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With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

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How Big Data and Risk Analytics Can Help Fight Climate Change

Data is all around us. We use it to calculate our calories and our steps to ensure a healthy body. We use it track our packages and ensure delivery to the right location. We look to it to check the weather for exercise, driving conditions, and in extreme cases, safety preparedness. But, could we use it to fight climate change? Could we use it to reign in swiftly rising temperature changes which could affect our food and ecosystems?  Greener Choices for Greener Products People have more choice than ever before. They also have information at their fingertips and can see at a glance the benefits or the drawbacks of purchases. From how their food is grown to how far their food is delivered to the practices of companies from oil and gas producers to the wearables on their wrist. Climate change and Big Data have been linked, but mostly to determine greenhouse gases and effects of pollutants. But with the rise of consumer advocacy groups, farm-to-table traditions, micro-and macro-farming, and a desire to know more about what we’re putting into our bodies, consumers are dictating greener options from the markets. The Business of Climate Risk Analytics As consumers take note of climate change, companies are merging knowledge of climate change risk into their financial decision making. How will climate change their business practices? How will it be scaled based on how climate science rules inform financial risk assessments not yet developed? The markets need just as much information as consumers when it comes to climate risk. These assessments are intended to businesses determine consequences, responses, and likelihood of the impacts of their actions. Enter climate risk analytics. Climate Risk Analytics uses risk assessment and risk management based on natural disasters and their impact. However, the climate is not in a static state. It’s ever-changing and those changes are often in the extremes with little information related to averages. This complicates risk assessments as do the differences in regional projections. How AI Can Help Big data combined with climate risk analytics is getting an additional boost from artificial intelligence. AI techniques are being used for a variety of situations such as disease tracking, crop optimization, and monitoring everything from our heartbeat to endangered species. Solutions from advances in Deep Learning and Machine Learning could solve global environmental crises while assuaging financial risk with predictive modelling. Yet barriers to effective solutions from AI include cost and regulatory approval. But if these items weren’t an issue? We could determine such vital information as water availability and ecosystem wellbeing. Water and ecosystems aside, AI can help: Track and monitor endangered speciesImprove energy efficienciesOptimize wildlife conservation Fight poaching of endangered speciesTrack mosquito populations to prevent diseaseWarn populations of upcoming storms• Inform agriculture, health, and climate studiesDetermine water, forest, and urbanization changesSome vineyards in California use AI to determine if vines receive enough or too much water. AI’s ability to process large amounts of information quickly are a boon to the ever-changing climate, its risk assessments for businesses, and its benefits to consumers and investors who want to know what businesses are doing to keep everyone safe. In Honor of Earth Day This week we celebrate Earth Day. It’s a day to remember and honor the earth who gives us our air, our food, our animals, plants, fish, and so much more. From Greta Thunberg’s School Strike for Climate to Naomi Klein’s book, The (Burning) Case for a Green New Deal, climate is front and center of our thoughts and our survival. Want to be part of the movement working with Climate Risk Analytics or the effect of Artificial Intelligence in our environment? Harnham may have a role for you. From Big Data & Analytics to the Life Sciences, there’s something for everyone interested in the Data industry. Check out our current vacancies or contact one of our expert consultants to learn more.  For our West Coast Team, contact us at (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to newyorkinfo@harnham.com.  

