The surprising history and bright future of data science

Nick Mandella our consultant managing the role
Posting date: 1/21/2015 9:14 AM

Data science is a young discipline, a multidisciplinary field requiring knowledge in sophisticated statistical modeling and software engineering. A strong grasp of information design doesn’t hurt, either. As a result, skilled practitioners are in high demand as increasingly data-driven enterprises and organizations in need of a unique skillset capable of reaping insights from big data. Meanwhile, there remains some confusion and debate as to what makes a data scientist.

The future of the discipline is bright, but it’s useful to look to its past to understand what it is and where it may be going. Data science arose from the convergence of two more mature disciplines. In a new post at Forbes, Gil Press presents a short history of how the discipline came to be, tracing its evolution back to a 1962 paper by mathematician John W. Tukey, “The Future of Data Analysis“. In Peter Naur’s 1974 book Concise Survey of Computer Methods, the computer scientist offered an early definition of data science, as “The science of dealing with data, once they have been established, while the relation of the data to what they represent is delegated to other fields and sciences.”

Beginning in the mid-’90s, the discussion leapt out of academic circles and turned towards potential business applications, with the advent of data mining technologies and their potential application in marketing and business intelligence. These developments also prompted the now-familiar challenge of storing and working with millions of rows of data. In 1999, Jacob Zahavi articulated this emerging issue, stating, “Scalability is a huge issue in data mining. Another technical challenge is developing models that can do a better job analyzing data, detecting non-linear relationships and interaction between elements… Special data mining tools may have to be developed to address web-site decisions.”

Data science came into its own during the last decade. As the strands of mathematics and computer science continued to intertwine in academia, new technologies were developed to mine, store, and analyze these massive data sets, while consumer internet giants such as Google demonstrated the business value of a data-driven approach to operations and innovation. A 2009 prediction by Google’s Chief Economist Hal Varian was particularly spot-on, with Varian telling McKinsey Quarterly, “I keep saying the sexy job in the next ten years will be statisticians…the ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades.”

Four years later, this statement seems like a forgone conclusion, as big data has reached buzzword status in the media, and become fundamental to the operations of enterprise, academic, and government organizations. Awareness of the value of data science has leapt out of academia and the business world and into mass culture, largely thanks to the accuracy of Nate Silver’s projections during the 2012 elections and his bestselling book The Signal and the Noise. The discipline’s prominence and impact is set to increase considerably in the next decade, with the advent of the Internet of Things, the industrial internet, and the democratization of its tools and techniques, which will transform fields from healthcare to agriculture, journalism to civic life.

To learn more about the history of data science and its rise to prominence, check out Gil Press’s Short History of Data Science at Forbes.

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Marketing Analytics - Then, Now & In the Future: A Q&A with Sarah Nooravi

We recently spoke to Sarah Nooravi, an Analytics professional with a specialism in Marketing who was named one of LinkedIn’s Top Voices in Analytics.  Sarah found herself working in Analytics after being attracted to the culture, creativity and the opportunity to be challenged. Having spent the first four years of her career working within the Marketing space, she has seen a real transition in the way that Analytics and Data Science has informed Marketing decisioning.  “I started my career in a Marketing agency within the entertainment industry, at the time it was doing things that most of the entertainment industry hadn’t considered doing yet”.  At the start of her career she’d meet entertainment giants with advertising budgets of millions of dollars who were, at the time, making mostly gut decisions with how to approach campaigns. “It was common that I’d hear, ‘I think our audience is females over the age of 35 with a particular interest and we should just target them’” she expands.  However, agencies quickly recognised the need for something more Data-driven. Entertainment businesses were going too narrow and were misunderstanding their audiences. The next step was to embed into these businesses the insights from a greater variety of sources, including social media, and to introduce more testing. That translated into a better media buying strategy that could be continuously optimised. It was a big step forward in the utilisation of Data within this realm and its clear focus on ROI.  Suddenly, the market was changing, “There was a massive spike of agencies popping up and claiming to leverage Data Science and Machine Learning to provide better optimisations for entertainment companies, mobile gaming – you name it. There was a huge momentum shift from using these gut decisions to leveraging agencies that could prove that”.  What she saw next seemed only natural, with more agencies offering Data-driven optimisation, companies looked to develop this capability internally. Sarah elaborates; “Now I am seeing these companies starting to take ownership of their own media buying and bringing the Marketing and Data Science in-house”. This shift in-house has been propelled by the major players, companies like Facebook, Google and Nooravi’s own company, Snapchat, working directly with companies to help them optimise their campaigns. This shift has changed the landscape of Marketing Analytics, specifically within the advertising space. Sarah explains, “You no longer need an agency to optimise your, for example, Facebook campaigns, because Facebook will do it for you. They are minimising the number of people behind the campaigns. You give up a little of your company’s Data for a well optimised campaign and you don’t have to hire a media buyer. There is definitely a movement now to becoming more Data-driven. Companies are really leveraging A/B tests and also testing out different creatives”.  It is this change in strategy that is seemingly taking the Marketing Analytics challenge to the next level. With opportunities to pinpoint specific audiences, companies are using their Data to understand how to approach their content, take the opportunity to experiment, and to find out what it takes to resonate with their audience. Sarah has seen the potential of this first hand: “We are starting to see a lot of AR and VR. There are meaningful ways to engage with technology to connect with the world. Moving forward, content will have to become more engaging. People’s attention spans are becoming shorter and with each decision someone makes it is changing the direction of content in the future. There has been a massive shift from static images to video advertisement and, more recently, from video into interactive video like playable adverts. People want to engage with adverts in order to understand a company’s message”.  It is within this space that she sees a gap for the future of ROI positive advertising:  “The biggest issue that I find with the creative and the content is that the value add is missing. The resonance with the brand or company, their values and mission is what is missing. Analytics alone cannot fix that. You need to understand what the company stands for, people want to connect with brands because of what they stand for – whatever it is. Especially in a time like we are dealing with right now, a pandemic, advertising spending has gone down. However, maybe there is a way to properly message to people that would resonate. Not that you want them to buy your stuff but maybe right now is the perfect time to do outreach and to help people understand your brand”. The ability to understand and predict customer behaviour is evolving, but with that, so is the customer. Whereas at the moment, you can build out experiments, you can create models that will be able to, as Sarah explains, “in real-time decide whether a user’s behaviour is indicative of one that is going to churn” and then try and create offers to increase retention.   This is the challenge of the current analytics professional – our behaviours in a global pandemic have shifted consumers into a new world. Now working for Snap Inc, she sees the potential of this from a new perspective. Naturally, like most social media channels and communication technologies, they have seen an increase in usage over the last month.  “People are wanting to communicate more as we are forced to social distance. However, we are seeing different regions engaging a lot more heavily. For example, it's Ramadan right now, people want to share those moments with one another and at the moment the way that they are having to do that is changing”.  So, it will be a question for all those required to predict behaviours to determine how many of these new lines of communication, these new habits, will have evolved. Once people are out of quarantine, are they going to continue to utilise the apps, games, social channels in the same way that they are currently? It certainly is going to be something that many within the marketing analytics space will be trying to forecast.  If you’re looking to take your next step in Marketing Analytics, or are looking to build out your team, Harnham 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. 

