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A data democracy built to last needs tools that empower everyone to work with data rather than relying on apps and data scientists. Tableau helped ignite the data revolution, and its IPO could help it keep going.
The democratization of data is a real phenomenon, but building a sustainable data democracy means truly giving power to the people. The alternative is just a shift of power from traditional data analysts within IT departments to a new generation of data scientists and app developers. And this seems a lot more like a dictatorship than a democracy — a benevolent dictatorship, but a dictatorship nonetheless.
These individuals and companies aren’t entirely bad, of course, and they’re actually necessary. Apps that help predict what we want to read, where we’ll want to go next or what songs we’ll like are certainly cool and even beneficial in their ability to automate and optimize certain aspects of our lives and jobs. In the corporate world, there will always be data experts who are smarter and trained in advanced techniques and who should be called upon to answer the toughest questions or tackle the thorniest problems.
Last week, for example, Salesforce.com introduced a new feature of its Chatter intra-company social network that categorizes a variety of data sources so employees can easily find the people, documents and other information relevant to topics they’re interested in. As with similarly devised services — LinkedIn’s People You May Know, the gravitational search movement, or any type of service using an interest graph — the new feature’s beauty and utility lie in its abstraction of the underlying semantic algorithms and data processing.
The problem, however, comes when we’re forced to rely on these people, features and applications to decide how data can affect our lives or jobs, or what questions we can answer using the troves of data now available to us. In a true data democracy, citizens must be empowered to make use of their own data as they see fit and they must only have to rely apps and experts by choice or when the task really requires an expert hand. At any rate, citizens must be informed enough to have a meaningful voice in bigger decisions about data.
The democratic revolution is underway
The good news is that there’s a whole new breed of startups trying to empower the data citizenry, whatever their role. Companies such as 0xdata, Precog and BigML are trying to make data science more accessible to everyday business users. There are next-generation business intelligence startups such as SiSense, Platfora and ClearStory rethinking how business analytics are done in an area of HTML5 and big data. And then there are companies such as Statwing, Infogram and Datahero (which will be in beta mode soon, by the way) trying to bring data analysis to the unwashed non-data-savvy masses.
Combined with a growing number of publicly available data sets and data marketplaces, and more ways of collecting every possible kind of data — personal fitness, web analytics, energy consumption, you name it — these self-service tools can provide an invaluable service. In January, I highlighted how a number of them can work by using my own dietary and activity data, as well as publicly available gun-ownership data and even web-page text. But as I explained then, they’re still not always easy for laypeople to use, much less perfect.
Can Tableau be data’s George Washington?
This is why I’m so excited about Tableau’s forthcoming IPO. There are few companies that helped spur the democratization of data over the past few years more than Tableau. It has become the face of the next-generation business intelligence software thanks to its ease of use and focus on appealing visualization, and its free public software has found avid users even among relative data novices like myself. Tableau’s success and vision no doubt inspired a number of the companies I’ve already referenced.
Assuming it begins its publicly traded life flush with capital, Tableau will not just be financially sound — it will also be in a position to help the burgeoning data democracy evolve into something that can last. More money means being able to develop more features that Tableau can use to bolster sales (and further empower business users with data analysis), which should mean the company can afford to also continually improve its free service and perhaps put premium versions in the hands of more types of more non-corporate professionals for free.
The bottom-up approach has already proven very effective in the worlds of cloud computing, software as a service and open-source software, and I have to assume it’s a win-win situation in analytics, too. Today’s free users will be tomorrow’s paying users once they get skilled enough to want to move onto bigger data sets and better features. But the base products have to be easy enough and useful enough to get started with, or companies will only have a lot of registrations and downloads but very few avid users.
And if Tableau steps ups its game around data democratization, I have to assume it will up the ante for the company’s fellow large analytics vendors and even startups. A race to empower the lower classes on the data ladder would certainly be in stark contrast to the historical strategy of building ever-bigger, ever-more-advanced products targeting only the already-powerful data elite. That’s the kind of revolution I think we all can get behind.
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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 firstname.lastname@example.org. For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to email@example.com.
