Download your copy of our insight paperIn these times of great technological change, unlocking the power of Business Intelligence (BI) is vital to the strategic decision-making process of any organisation. Download our latest insight paper which features top tips on increasing your BI capability along with case studies from leading Business Intelligence experts operating in different sectors. Download
04 Sep 2020
Increasing Business Intelligence capabilityIn these times of great technological change and ongoing economic uncertainty, unlocking the power of Business Intelligence (BI) is vital to the strategic decision-making process of any organisation. But despite countless ways to collect information and connect people, the world has become more fragmented than ever before, and it’s holding businesses back.Data analytics provides accurate analysis and interpretation of business-wide internal, and macroeconomic external data, to the right people at the right time, equipping leaders with much needed foresight in these unprecedented times.As such, organisations, both large and small, are increasingly preoccupied with growing their overall BI capability. They are investing in talent with these skills, enabling employees to use self-serve data analysis tools and seeking ways to improve the accessibility of data to help a greater proportion of the workforce.Moving to the cloudTraditionally, business intelligence is delivered by Business Analysts using on-premises data warehouse solutions. This process can be slow with some Analysts spending the majority of their time extracting, moving and combining data instead of conducting analysis.That’s why many organisations are now creating data analytics platforms in the cloud, with accessible tools that can be used by people across different business functions across the entire organisation, instead of just a select few.Another endeavour which seeks to increase the overall BI capability of an organisation is the creation of a Business Intelligence Centre of Excellence (CoE).Establishing a Business Intelligence Centre of ExcellenceEstablishing a Business Intelligence CoE is a proven approach to achieving a strategic, cohesive, enterprise-wide BI environment. The CoE is an internal group that provides services and oversight to the various departments within an organisation and guides BI initiatives to achieve common strategic objectives i.e. service improvement, efficiency gains, cost savings, revenue growth etc. The overriding goal is to increase the BI maturity level of the organisation as a whole, to derive more value, more quickly from data insights and enable strategic decision-making at the highest level.Business Intelligence maturity levels according to GartnerIn 2015, Gartner published their much-sited maturity model giving leaders a yardstick to understand how effectively a BI or data function is supporting enterprise level goals. The model asserts that there are five BI maturity levels:Maturity level 1: UnawareAccording to Gartner at this level the company has no information infrastructure and there is only ad hoc BI. Departments have not developed formal processes or practices and workers try to gather information with the random applications they happen to have at their disposal, predominately spreadsheets in Excel. Maturity level 2: OpportunisticAt this level there are BI and analytics projects in the organisation, but business units carry them out individually to optimise a process of their own or to make unit-specific decisions. Employees use data integration tools, databases and analytics platforms. They disseminate information via reports, ad hoc queries and dashboards.Maturity level 3: StandardsAt the third level there is coordination between people, processes and technologies across the organisation. A champion for BI and analytics emerges and there are process and IT managers who oversee projects across multiple business processes (instead of one process) that need to share analysis and decisions. Technology standards start to emerge, including standards for information infrastructure, data warehouses and BI platforms.Gartner proposes that at this level most organisations implement a BI Competency Center (BICC) or BI Center of excellence (CoE) consisting of business users, IT professionals and analysts to share expertise and improve consistency for specific applications or uses of information. Maturity level 4: EnterpriseAt the fourth level, BI is sponsored by the senior management. A second feature is that the organisation has linked multiple processes to its revenue and other goals and defined a framework of performance metrics through which processes and action towards those goals are evaluated. Gartner says that these metrics guide the implementation of enterprise strategy. Thirdly, BI applications support cross-functional or enterprise-wide decision-making processes meaning they are not limited to individual functions or processes. Gartner says that at this level, everyone from analysts to managers and senior executives, uses the same BI and analytics systems. Maturity level 5: TransformativeAt this fifth maturity level, BI and analytics are run by business and IT functions together and are supported and governed at the highest level of the organisation. The CEO sponsors the BI program or if not, the position of Chief Analytics or Data Officer has been established.Information, analytics and the whole BI system are regarded as vital for implementing the enterprise strategy. BI is used actively to increase sales, productivity and customer satisfaction and to decrease costs. The performance metrics framework is now complete and covers also partners and customers. The organisation uses decision processes like decision simulations which incorporate best practices in decision-making and optimisation technologies. Top tips for