Ross Whatling - Recruiter Profile | Harnham Recruitment

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Ross Whatling

Associate Director - UK

Bio

Ross joined Harnham as a graduate in 2013 as part of the Data and Technology team. He helped subspecialise and grow the team, and now is a Manager leading Big Data Engineering, Business Intelligence, Data Governance, and Software Engineering recruitment. He brings a passion for data technology with a high-level of service, looking to make your recruitment process as easy, fast, and efficient as possible.

Harnham blog & news

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.

Weekly News Digest: 22nd - 26th Feb 2021

This is Harnham’s weekly news digest, the place to come for a quick breakdown of the week’s top news stories from the world of Data & Analytics. Search Engine Journal: 4 ways call tracking is changing (and why it’s a good thing) Call tracking is no longer about a customer seeing an ad, calling up the company, telling them how much they loved the ad and then deciding to purchase goods. This is a positive thing really because it wasn’t the most effective way for businesses to track how well adverts were doing anyway - who really remembers where they saw a billboard that took their interest, or what time of day an advert popped up on the TV? As call tracking technology becomes more advanced, call analytics have become much more accessible for all. Not only have they been able to transform how businesses of all shape and size advertise and track their success, but also how they market to potential audiences and track their sentiment. This article from Search Engine Journal looks at the evolution of call tracking and call analytics from its most basic form, how it works now and what the future of this crucial set of analytics will look like in the future. Read more on this here. Towards Data Science: Data Science Year Zero Skills or qualifications in Data Science are becoming incredibly sought after by many employers, but the knowledge of how to break into the sector is still a little unclear for potential candidates. In this article by Towards Data Science, they break down the crucial elements of how to successfully enter the industry in four easy steps. What the author, Bala Vishal, lacked when he started and how you can set off on a better footing.The most important skills and tools to have under your belt.Which skills should you home in on first.How to thrive in the workplace. This incredibly insightful piece should be a ‘must-read’ for any budding Data Scientist looking to break into Data in 2021 and beyond. Read more here. KD Nuggets: 10 Statistical Concepts You Should Know for Data Science Interviews This article is perfect for anyone in the Data Science industry. Whether you’re new to the game or looking to take the next step on the career ladder, make sure you brush up on these crucial statistical concepts you should know inside out before entering interview. A few, in no order, include: Z tests vs T tests An invaluable piece of knowledge that will be used daily if you are involved in any statistical work.Sampling techniques Make sure you’ve got the main five solidified in your knowledge bank - Simple Random, Systematic, Convenience, Cluster, and Stratified sampling.Bayes Theorem/Conditional Probability One of the most popular machine learning algorithms, a must-know in this new era of technology. Want to know about the other seven? Read more here. Forbes: 48 per cent of Sales Leaders Say Their CRM System Doesn’t Meet Their Needs. The Good News Is That This Is Fixable. This article by Gene Marks explores why teams aren’t happy with their current CRM systems, and how this can be remedied. New research from SugarCRM found: 52 per cent of sales leaders reported that their CRM platform is costing potential revenue opportunities.50 per cent of the companies said they cannot access customer data across marketing, sales and service systems.Nearly one-third complained that their customer data is incomplete, out of date, or inaccurate. While damning statistics, Marks then goes into how this worrying situation can be fixed for good. He says: “Like just about all problems in business, this problem comes down to two factors: time and money. The blunt fact is that most companies are not willing to spend the necessary time or money needed to enable their CRM systems to truly do what they’re designed to do. CRM systems are not just for sales teams. And they're not just for service teams. For a CRM system to be effective, a company must adapt it as its main, collaborative platform.” Read more on this here. We've loved seeing all the news from Data and Analytics in the past week, it’s a market full of exciting and dynamic opportunities. To learn more about our work in this space, get in touch with us at info@harnham.com.

