Connecting Jethro to Hadoop

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Learn how to connect Jethro to Hadoop.Learn how to connect Jethro to Hadoop.

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Lohika

Lohika provides premium software development and consulting to top technology companies as well others seeking breakthrough innovations. Headquartered in San Mateo, California, in the heart of Silicon Valley, founded in 2001 by venture investors including Altos Ventures and Draper Richards. Today Lohika is part of the Altran Group.

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Put Publicly Available COVID-19 Data to Work for Your Business—Fast

Article | April 13, 2020

There’s a lot of information out there related to COVID-19. But right now—when it’s more important than ever to quickly access and analyze data —figuring out how to effectively use COVID-19 data to better manage your business can still be a challenge. We can help. Several customers have leveraged their Incorta platforms to instantaneously integrate COVID-19 data into their enterprise data and analytics dashboards.

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How to Overcome Challenges in Adopting Data Analytics

Article | April 13, 2020

Achieving organizational success and making data-driven decisions in 2020 requires embracing tech tools like Data Analytics and collecting, storing and analysing data isn’t.The real data-driven, measurable growth, and development come with the establishment of data-driven company culture.In this type of culture company actively uses data resources as a primary asset to make smart decisions and ensure future growth. Despite the rapid growth of analytic solutions, a recent Gartner survey revealed that almost 75% of organizations thought their analytics maturity had not reached a level that optimized business outcomes. Just like with any endeavor, your organization must have a planned strategy to achieve its analytical goals. Let’s explore ways for overcoming common blockers, and elements used in successful analytics adoption strategies. Table of Contents: - AMM: Analytic Maturity Model - What are the blockers to achieving a strategy-driven analytics? - What are the adoption strategies to achieve an analytics success? - Conclusion AMM: Analytic Maturity Model The Analytic Maturity Model (AMM) evaluates the analytic maturity of an organization.The model identifies the five stages an organization travels through to reach optimization. Organizations must implement the right tools, engage their team in proper training, and provide the management support necessary to generate predictable outcomes with their analytics. Based on the maturity of these processes, the AMM divides organizations into five maturity levels: - Organizations that can build reports. - Organizations that can build and deploy models. - Organizations that have repeatable processes for building and deploying analytics. - Organizations that have consistent enterprise-wide processes for analytics. - Enterprises whose analytics is strategy driven. READ MORE:EFFECTIVE STRATEGIES TO DEMOCRATIZE DATA SCIENCE IN YOUR ORGANIZATION What are the blockers to achieving a strategy-driven analytics? - Missing an Analytics Strategy - Analytics is not for everyone - Data quality presents unique challenges - Siloed Data - Changing the culture What are the adoption strategies to achieve analytic success? • Have you got a plan to achieve analytic success? The strategy begins with business intelligence and moves toward advanced analytics. The approach differs based on the AMM level. The plan may address the strategy for a single year, or it may span 3 or more years. It ideally has milestones for what the team will do. When forming an analytics strategy, it can be expensive and time consuming at the outset. While organizations are encouraged to seek projects that can generate quick wins, the truth is that it may be months before any actionable results are available. During this period, the management team is frantically diverting resources from other high-profile projects. If funds are tight, this situation alone may cause friction. It may not be apparent to everyone how the changes are expected to help. Here are the elements of a successful analytics strategy: • Keep the focus tied to tangible business outcomes The strategy must support business goals first. With as few words as possible, your plan should outline what you intend to achieve, how to complete it, and a target date for completion of the plan. Companies may fail at this step because they mistake implementing a tool for having a strategy. To keep it relevant, tie it to customer-focused goals. The strategy must dig below the surface with the questions that it asks. Instead of asking surface questions such as “How can we save money?”, instead ask, “How can we improve the quality of the outcomes for our customers?” or “What would improve the productivity of each worker?” These questions are more specific and will get the results the business wants. You may need to use actual business cases from your organization to think through the questions. • Select modern, multi-purpose tools The organization should be looking for an enterprise tool that supports integrating data from various databases, spreadsheets, or even external web based sources. Typically, organizations may have their data stored across multiple databases such as Salesforce, Oracle, and even Microsoft Access. The organization can move ahead quicker when access to the relevant data is in a single repository. With the data combined, the analysts have a specific location to find reports and dashboards. The interface needs to be robust enough to show the data from multiple points of view. It should also allow future enhancements, such as when the organization makes the jump into data science. Incorta’s Data Analytics platform simplifies and processes data to provide meaningful information at speed that helps make informed decisions. Incorta is special in that it allows business users to ask the same complex and meaningful questions of their data that typically require many IT people and data scientist to get the answers they need to improve their line of business. At the digital pace of business today, that can mean millions of dollars for business leaders in finance, supply chain or even marketing. Speed is a key differentiator for Incorta in that rarely has anyone been able to query billions of rows of data in seconds for a line of business owner. - Tara Ryan, CMO, Incorta Technology implementations take time. That should not stop you from starting in small areas of the company to look for quick wins. Typically, the customer-facing processes have areas where it is easier to collect data and show opportunities for improvement. • Ensure staff readiness If your current organization is not data literate, then you will need resources who understand how to analyze and use data for process improvement. It is possible that you can make data available and the workers still not realize what they can do with it. The senior leadership may also need training about how to use data and what data analytics makes possible. • Start Small to Control Costs and Show Potential If the leadership team questions the expense, consider doing a proof of concept that focuses on the tools and data being integrated quickly and efficiently to show measurable success. The business may favor specific projects or initiatives to move the company forward over long-term enterprise transformations (Bean & Davenport, 2019). Keeping the project goals precise and directed helps control costs and improve the business. As said earlier, the strategy needs to answer deeper business questions. Consider other ways to introduce analytics into the business. Use initiatives