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Big Data In Politics – Win, Lose, Or Draw

Big Data In Politics – Win, Lose, Or Draw

In the movie Definitely, Maybe starring Ryan Reynolds, there’s a scene in which he must sell tables for a political campaign dinner fundraiser. He makes call after call with no luck. Finally, in frustration, he speaks plainly and finds a connection between the politician and the prospective donor. In an instant, he understands. Make the connection and you can’t go wrong. This is the 90’s version of micro-targeting. Online advertising today has honed targeted Marketing to an art form and it’s infused every industry from Fisherman’s Wharf to Wall Street to Washington. Messages are crafted on detailed profiles of what makes us unique such as hopes, fears, dreams, emotional triggers, and more which is then taken out of the hands of humans. Enter such deep, personal details into automated technologies and you’ll get automated reactions. How did we get here? Ever since Cicero’s brother, Quintus, who approached politics with a do anything to win mindset, we’ve been working toward this point. But, when it comes to technological advances within politics, George Simmel put it best when he wrote around 1915, “the vast intensive and extensive growth of our technology…entangles us in a web of means, and means toward means, more and more intermediate stages, causing us to lose sight of our real ultimate ends.”  What does this mean? It means we have moved so quickly and with such intensity as we push inwards while reaching outward, we get tangled up in our own systems. Before we know it, it’s difficult to separate the means from their ends, and we lose sight of our purpose. In other words, it can be hard to keep our sense of direction with our constant distraction of tasks, systems, and processes. According to Simmel, this would soon morph into what he called a ‘fragmentary character.’ Like a mosaic, we put the pieces back together and assemble the bits to fit our concept of the world.   The Digitizing of Campaigns Traditional campaigning has traditionally looked much like the movie scene mentioned above with phone banks, whiteboards, and handmade signs. But, today, things are changing. Everyone has at least one smart device which can sync information in real time to a range of devices. Algorithms and predictive modeling help reduce the guesswork, though gut feeling and instinct still prevail. At least, for now. Our machines are learning how to learn about us and define what we believe and wish to see by historical Data, or rather our past behaviors. Where psychographic profiling meets micro-targeting. What was once only seen in the Marketing world has now entered politics. Just like marketers want to know what people are interested in, so to do politicians wish to know what voters think. To do this, both industries will study behavioral and attitudinal profiles to help understand a demographic better or discern a gap in the marketplace. In consumer research, companies rely on psychographic micro-targeting to reach smaller groups and individuals. The key question here is to ask is to what extent are politicians prepared to pass laws that restrict their own opportunities to know more about voters. Just as the next generation of voters are coming, so too are the next generation of tools being developed.  One Final Thought… Over the last 20 years or so, we have built an immense Data structure from mobile devices to social media to modelling processes and more. With this kind of connectivity combined with fragmentary media, the use of Data Analysis has a big role to play going forward. If we seek change in our political and social infrastructures, we will have to reimagine the structures currently in place. From algorithmic modelling to AI and Machine Learning, the possibilities for new ideologies has emerged blurring the lines between context and production in which Data underpins capitalism. As those in Data Analytics continue to pursue an uninterrupted (read: non-fragmentary) vision of the world, we find ourselves at a new stage in history of where both looking back and looking forward at the same time informs our future.   Where would you like to go? If you’re interested in Big Data & Analytics, we may have a role for you. Take a look at our latest opportunities or contact one of our expert consultants to find out more:  For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

Going Green With Big Data

Going Green With Big Data

Greta Thunberg sailed the Atlantic to come the UN to talk about climate change. Her mother, a renowned opera singer, has given up air travel to support her daughter’s efforts. There is a zero-waste movement to lessen our trash and help alleviate the carbon footprints from our buying, traveling and more. These are steps humans have made. Yet technological advances may make it possible to flip the script for the environment and Big Data has a big role to play.   What are Some of the Advances Taking Place? Technological advances have brought us breakthroughs in modern science and in every industry. Now, we are at a time and place in where our technologies cam help tackle climate change. From modeling to predictions, we can begin to build not just a map of environmental concerns, but begin to build a road toward a solution. Below are just a few of the ways technology is being used to advance solutions for climate change. AI modeling makes it easier to identify problemsPredictive Analytics models can create different scenarios to see ‘what happens if?’Big Data is used to identify areas which need immediate attention This is just the tip of the iceberg when it comes to using technology to predict and identify climate concerns. While some parts of the world contribute more to the problem than others, Big Data has made it possible to draw conclusions where the hardest hit areas are and is key to addressing the problem. But whatever Data brings, the information is useless if it isn’t used to formulate and put forward better environmental practices and policies.  Ways to Upscale Urban Data Science  Manhattan, Berlin, and New Delhi, as varied as they are, have one thing in common. They’re often sites for case studies when it comes to analyzing our environment. However, our advances continue to improve and we’re able to learn from state-of-the-art Data infrastructures. These can include such things as social media data combined with earth observations to see how they might better integrate. A research publication in Berlin suggest three routes for expanding knowledge. They are: Mainstream Data collectionsAmplify Big Data and Machine Learning to scale solutions and maintain privacyUse computational methods to analyze qualitative Data With these advances in place, there is a chance urban climate solutions could effect change on a global scale. With the proper Data of urban areas in place, including that of related greenhouse gases, socio-economic issues, and climate threats, Data professionals can get a clearer picture of what needs to be done. Building on the advances that are in place with the integrated technologies of AI, Predictive Analytics, and Big Data helps make big strides in combatting climate change. According to reports, only about 100 cities make up 20% of the global carbon footprint. Yet 97% of climate concerns are focused in urban areas. There’s still a lot which remains to be done to combat the greatest issue of our age, but working hand in hand – machine and human – we just might find ourselves on reprieve and the chance to leave the world better than we found it for the next generation. The next Greta Thunbergs of the world. If you’re interested in Big Data & Analytics, we may have a role for you. Check out our current opportunities or get in touch with one of our expert consultants to learn more.  For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

Data Engineer Or Software Engineer: What Does Your Business Need?