by Ross Whatling

26. February 2021

Weekly News Digest: 21st - 24th December

This is Harnham’s weekly news digest, the place to come for a quick breakdown of the week’s top news stories from the world of Data & Analytics. Which-50: The Art and Science of Data-Driven Decision Making Data-driven decision making when actioned effectively can be a real success. However, marketing and analytics leaders in the digital marketing space recognise that, for the most part, managing the cross-channel customer experience, taming data silos, and creating and managing cultures inside companies all stand in the way of this. Using data in a way that sets a business up for continued growth is critical and Which-50 heard from some senior leaders in analytics and marketing to share their thoughts on these obstacles. Steffen Daleng, the CMO of Booktopia, says the first problem organisations face is that they have too many platforms. “We have data sitting in so many silos that we need to try and connect them to one single customer view, as they like to call it.” Campbell Holt, the Chief Customer Officer of Mercer says, “The single customer view is not a reality, despite what many claim, in a federated data model.” Managing the vision of implementing the data and the reality is key. Read the full article here. KDnuggets: Top nine Data Science courses to learn online Within Data & Analytics, there have certainly been a range of key learnings we have taken from the past year. From implementing remote working and understanding the technology needed to support it, to a renewed focus on employee wellbeing, organisations will now be moving into the new year with a clearer vision for their growth and hiring plans. What is also important to consider, as outlined by KDnuggets, is that preparing for a career in data science has also drastically changed. The skills gap in the tech industry is widening, and for professionals seeking to move into the data science discipline, it is crucial to think about furthering your education and opportunities by undertaking online courses. In the USA, these are: Post-Graduate Program in Data Science from Purdue University and SimplilearnGraduate Data Science Certificate Program from Michigan Institute for Data Science, University of MichiganData Science Graduate Certificate from Harvard Extension SchoolCertificate in Data Science from University of Illinois at Urbana-ChampaignData Analytics Certificate Program from University of Texas at Austin (Center for Professional Education)Certificate in Data Science from Georgetown UniversityCertificate Program in Data Science from UC Berkeley ExtensionData Analytics - Cornell Certificate Program from eCornellProfessional Certificate in Applied Data Science from Thayer School of Engineering at Dartmouth Read more on how you can further your learning here. The Drum: Do you know how ITP is affecting your Google Analytics data? A large number of marketers are likely to be unaware of the impacts that Apple’s Intelligent Tracking Prevention (ITP) is having on their Google Analytics data. A feature in Safari, ITP prevents a website from tracking its visitors around the web. A bit of a headache, right? The Drum explores the impact this can have on marketers who will rely on this to understand and enhance the customer journey for Safari users. This article takes a look at how reporting may be misguided by this limitation of data. Read more about this here. Engadget: The New York Times brings its crossword to AR As it’s Christmas week, we thought it would be fun to bring you this story as our last in the weekly roundups from Harnham. We’ve all become familiar with the New York Times crossword in the newspaper, online and on mobile. But now, it’s coming to you in augmented reality too! The new version of the popular puzzle is called Shattered Crosswords and brings the game to life. It’s great to see how our uses of technology are changing across different sectors. We’re loving this, and you can find out how to get involved here. We've loved seeing all the news from Data & Analytics in the past week, it’s a market full of exciting and dynamic opportunities. To learn more about our work in this space, get in touch with us at info@harnham.com.