establishing a BI Centre of ExcellenceSo, when it comes to establishing a Business Intelligence CoE, to increase the BI maturity level of an organisation, what are the key considerations and actions? We spoke with a range of Directors and ‘Heads of’ Business Intelligence who have implemented a BI Centre of Excellence across a variety of companies, industries, and global remits. It was relatively clear to spot the patterns in what they believed, joint with human elements of creating a technical solution, would be the ‘key tips’ to think about – these can be grouped into three headings:Talent Planning Change Management Interestingly, and perhaps unsurprisingly, the key challenges were all about people and changing behaviours. Every leader that we spoke with was clear that if you get the talent right, the tech can look after itself, it’s then about how you make it part of the company’s DNA. Once consolidated, the top tips for building an effective BI CoE were:Talent:1. Deeply evaluate the skills of the BI team to focus training and hiring activities. A high performing, highly skilled team is essential. Find and balance the technical skills, project discipline and ability to evangelise within the business. 2. Do not underestimate how time to hire can derail a programme. A clear people roadmap will prevent long delays.3. Prioritise training for end users to realise real value from the CoE. Planning:1. Obtain a clear business mandate before anything else. 2. Ensure that you can support ongoing business requests (within reason) during the implementation of the BI solution / CoE.3. Spend time understanding the balance between centralised and localised BI activities.4. Always work with trusted information with consistent definitions and known data sources.5. Automate high-frequency, labour intensive regular report generation. 6. Clearly demonstrate value add through meaningful KPIs. Take time to determine ROI and to prove the tangible and intangible value of the CoE but don’t wait until the end to deliver value. Incremental wins support engagement and business take up. Change Management:1. Fix data issues at the source so users don't waste time manually adjusting or correcting data while generating insights.2. Create a demand management forum to prioritise non-project related information required by executive stakeholders.3. Do not scrimp on Change Management or under-estimate the extent of business change required. Keeping users and the business community engaged from kick off and up to date with what the CoE is doing is the single most important success factor.4. Identify and train Super users within the business community to act as ‘change agents’ and promote the value of the CoE.5. Spread the data culture of data analysis from the CoE across the organisation. Executive support is critical in making this happen.6. Become an unashamed evangelist for self-service analytics. Run events to bring users together, demystify BI technology and identify first adopters.7. Use engagement analysts to collaboratively work with different departments to help them self-serve. • Integrate the BI / Analytics function into any enterprise-wide transformation. We hope there is something in here that will support you in your progression through Gartner’s well-trodden path. Share your insightsIf you need help finding top BI talent to provide the insights you need to transform your business please get in touch. We’d also love to hear from leaders on how you are increasing your BI capability in these challenging times.
09 Jun 2020
By now you may have heard the news that Dominic Cummings, the Chief Special Advisor to our Prime Minister has called for Data Scientists and Software Developers to help them reshape the UK state. No small task I might add and while it reads as slightly satirical, we have something to say about it. Posted in a personal blog last week, Cummings called for ‘an unusual set of people with different skills and backgrounds to work in Downing Street with the best officials, some as spads [special advisors] and perhaps some as officials’. He expressed a strong preference for those with STEM experience to fix historic issues in government to which he refers to as profound problems that sit at the core of how the British state makes decisions. Citing Brexit and recent changes to legislation, Cummings spoke of a huge amount of low-hanging fruit lying on the street in the intersection of Data Science, AI and cognitive technologies that could be used to improve policy and project management. Sticking to a very brief, brief – the Chief Special Advisor has offered ‘a rough set of categories’; Data scientists and software developersEconomistsPolicy expertsProject managersCommunication expertsJunior researchers one of whom will also be my personal assistantWeirdos and misfits with odd skillsAsking only for applications from those with exceptional academic qualifications from one of the world’s best universities or evidence of doing something that demonstrates equivalent skills, a PhD or MSc, outstanding mathematical skills and experiences of using analytical skills – I can’t help but question their understanding of Data Science as Data Scientists cannot be categorised, especially not in this manner. We recently produced a white paper to determine what the Ideal Data Scientist looked like and interviewed more than 1,000 professionals in the space to help us get there. Not only did we discover that there was no such thing as an ‘ideal’ Data Scientist but we also discovered, there were several debates being had over the salary, experience, education, age and location of the ‘ideal’ professional. Taking Education as an example, 50% of hiring managers believe a Data Scientist should have at least one PhD and 46% believe they should have no degree education at all, rather, being self-taught with practical and hands-on experience. This means that from a recruitment perspective, Data Scientists are tremendously hard to find and a very specific list of requirements including a PhD in my opinion, is not likely to produce the right pool of candidates – especially not when placed alongside weirdos and misfits with odd skills. Please follow the link below for access to the paper but in the meantime, I’d love to hear your opinion – do you think Cummings is on the right track with his hiring efforts? Download our white paper
