability of particular tools with regard to the stages of the BI solution from Data Warehousing?
03. May 2016
...And how they affect your career.We have said it before, and I am sure we will say it again. The industry of data analytics and the associated positions within it are still, in relation to other industries at least, in their infancy. While there have been associated roles such as I.T. focused or predictive analysis based careers for some time. It is only with the advent of large, disparate and often difficult to mesh data sets, that specific roles such as Chief Data Officers (CDO) have risen to prominence. We discussed some of the challenges facing the CDO in a recent article, not the least of these being the integration of silo mentality departments into the larger whole. In this article, we would like to consider the wider issues around data analysis and how you as a front line worker, need to consider them in relation to your career.Let me be clear, this article is not going to be the 'be all and end all'. It’s more a general musing on the situation than a stone tablet of universal guidelines. For a start, I am sure there could be a lot more on our list of 3 challenges. In fact the list could probably be hundreds of lines long but, for the sake of convenience, let’s just look at three big areas of consideration in the data analytics world. Understanding the potential of big data issues. As I am sure you are aware ‘Big Data’ has been the Holy Grail buzzword for the last few years. The phrase seemed to go through a distinct cycle of buzzword, keyword, questioning it’s meaning, doubt and finally acceptance that it really means little in isolation. We know what big data is, we probably always have, but the real question lies in its application in real world environments. Clearly the effective data analyst is going to be not only aware of the potential here but be able to see the application and implementation of results in the context of the workplace. A data analyst would probably therefore be well advised to not only understand the big picture but to concentrate their focus on application.Real-Time Integration. The sheer amount of stored data available is one issue, the input of new information is another entirely. If you consider the data flow of even a multi-site retail outlet such as one of the big supermarkets, you begin to realise the importance of immediacy in the analysis of data. This is a key factor in your potential career development. A talented analyst who understands the area of real-time data is likely to be in great demand. In many cases, of course, the result of real-time data may arrive faster than the business can utilise, and this is where integration experts will find themselves very welcome.Bridging the ‘talk’ divide. The reality is that the implementation of results, the use of refined data, and the practical application of your energies is, for the most part, likely to end its cycle with a non-data aware person. Accompanying this, there is, therefore, a language divide. A good, real life desirable skill to acquire is the ability to simplify and practically explain your work. Talk the talk of the user, and you will make many friends in the workplace. Of course, we are oversimplifying here and even being a touch flippant but the message running through all this is simple. The data world is fast changing and difficult. A career in this (or indeed any) industry is often built on not only being an expert, but on being the expert who understands how to fit their industries demands and anticipate new ones.
31. May 2015
BBC's Horizon tests the limits of the data revolution, crime prediction in LA, financial formulas in the city of London and a South African attempt to catalogue the entire cosmos. In Los Angeles, a remarkable experiment is underway; the police are trying to predict crime, before it even happens.At the heart of the city of London, one trader believes that he has found the secret of making billions with maths. In South Africa, astronomers are attempting to catalogue the entire cosmos. These very different worlds are united by one thing - an extraordinary explosion in data.Horizon meets the people at the forefront of the data revolution, and reveals the possibilities and the promise of the age of big data.Watch now.
12. February 2013