by Ross Whatling

24. December 2020

WE HAVE TO TEACH SPECIALISATION, WE CAN’T EXPECT IT: A Q&A WITH VIN VASHISHTA

We recently spoke to Vin Vashishta, a consulting Data Scientist and Strategist who was named one of LinkedIn’s Top Voices in Data Science. Having started off in the tech world 25 years ago and progressing from web design and hardware installation to Business Intelligence Analytics, Vin found for many years that enterprises were reluctant to adopt AI technologies and embrace the value of Data. In fact, it wasn’t until the beginning of the decade just passed that companies started to think about their Data more strategically and the world of Data Science was born, albeit hesitantly: “When I first started, it was a lot of experimentation, everyone wanted a proof of concept,” he says. “A lot of work was creating models that could go from whiteboard to production and productise and show their value.” However, it wasn’t until halfway through the decade that he began to see businesses who had adopted Machine Learning move away from experimentation into incorporating it more deeply into their companies, relying more on analytical and optimisation models to make strategic business decisions. “After that, in about 2017/2018 the maturity changed. It went from being a one off implementation to it being a comprehensive tool within an organisation where we have full lifecycles of model implementation and full models that were full views of the system. The key component of development was allowing users to access a small part of the system to do their job better without having to understand the whole thing. And that’s where we are now. We have this applied Deep Learning and we are seeing, especially this year, attempts to optimise that, make things go faster and make them more repeatable.” But, as we all know, with great power comes great responsibility: “There’s this whole depth we are getting into, the expectations are so much higher, people don’t just expect it to work they expect it to work the way they want it to and in a way they can adopt.” So, with so much expected and required of Data Scientists in 2020, building the right team is more important than ever. However, many businesses, Vin believes, are yet to get their hiring processes right: “A lot of the measures that we use to sort of evaluate employees are fictional – when you say years of experience, it has no correlation to employee outcomes or the quality of employee you get long term. It’s the same thing as college degree, there’s no correlation.” So when Vin is trying to build a highly specialised team, what does he do? “We have to teach specialisation, we can’t expect it. We can’t bring someone in and call them a Data Scientist and hope that they train up. You end up with teams that are exactly the same because they have hired the same people, people who reinforce the bias of what they do, and that is where true leadership needs to come in.” A specialised team made up of individuals who bring their own ideas to the table is more important than ever, particularly as businesses demand more from their Data teams. Gone are the days of one-size-fits-all models. Businesses now want something tailored to them: “Custom models are huge. The “import from…” Machine Learning development from three years ago adds value when it comes to wrangling and doing the Analysis, but when it comes to creating models companies are now expecting it to become a competitive advantage. Companies no longer want the same model that everyone else has, now it has to be differentiating.” These smart, customised models, he adds, will help businesses through the current pandemic. “The best models right now are adapting rather than reacting.” However, he’s sceptical about the Data Science community becoming too preachy: “When it comes to COVID-19 one message I want to send to the Machine Learning and Deep Learning community is ‘shut up’. We don’t have the Data! We have so many Data Scientists talking about something that’s very important to get right. If you get it wrong the consequences and the credibility we will lose as a field is enormous.” Indeed, discussions about the lack of quality Data on COVID-19 are widespread at the moment and raise concerns for Vin: “What the last two and a half months has revealed is the danger of bad Data, the danger of assumptions that are hidden in Data that hasn’t been looked over well or wasn’t gathered well and was fed into these models that now aren’t robust. Of course, no model can account for something this drastic, but they should still be performing far better than they are right now.” Despite these concerns, Vin believes any change in the world brings about opportunities for those in the Data and technology space. “What I’ve been trying to do ever since I joined the technology space is figure it out. It’s constantly evolving and it’s constantly changing. That’s really what has driven my journey. I’m always trying to figure out ‘what’s next’ over the next five years, ten years whatever it may be.” If you’re looking for your next Data Science, Machine Learning or Deep Learning role, or want to build out your own highly-specialised team, we may be able to help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more.

by Ross Whatling

23. April 2020

Why Now Is The Perfect Time To Connect With Data & Analytics Professionals

Covid-19 has presented a challenge for recruiting teams and businesses across the globe. Many businesses have adjusted to remote working in order to help stop the spread and eliminate the need for their employees to travel to the office. As businesses continue to hire, they are using technology to move from a traditional face to face process to video interviews. The good news for companies who are on the hunt for top talent, however, is that now is a prime time to continue speaking and networking with Data & Analytics professionals who could be a great addition to their business. Here’s why: AN EXCESS OF FREE TIME Now that people are working from home, they’re saving time on commuting, and aren’t working in close proximity with their co-workers or boss so they can make time to speak. Any previous struggles with arranging times to speak because of clashing schedules are now significantly reduced and there is now plenty of time to book in a call. Isolation is a prime time to hold a conversation with potential future employees as it’s highly doubtful anything is going to pop up last minute and interrupt your meeting. Plus people are more keen than ever to keep connected to others and engage with conversation. It’s the perfect opportunity to softly sell your company and what is on offer, longer term company visions, discuss trends in the market, plans on growing and where the best Data & Analytics talent around can fit into this. WE’RE ALL TECH READY With the world now set up for remote working, this could be a great time to book in virtual coffee meetings over the likes of Zoom, Google Hangout or Skype. Adjusting from face to face meetings to virtual ones, means there is no need to cancel meetings and if anything means networking with talent is easier. There are plenty of opportunities to ‘meet’ with talent and build relationships in ways that may have been harder to arrange when trying to find a physical time and place. This also means that onboard remotely is a very achievable reality. Virtual meetings with new starters offer an easy way to stay connected and build a relationship before they join the business. For example, a candidate who accepted a job offer in February but now may not start until August could be feeling uncertain as to whether there is still a job on offer. By arranging virtual meetings with people who are still set you join the company you’ll be able to stay in contact,. keep them engaged with your brand, and actually have longer to build a pre-onboarding relationship with them than you would’ve done. CANDIDATES ARE READY TO GO The best talent doesn’t wait around for long and, if projects have been postponed, they’ll be keen to keep developing their skillset Specialist and highly skilled candidates who may not have been looking for new opportunities are now actively searching and more than willing to network with hiring managers. Even if you’re not imminently hiring, now is a good time to begin initiating longer term conversations with professionals and creating a pool of talented candidates who are engaged with the business. Then, by the time you are ready, there will be a talent pool to begin interviewing with rather than starting from scratch. Naturally, some candidates may no longer be on the market, but if they’ve been left with a positive impression, there’s no harm staying in touch until the right time does come along. If you’re looking to connect with top Data & Analytics talent or businesses, we can help. Get in touch to hear about our network of thousands of top Data professionals, or take a look at our latest opportunities here.