We are in a time in which what we do with Data matters. Over the last few years, we have seen a rapid rise in the number of Data Scientists and Machine Learning Engineers as businesses look to find deeper insights and improve their strategies. But, without proper access to the right Data that has been processed and massaged, Data Scientists and Machine Learning Engineers would be unable to do their job properly.   So who are the people who work in the background and are responsible to make sure all of this works? The quick answer is Data Engineers!... or is it? In reality, there are two similar, yet different profiles who can help help a company achieve their Data-driven goals.  Data Engineers  When people think of Data Engineers, they think of people who make Data more accessible to others within an organization. Their responsibility is to make sure the end user of the Data, whether it be an Analyst, Data Scientist, or an executive, can get accurate Data from which the business can make insightful decisions. They are experts when it comes to data modeling, often working with SQL.  Frequently, “modern” Data Engineers work with a number of tools including Spark, Kafka, and AWS (or any cloud provider), whilst some newer Databases/Data Warehouses include Mongo DB and Snowflake. Companies are choosing to leverage these technologies and update their stack because it allows Data teams to move at a much faster pace and be able to deliver results to their stakeholders.   An enterprise looking for a Data Engineer will need someone to focus more on their Data Warehouse and utilize their strong knowledge of querying information, whilst constantly working to ingest/process Data. Data Engineers also focus more on Data Flow and knowing how each Data sets works in collaboration with one another.    Software Engineers - Data Similar to a Data Engineers, Software Engineers - Data ( who I will refer to as Software Data Engineers in this article) also build out Data Pipelines. These individuals might go by different names like Platform or Infrastructure Engineer. They have to be good with SQL and Data Modeling, working with similar technologies such as Spark, AWS, and Hadoop. What separates Software Data Engineers from Data Engineers is the necessity to look at things from a macro-level. They are responsible for building out the cluster manager and scheduler, the distributed cluster system, and implementing code to make things function faster and more efficiently.  Software Data Engineers are also better programers. Frequently, they will work in Python, Java, Scala, and more recently, Golang. They also work with DevOps tools such as Docker, Kubernetes, or some sort of CI/CD tool like Jenkins. These skills are critical as Software Data Engineers are constantly testing and deploying new services to make systems more efficient.   This is important to understand, especially when incorporating Data Science and Machine Learning teams. If Data Scientists or Machine Learning Engineers do not have a strong Software Engineers in place to build their platforms, the models they build won’t be fully maximized. They also have to be able to scale out systems as their platform grows in order to handle more Data, while finding ways to make improvements. Software Data Engineers will also be looking to work with Data Scientists and Machine Learning Engineers in order to understand the prerequisites of what is needed to support a Machine Learning model.   Which is right for your business?  If you are looking for someone who can focus extensively on pulling Data from a Data source or API, before transforming or “massaging” the Data, and then moving it elsewhere, then you are looking for a Data Engineer. Quality Data Engineers will be really good at querying Data and Data Modeling and will also be good at working with Data Warehouses and using visualization tools like Tableau or Looker.   If you need someone who can wear multiple hats and build highly scalable and distributed systems, you are looking for a Software Data Engineer. It's more common to see this role in smaller companies and teams, since Hiring Managers often need someone who can do multiple tasks due to budget constraints and the need for a leaner team. They will also be better coders and have some experience working with DevOps tools. Although they might be able to do more than a Data Engineer, Software Data Engineers may not be as strong when it comes to the nitty gritty parts of Data Engineering, in particular querying Data and working within a Data Warehouse.  It is always a challenge knowing which type of job to recruit for. It is not uncommon to see job posts where companies advertise that they are looking for a Data Engineer, but in reality are looking for a Software Data Engineer or Machine Learning Platform Engineer. In order to bring the right candidates to your door, it is crucial to have an understanding of what responsibilities you are looking to be fulfilled. That's not to say a Data Engineer can't work with Docker or Kubernetes. Engineers are working in a time where they need to become proficient with multiple tools and be constantly honing their skills to keep up with the competition. However, it is this demand to keep up with the latest tech trends and choices that makes finding the right candidate difficult. Hiring Managers need to identify which skills are essential for the role from the start, and which can be easily picked up on the job. Hiring teams should focus on an individual's past experience and the projects they have worked on, rather than looking at their previous job titles.  If you're looking to hire a Data Engineer or a Software Data Engineer, or to find a new role in this area, we may be able to help.  Take a look at our latest opportunities or get in touch if you have any questions. 