Bridging the Gap: The Role of a DevOps Engineer

Siloed teams are swiftly becoming a thing of the past as organizations learn collaboration is key. Businesses are embracing transformation. But some may not know where to turn to help them manage such a massive restructuring of operations. Enter the DevOps Engineer. Yes, Virginia. The unicorn employee does exist. What is a DevOps Engineer? For many businesses, it’s a dream to find a technical person who can also communicate across departments. In the DevOps Engineer role is an IT Generalist who not only has a deep understanding of codes, infrastructure management, and agile familiarity but who also possesses interpersonal skills. It’s this combination that makes this role so imperative to businesses. Working across siloes and bringing teams together for collaboration bridges the gap between the technical and non-technical departments. One of their most important roles is as advocate. Moving from siloed teams to the more collaborative environment of a DevOps culture can be difficult for engineering team members. But as advocate for the benefits, the DevOps Engineer can explain it best to those with whom they’ve worked. Their technical expertise puts them on par with their peers and their interpersonal skills offer a way to communicate across the organization.   Want to Restructure Your Skills toward DevOps? If you’re an IT Generalist with great communication skills. DevOps Engineer could be your next role. But what skills do you need and how might you streamline what you already know into this key role for many businesses? Technical skills depend on team structure, technologies in place, and tools already in use. But the key element of a DevOps Engineer is their strong communication and collaborative skills. Can you morph your technical world into layman’s terms for the executives? Can you translate different needs across teams from QA testers to software developers, generalists and specialists alike? It’s this deep understanding which makes you so valuable to employers. For many organizations, this is the best of both worlds.  Knowing the pros and cons of available tools. Understanding the components of a delivery pipeline. And strong communication skills to bridge once siloed teams into a cohesive and collaborative environment. More technical skills include, but aren’t limited to System administration – such as managing servers, database deployment, and system patching just to name a few.Experience with DevOps tools – understand the lifecycle from building and infrastructure to operating and monitoring a product or service.Configuration management – experience with configuration management tools such as Chef, Puppet, or Ansible to automate admin tasks.Continuous Integration (CI) and Continuous Deployment (CD) – this is a core practice of DevOps. It’s this role’s approach to software development with tools to automate the building, testing, and deploying of software processes. System architecture and provisioning – ability to design and manage computer ecosystems whether in-office or in the cloud. Within this skillset is the importance of Infrastructure as Code (IaC). This is an IT management process that applies best practices from software development to cloud infrastructure management.  Collaborative management skills – while the CI/CD skills are core to the technical side, this is one of the key components for the soft skills required for a DevOps structure. In a Nutshell DevOps (Development + Operations) is a practice that involves new management principles and requires a cultural change. And a DevOps Engineer is the heart of the transformation. Yet they can’t do it alone. A good DevOps Team has more than just one engineer. It involves a mix of generalists and specialists to implement and improve these practices within the software development cycle. A few of these roles include:  DevOps evangelist Automation expert Software developer Quality assurance  If you’re interested in Big Data and Analytics, Harnham 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, contact us at (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to newyorkinfo@harnham.com.  

Amped Up Analytics: Google Analytics 4

Google Analytics 4 has amped up data insights into the behaviors and preferences of your customers. Where once each touchpoint only tracked what had been clicked, GA4 is bringing it all together in a more wholistic approach to the customer journey. As the fourth quarter of 2020 dawned, Google upped its game. Crafting a compelling array of features with machine learning at its core, this new platform offers a more customer-centric approach to data-driven insights, rather than split data across platforms and devices.   Though still in its infancy, there are some dramatic new changes afoot. And while it’s not a good idea to get rid of the old Universal Analytics platform before ringing in the new one, it is a good idea to understand what’s available now and what may come to be over time. Four Advantages to Google Analytics 4.0 From our desktop to our laptop to our smartphone, we carry our office in our pocket or on our lap. So, what better way to integrate what was once called “App + Web properties” into a more cohesive trackable measurement of data. Add to this the privacy protocols in place to protect customers, and Google Analytics 4 offers flexibility for future cookieless tracking and permissions, and advantages are revealed. Combined Data and Reporting Rather than focusing on one property (web or app) at a time, this platform allows marketers to track a customer’s journey more holistically.  The platform’s premise is that there is a pattern everyone follows. From the moment a customer visits your website to clicks on a button subscribing to your newsletter or blog – Acquisition and Engagement. To the moment your customer makes a purchase, is happy with the product or sevice, and comes back again – Monetization and Retention.  Designed for marketers who want to track users across multiple formats, Google Analytics 4 hopes to solve with Data Streams. These Data Streams merge to paint a picture of the customer journey from website visit to purchase. A Focus on Anonymized Data This anonymization answers the call to Data Privacy and third-party data collection. Crafting a unified user journey centered around machine learning to fill in any gaps, marketers and businesses have a way to get the information they need without diving into personal data issues. This is a key change in that Google is moving away from client-side focus and using server-side and customer-centric capabilities. With GDPR and privacy laws in full swing, marketers face enhanced privacy regulations as cookies are phased out or blocked. Predictive Metrics and Audiences Using Machine Learning to predict future transactions is a game changer for the platform. These predictive metrics for e-commerce sites on Google properties allow for targeted ads to visitors who seem most likely to make a purchase within one week of visiting the site.  Though focused on e-commerce sites now and based on transactions and revenue, there is an opportunity for marketers to identify and convert based on such leads as video views or form submissions. Machine Learning-Driven Insights The launch announcement for GA4 explains it “has machine learning at its core to automatically surface helpful insights and gives you a complete understanding of your customers across devices and platforms.” Machine Learning-driven insights include details that elude human analysts.  What These Changes Mean on the Digital Frontier We’re all reaching for higher value and Google Analytics 4.0 brings it into one unified platform for the future. As we make the shift from traditional Google Analytics to its 4.0 version, there is opportunity to get more creative.   Wondering if you should upgrade? This article breaks down the pros and cons to help you decide.  If you’re interested in Big Data & Analytics, Harnham 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, contact us at (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to newyorkinfo@harnham.com.  