that target smaller areas of the company to build competencies. Provide an analytics sandbox with access to tools and training to encourage other non-analytics workers (or citizen data scientists) to play with the data. One company formed a SWAT team, including individuals from across the organization. The smaller team with various domain experience was better able to drive results. There are also other approaches to use – the key is to show immediate and desirable results that align with organizational goals. • Treating the poor data quality What can you do about poor data quality at your company? Several solutions that can help to improve productivity and reduce the financial impact of poor data quality in your organization include: • Create a team to set the proper objectives Create a team who owns the data quality process. This is important to prove to yourself and to anyone with whom you are conversing about data that you are serious about data quality. The size of the team is not as important as the membership from the parts of the organization that have the right impact and knowledge in the process. When the team is set, make sure that they create a set of goals and objectives for data quality. To gauge performance, you need a set of metrics to measure the performance. After you create the proper team to govern your data quality, ensure that the team focuses on the data you need first. Everyone knows the rules of "good data in, good data out" and "bad data in, bad data out." To put this to work, make sure that your team knows the relevant business questions that are in progress across various data projects to make sure that they focus on the data that supports those business questions. • Focus on the data you need now as the highest priority Once you do that, you can look at the potential data quality issues associated with each of the relevant downstream business questions and put the proper processes and data quality routines in place to ensure that poor data quality has a low probability of Successful Analytics Adoption Strategies, continuing to affect that data. As you decide which data to focus on, remember that the key for innovators across industries is that the size of the data isn’t the most critical factor — having the right data is (Wessel, 2016). • Automate the process of data quality when data volumes grow too large When data volumes become unwieldy and difficult to manage the quality, automate the process. Many data quality tools in the market do a good job of removing the manual effort from the process. Open source options include Talend and DataCleaner. Commercial products include offerings from DataFlux, Informatica, Alteryx and Software AG. As you search for the right tool for you and your team, beware that although the tools help with the organization and automation, the right processes and knowledge of your company's data are paramount to success. • Make the process of data quality repeatable It needs regular care and feeding. Remember that the process is not a one-time activity. It needs regular care and feeding. While good data quality can save you a lot of time, energy, and money downstream, it does take time, investment, and practice to do well. As you improve the quality of your data and the processes around that quality, you will want to look for other opportunities to avoid data quality mishaps. • Beware of data that lives in separate databases When data is stored in different databases, there can be issues with different terms being used for the same subject. The good news is that if you have followed the former solutions, you should have more time to invest in looking for the best cases. As always, look for the opportunities with the biggest bang for the buck first. You don't want to be answering questions from the steering committee about why you are looking for differences between "HR" and "Hr" if you haven't solved bigger issues like knowing the difference between "Human Resources" and "Resources," for example. • De-Siloing Data The solution to removing data silos typically isn’t some neatly packaged, off-the-shelf product. Attempts to quickly create a data lake by simply pouring all the siloed data together can result in an unusable mess, turning more into a data swamp. This is a process that must be done carefully to avoid confusion, liability, and error. Try to identify high-value opportunities and find the various data stores required to execute those projects. Working with various business groups to find business problems that are well-suited to data science solutions and then gathering the necessary data from the various data stores can lead to high-visibility successes. As value is proved from joining disparate data sources together to create new insights, it will be easier to get buy-in from upper levels to invest time and money into consolidating key data stores. In the first efforts, getting data from different areas may be akin to pulling teeth, but as with most things in life, the more you do it, the easier it gets. Once the wheels get moving on a few of these integration projects, make wide-scale integration the new focus. Many organizations at this stage appoint a Chief Analytics Officer (CAO) who helps increase collaboration between the IT and business units ensuring their priorities are aligned. As you work to integrate the data, make sure that you don’t inadvertently create a new “analytics silo.” The final aim here is an integrated platform for your enterprise data. • Education is essential When nearly 45% of workers generally prefer status quo over innovation, how do you encourage an organization to move forward? If the workers are not engaged or see the program as merely just the latest management trend, it may be tricky to convince them. Larger organizations may have a culture that is slow to change due to their size or outside forces. There’s also a culture shift required - moving from experience and knee-jerk reactions to immersion and exploration of rich insights and situational awareness. - Walter Storm, the Chief Data Scientist, Lockheed Martin Companies spend a year talking about an approved analytics tool before moving forward. The employees had time to consider the change and to understand the new skill sets needed. Once the entire team embraced the change, the organization moved forward swiftly to convert existing data and reports into the new tool. In the end, the corporation is more successful, and the employees are still in alignment with the corporate strategy. If using data to support decisions is a foreign concept to the organization, it’s a smart idea to ensure the managers and workers have similar training. This training may involve everything from basic data literacy to selecting the right data for management presentations. However, it cannot stop at the training; the leaders must then ask for the data to move forward with requests that will support conclusions that will be used to make critical decisions across the business. These methods make it easier to sell the idea and keep the organization’s analytic strategy moving forward. Once senior leadership uses data to make decisions, everyone else will follow their lead. It is that simple. Conclusion The analytics maturity model serves as a useful framework for understanding where your organization currently stands regarding strategy, progress, and skill sets. Advancing along the various levels of the model will become increasingly imperative as early adopters of advanced analytics gain a competitive edge in their respective industries. Delay or failure to design and incorporate a clearly defined analytics strategy into an organization’s existing plan will likely result in a significant missed opportunity. READ MORE:BIG DATA ANALYTICS STRATEGIES ARE MATURING QUICKLY IN HEALTHCARE