Sean Byrnes, CEO Of Outlier.Ai, On Creating A Business With Values

Sean Byrnes, CEO Of Outlier.Ai, On Creating A Business With Values

We sat down with Sean Byrnes, CEO of Outlier, an Analytics solution provider, to learn more about Outlier and its values. He also shared us with the power a candidate has when applying and interviewing for jobs in the tech industry today as well as how employers can retain their top talent. How long has Outlier been in business? We’ve been in business about four years. Prior to starting Outlier, I had another company called Flurry which we sold in 2014. When we sold it, I took a year off to reset my internal counter. I needed to reorient my work/life balance and then, when I felt I was in good shape to get back into things, we kicked off the launch of Outlier.  When you began Outlier, did you plan to map out your values of diversity, inclusion, transparency, and work/life balance? Or did it evolve as the company grew? Planning out our values is one of the first things my co-founder, Mike Kim, did when we were starting the company. It was intentional. We sat down and wrote down those values you see on the website.   I learned a lot in my previous company and there were a lot of things I did which followed the values we have which we didn’t follow intentionally. It just felt…right. Growing up in New York, a very diverse place, having a diverse team always felt more natural to me than a homogenous team. Add to that, having just spent a year with my new daughter and adjusting to being a parent and what that meant; it put things in perspective.  If there’s one thing I’ve learned it’s this. There’s no reward for running a company well. There’s no reward for following good practices and treating your employers with respect. In fact, the reason I started my first company was that I’d worked a lot of places that treated employers like resources or like widgets.  You put salary in one side and productivity comes out the other. I wanted to work somewhere that treated people like people. So, I started Flurry, and now have come into Outlier with a solid idea of our values and company culture. As important as tech skills are, there’s no data to support a feedback mechanism and predict success. Success comes from unexpected places.  So, by sitting down and writing out those values, we weren’t just signing up for a contract for how we would treat the people.  In our way, we were standing up as an example of how you can build a tech company that didn’t follow these bad habits.   Someone said, not too long ago, that the best employees stay in the same company for about 20-months before moving on to their next project. Often, it’s because the employee no longer feels creative.  In thinking through your company values statements, what would you say to a business who’s hoping to both attract and retain their top talent? Or do you think it’s better for these individuals to rotate off in order to keep things moving?  Tech companies typically have two problems: Companies are so desperate to keep going, they do whatever they can to survive. They promise themselves they’ll make compromises and fix the short cuts when the time is right.  So, they hire people who need not share their values or may not meet their criteria with a promise that when the company is successful, they will fix it and that time never comes. You’re never at a point where you’re so successful you can go back and remake those mistakes or fix them.For many, recruiting is a one-way system. You search for and hire a candidate, then another, and another without really putting much thought into it.  The reality is that you can’t build a high growth business that way. What you have to believe is that if you spend the time to find the great people that enjoy working on what you working on, where you treat them with respect, you give them not just responsibility but also the authority to do things that you create gravity. You create a world where the people you hire pull in the next group of people because people really want to work them, they want to be in that environment. A lot of our most recent hires here at Outlier are people that came to us, who wanted to work in this environment and when they saw how great our team are they want to add to that and it becomes a self-fulfilling cycle where the larger your core of great people the better your gravity is, the more great people it pulls in and so the gravity needs to expand and that’s how you build a high grow organization. You don’t build a high growth organization by having the best sourcing process, by having the largest recruiting team, the best employ onboarding. You grow the fastest if you can create a community and an environment people want to work in and when it becomes self-evident of that then you start to pull people in. And so those become the culmination.  How do the values that you’ve initiated affect your company? Have you found that those values foster a deeper loyalty and higher moral than in other places and other business that sound a little bit like yours?  We have a very low attrition rate. Yeah, people have left due to life changes, but otherwise we still have the same people we started with though their roles may have changed as we grew. There was nothing we set out to do, nothing on purpose to keep them, but just did things which felt right. In retrospect, it comes down to this. If you create an environment where people are learning, where they feel valued, and they enjoy the work, why would they leave? Where would you go? What could you prefer to that environment? I think by focusing on this early, it’s led to more employee retention.  My hope is that everybody’s who’s here will continue along the journey as we build the company together. Another example is our work/life balance. Both my co-founder and I are parents of young kids. My first few hires were parents of young kids. So, being family friendly has always been a core value of ours from the beginning.   Being family friendly is both a conceptual and a practical concept. Our policy is to be in the office three days per week and work from home two days per week. This gives our parents a chance to do things like take their kids to the doctor or attend a parent/teacher meeting. It’s become an enormously strong aspect for us because there’s a lot of people who have young kids and they don’t want to work in your stereotypical tech company. You know the one, the company who expects you to be in the office for twelve hours a day seven days a week.   Our employees like the challenge of building tech companies, but they need the flexibility we offer, too. So, just by having a simple stand of valuing people as people and parents as parents, means we have a competitive advantage in recruiting for roles. The senior people who can contribute at a very high level where they suddenly didn’t have any options and this is a chance for them to keep doing what they doing. They’re able to do what they love and not feel they have to compromise their family on its behalf.   It’s the kind of thing where very simple principles becomes an enormously strong competitive strong advantage in todays market. While it hasn’t been true for very long, it has definitely been the case so far for us. If you’re interested in Big Data and Analytics, we may have a role for you. Take a look at our latest opportunities or contact one of our expert consultants to learn more.  For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.   For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

MACHINE LEARNING ENTERS BIOINFORMATICS AND ITS FUTURE IS BRIGHT

Machine Learning Enters Bioinformatics and its Future is Bright

Ever wondered how your email system knows which emails to show you and which to put in your junk or spam folder? Enter Machine Learning. It learns what you open and read and after a time can differentiate what you ignore, toss, or move to spam. Now imagine that same type of learning in the life sciences. As scientific advances move toward Data and Machine Learning to scale their knowledge, you can imagine the possibilities. After all, as you read this, trends in the life sciences, specifically with an eye toward bioinformatics showcase machine learning such as genome sequencing and the evolutionary of tree structures. Human and Machine Learning with a Common Goal There has been so much data provided over the past few decades, no mere mortal could possibly collect and analyze it all. It is beyond the ability of human researchers to effectively examine and process such massive amounts of information without a computer’s help.  So, machines must learn the algorithms and they do so in any number of ways. For the most part, it’s a comparison of what we know, or is already in a databank, with the information we have and don’t yet know. Unrecognized genes are identified by machines taught their function. The Future is Bright Machine Learning is giving other fields within the life sciences both roots and wings.  Imagine scientists being able to gain insight and learn from early detection predictions. This type of knowledge is already in play using neuroimaging techniques for CT and MRI capabilities. This is useful on a number of levels, not the least of which is in brain function; think Alzheimer’s Research, for example.  The hurdle? It isn’t the availability of such vast amounts of data, but the available computing resources. Add to that, humans will be the ones to check and counter-check validity which can in turn become more time-consuming and labor intensive than the computer’s original analysis. And it’s this hurdle which leads to a caveat emptor, or “buyer beware” of sorts. Caveat Emptor: Continue to Question Your Predictions In other words, how much can you trust the discoveries made using Machine Learning techniques in bioinformatics? The answer? Never assume. Always double check. Verify. But as you do so, know this. Work is already in progress for next-generation systems which can assess their own work.  Some discoveries cannot be reproduced. Why? Sometimes it’s more about asking the right question. Currently, a machine might look at two different clusters of data and see that they’re completely different. Rather than state the differences, we’re still working on a system that has the machine asking a different kind of question. You might think of it as a more human question that goes a bit deeper.  Imagine a machine that might say something noting the fact that some of the data is grouped together, but if different, it might say while it sees similarities, but am uncertain about these other groups of data. They’re not quite the same, but they’re close.  Machine Learning is intended to learn from itself, from its users, and from its predictions. Though a branch of statistics and computer science, it isn’t held to following explicit instructions. Like humans, it learns from data albeit at a much faster rate of speed. And its possibilities are only getting started. Want to see where Bioinformatics can take your career? We may have a role for you. If you’re interested in Big Data and Analytics, take a look at our our current vacancies or contact one of our recruitment consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

