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When developing your organization’s data monetization strategies, the first ideas that come to mind are probably not the best. For example, you might assume that all you need to do before you can start selling your customer data to external organizations is to obfuscate or remove a few sensitive fields – think again. Doing only that could potentially risk your organization’s very existence. The best data monetization strategies begin by applying an organization’s intellectual capital to raw data elements to create truly new and unique insights. That is the fastest path to creating ‘data products’ that a broader market will be motivated to purchase. Finding the right people Naturally, data monetization strategies are most effective when an organization has the right people analyzing the data. Your leadership team might reasonably think that the best person to tap for this role would be the Chief Information Officer, or possibly the Chief Marketing Officer. Such individuals might indeed be fine for the task, but in my opinion, you should be looking beyond titles to find individuals with the innate sensibilities and personal preferences that make them suited to the practice. To be specific, I recommend finding those rare individuals with analytic skills in both qualitative and quantitative areas. In a sense, they need to be ‘hardwired’ for this type of insight and practice. In my experience, it’s much more important to find analysts with such insights instead of requiring that they had this or that previous job title, or even a particular degree. As a matter of fact, when I’m trying to fill data scientist positions, I’m far more interested in seeing how the individual rates on the Keirsey Temperament Sorter , a personality instrument similar to the Myers-Briggs, in conjunction with their quantitative reasoning. It’s not as well known as the Myers-Briggs, but I find it gives me more useful insights into the preferences, abilities and ways in which a candidate prefers to think that I value most in a data scientist. To drill down a little more, I tend to look for rational, curious and analytical people. They might not even need to be familiar with my particular industry. My reasoning is that by the time they’ve sliced and diced my data to derive truly new inferences from it, it’s likely that it will be relevant to (and valued by) organizations in other industries. To go even further on this point, it might make strategic sense to focus on developing data products with the specific aim of selling them to companies with whom you could never directly compete. By the same token, your new data should feature insights that are based on your customer information, but bear little or no resemblance to the original data. Ideally, your data analytics team members should be able to learn rapidly and also be familiar with the latest data analytics products. A few years ago, that would have probably been Hadoop, but in today’s data analytics world, technologies like Spark and Storm have begun to attract more market attention. The stakes are high – and your standards should be, as well The better decisions you make about monetizing data, the greater your likelihood of maximizing the revenue it will generate. In addition, by making sure that you produce and sell only data that is truly yours, you can minimize the risk of angering or alienating customers, shareholders, regulators and other interested parties. The world of data analytics is changing – not only in the types of data monetization strategies that organizations are pursuing but also in the types of technology, processes and people involved. If you think that data monetization is worth doing (and there are plenty of reasons to think so), it’s also worth doing right. For those and other reasons, it pays to start by hiring the right people for the task. Article written by Reuben Vandeventer Image credit by Getty Images, DigitalVision, Thomas Barwick Want more? For Job Seekers | For Employers | For Influencers
Understanding various information and putting them into perfect arrangements and creating applications for converting them into newer, dependable big data, all comes under a technology called data science. And currently, there is a high demand to secure people to fill data scientist roles. While programming is a must-have skill for candidates, there are certain additional skills and understandings required of both the potential employee and the employer. Here are nine tips on how to hire a data scientist for your company. 1. Select curious minded people Scientists should be eager to dive to the depths of their study. So seek candidates who are always curious to know about new innovations. They should be ultimate thinkers. They should be detail-oriented and should be eager to know more and more. That’s what a scientist should be like. 2. Consider his or her ability to program newer languages A data scientist should be a good programming engineer. Avoid hiring one if you see few programming skills or ability. Instead, hire one who has the desire to know and learn new programming languages. As a scientist, he or she has to delve deep in data science technology, and any language may be necessary at any time. So the one whom you hire should have the hunger to learn and know new coding terms. 3. Notice if statistics are at the tip of his or her finger Data science is all about information – the arrangement, calculation, comparison and ultimate results. Playing with information needs more statistics. And one who will study and work on information research should obviously know about statistics. The understanding of the behavior of data and the certainty of how they will react on given arrangements fall under statistics. Statistics is a must. 4. Make mathematics the foundation Mathematics is the basics of data science. To even think about becoming a data scientist, an individual must have an excellent foundation of mathematics. A perfect mathematician only can become a good data scientist. Data is all about information and calculation. Arrangements of data in rows and columns all require mathematics. A basic mathematics knower is not fit to be a data scientist. In most cases, people seem to hire advanced coders without concentrating on their mathematics ability. In such situations, it is difficult for the scientists and coders to communicate. Since data science stands on the pillars of mathematics, a person with mathematics background is the best hiring choice for a data scientist position. 5. Stay away from puzzling tests Don't ask your candidates to pass a test on puzzles. Solving puzzles does not mean that he or she will be great in research work. Many people do not solve those puzzles because of limited time, while some do because they are good at it. If given more time, the failed candidates may have nailed it. Research needs time. You cannot always put a time frame in research. It isn't proportional to solving puzzles. So better not to include it in the interview process. 