by Ross Whatling

09. April 2020

What Will Happen In The World Of Data & Analytics In 2020?

The New Year, and the new decade, have arrived. The past ten years saw Data move to the forefront of public conversation following a number of big leaks and controversies. But, realistically, the impact of the ease of access to a surplus Big Data has only just begun to be felt. Whilst many are predicting what the world will look like by the end of the 2020s, discussing how far AI will have come and the consequences of automation on the job market, we’ve decided to look a little closer to home. With that in mind, here are a few trends we expect to see over the next year. ACCESS TO DATA SCIENCE WILL BECOME EASIER Data Scientists have traditionally been limited in number, a key group of individuals with PhDs, honed skills, and a vast understanding of Data & Analytics. However, with the advent of a number of new tools, more and more users will be able to perform Data Science tasks. However, many of the more sophisticated processes are still far from being replicated, so those currently working in this area shouldn’t be concerned. In fact, the more standard tasks that can be automated, the more time Data Scientists will have to experiment and innovate. THE 5G EXPLOSION Whilst there may have been a soft launch last year, the introduction of 5G will have a much more significant impact over the next year. With a flurry of compatible mobile devices around, and many more expected to come, we’re likely see 5G networks hit the mainstream. In the world of Data, this is likely to have a huge impact on how businesses use the Cloud. Indeed, with mobile upload and download speeds set to be so fast, there is a chance that an online middle-system may no longer be as necessary as it once was. THE RISE OF THE EDGE On the subject of the Cloud, it’s worth talking about Edge Computing. No, this has nothing to do with the pizza or the guitarist. Edge Computing has been a trend for a few years now, but, following an announcement from AWS, it looks set to become much more prevalent in 2020. Concerned with moving processing away from the Cloud and close to the end-user, Edge Computing is already beginning to have an impact across a number of industries. A NEED FOR AUGMENTED ANALYTICS It’s no surprise that the use of AI, Machine Learning and NLP is set to increase over the next year, so it shouldn’t come as a shock that Augmented Analytics are set to become more popular too. The opportunities, and extra time, offered by using the automated decision making offered by Augmented Analytics are the perfect fit for the increasing number of organisations who find themselves with more Data than processing capabilities. DATA WILL HELP FIGHT THE CLIMATE CRISIS Whilst there is a fair argument that the amount of processing required by the world of Data & Analytics is detrimental to the climate, the benefits any insights can offer are likely to outweigh any negative impact. Indeed, the UK government are already using Satellite Data to help reduce the impact of flooding, whilst Google’s EIE is being used to map carbon emissions with a view to better plan future cities. Given the recent, and tragic, bushfires in Australia, this is going to become an even more pressing issue over the next 12 months. If you want to be at the forefront of the latest innovations in Data & Analytics, we may have a role for you. Take a look at our latest opportunities, or get in touch with one of our expert consultants to find out how we can help you.