DATA SCIENTISTS MOVE THE NEEDLE TO SCALE BUSINESS

Data Scientists Move the Needle to Scale Business

Today’s companies know how important it is to add Machine Learning and AI into their business, but without a plan, things can easily go sideways. Hiring Data Scientists for your business involves more than just hoping for the next Facebook or Google success. It’s about moving the needle on your own business. So, how do you do that?  Well, first you’ll want to think about what is you want from your business and from there, build a team to help get you there. We know that sometimes these two things pose challenges as well. So, where should you should begin? Ensure you have the right leadership structures in place In their role as Stakeholder, it’s important for top executives to have a clear understanding of where their business is and solid framework for where they want it to go. In other words, it’s important to ask yourself, is your business ready for that sort of growth and transition? Before you say, yes, and start looking for that unicorn employee, there’s a few things you need to know. Data Scientists aren’t magicians. They cannot wave a magic wand and make you ten times profitable overnight. Efficiency will come, but it will take time. In reality, if you don’t have the right Data sets, the right people in place, or the right backing and investment, then it will be a hard road to success and can lead to failed initiatives. So, how do you avoid that and get yourself set back on the right path? Here are a few key steps to consider: Don’t Put the Cart Before the Horse Understand your focus and the why of your business. Align your teams with a clear view of where you are and where you’d like to go in your business. Set clear expectations for yourself and your team.Decide What Your Team Should Look Like Ask yourself, “How much talent do I need?” Well, that depends on how much Data you’ll be working with and where you want those initiatives directed. For example, if you’re building a team just to focus on work recommendation systems, then you’ll need a far smaller team than if you were overhauling an entire platform or product line. Stop Chasing Shiny Objects Be realistic in your expectations as you build your team. So many businesses, when they think of Data Scientist, focus on the word scientist. And their first thought is they’d like to get someone with a PhD from Stanford who’s worked ten years at Google. A couple of things come to mind here, when I hear this and the first is this: When businesses first reach out, they talk to me about how they want someone from Google or Facebook or Netflix, but the reality is 9 times out of 10, you’re not going to be able to access that kind of talent.Be realistic about what sort of talent you can gain access to and ask yourself, if you could get someone from Google, Facebook, or Netflix, why would they leave that job to come work for you? What can you offer that those businesses cannot? It's important to understand the goal here is not to chase the shiny stereotypes, but to have clarity and desire to set up for success those that you do hire. For some businesses, the initial reaction is to just throw Data Scientists at a problem and believe they can fix things or move you forward faster. But like everything you need to put steps in place to get you from where you are now to where you want to be.  Learn, Grow, Pivot For most people who don’t come from a Data Scientist background, there are two schools of thought. Data Scientists are bright shiny objects who will fix all of their business problems overnight orA waste of time. To build a successful team, begin by educating those in the dark about what Data professionals are capable of and ensure everyone is aware of what is realistic. It’s important to understand that it may take six months to a year for a business to see any real outcomes. This doesn’t mean things aren’t working. It’s about investing time and not getting itchy, if something doesn’t instantly showcase results.  Rethinking Stability The gold watch after 40 years of working for the same company is a thing of the past. Good Data Scientists today, typically stay on for about 20-months, then move on to their next creative endeavor. This can be scary for a world that expects people to stay in positions for four or more years, particularly if you’re not from a tech background. However, the definition of a scientist is someone who researchers, someone who tries to find new ideas and new concepts, so these are people who are naturally inclined towards learning and towards being in new situations and these people get very, very bored very quickly. Understand that if you build your team well, people will move on and drop away. This is a good thing. It means you built a strong team and the ones who have moved on have got you to this place, now you need the next team to help you get to the next level. A constant state of flux is scary, but it can also mean your business is scaling faster, and your team of Data professionals are doing their job to move the needle. Give your team the freedom to learn, give the freedom to work on projects outside of their natural scope to be able to bring value to your business, although even within that year 18-month framework you might not see proof straight away. You need to be ok with the fact that you going to lose people. That typically means you’re doing something good.  If you’ve got a Data Scientist who is happy to just stay at your company and work on the same projects for six, seven, eight years, that’s probably a red flag; how will they keep improving? “What Got Us Here, Won’t Get Us There” The needs of your group are going to change and shift and so the people you need are going to change and shift. Again, it’s about being adaptable and being able to ride those waves and change as and when we need to. If you’re looking for more guidance in scaling your business using Data Scientists and building your Data team, Harnham can help. If you’re a candidate interested in Big Data & Analytics, we may have a role for you. Check out our current vacancies or contact one of our expert consultants to learn more.  For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