07 Jan 2020
Data as we know has the capacity to change the world. Not just change the way we do things, see things or interpret things but make the unthinkable possible. We have now reached a stage in our technical evolution where we have a comprehensive understanding of data and how it can impact our everyday lives but isn’t it time we started using it for good? I don’t mean good as in creating conversions or good as in building Sophia 2.0 I mean good as in using data to create genuine change, for the better. Clive Humby stated in 2006, ‘Data is the new oil’ and The Economist followed in his footsteps just two years ago when they wrote ‘The world’s most valuable resource is no longer oil, but data’. Data is ‘officially’ the most valuable resource in the world, surely we can find a more valuable place for it than business? I recently attended the Big Data LDN conference and heard a really fascinating talk by a company called TIBCO. They were discussing how data and AI can help reduce homelessness, prevent hunting and even help us go green by monitoring rain-fall in certain parts of the world. This really got me thinking about how I talk about data every day. We either talk from a business perspective about GDPR and regulations we have in place to protect our data or we speak to businesses hiring a new data team to monetise their vision. This was a completely fresh concept for me – using data for good and it’s made me really want to learn more about the social responsibility, of data. I’d like to hear from you – do you use data for good or have you come across a company that has? I would love to hear about your experiences with ‘responsible data’ and how together, we could enhance the cause.
25 Nov 2019
With an increased awareness in the protection and abuse of our personal data, it’s now commonplace to find the words ‘ethical’ and ‘moral’ thrown into everyday conversations about data but, while we need to be having this conversation, do we all understand what it means? The debate about ethics is vital. As technology professionals we carry a responsibility to not just protect the data we come into contact with but also reassure the professionals, consumers and spectators that sit outside of the industry – that we’re the good guys. But, what I find fascinating is not perhaps the debate itself but rather, the disparity in definitions of ‘Ethical Data’ and how drastically they can differ. I started thinking about this almost a year ago when we produced our first tech-focused white paper on the Human Face of Data Science. It was an interview with former Data Science lead at NASA, Adi Andrei who really got me thinking about this human side of data, not just the humans effected by poor use of data but how the philosophy of it can have a huge impact on society. He spoke about the fear-factor of data and how implications of roles being replaced with robots does nothing to help our economy, instead it produces resentment to innovation and leaves professionals questioning their survivability in a modern age. Adi suggested that we as professionals have a responsibility to promote technology as an empowering tool and not a debilitating one. He also spoke about the responsibility he has as a Data Scientist to work solely with ‘ethical companies’. This means not working with organisations who abuse the data you waive the rights to or who abuse your behavioural traits and translate them into profit. While this seems like a simple explanation of ‘Ethical Data’ some people have very different definitions. Some for instance believe that by simply delving into this data, without using it, is unethical as it abuses the privacy of its owner but I’m not sure I agree with this one. For me, that’s the magic of data, being able to delve into the behavioural habits behind spending and the psychology behind clicks to come up with the answers to a whole host of things - but this doesn't mean it has to be abused. I agree with Adi that we all have a responsibility to be ethical, righteous and sensitive with the data that falls in our laps but that shouldn't have to restrict us. I attended Big Data LDN this week and was wowed by some of the incredible technology and tools on display – a giant Pac Man amongst other things – but the thing that really blew my mind was a company called TIBCO which through analytic insights is able to monitor Lewis Hamilton as he is driving. The behaviours and decisions behind the manoeuvres in order to present him with different ways to drive faster, push harder and be better. This for me was magic but others may deem it as unethical. I’d like to hear your thoughts on ‘Ethical Data’ – what does it mean to you?
18 Nov 2019
Senior Commercial Analyst £45,000 - £50,000 per annum + bonus + benefits package Buckinghamshire Senior Data Scientist £600 - £650 per day London Enterprise Security Architect Negotiable UK wide Back End Developer (AEM) £375 - £550 per day Edinburgh Finance Analytics Manager £70,000 - £75,000 per annum + bonus + benefits packageSurrey Senior Business and Data Analyst £475 - £525 per day Hampshire Finance Manager £60,000 - £70,000 per annum London A wider selection of current vacancies can be viewed on our opportunities page or get in touch for a confidential discussion about how Stanton House can help you hire great people or assist with your own career goals.