Making Sense of Unstructured Data with NLP

Natural Language Processing. It seems a simple enough explanation. The idea is to make computers sound like native speaking humans regardless of their language. Except there’s one problem. When we speak, we don’t follow our own rules of grammar. We use idioms, metaphors, abbreviations, and oftentimes use more body language to communicate than we realize.  So, what’s a poor machine to do when confronted with such an unstructured melee of data? Well, since semantics is not what you say it’s how you say it, we must teach computers to read between the lines. Of code. Enter NLP. The semantics of human language written for a machine to help make sense of our human behaviors gets organized. The Perfect Imperfections of Language Computers require structure. Natural language does not. Teaching machines how we communicate is no easy task, and yet we use machines that can do this every day. By combining technology and Machine Learning we begin to teach computers how to understand us. We teach them how to interpret and determine what it was we want done. When you’re asking Siri or Alexa a question, you’re helping them to learn how you ask, so they can better respond, and they make you more efficient. It’s a win-win for everyone. In business, using NLP techniques to drive business decisions is even more important. Now, the computer must decide what information is the most valuable to pull from a pile of Data. Understanding our choices, our tone, even the words we choose to use, helps our machines learn what we want to do or need done. Where is NLP Used? Since we use different rules when we speak than when we write, our computers learn how we talk and how to use language more naturally. Wondering where NLP might be used? In a word or two? Nearly everywhere. You are scheduling a meeting and when it’s time, a calendar reminder pops into your phone which says estimated drive time to the meeting based on traffic conditions in your area. Or you ask Alexa to play your favorite music list from Pandora.  Every touchpoint in this scenario is using NLP. We naturally might get into our car, ask our Virtual Assistant navigation system for directions, or to play our favorite music. Our choices don’t fit in a box and may not be logical, but the more we teach the machines, the closer they may get to understanding the nuances of our language. Here are 5 more ways we use NLP every day: Predictive text on your phone or in your Word document. Chatbots and Virtual Assistants to ensure customers are acknowledged in a timely manner, answer basic questions or redirect to appropriate personnel, and making suggestions to improve the customer experience.Curating social media feeds to determine customer needs and interest.Grammar correction software so our emails and business documents are error-free.Analyzing customer interactions using comments and reviews for customer feedback about a product or service. There’s a ton of information to be filtered, sorted, sifted, and analyzed, and NLP is just one of the tools Data Scientists use. Interested in the subfield of NLP? Check out this article for 6 techniques you need to know to get started. Already well-versed in the industry and looking for a new challenge? If you’re interested in Big Data and Analytics, Advanced Analytics, Life Sciences, Data Science, or any of our Data professional fields, we may have a role for you. Review our current vacancies or contact one of our expert consultants to learn more.   For our West Coast Team, contact us at (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.   For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to newyorkinfo@harnham.com.  

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