Web Analytics Trends

Web Analytics Experts On The Year’s Biggest Trends

Size. Scale. Strategy. Metrics. The measurement of the customer experience is inherent throughout our marketing and advertising efforts in today’s world. So, we thought we’d take a step back and ask Data professionals about their thoughts on current and upcoming trends in the realm of Web Analytics.Their answers were as wide and varied as the people who gave them. But to a one, these are all in executive leadership and are exclusively focused on Web Analytics and its effects on consumer behavior. Personalization Gets More Personal  "With IoT, today service providers have a huge amount of data about their consumers. In Web Analytics, this has led to analysis of individual customer behavior, which is enabling companies to offer personalized services to their customers.  Therefore, on-page analysis has become popular because the aim here is to convert the visitor to a purchaser when the opportunity presents itself. With voice search on the rise, this has led to a push towards analyzing voices- so as to understand emotions and identify points of frustration which lead to abandoned carts."  - Avinash Chandra, Founder and CEO at BrandLoom  Privacy Focus  “Many people are concerned about how the likes of Google and Facebook handle the analytics data they collect. Google Analytics has been the default choice for most tech teams for years now, but it's being installed on fewer and fewer sites nowadays. Instead of handing your website's visitors' data to Google, many young companies want to be in control of their visitors' data.”  - Uku Tehrat, CEO at Plausible Insights Reading the Tea Leaves of Web Analytics “Access to deep information has a drawback; it’s made us reliant on the instantaneous raw data from each analytics channel to inform strategy. However, that data isn’t always accurate. To solve the problem, marketers and analysts will need to take a more holistic view of the data.  Instead of, for example, tracking revenue by channel solely in analytics, the analytics professional will be looking at the real revenue of the business and evaluating the changes in the marketing that have led to that result. By looking at the real business KPIs and creating narratives that reflect the whole of the data, an analyst will be able to avoid the short-term thinking that comes from the constant analysis of daily analytics reports.” - Doug R Thomas, Marketing Consultant at Magniventris  The Third Wave of Business Intelligence  Sean Byrnes, the CEO of Outlier, an analytics solution provider, is seeing an interesting shift in Data Analytics. This shift has us entering the third wave of Business Intelligence (BI).  The first wave, data centralization, aggregated business data in a single place, making it easy to know where to look for answers. Next, data visualization tools made extracting answers easy and accessible, allowing anyone in the organization to make use of them.  The third wave is Automated Analysis. This wave will have a big impact on data scientists and how they do their jobs. In this third wave of BI innovation, automated analysis systems will constantly examine all of a business' data and provide "curated" insights related to specific and actionable changes in the business. This is vastly different than today's dashboards, giving data scientists specific, daily direction on which parts of the business to focus on. This also helps data scientists elevate their own brand to one of a data counselor. Helping define how to use data insights to fine-tune the business.  - Sean Byrnes, CEO at Outlier. Video Marketing is Here to Stay “From a marketing perspective, the biggest trend I see moving forward is the continual rise of video consumption.  Consumers are shifting from reading blogposts to watching YouTube videos, and from reading books to watching Netflix. We know that the currency of the online world is engagement. This means that whichever large company, small business, or individual can engage their audience most effectively, wins.  When it comes to web analytics, it is important to identify the metrics that signal strong user engagement and to work to improve upon the mover time. Some of these metrics include watch time, average view duration, re-watches, returning viewers, etc. The better you can get these metrics, the more engaged your audience will be.” - Jeremy Lawlor, Co-Founder & Chief Strategist at Active Business Growth AI-Enabled Data Interpretation  “AI is constantly being mentioned as an enhanced way to interpret and help visualize data, and we are seeing various tools that promise to do that. I also see a lot of potential in using Web Analytics to predict consumer behavior and have better forecasts of performance. In general, we will have better ways to decipher them in order to better serve our needs in understanding the historical data and identifying trends and risks.”  - Gabriel Shaoolian, Founder and Executive Director at DesignRush A Three-Pronged Shift of Integration and Visualization Data connection web + mobile + offline (data integration)  DataViz development (data visualization) Development of the data-driven business model  "This shift towards a more integrated and data-driven approach should be important to business. After all, it’s the business model not just of the future, but now." - Krzysztof Surowiecki, Managing Partner at Hexe Data, a Data Analytics company A Growth Spurt   “Web Analytics should expect a growth spurt in the next decade. Technology has gotten so complicated, we need people – senior level people – with experience to understand and distill data to the business. Even something as simple as Salesforce, needs experts. Jobs aren’t going anywhere.  Businesses are desperate for educated individuals to help them make sense of all the data and filter out that which “fake” (read: bot traffic) in order to really understand the numbers and what that means for their business.”  - Daniel Levine, trends expert and keynote speaker at DanielLevine.  Web Analytics has been backstage long enough. As businesses aim to strike a balance between the data measurement of the customer journey, ensuring customers’ data privacy, and tailoring each experience, there’s plenty of work here to go around. And with the estimated growth spurt in the next decade, now’s the time to jump in. If you’re interested in Web Analytics, we may have a role for you. Check out our current vacancies or get in touch with one of our expert consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com. 

