Graphing the sensitive boundary between personally identifiable information and publicly inferable insights

| May 1, 2018

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Sleuthing is the art of making intelligent inferences from sparse, disconnected and seemingly random data. Detectives, like any skilled analyst, are adept at using these inferences to solve real-world problems. Analysis is a profession that often demands ingenuity in how you play with the data that comes into your possession. When analysts encounter sensitive data, they must be careful not to play fast and loose with it. As the European Union’s General Data Protection Regulation compliance deadline approaches, many enterprises are revisiting their procedures for analyzing, storing and protecting customers’ personal data. Privacy is serious business. Rather than assume that all customer data is fair game for them to explore, analysts must observe the bounds of what uses the customer has explicitly opted into. GDPR strongly protects personally identifiable information, or PII, that’s maintained in digital data stores of all sorts. To that end, enterprises everywhere, not just in EU member states, are investing in platforms and tools, such as SugarCRM’s latest, to beef up privacy protections as a standard operating procedure that involves: Inventorying all customer PII; Establishing processes for gaining customer consent to acquire, store, profile, process, disclose and manage PII, as well as to erase, correct, withhold and restrict processing of it; Flagging PII records for erasure, correction, nonprocessing and nontransfer; Providing customers with comprehensive access to their stored PII; and Logging all customer requests related to protecting their PII.

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GSTEP

The GSTEP is a Portuguese company specializing in Business Intelligence (BI), Enterprise Performance Management (EPM) and Business Analytics (BA). Thanks to the solutions provided by GSTEP, more than a hundred organizations around the world can analyze large volumes of data in order to make the best decisions and distribute knowledge for all stakeholders. Constituted by an experienced and competent staff in different business areas and different technologies, in Portugal and abroad, the mission of GSTEP involves adding value to its customers' activity through solutions and innovative and pioneering services. For this reason the GSTEP is constantly attentive to the projections of the future. Recognition of various companies related to Technology and Consulting, allows the GSTEP asserts itself increasingly as a provider of certified services and excellence solutions. Always attentive to new trends in the field of Information Technology, the GSTEP worries in anticipating the needs of its

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Saurav Singla, the machine learning guru, empowering society

Article | December 10, 2020

Saurav Singla is a Senior Data Scientist, a Machine Learning Expert, an Author, a Technical Writer, a Data Science Course Creator and Instructor, a Mentor, a Speaker. While Media 7 has followed Saurav Singla’s story closely, this chat with Saurav was about analytics, his journey as a data scientist, and what he brings to the table with his 15 years of extensive statistical modeling, machine learning, natural language processing, deep learning, and data analytics across Consumer Durable, Retail, Finance, Energy, Human Resource and Healthcare sectors. He has grown multiple businesses in the past and is still a researcher at heart. In the past, Analytics and Predictive Modeling is predominant in few industries but in current times becoming an eminent part of emerging fields such as health, human resource management, pharma, IoT, and other smart solutions as well. Saurav had worked in data science since 2003. Over the years, he realized that all the people they had hired — whether they are from business or engineering backgrounds — needed extensive training to be able to perform analytics on real-world business datasets. He got an opportunity to move to Australia in the year 2003. He joined a retail company Harvey Norman in Australia, working out of their Melbourne office for four years. After moving back to India, in 2008, he joined one of the verticals of Siemens — one of the few companies in India then using analytics services in-house for eight years. He is a very passionate believer that the use of data and analytics will dramatically change not only corporations but also our societies. Building and expanding the application of analytics for supply chain, logistics, sales, marketing, finance at Siemens was a very fulfilling and enjoyable experience for him. Siemens was a tremendously rewarding and enjoyable experience for him. He grew the team from zero to fifteen while he was the data scientist leader. He believes those eight years taught him how to think big, scale organizations using data science. He has demonstrated success in developing and seamlessly executing plans in complex organizational structures. He has also been recognized for maximizing performance by implementing appropriate project management tools through analysis of details to ensure quality control and understanding of emerging technology. In the year 2016, he started getting a serious inner push to start thinking about joining a consulting and shifted to a company based out in Delhi NCR. During his ten-month path with them, he improved the way clients and businesses implement and exploit machine learning in their consumer commitments. As part of that vision, he developed class-defining applications that eliminate tension technologies, processes, and humans. Another main aspect of his plan was to ensure that it was affected in very fast agile cycles. Towards that he was actively innovating on operating and engagement models. In the year 2017, he moved to London and joined a digital technology company, and assisted in building artificial intelligence and machine learning products for their clients. He aimed to solve problems and transform the costs using technology and machine learning. He was associated with them for 2 years. At the beginning of the year 2018, he joined Mindrops. He developed advanced machine learning technologies and processes to solve client problems. Mentored the Data Science function and guide them in the development of the solution. He built robust clients Data Science capabilities which can be scalable across multiple business use cases. Outside work, Saurav associated with Mentoring Club and Revive. He volunteers in his spare time for helping, coaching, and mentoring young people in taking up careers in the data science domain, data practitioners to build high-performing teams and grow the industry. He assists data science enthusiasts to stay motivated and guide them along their career path. He helps fill the knowledge gap and help aspirants understand the core of the industry. He helps aspirants analyze their progress and help them upskill accordingly. He also helps them connect with potential job opportunities with their industry-leading network. Additionally, in the year 2018, he joined as a mentor in the Transaction Behavioral Intelligence company that accelerates business growth for banks with the use of Artificial Intelligence and Machine Learning enabled products. He is guiding their machine learning engineers with their projects. He is enhancing the capabilities of their AI-driven recommendation engine product. Saurav is teaching the learners to grasp data science knowledge more engaging way by providing courses on the Udemy marketplace. He has created two courses on Udemy, with over twenty thousand students enrolled in it. He regularly speaks at meetups on data science topics and writes articles on data science topics in major publications such as AI Time Journal, Towards Data Science, Data Science Central, Kdnuggets, Data-Driven Investor, HackerNoon, and Infotech Report. He actively contributes academic research papers in machine learning, deep learning, natural language processing, statistics and artificial intelligence. His book on Machine Learning for Finance was published by BPB Publications which is Asia's largest publisher of Computer and IT Books. This is possibly one of the biggest milestones of his career. Saurav turned his passion to make knowledge available for society. Saurav believes sharing knowledge is cool, and he wishes everyone should have that passion for knowledge sharing. That would be his success.