25 Sep 2019
We recently produced a white paper, focused on the human face of Data Science. We explored the evolution of the Data professional, the relevance of the phrase ‘Scientist’ and questioned whether we should scrap the term altogether, replacing it with a list of more specific job titles. We also battled with the idea of the Ideal Data Scientist and questioned what they looked like. Were they educated? Personable? Analytical? Or, perhaps creative? To reach some sort of conclusion, we decided to ask the question to our network; made of more than 2,000 Senior Data Science Professionals and the organisations that home them. They were equally torn on the matter of the ‘ideal education’ as 50% stated that the ideal professional had a degree, PhD and further post-doctorate while 46% believe they should be non-degree educated and just 4% said a degree with no post-degree qualification would be ideal. For me, this debate is fascinating. I completely agree with the principle that Data Professionals may or may not require an education but there are many different considerations that need to be taken into account first. This includes their primary objective as a new hire, the working environment and the type of industry. If you are working for a media company for example, you need to be more creative to fit both the brand and its internal messaging while if you work in the Financial Services sector – you need to be more analytical. Don’t get me wrong, a Data Scientist always needs to be analytical. They need to be able to spot the trends and then come up with a solution to any problem that arises as a result, however, if we think to the profile of a Data Engineer, they need to be more hands on and technical and a PhD would almost be irrelevant. It is vital when considering a new hire that you think about the role they play and not just the title we bestow upon them as the term ‘Data Scientist’ spans across so many different disciplines. Equally, there isn’t a one-size-fits-all approach. Every company should look at their own requirements before thinking of a key profile of a person they need to solve the problems. You need to think firstly, what are your key problems and then secondly, how are you going to solve the problem or who is going to help you? Do you think the ‘Ideal Data Scientist’ exists? Or, do we need to think about the professionals we bring in to carry our businesses forward on an ad-hoc basis? Join the conversation and in the meantime, download a copy of our white paper ‘The Human Face of Data Science’ below. Download our Data Science white paper
10 Sep 2019
Tech people are known for two things. Being expert in all things technology of course but also, being adaptable. Being able to change with the time, the tools and the people. As we know technology moves at a rapid pace and the world hasn’t quite caught up but it’s the job of the technology professional to move our businesses and our attitudes towards the technology along. The problem technology professionals have however is with the latter of the two – changing mindset. More and more of my candidate pool tell me that despite being employed to implement new technology or help the organisation become more data-driven, they are being told technology isn’t important. It’s either irrelevant, unnecessary or something that ‘doesn’t apply to them’ as they are already perceive their business to be successful. Despite technology being at the forefront of all of our conversations, it’s still being treated as if it’s something out of this world, unneeded or something we just aren’t ready for yet. Organisations either welcome the change but don’t know what to do with it or tell their hired help to stick with the basics but, rather than balancing the books – they’re jeopardising their revenue generating potential. The future is technology and leaders aren’t always able to see the benefits of bringing in innovation and as a result, technology professionals are finding it hard to change mindset and influence change. I’m keen to hear your thoughts on the matter – are you a technology professional working for an organisation that lacks innovation or the ability to see that it matters? Or, are you struggling to change mindset or influence change?
13 Aug 2019
Data Analysts belong to a growing talent pool, a candidate-driven pool that is overpopulating the marketplace. For every job advert I post, I receive hundreds of messages and admittedly, it’s hard to reply to them all so I can only imagine what hiring managers go through to find the perfect match. I believe Data Analysts need to bring something unique to the table otherwise, I fear they’ll drown in a pool of equally-talented people with the same resume. Data Analysts need to get creative with how they sell themselves, in a role that from the outset, appears to accommodate for little creativity. I meet Data Analysts daily and realise only upon meeting them that they bring incredible differences to the table. Their sheer creativity, enthusiasm and energy around data as a whole is astounding, but unfortunately, it doesn’t translate on paper. Something that really stood out to me was the impressive career diversity that many of these professionals offer. They have worked with several data migration projects, had hands-on project experience and understand data across different platforms while formulating the best way to utilise it. Some of these conversations have led me to interesting debates surrounding the importance of stakeholder engagement. Do Data Analysts need to be able to build strong relationships with their key stakeholders or, are they expected to remain hands-on, head down and technically savvy? I’d love to hear your thoughts. What should a Data Analyst do, to stand out?