Our Interview with Amit Jnagal

Our Interview With Amit Jnagal, CEO Of AI Firm Infrrd

Amit Jnagal is the CEO of Infrrd.ai, an award-winning Artificial Intelligence software firm in San Jose. We reached out to learn what had inspired Amit to start the business and what trends he predicted for the future of the industry. In addition, we asked his take on diversity in business as well as what he looked for in prospective candidates. Here’s what he had to tell us. What inspired Infrrd.ai? It’ll be ten years next month that I started Infrrd. The same age as my son, who inspired it. Well, I’d wanted to start a venture of my own since I graduated and spent the next 12 years working for two large organizations. These experiences led to work with some exceptionally talented people. So, being a forward-thinker, I made a mental list of their names and resolved to have them work for me once my own venture took off.  I was cruising with a high-flying career when our first child was born. It was then I realized if I was ever going to start something of my own, the time was now. So, I quit my corporate job when my son was 10 days old and got to work. I’ve been an entrepreneur ever since and was successful in getting most people on my mental list to join me - some agreed in a second, some took a couple of years of convincing, a few took more than five years, and there are a few people that I am still working on. While I’ve had my share of failed ventures, this is my success and it’s taken a decade to get here.  How have trends in the industry affected your business and what do you see in the future of say, the next one to five years?  In my more than two decades of experience, I have witnessed two revolutions which have fundamentally changed the way the world works. The first was the advent of the internet and the second was smart phones.  We are at the beginning of another such revolution in AI. It amazes me still, the things we can do for our clients using AI for automation. The demand means jobs, yet at the same time, there are a few which will cease to exist, which I wrote about earlier this year.  My son, now 10-years old wants to be a pilot when he grows up. But I suspect that will be one of the jobs which ceases to exist by the time he enters the workforce. AI is here. And over the next one to five years, it’s important for businesses to know how to redesign their business around it. In order to thrive, they’ll want to be AI enabled. What are your recommendations for building a team within a startup? Where do I start? Well, I have plenty of recommendations of how to NOT to build a team within a startup. But when you’re first scaling your business, there are different phases as you start up. As you grow, you’ll need people with different specializations.  During the first few years, you’ll need people who are generalists - folks who are happy to do whatever needs to be done to move the company forward. Sales on Monday, Customer Support Management on Thursday, and throughout the week continuing to build the product. People who thrive on ever-changing responsibilities would do well in the beginning of a startup. In fact, it’s impossible to get off the ground without them.   But if they do their job right and you start to grow, a time will come when you will need specialists that can create a system for each part of the organization and scale it. This is when you start hiring separate VP of Sales, VP of Customer Success, VP of Marketing, and fine tune your leadership within your data professionals team; let them build their teams. People who get the startup started and those who scale it have very different skills and styles of working. It is important to get the right people at the right time. What are some things to consider both as a business and a candidate in regard to diversity?  Here’s an answer that you might not expect. You need to consider nothing when it comes to diversity; rather you need to 'un-consider' stuff that might cloud your judgement.  With experience, everyone starts collecting heuristics that prejudice how they look at people. More often than not, this prejudice blocks you from hiring people by 'considering' heuristics that are irrelevant. Learn to evaluate people for what they bring to the table rather than their demography.  I was surprised when we won the diversity award for 2018. I had never looked at my team using a demography lens to figure out what kind of people to hire. To our mind, as long as you have the right skill and experience to do the work, you’ll get a shot at proving yourself at Infrrd. What are companies like yours looking for in a candidate hire?  We go through a ton of profiles before we make an offer to someone. What we look for in a candidate varies by experience.  At the entry level, we look for people who have shown some spark and done something that most other people have not - creating new algorithms, getting certified in some technology, presented paper at conferences or technical events, etc.  For mid-level hires, our primary concern besides skill is whether this person exhibits our values and if he or she will gel with our team. High performers are great. But make sure they’re a good fit with the rest of your team. Remember when I said I knew how NOT to build a team? We’ve had our fair share of people and teams who just didn’t click and it was detrimental to business.  At a senior level, we look for good experience, shared values and an ability to lead people. A good leader can get outstanding results from any team. That is what makes an awesome leadership hire for us. If you’d like to learn more and are interested in working with AI. Check out our current vacancies or contact one of our expert consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.   For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

The Art And Science Of Tech In HR

The Art & Science Of Tech In HR

Human Resources. Talent Acquisition. People Officer. No matter what title a person has whose responsibility is finding the right person, for the right job, at the right time, the heart of the matter is they’re dealing with people. You know, humans. So, as the word “tech” becomes a suffix to every industry, think FinTech, MarTech, AdTech, and so on disrupting the old ways to make way for new, more efficient ways, what does that mean for the human resource industry? While there remains some concern about the industry in the age of the fourth industrial revolution, for the most part, many companies see technology in HR as good overall. But first, let’s get some misunderstandings out of the way. What Tech in HR Is Not Automation, Robotics, and AI are not taking over jobs, they’re helping to make a Hiring Manager’s job easier. By automating certain processes, it can give hiring professionals the time they lack to get back to the human side of things. You know, human-to-human communication. In fact, it’s not really disrupting jobs at all, rather it’s disrupting tasks. It’s either substituting or augmenting, just as humans do but wish they didn’t have to, so they could spend more time on the people side of things. Though we’ve mentioned a few things, Data Science in HR is not, there still remains some debate. Check out this article on whether or not Data Science will save the industry or forever change it leaving years of best practices in the dust? What Tech in HR Means for Growth Today’s tech world is a robust ecosystem of art and science. In Human Resources, the merging of a person looking for a job with a company looking for someone qualified to fill it takes a delicate touch of personality and technical prowess. HR has always been a science, of sorts. Now, it’s got the tools to help your company grow and keep up with your competitors. These tools include software applications which can offer improved reporting metrics, people analytics, Machine Learning, and NLP to help you improve such areas customer service. Would you rather have a person at the computer 24/7 or a chatbot to answer basic questions until a human can get online and take the conversation further? This is just one way in which technology is helping to improve the industry, not hurt it. Besides, when your customers are happy, so is your company’s bottom line. There is not a one-size-fits all approach and as technology learns to navigate the complex systems of people, businesses, and their behaviors, but there is much to learn. However, with the near limitless storage, computer processing ability and availability, and data models for predicting outcomes, the sky really is the limit. The mark of HR will be its ability on using data to steer the organization’s future with an upskilled and reskilled workforce. How to Not Get Left Behind There’s comes a time in every organization’s life in which it knows what it needs to grow, but isn’t yet ready to part with the old ways. The only problem, especially in today’s world, is that world doesn’t wait, and if you’re not ready and willing to pivot, you could get left behind. KPMG International’s Future of HR global study, which surveyed 1,200 global HR executives, reports conflicting attitudes and approaches to the challenge between action and inertia. According to the report, only 41% of respondents recognized the need for transformation but were in a “wait and see” position, while 72% of CEOs reported they’d rather be the disruptor than the disrupted. So, where is the disconnect? We know now, that even with a degree, pedigree, and a solid set of technical skills as a foundation, transformation is happening at a breakneck speed. So how can companies ensure their employees can keep up? One of the hottest topics across industries is reskilling and upskilling. Following in the footsteps of Google, IBM, and Microsoft, businesses today are more focused on ensuring individuals can demonstrate their skills, not just list them on a document. Think skills assessments and simulations, practice projects, virtual and mock experiences. It’s the “try before you buy” model. This is a near 360-degree shift from historical practices.  Every industry had its growing pains as it marched forward toward progress, but with the demand for highly skilled data professionals who can also resonate at personal level across the business landscape (read: soft skills), then the potential, if executed well, could be a boon for the industry.  Disruptions are meant to shake up the way we do business and answers the needs of our buyers. Easier, faster, more efficient, smoother, and the list goes on. Technology can free up time by taking on those repetitive and redundant tasks, giving you more time to be with your customer, whoever and wherever they are. Are you looking for a new opportunity in Data & Analytics? We may have a role for you. Check out our current vacancies or get in touch with one of our expert consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