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Why Data Science Needs DataOps

Article | December 10, 2020

DataOps helps reduce the time data scientists spend preparing data for use in applications. Such tasks consume roughly 80% of their time now.We’re still hopeful that the digital transformation will provide the insights businesses need from big data. As a data scientist, you’re probably aware of the growing pressure from companies to extract meaningful insights from data and find the stories needed for impact.No matter how in-demand data science is in the employment numbers, equal pressure is rising for data scientists to deliver business value and no wonder. We’re approaching the age where data science and AI draw a line in the sand for which companies remain competitive and which ones collapse.One answer to this pressure is the rise of DataOps. Let’s take a look at what it is and how it could provide a path for data scientists to give businesses what they’ve been after.

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CYBERSECURITY STRATEGIES TO MAKE IT NETWORKS MORE RESILIENT TO CYBERATTACKS

Article | December 10, 2020

The increasing use of advanced technologies and the internet have created an attack surface for malicious attackers. With these progressions, businesses’ IT systems are now more vulnerable which has led them to leverage innovative cybersecurity strategies that can thwart and make their networks more resilient to cyberattacks. Cybercriminals can use a variety of attacks against individuals or businesses like accessing, changing or deleting sensitive data; extracting payment; interfering with business processes and more.These kinds of attacks present an evolving danger to organizations, employees and consumers, and can cost them reputation, finances and personal lives to some extent. So, in order to protect IT networks from cyberattacks, it is significant to be aware of the various aspects of cybersecurity.

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Choosing External Data Sources: 4 Characteristics to Look For

Article | December 10, 2020

Decision-makers at consumer brands are finally realizing the full transformative potential of external data - but they’re also realizing how difficult it is to source. Forrester reports that 87% of decision-makers in data and analytics have implemented or are planning initiatives to source more external data. And those initiatives are growing outside of the IT team; 29% of those surveyed say that IT has primary ownership of data sourcing, down from 37% in 2016. To support these projects, organizations are increasingly turning to a new specialist: the data hunter, who identifies and vets external data sources. It’s a lot of work to build external data-focused teams, and many leaders are realizing that external data is difficult to scale as the source list grows. Perhaps that’s why 66% of those decision-makers surveyed by Forrester report that they’re using or planning to use external service providers for data, analytics, and insights.

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Spotlight

GSTEP

The GSTEP is a Portuguese company specializing in Business Intelligence (BI), Enterprise Performance Management (EPM) and Business Analytics (BA). Thanks to the solutions provided by GSTEP, more than a hundred organizations around the world can analyze large volumes of data in order to make the best decisions and distribute knowledge for all stakeholders. Constituted by an experienced and competent staff in different business areas and different technologies, in Portugal and abroad, the mission of GSTEP involves adding value to its customers' activity through solutions and innovative and pioneering services. For this reason the GSTEP is constantly attentive to the projections of the future. Recognition of various companies related to Technology and Consulting, allows the GSTEP asserts itself increasingly as a provider of certified services and excellence solutions. Always attentive to new trends in the field of Information Technology, the GSTEP worries in anticipating the needs of its

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