6. Know that good communication is an added advantage At times, scientists may be considered awful communicators. But communication is actually very important. Whether you are a data scientist, writer or marketer, you will not work alone. At some point, you have to communicate with colleagues or vendors. A data scientist with great communication skills will be able to explain his or her study to others and spread knowledge. And clear communication is critical for scientists to transfer their research and studies to the next scientist, passing knowledge generation after generation. 7. Take soft-skills into account Give importance to a candidate's personality.  Consider whether or not the candidate possesses leadership qualities, a responsible nature and a positive mindset. 8. Schedule minimal interview sessions Be sure your interviews include a team of seniors. Instead of one-to-one interviews for several different times, schedule an interview for a single time with a group of experts. Add scientists, coders, mathematicians and managers in the interview round. Based on everyone’s varying perspectives, select the best finalists or one candidate. 9. Let senior data scientists join the interview session Although recruiting is the job of HR, when it comes to selecting staff for a technology team, it's better to include technology experts, too. If you want to recruit data scientists, try to invite experience employees working in the same technology to participate in the hiring process. They have gone through the same work and functions, and they know the exact qualities required to hire a data scientist. Time and energy is a worthwhile investment in order to get the best candidates and fill open data scientist roles in your organization. In this big data era, data is as important to a company as ever, and the need to fill data and technology positions will not disappear anytime soon. But don't fret about the hiring process – just follow the above instructions, test and select the deserving candidates. You will be doing a great deed in the data science world. For additional resources related to this article check out the data science jobs index  + data scientist salary index + icrunchdata's job posting platform to hire data scientists. Article written by Vaishnavi Agrawal Image credit by Getty Images, E+, Yuri_Arcurs Want more? For Job Seekers | For Employers | For Influencers
Data is all-powerful. Data is all-encompassing. Data is all… too intimidating to use? This seems to be the case at many organizations, including those deploying people analytics solutions to address recruitment, onboarding, training and other HR and talent management needs. More than three quarters of companies are investing or are planning to invest in big data initiatives, according to Gartner. However, 72 percent of organizations are gathering data that they never use , according to survey research from Pure Storage. Why? Because they feel that data processing is too time consuming (as cited by 48% of survey respondents); they lack the internal skills to do so (46%); and/or they don’t have the proper tools to sufficiently process data (30%). All of which creates the impression that you can’t derive value out of talent management or people analytics because the data is too big or too complex to tackle – or both. Thus, too much of it sits somewhere in your network, untapped of its great potential. So how do you reverse this scenario – to not only “tame” people analytics and Big Data but fully optimize it as a strategic resource? By pursuing what I call the following Three Stages of HR Data Empowerment: 1. Presentation Did you ever attend an industry seminar of sorts in which a really smart person was doing the presentation? And you realized the presenter had plenty of “good stuff” to offer – yet you learned nothing because he or she couldn’t convey the key thoughts in a clear, accessible way? Let’s face it: Given as little as you take away from these sessions, the presenters could just as well be speaking in a foreign language. Well, HR-based analytics solutions can be a lot like this. So you should invest in products designed with simple, utilitarian dashboard displays that any staffer – including the least “techie” of employees – can immediately grasp and start using. Streamlining the data goes a long way here. When you pick up a magazine off the rack, after all, you usually go right to the sections and articles that are most relevant to you and your interests or informational needs. In the same manner, users should be able to manipulate analytics tools so that only the information that is important to them and/or their role is depicted. 2. Collaboration and Sharing At this stage, your solution’s presentation has eliminated the “data intimidation” factor. Even relatively technophobic staffers feel comfortable taking a “deep dive” to obtain previously unavailable insights – and, every day, their enthusiasm for analytics grows. What’s more, the enthusiasm is contagious, and your entire organization is constantly making new discoveries via analytics. To take full advantage of the “building buzz,” your tools must make it easy for users to share and collaborate. When a person gains knowledge that can enhance talent management policies and practices, it’s a good thing. But when a person gains such knowledge and shares it enterprise-wide, it’s a better thing. And when employees throughout your organization – no matter where they work in the world – collaborate upon the knowledge and expand its value through comments and recommendations, the potential for tangible impact is limitless. 3. Storytelling By now, you’ve completed the first two stages of data empowerment. As part of their daily routine, your talent management team members are extracting new information which greatly supports them in their accomplishment of HR objectives, with maximum efficiency and cost-affordability. They’re sharing and collaborating upon the information to further extend its capacity to make a difference. This is when data ascends from being “a huge collection of numbers” to something more transcendent. Indeed, it has emerged as a “storyteller,” revealing to users new thematic threads about the most promising regions/colleges for recruitment; the likeliest department areas for retirements (i.e. those requiring proactive succession-planning); and the best programs for onboarding, training, etc. For the first time, users gain total visibility of “storylines” within the past and present to make insightful, actionable decisions about the future. For them, data once resembled an indecipherable code. But the code has been “broken,” and they now see pattern after pattern that significantly augments your organization’s ability to recruit and manage talent. Data can do so many things, but not if we continue to conclude that only a scientist can make sense of it. When your talent management solutions demystify effective people analytics for everybody – even staffers who consider themselves tech-challenged – through the three described stages, you’ll truly empower all of the information you own. And you’ll empower your HR team members as a result. Article written by Joe Abusamra Image credit by Getty Images, Cultura, Monty Rakusen Want more? For Job Seekers | For Employers | For Influencers
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