AR & VR Breathe Life Back Into Our Lives

AR & VR Breathe Life Back Into Our Lives

Remember the bicycles piled in the yard that showed where your friends were? What about the jingle of coins in your pocket as you headed to the arcade? Stranger Things may have recalled these distant memories, but what if you could still see bicycles piled against your neighbors’ door or listen to the jingle of coins as you played with your friends? This is Augmented Reality and its aim now is to get you off the couch, exercising, playing, and enjoying camaraderie. Ready, Player One? Five Trends to Watch As we navigate the next stage of our virtual and augmented realities, there are a few trends to watch. AR, VR, and AI Will “See” Objects - Augmented Reality (AR), combined with Artificial Intelligence (AI) and Computer Vision, will help computers “see” and label what’s being seen. Machine Learning will ramp up to offer increasingly correct identification of objects whether it’s the dinosaur exhibit at the natural history museum or connects the dots for stargazing in a planetarium.VR Gets More “Real” for the Mainstream – In other words, developers are able to offer more immersive experiences. New developments in hardware technology, such as eyeball tracking and field-of-view help power the idea users can interact and explore less like a video game and more like real-life.AR Can Help Keep Your Eyes on the Road – Remember KIT? The car who talked, could self-diagnose, and navigated with barely a hand on the wheel? It’s not so far-fetched now as it once seemed as vehicle manufacturers increasingly opt for voice assistants and some begin to offer graphics’ overlay of footage around the car. Others go a step farther, projecting data onto the car windshield, assisting with navigation, lane identification, and potential hazards along the way.AR-Based Entertainment Branches Out – No more piled high pizza boxes, sitting in the dark of your parent’s basement. AR today is about getting moving; physical activity and balancing tech life with real life. Toys and Gaming companies are on the bandwagon to get families moving. Location-based gaming has grown by leaps and bounds as has interactive projection-based technology. Some applications can transform your immediate environment into an immersive gaming experience while others can transform playgrounds which, when unlocked by parents, can offer choose-your-own adventure types of projects to complete. Enter whole being of mind, body, spirit. And we’ve barely scratched the surface.Worldbuilding, Personalized Character Building, and Augmented Board Games – Each of these are about bridging the gap between the physical and digital world and is most changed when it comes to board games.  As exciting as these trends are, it’s not all fun and games when it comes to the next advances of AR. This technology is also being used to help discover early onset of Alzheimer’s and most recently has found its way into the operating room. Or at least, it will be soon as companies look to bring the AR and VR technologies into the OR. Training and Teaching with AR AR & VR technologies are being used for teaching and training in just about every industry it seems. From Walmart to the Military, business is seeing the benefits of allowing individuals to practice their roles without the associated real-world risks. But when it comes to the healthcare industry, this is where AR begins to really shine. What if we could predict not only detect early onset dementia, but catch the onset of mental illness as well? One company, recently cleared by the FDA, is working toward just that, with the specific goal of predicting Alzheimer’s early. With an estimated cost of around $290 billion which could rise to over $1 Trillion by 2050, their claims of 94% accuracy in detection 6 to 10 years early is good news for families. Memories are how we share our stories and no family wants to miss theirs. Could technology as disparate as it once made us be bringing us closer together? With these latest, that just may be the case. So, whether you want to let out your inner child, practice a new skill without real-world ramifications, or simply know your memories will be with you for a lifetime, AR offers something for everyone. Every industry. Every business. It has infused our world and is breathing life back into our lives.  Want to be the wizard behind the curtain, the Data Analyst to know what’s next, or the Data Engineer who builds the next great technological advance in the AR spectrum of services and capabilities? We may have a role for you. Check out our current vacancies or contact one of our recruitment consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