30 Jul 2019
Adi Andrei has more than 20 years of experience building real-world intelligent systems for NASA, Phillips, Unilever, British Gas, Sixt and others. He is an expert in Machine Learning, Artificial Intelligence and Process Optimisation and has become a thought-leader in Data Science, known especially by his two Patents and three Certificates of Recognition for contributions to science from NASA. Despite his endless achievements in the field, Adi describes the mystery involved in Data Science and how it should be seen, by its human face. The human race did not stop evolving when it reached homo sapiens. Even though physically we may be identical to our ancestors from 200,000 years ago, when we look back at how much our view of the world and ourselves has changed; even in the last few millennia, there is no doubt that, as a group, we are going through a continual process of evolution in awareness of our world and of ourselves. This is reflected in our way of life, and especially nowadays in the technologies we are using. Everything we do in Data Science is not new. It has been done in different ways since the 50s in fields like operations research, industrial engineering, statistics, and artificial intelligence. What is new is that we have reached a point in time where we produce more data in a day than we had done in the whole of humanities history before. But data by itself cannot directly be used; we need to make sense of it, to understand what it means. The concept of Data Science emerged as a way to define this need and the tools and technologies used to address it. As a profession, it has appeared in a point of time in our evolution when we are moving into a new level of awareness and it is bringing with it new concepts. Data Science is as mystical as you can get in an everyday business environment and Data Scientists are sometimes being seen as magicians and revered almost as a new kind of priesthood. For full access to Adi's interviews alongside our other features and research. Download our white paper 'Identifying the Human Face of Data Science' below. Download your copy of 'Identifying the Human Face of Data Science'
04 Jul 2019
Abdel Whab Turkmani is the co-founder and CTO of Util which is on a mission to democratise impact investing, existing to support an investment community that is increasingly being assessed on both its financial and non-financial practice and performance. Abdel brings experience from the Financial Services industry and founded the organisation having graduated from the University of Oxford with a degree in Computer Science and a keen interest in Machine Learning and Data Science.He supports the idea that the term Data Scientist is both too broad and essentially, redundant. The term Data Scientist has become muddied, a term that is so misunderstood and overused it makes me wonder if the perfect Data Scientist exists. I don’t even like to use the term anymore because actually, I have no idea what it means. It’s like a unicorn with so many required skills and specialisms that they can’t possibly exist. How can one person be a Machine Learning Engineer, Data Engineer, Researcher and Analyst? We are hiring at Util but we don’t hire Data Scientists. We hire for three different roles within our Data Department. Different job descriptions, different requirements and different responsibilities. The term Data Scientist is so broad that it’s impossible to find someone who can do everything but organisations seem to believe they can find someone to do all three. You need Data Engineers to build the data pipelines, Machine Learning Engineers to build predictive models, and depending on how advanced your technology is you might need Researchers (Research Scientists) to constantly explore new ideas, keep on top of the state-of-the-art and evaluate the applicability of new models. In some cases, you also need the Analysts who are able to look at the output of models and understand and interpret them in a commercial context. Within those roles, you also have further specialisms. For example, you have Machine Learning Engineers who are specialists in language, image, vision, sound, reinforcement learning or others. That’s not to say that all of the individual roles set above do not intersect with one other. They integrate together tightly as anyone who joins the Data Science team will have to be cross-functional, they need to understand what everyone else does but have an edge towards one direction. Everybody has to know a little about everything but you need to know a lot about one thing. To a certain level of sophistication, you can easily transition between the different roles but once you get to an advanced stage you need the specialty and it’s highly unlikely that one single person; or rather a single Data Scientist, is that knowledgeable. The parallel would be something like Natural Science. At some point, perhaps 800 years or so ago, there would have been people who are “Natural Scientists” but today, as the field has developed, you can no longer be a Natural scientist. You have to be a physicist or a biologist, something more niche and specific. The same thing is now happening within the Data Science field as it continues to mature. The field has developed enough depth to merit specialists, and the search for the ultimate Data Scientists is increasingly impossible. For full access to all of our interviews and our white paper on Identifying The Human Face of Data Science, please download a copy below. Download our white paper
12 Jun 2019
PMO Manager £70,000 - £80,000 per annum + bonus + benefits London Head of Employee Relations & Policy £110,000 - £130,000 per annum + competitive package including. LTIPS Berkshire Data Science Manager Negotiable London Revenue Accounting Manager £70,000 - £80,000 per annum + car allowance + bonus + benefits Reading Business Analyst - TechnicalUp to £55,000 per annum + benefitsEdinburgh Finance Director£800 - £900 per dayBerkshire A wider selection of current vacancies can be viewed on our opportunities page or get in touch for a confidential discussion about how Stanton House can help you hire great people or assist with your own career goals.
24 Apr 2019