by Ross Whatling

09. January 2020

Why it is hard to build a Big Data team

Increasingly, I speak to managers who are adopting big data tools and developing PoCs to prove how they can make use of them. Just last week I spoke to a data architect who mentioned that if he didn’t get exposure to big data tech sooner rather than later, his current RDBMS skills may become redundant within the next few years. While that is likely an exaggeration, it is certainly an interesting point. Companies that would have never previously had the capability to interpret ‘Big Data’ are now exploring a variety of NoSQL platforms. In particular, the massive performance benefits gained from Spark and real-time/streaming tools have opened up a whole new world beyond just MapReduce. I don’t claim to be a data engineer, but as a recruiter for this sector, what I do is spend all day, every day interacting with big data developers, architects and managers (as well as keeping a close eye on the latest Apache incubator projects). Due to this, I have seen some recurring themes that have become trends when companies look to create and build their big data teams that are coming to the fore. Candidate demand The demand for Big Data professionals is very much a present day issue as the data companies have grand plans for is waiting for the right data developer to use the best tech to extract valuable insights from it. The best candidates receive massive interest, often gain multiple offers from a range of companies. Your business is now no longer just competing with large corporations such as Facebook, Twitter or Yahoo. Startups and SMEs are also vying for the best candidates. Candidates are seeing pay rises twice that of the normal rate, as illustrated in our salary guide. Candidate shortage The number of candidates with hands-on, production level Big Data experience is incredibly limited. We go to great lengths to find the candidates who can add real value to companies. The growth and exciting future for the big data industry has led to increased interest in big data jobs, particularly for those from RDBMS or software. engineering backgrounds. This leaves the industry in a difficult predicament: high demand + low supply = massive competition. There are countless examples of companies that have failed to recruit a Big Data team after a year of looking. Competition to get ahead and stand out Planning - Companies need to have a data road map detailing their future plans. Candidates want to clearly know what they are getting into and what to expect from a job. Innovation - Why get stuck on batch processing? The most exciting positions that candidates love are in data innovations teams, playing with real-time/streaming tech and new languages. Personal development, growth and training – with the data science market experiencing similar growth, many big data engineers are looking for a job that not only offers the chance to work with machine learning and similar fields; but training, mentoring towards clear career progression as standard. Speed – the length of the interview process is often seen as a reflection of the amount of red tape developers have to go through to get a job. The longer and more convoluted the process, the more put off some people may be. Complacency – don’t rest on your laurels, it’s unlikely that you’ll get 10s of CVs through when you are looking to fill a data role, so when you find a candidate you like, move swiftly to show your interest to them as quality candidates don’t come around often. By implementing these small but effective improvements to your recruiting process and how you develop data talent will see you create a team that is a success in this ever more digital analytics landscape. Companies who don’t create and nurture strong, dynamic teams will fall by the wayside. It’s Harnham’s job to help you achieve this goal. Get in touch with us to tell you how. T: (020) 8408 6070 E: info@harnham.com