Diversity in Data

Coming Together: Diversity In The Workplace

Diversity. It’s a hot topic fused into every discussion from the board room to the upper echelons of government. And it’s more than the gender pay gap. It’s about bringing people together with different backgrounds, ideologies, processes, and creative persuasions. This is how you create your dream team – different ideas focused on a common goal.  We all know this at our core, but what does it mean to put it into action? In our 2019 Salary Guide, we covered a wide spectrum of how to hire and retain employees and within it discovered where the diversity gaps lay within the Data & Analytics industry. Marketing & Insight roles may have had the smallest gap, but Data Science teams had the largest. On the heels of our Salary Guide, keep an eye out for our upcoming Annual Diversity Report. But before we can do anything, we need to talk about culture. Specifically, changing our business culture to embrace diversity and inclusion. Did you know this change could help attract top talent and drive stronger teams to more innovative results? How to Put Diversity Initiatives Into Practice According to a 2018 study from McKinsey, diverse companies from their general workforce to their leadership are 33 percent more likely to have higher profitability than their competitors who are not as diverse. For gender diversity, the margin is 21 percent. Add to this, government regulations, candidates’ evaluation of diversity in an organization, and the company’s own plans to improve their efforts, and the task seems daunting enough. Diversity questions are no longer relegated to the just one group or another, now the question comes from every direction. Every location.  Below are just a few ways to begin your Diversity and Inclusion Initiatives Be Open to New Things and Establish a Sense of Belonging. Create a space where each person can bloom and shine. Establish a connection in which people are relaxed and can be themselves. Give them opportunities to create and engage in the workplace. Leadership is Key. Lead by example. Show empathy and avoid making diversity and inclusion the exclusive domain of HR. This is a time for everyone to get involved, to understand, and to remember why diversity is important. It’s more than ticking a box and it’s not one-size fits all. But each step forward is for the betterment of the business.Make Inclusion Part of Your Company Culture. Don’t think of diversity and inclusion as a one-day workshop. This is the time where you bring everyone together to learn what it means to be inclusive. Have you ever felt out of place or uncomfortable in a situation? Did anyone come to include you into the goings on or were you left to your own devices? Put yourself in other’s shoes and have your team do the same. Then identify behaviors and build new habits which support open and honest communication. It is okay for people to disagree, it’s what leads to real change. The key is to not let biases be mistaken for healthy discussion.You are Your Brand. Wear it Well. Company culture and brand are linked. They are infused in the products and services you offer to the world. Does your company culture of diversity reflect that of your customer base? What do you want your brand to say? How do you want to be known as a company? What new idea might you come up with divergent voices giving their thoughts and opinions? These are just a few of the ways you begin to establish a diverse and inclusive culture in your business. Now is the time to adapt your processes to scale for these behaviors. Ask yourself about the makeup of your meetings. Who attends? Who speaks? Is anyone being left out whose input you value? People First, Then Data As companies struggle to begin their diversity initiatives, there are still some caveats. The first is to remember you’re hiring people, not the data on a spreadsheet. Every business knows the importance of data and People Analytics is no different. But the problem begins with too small numbers as businesses try to pinpoint where they need to improve their diversity efforts.  Limited data about certain groups within the larger can be misleading. So, while it may seem counterintuitive, the answer is to broaden categories. Rather than focus on ethnicities, age, background consider the group overall. It’s the breaking down of demographics, where businesses begin to misstep on their path to diversity. So, what are some steps you can take to help improve your diversity initiatives? Well, here are a few to get you started. Avoid sample-based analyses. Focus on a range of outcomes. For example, are women represented well? What about women of color? Is there a wider variety of ages? Is the male contingent homogenous or are different demographics represented?Talk to employees and dig past first glances. Interviews with staff help to remind you these are people who cannot be defined by a statistic. It’s during this time you can learn more about them, their struggles, aspirations, and cultural insights. Descriptions take into account surface information such as race, gender, socio-economic status, and so on. Decisions based on data ripple through people’s careers and can affect their livelihoods.Ensure managers are engaged as allies. Just as leadership is key to ensuring the company culture embraces diversity, so too are the managers. It is they who are crucial as they make key hiring decisions, determine projects, and develop employees for advancement. Leadership offers an overview, but it’s the managers who shape the day-to-day of the employee experience.  One Final Thought… Most of us don’t start out intending to exclude anyone, but we naturally gravitate toward people like us or whom we imagine can best benefit our business. But when we open ourselves up to the possibilities of a more diverse workforce, the possibilities are endless. To begin, however, we must understand where the problems are, and from there fix them. At Harnham, we’re proud to be diverse in our company culture. In our inaugural Diversity Report, we showcased our near 50-50 split within our leadership and our efforts to be inclusive throughout our organization.  If you’re interested in Big Data and Analytics, we may have a role for you. Take a look at our current vacancies or contact one of our expert consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

Data Privacy Rules

Privacy Rules. Or Does It?

Privacy and transparency are two sides of the same coin. With the amount of Data we give and companies consume, we want to know our personal information is safe. We want to know we are safe from illegal use of our information. But as conversations about FaceApp, Facebook, Cambridge Analytica, and Google privacy issues make the rounds again, we now know to be much more cautious. Companies would be wise to follow. States Take the Reins to Enact Privacy Rules Though the General Data Protection Regulation (GDPR) passed last year in Europe, businesses in the U.S. have not been so constrained. While there is no overarching federal law, states have taken steps to protect the privacy of their residents and have passed their own Data protection laws. Though all 50 states have enacted notification laws to inform consumers if personal information has been compromised, only California and Vermont have instituted laws requiring businesses to make real change in their Data operations. Other states, from Oregon to Virginia have expanded their definitions of identifying information and increased fines to $500,000 for breaches of privacy. These more stringent rules affect such information as that from Electronic Health Records to tax preparers. And when it comes to Data disposal, companies are required to shred or modify in some way any personal information before tossing it away. Student information is particularly protected in Iowa, in which online efforts against selling their information or otherwise siphoning from online profiles are expressly forbidden by state law.  These are just a few of the rules in place and vary slightly from state to state. So, how can you ensure you’re in compliance? Some Tips to Ensure You’re in Compliance If you need to create or amend your Data Management program, here are a few tips to consider: Conduct a gap assessment. What existing procedures are in place which may need to be revised?Ensure your legal teams work closely with your IT, business, and marketing teams to monitor changes and reassess your company’s mitigation controls. How effective are those controls within this legal landscape?Ensure the consumer Data you’re collecting is “critical” to the company. Create a process to receive, review, and fulfil customer requests. But also consider how you handle their information should a customer wish to opt out.Train employees on how to handle personal information. Create and maintain procedures on policy changes and best practices for your Data protection policies. A final note on the above tips, though each state has their own laws, it’s important to consider Golden Rule when it comes to privacy; how would you want your personal information handled? Data is a Commodity. Trust is Valued.  Broken promises have tarnished trust in companies. According to a recent survey by SAP, nearly 70% of customers said they don’t trust brands with their personal information. So as companies strive to offer the best customer experience, remember it’s more than flashing lights and deep discounts.  Customers want to know their personal Data is safe. So how you ensure this is the case and maintain your customer’s trust? Be transparent. Collect customer information with clear intentions and keep your customers informed of changes to policies.  Legal verbiage in policies are to protect companies. It’s time to rethink this strategy and enact policies to protect customers. Though their wariness is warranted, consider how not being transparent and protecting your own business has been detrimental to the customer experience. By being proactive in Data policy compliance laws, you let customers know you’re putting their needs first. That builds trust and loyalty to your business. Isn’t that what every business strives to attain? Even the tech companies realize its import and impact. Earlier this year, tech companies laid out what they’d like to see in federal Data privacy laws. The key takeaway? One set of rules for all is preferred over the slightly differing state laws. If you’re interested in Big Data and Analytics, we may have a role for you. Check out our current vacancies or get in touch with one of our expert consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

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