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Artificial Intelligence and Machine Learning on Agricultural Drones Market investigated in the latest research

Article | April 13, 2020

The agricultural drones market is projected to grow from $1.5 billion in 2018 to $6.2 billion in 2024, experiencing a 25.0% CAGR during 2019–2024 (forecast period). Crop spraying was the largest category in 2018, based on application, owing to the rising prevalence of fungal plant diseases caused by the Verticillium and Rhizoctonia fungi, which are spread by bollworm and flat armyworm.As these diseases destroy the yield, the agrarian community is deploying drones, also called unmanned aerial vehicles (UAV), to kill the pathogen.The rising adoption of such platforms for crop spraying is one of the key agricultural drones market trends. With UAVs, farmers can track their crops in distant locations in real time.Further, such vehicles ensure efficiency, by spraying only the required amount of liquid, which also checks wastage. For the purpose, multi-rotor UAVs are the most preferred choice, as they can hover over the spray zone.Currently, North America witnesses the heaviest utilization of drones for spraying insecticides and pesticides.The major driver for the agricultural drones market is the focus of farmers on enhancing the yield. Images to asses soil and field quality, crop growth and health, and hydric-stress areas are provided on a real-time basis by UAVs.

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Saurav Singla, the machine learning guru, empowering society

Article | April 13, 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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Spotlight

Lohika

Lohika provides premium software development and consulting to top technology companies as well others seeking breakthrough innovations. Headquartered in San Mateo, California, in the heart of Silicon Valley, founded in 2001 by venture investors including Altos Ventures and Draper Richards. Today Lohika is part of the Altran Group.

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