by Ross Whatling

06. June 2017

Hiring a BI Manager – Trends and Challenges

With all the talk of big data and data science being able to predict what colour shirt I will buy in four years’ time (probably white or blue for those who don’t know me!), effective business intelligence is sometimes passed by or considered old news. The reality is that companies are realising that they can get much more from their business intelligence and are changing their strategies to deliver interactive, insight-driven and visualised reports. Not every data-driven decision needs machine learning algorithms behind it, and quality business intelligence enables all managers to be effective decision-makers. These strategies are creating some obvious trends in the market, resulting in a change in expectations when hiring a BI Manager. Key BI TrendsData Visualisation – Companies of all sizes are implementing Qlikview and Tableau (amongst many other tools) to create attractive, interactive visualisations, to harness intelligence, in a way that will capture attention in a presentation. Insight Driven - A BI professional can’t simply develop automated reports anymore. Analysts are often required to offer suggestions for business change and present insight to decision makers. Hands-on Management – BI managers and even heads of business intelligence are expected to keep coding well into their management years, with the logic that problems can be spotted quicker when they are in the trenches, coupled with strategic and line management work. Data Ambassadors – BI professionals are becoming door-to-door data sellers, coaching teams in a business on the benefits of using data to optimise their teams and decisions to save or bring in more money. Heads are in the Cloud – Companies are using cloud-based data warehouses such as Redshift to save on storage costs, whilst creating a centralised data warehouse for BI. Alternative Data Sources – Companies are looking to use the web and social media data, alongside numerous other sources to generate deep insights for managers. The BI Manager EffectI am completely sold that all of these features represent the future of business intelligence. The few companies that are doing all of the above well enough, are doing advanced work in the area and these companies will be leveraging big commercial gains from their business intelligence teams. The problem is that only a few businesses are doing all of the above, so only a handful of professionals have the relevant experience, and as a result expect top dollar to bring all of those skills. Therefore, it is prudent to be flexible with your hiring requirements. Look for a bright, passionate candidate, who can readily grasp the shift in business intelligence trends, and is keen to plug skills gaps. An enthusiastic business intelligence professional will get up to speed with whatever they were missing. Don’t be too quick to dismiss those who are not ready-made BI managers on paper. Message to CandidatesFor all aspirational or existing business intelligence managers and leaders, I would advise you try to stay hands on as long as possible. I know some of you dream of never seeing a line of SQL code again, however, the trend in hiring for hands-on business intelligence management positions means that keeping your tech skills sharp will really keep your options open moving forward. It would be great to hear your experiences, so please feel free to comment below on the trends you see in your business. Have you needed to remain hands on as you progress within your career? Or are you looking for a multi-skilled BI manager, and it is proving hard?

by Ross Whatling

06. July 2016

Lines in the sand: what the introduction of a CDO can mean to working practices

Introducing New PracticesThe introduction of a new methodology into a business structure should, in theory at least, be about the simple integration of a new practice into an existing framework. The truth is that this is rarely simple. Over the weeks, months and sometimes years of development of something such as information and data distribution systems they will naturally develop a set of practice based functionalities. Commonly (despite how this can feel at the time) these structures are in fact perfectly sound and probably just require minor adjustments to make them fit new circumstances. At other times due to either internal or external pressures such as changes in the market, technological advances or organic development and growth, the systems can be in need of a more radical change. In either case issues can arise within the distribution of responsibility for the specifics of the infrastructure.The introduction of the position of Chief Data Officer (or CDO) in a large business environment is very likely to be a catalyst for change. As discussed in a previous article the role of the CDO is varied at times; but it does have a very specific set of common elements. One certain commonality in the role is the need for the CDO to oversee the collation of systems and data flow processes into the larger ‘whole’ of the business. This will very likely require a redistribution of responsibility. One potential area of contention for example could be the methods and aims of the data engineers and associated colleagues compared to those of the technologists and hardware related areas. Clearly these are associated and intimately linked in that, to state the obvious, they have a symbiotic relationship with the flow of data from storage to user but these are often different departments with disparate operational procedures and methodologies. To the CDO however they will need to be seen as an operational component of the wider system. That means specific lines will need to be drawn to ensure efficient use of resources and the effective utilisation of the data. In short the poor CDO may find themselves in the unenviable position of trying to disentangle a complex weave of different threads of operations. Once this is done he can then set about the equally arduous task of re-weaving them into a new structure.Reweaving The ThreadsClearly a moments thought informs us that there is not going to be a quick fix for this, and no one size fits all plan is available. For the new CDO one of the first tasks will be to clearly understand the roles and responsibilities of the team and, with long term and deeply embedded working practices, this is not likely to be a matter of reading the job descriptions. Once the system is understood then the redistribution of workflow and responsibility can commence. Of course the CDO will be responsible for more than the mechanics of the situation. All business has people at the heart of the operations and a good CDO will understand this. Team player and leadership qualities may well be just as important to the new CDO as his technical and managerial skills when it comes to forging his position in the structure.