15. April 2021
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 firstname.lastname@example.org. For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to email@example.com.
08. April 2021
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 firstname.lastname@example.org. For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to email@example.com.
25. March 2021
Ever wondered what’s new at the dentist’s office? If you’re in the hot seat for dentures, crowns, or braces, you may be surprised at the speed you find yourself with a new smile. Imagine a new set of teeth printed layer by layer before your eyes. Ok, before your dentist’s eyes. 3D printing has been used to print prosthetic limbs, orthopedic and cranial implants, surgical instruments, crowns, and dental restorations. Electronic Health Records. AI-assisted surgeries. Machine Learning algorithms for more efficient workflows in hospitals and doctors’ offices. Medical technology isn’t new. But what about dental technology? In the Life Sciences field, technology is helping to shape the future of how we heal. What is 3D Printing? According to the FDA, “3D printing is a process that creates a three-dimensional object by building successive layers of raw material. Each new layer is attached to the previous one until the object is complete. Objects are produced from a 3D file, such as computer-aided design (CAD) drawing or a Magnetic Resonance Image (MRI). The flexibility of this technology allows creation of individualized products such as prosthetics, dentures, or crowns specific to the individual requiring the device. “It’s Not the Drill, It’s the Bill” Borrowed from an old commercial, the tagline originally implied patients weren’t afraid of the dentist, but of the bill at the end of the appointment. But with today’s technologies, particularly through the benefits of 3D printing, this tagline isn’t quite so dramatic. Here are a few ways, 3D printing in dentistry is benefitting both doctor and patient. 1. The Lab is Onsite Cost savings begin here. When the dentist can do his or her own lab work onsite, it’s less cost to consumers and to the dentist office’s bottom line. Add in the user-friendliness of the available 3D machines which allows dentists to produce molds, models, crowns, bridges, there’s plenty of opportunity to be more efficient and have more control over time and quality of the product. 3D Printers range in price from $20,000-$100,000+ for industrial printers. If you have a dental practice, you could most likely snag a desktop model for around $6,000 or less. Compare that to over $100,000 for outsourcing lab work, labor, and shipping costs included. 2. Getting it Right – More Accurate and Faster Services Reduce errors and increase accuracy when using 3D printing to convert digital images into physical objects within minutes. Watch as your patient’s dentures, for example, are printed layer-by-layer and usable with minutes, not hours or days. Your technician can get to work as soon as the scan is ready and won’t be inhaling plaster or grinding dust while they work. A clean work space is a safe work space, no matter the industry. 3. Better Quality Products Skilled dental technicians are still in high demand. But with the advent of 3D printing, their jobs are made a bit easier, and they’re able to design and create better quality products. Milled models could wear down over time. But a 3D model offers more stability and durability than its predecessor. Additionally, this digital model creates a more complex structure and offers a higher level of detail that may not be available in more traditional modeling techniques. 4. Enhanced Patient Experience 3D printing technologies have enhanced patient experience by reducing anxiety and increasing patient acceptance. How? Well, when you can print a model to help explain what’s going to be happening to identify and solve a patient’s problems, it can help alleviate their stresses of the unknown. Add to this a more efficient workflow, more aesthetically pleasing products, and less invasive treatments which make the patient’s visit go more smoothly, and you have a satisfied customer. 5. Save Money Last, but not least, is probably the biggest benefit to both patient and provider. Saving money. Though the upfront investment in a 3D can run into around $20,000 for a top model, it includes all the necessary components printer, reduces the need for skilled staff to produce dentures, implants, and other dental restorative models. These savings are then passed on to the patient not only monetary value, but in time. The more accurate, efficiency, and speed of 3D printers means less time at the dentist’s office. Less return visits. Less error. With an estimated savings up to 80 percent depending on patient’s needs Smile. Tech is transforming the dental industry. Want to see where it can take you? If you’re interested in Big Data & Analytics, Advanced Analytics, Life Sciences, Data Science, or any of our Data professional fields, 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, contact us at (415) 614 - 4999 or send an email to firstname.lastname@example.org. For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to email@example.com.
18. March 2021