How To Write The Perfect Tech Job Description

It’s a challenge finding the right Data & Analytics candidate. Add in the number of companies fighting for that perfect profile and for many it can seem like an uphill battle. But there’s a simple way to cut through the noise; better job descriptions.  As a specialist recruitment agency within the Data & Analytics space, we have seen a real variety of job descriptions over the years, from the bright and innovative to the long and technical. And it may surprise you to learn that candidates still ask regularly to see official job descriptions and it is part of their decision-making process.  Unfortunately, they are also often a part of the recruitment process that can be rushed or created from out-of-date previous descriptions. There are some real benefits, however, to putting the time and effort required into creating something fresh.   If you’ve recruited a role like a Data Scientist before, you know that the problem isn’t usually getting enough candidates through the door, it’s about getting the right ones. A well-crafted job description leads to better quality applicants. It also helps those candidates become more engaged and excited about your business.  So, with that in mind, here are our five top tips for businesses looking to help their role stand out from the crowd.  CHECK YOUR JOB TITLE  You might think that calling your BI Analyst a ‘Data Ninja’ is going to get you the top talent, but it would probably mostly cause confusion. It is important that you align the job title to a clear and market relevant job title. Often internal job titles can be the biggest blocker in aligning your vacancy to the market.  Consider changing the job title for external purposes to make it more closely aligned to the market. Here are some common examples:  An AVP Analyst within a Marketing Analytics team is more closely aligned to a Senior Marketing Analyst. A Data Scientist job title aligned to a role with no machine Learning or algorithmic development may be better titled a Statistical Analyst.  CREATE A COMPELLING JOB RUN-THROUGH  Our consultants agreed unanimously that one of the weakest areas of job descriptions tends to be the more detailed description of what the role actually is. Too often job descriptions just list lots of different responsibilities, but these are often very generic or basic.  Before starting to write the job brief, ask members of your team that do the role already – what gets them excited?  You will likely find that it has to do more with the types of projects i.e. the application of technical elements, that appeals most to candidates. If you can, bring the role to life in a meaningful way. For example, relating it to projects that your team has done is a really enticing method of exciting a candidate about the potential of the role. Create A Tailored Experience Section Uninspiring job descriptions often have long lists of key skills required, often with irrelevant skills included. Keep your requirements to around 5 or 6 key bullet points, asking yourself what the most important requirements are and clearly laying those out.  On top of that often companies get too focused on requesting years of experience. We strongly discourage companies from specifying years of experience in a job advert as, within the UK, most European countries and a number of US states this is classified as age discrimination. Instead of including years of experience, carve out what it is that you want your ideal candidate to have done before instead, this will often correlate to their experience level. For example: 5+ years' experience in a Marketing Analytics could easily be transformed to Proven commercial experience in a Marketing Analytics environment with exposure to pre and post campaign analysis, customer analysis,  customer segmentation and predictive modelling.  DON’T FORGET TO SELL YOURSELVES Another key area where many companies fall down is effectively selling their opportunity and company to the prospective candidates. Whether an active or passive job-seeker, candidates are likely deciding whether this is the right fit for them based on what they are reading. Many job descriptions completely forgo any type of sales pitch above an initial description of what the company does, perhaps because they expect the candidate to know them and want them.  These are the areas we’d suggest bringing to life to effectively sell your opportunity: Writing in your brands personality. Consider the right tone of voice to match your company culture and style of working. Introduce yourself. Whether you’re a brand name or not, use this chance to actually tell people about what you really do and what you really stand for. Share what it’s like to work for the company. Include the culture, work environment, targets, challenges and of course reference to perks and benefits on offer too. Consider the candidate. What appeals to a talented Data Scientist will differ from what appeals to an HR professional. Make sure you tailor your overall pitch to the type of candidate you are seeking.  WORK ON THE LOOK AND FEEL  A little effort on the aesthetic look of your job description an go a long way.  On top of a nice overall look, keep the length to a maximum of 1.5 pages. Utilise bullet points and bold formatting to keep the description some-what ‘skimmable’.  If you’re looking to hire a Data & Analytics professional, Harnham can help. Get in touch with one of our expert consultants to find out more. 

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