by Ross Whatling

16. January 2015

Big data and its big impact on sustainability

Gathering and analyzing millions of data points can be difficult, but big data can tell business where to focus its efforts. Advances in data gathering, computing power and connectivity mean that we have more information than ever before at our fingertips. IBM estimates that by 2020 there will be 300 times more information in the world than there was in 2005 – a total of 43tn gigabytes. And this data is being put to good use. Increasingly we hear how properly understanding data leads to positive results, whether this is Moneyball in sport or Nate Silver's predictions of the US elections. We are only just starting to scratch the surface of how businesses can process, analyze and otherwise make use of all this extra information to help them make money, save money and become more sustainable. But when it comes to sustainability the great thing about big data is that it is unlocking the ability of businesses to understand and act on what are typically their biggest environmental impacts – the ones outside their control. For pharmaceutical giant GSK only 20% of its carbon footprint is within its own boundaries: 80% comes from indirect emissions, with 40% of that coming from the use of its products such as propellant inhalers. Big data's potential big impact on sustainability hinges on three simple facts: • taking meaningful action on corporate sustainability requires an understanding of all the impacts that the business world and the natural world have on each other; • the business world is a very complicated place, with lots of interactions between consumers and companies and suppliers and markets; • the natural world is even more complicated, with lots of interactions between people and resources and ecosystems and climate. Until relatively recently businesses struggled to get a full picture of the impact of their own operations. The information required to get an accurate understanding of even something relatively simple such as energy consumption was kept in separate documents, in varying formats, and across multiple sites. But now leading businesses such as Nike and Ikea are trying to understand the entire end-to-end impact of their businesses, throughout the value chain. This includes looking at what's happening outside the boundaries of the business, including raw materials, suppliers, employees traveling, customers using products, how waste is dealt with, and investments that have been made. Businesses know that measurement is one of the keys to management. Collecting and understanding data about how an organization operates leads to knowledge that can improve decision making, refine goals and focus efforts. When the Carbon Trust worked with BT, we found that emissions outside its direct control accounted for 92% of the total. To add to the complexity, two thirds of those emissions were from BT's supply chain, which involves 17,000 suppliers around the world providing products and services worth £9.4bn. Big data has the power to transform how large businesses – the ones with biggest environmental impacts, but also access to large volumes of information – can take action on sustainability. A drive for data collection can also incentivize smaller suppliers to be more responsible in their own operations, creating a domino effect. Companies such as Hitachi are already providing an online platform for suppliers to submit how they meet sustainability criteria. Providing quality data in the right format is becoming an increasingly important factor in whether a supplier is chosen. The worlds of data collection and analysis, sophisticated business software applications, and accepted measurement standards are coalescing to help drive transparent and improved sustainability performance for companies and their supply chains. Measuring and understanding how doing business really does affect the natural world will open up new opportunities for bringing sustainability inside an organization: creating change, cutting costs and boosting long-term profitability in a resource-constrained world. It isn't easy. There are challenges around gathering external data, as well as in analyzing and interpreting hundreds of thousands, or millions, of data points. But we are already seeing the pioneers in sustainability leading the way, bringing suppliers and customers along for the journey. Author John Hsu is an expert in sustainability data at the Carbon Trust Click here for the article on the web.

by Ross Whatling

18. February 2014

BBC to spend £18m on big data

The BBC are set to splash out £18 MILLION of licence fee cash... on Big Data.The Beeb are planning to throw up to £18m worth of licence fee payers' cash at data analytics suppliers to work their dark arts for the corporation.A tender for a three-year Next Generation Digital analytics Services agreement appeared in the Official Journal of the European Union at the weekend."The BBC is looking for a framework of suppliers to provide web and data analytics tools and services, and associated activities," the tender stated.The framework will be split into two lots: the first is a single supplier lot for a core analytics platform designed to provide "insight" into web reporting, advanced predictive analytics and regulatory reporting requirements.This is worth between £6.3m to £9.9m for the supplier that wins the only seat on the framework.The second lot is a multi-supplier framework which covers enhanced reporting and analytics tools, worth between £5.5m and £7.92m. Up to 24 suppliers can make it onto this agreement.Lot Two includes multivariate testing, multi attribute segmentation, models to drive algorithmic content recommendations, visualization and social media analytics.Anyone wanting to chance their luck should note the closing date for submissions is noon on 18 November.Venerable analyst house TechMarketView described the deal as "eye-catching" because at up to £18m, the data analytics gig was a "medium-sized project" in a sector where "advanced analytics projects tend to be small or exploratory"."We expect all the usual suspects will be interested in the analytics tender but will be insightful to see who they [the BBC] pull in from the specialist analytics vendor community. This is a valuable opportunity for the 'little guys'," said TMV's Angela Eager. Click here for the article on the web.

by Ross Whatling

08. November 2013

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