Attunity CloudBeam Now Transferring Data to Google Cloud SQL

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Today, Attunity announced a new cloud offering, Attunity CloudBeam for Google Cloud SQL. Attunity CloudBeam now accelerates data transfer between on-premises enterprise data sources and Google Cloud SQL which customers can leverage for cloud-based business intelligence (BI) and Big Data analytics applications.

Spotlight

CrowdFlower Inc.

CrowdFlower is the essential human-in-the-loop platform for data science teams. CrowdFlower helps customers generate high quality customized training data for their machine learning initiatives, or automate a business process with easy-to-deploy models and integrated human-in-the-loop workflows. The CrowdFlower platform supports a wide range of use cases including self-driving cars, intelligent personal assistants, medical image labeling, content categorization, customer support ticket classification, social data insight, CRM data enrichment, product categorization, and search relevance.

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Data-driven Content Marketing for 2022

Article | September 13, 2021

In this data-driven age, marketers have access to all the necessary information about their customers. There are different tools they can use to capture the exact data required for specific campaigns. We have come a long way from mass broadcasting campaigns. Growth in digital marketing has given rise to pinpointed targeting. The marketing industry is still accepting and learning data technology. However, there is more importance given to the content creation side of things when there should be a clear balance between data-driven efforts and content. It can be challenging to re-route pure content creator’s attention to data marketing, but it cannot be ignored for long. It is no longer enough to rely on gut instinct and ‘good content’. The rise in popularity in data-driven marketing has been lead by the revolution in big data. Big data has enabled massive amounts of data to be collected, analyzed, and organized, which helps in creating a personalized customer experience. Since the start of the Covid-19 pandemic, more and more people have started to spend time online. As a result, online user behavior has changed in just a matter of months rather than years. Data-driven marketing efforts can also help marketers to maximize their success as their results will now be data-backed with metrics that will change the way they conduct their business online. We have highlighted the steps you need for successful data-driven marketing. 5 Steps to Take for Successful Data-Driven Content Marketing Layout Objectives For any campaign to succeed, it is imperative to have a list of attainable objectives. You can set these objectives by studying historical data and know-how your marketing campaign will perform. For a successful content marketing strategy, make sure to concentrate on raising brand awareness, retaining current customers, and tracking sales. If you're not putting out relevant content in relevant places, you don't exist. Gary Vaynerchuk, -American entrepreneur, author, speaker, and Internet personality. Customize Campaigns for Target Audience Before you create any data-driven campaigns, know your customers well. Then, with the abundance of data at your fingertips, you can easily create personalized campaigns for them. Figure out and solve any problems they may be facing, if they need any solution, what they’re looking for and where. Creating user profiles will help you avoid targeting generalized strata rather than help you be more precise in your marketing efforts. Regular Content Optimization One of the best ways to ensure successful marketing results is through content optimization. Google algorithms are constantly changing. So what’s ranking on the first page today may not always rank the same next day. Set campaign-specific KPIs and work towards achieving those targets. Use different tools to track whether your campaign is working according to your goals or needs some serious upliftment. Keep running SEO audits on your pages regularly to keep your content in the best shape possible. Content Repurposing Repurposing content is the oldest trick in the book to gain a higher ROI on your existing content. For instance, if you have an article published on your company’s website, adapt that blog into an infographic and publish it on various social platforms. Content repurposing will help you boost your SEO, reach a broader and newer audience, help drive traffic to your website, and raise your brand’s awareness. Track Analytics Every platform has a different reach. Use your platforms according to the KPIs you have set for your business. For example, Twitter can help you raise your brand’s awareness, while LinkedIn will help you generate leads. Different platforms will have different metrics you will need to track. There are online tools available that help marketers track metrics. Each of these metrics will help you to achieve your marketing objectives. Final Thoughts In today’s competitive market, content marketing will have to be data-driven. The data-first approach will help you and your business in reaching the maximum number of people. In addition, a performance-oriented approach will ensure the success of your campaigns. Investing in high-quality marketing technologies will help you get balanced, data-driven, and goal-oriented results preparing you to become a content marketer ready to take on any challenges. Frequently Asked Questions What is the future of content marketing? Data-driven content marketing strategies can help marketers to maximize their success as their results will now be data-backed with metrics that will change the way they conduct their online business. What are the top content marketing trends for 2022? 1. Layout Objectives 2. Customize Campaigns for Target Audience 3. Regular Content Optimization 4. Content Repurposing 5. Track Analytics How is content-based marketing a proven strategy? Content-based marketing is a marketing strategy designed to attract, engage, and retain target audience. This works by creating and sharing relevant content such as articles, podcasts, infographics, videos, and other content marketing materials. This approach lays down expertise, helps brand awareness. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What is the future of content marketing?", "acceptedAnswer": { "@type": "Answer", "text": "Data-driven content marketing strategies can help marketers to maximize their success as their results will now be data-backed with metrics that will change the way they conduct their online business." } },{ "@type": "Question", "name": "What are the top content marketing trends for 2022?", "acceptedAnswer": { "@type": "Answer", "text": " A. 1. Layout Objectives 2. Customize Campaigns for Target Audience 3. Regular Content Optimization 4. Content Repurposing 5. Track Analytics" } },{ "@type": "Question", "name": "How is content-based marketing a proven strategy?", "acceptedAnswer": { "@type": "Answer", "text": "Content-based marketing is a marketing strategy designed to attract, engage, and retain target audience. This works by creating and sharing relevant content such as articles, podcasts, infographics, videos, and other content marketing materials. This approach lays down expertise, helps brand awareness." } }] }

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MODERNIZED REQUIREMENTS OF EFFICIENT DATA SCIENCE SUCCESS ACROSS ORGANIZATIONS

Article | September 13, 2021

Does the success of companies like Google depend on that of the algorithms or that of data? Today’s fascination with artificial intelligence (AI) reflects both our appetite for data and our excitement about the new opportunities in machine learning. Amalio Telenti, Chief Data Scientist and Head of Computational Biology at Vir Biotechnology Inc. argue that newcomers to the field of data science are blinded by the shiny object of magical algorithms and that they forget the critical infrastructures that are needed to create and to manage data in the first place.Data management and infrastructures are the little ugly duckling of data science but they are necessary for a successful program and therefore need to be built with purpose. This requires careful consideration of strategies for data capture, storage of raw and processed data and instruments for retrieval. Beyond the virtues of analysis, there are also the benefits of facilitated retrieval. While there are many solutions for visualization of corporate or industrial data, there is still a need for flexible retrieval tools in the form of search engines that query the diverse sources and forms of data and information that are generated at a given company or institution.

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Is Augmented Analytics the Future of Big Data Analytics?

Article | September 13, 2021

We currently live in the age of data. It’s not just any kind of data, but big data. The current data sets have become huge, complicated, and quick, making it difficult for traditional business intelligence (BI) solutions to handle. These dated BI solutions are either unable to get the data, deal with the data, or understand the data. It is vital to handle the data aptly since data is everywhere and is being produced constantly. Your organization needs to discover any hidden insights in your datasets. Going through all the data will be doable with the right tools like machine learning (ML) and augmented analytics. According to Gartner, augmented analytics is the future of data analytics and defines it as: “Augmented analytics uses machine learning/artificial intelligence (ML/AI) techniques to automate data preparation, insight discovery, and sharing. It also automates data science and ML model development, management, and deployment.” Augmented analytics is different from BI tools because ML technologies work behind the scenes continuously to learn and enhance results. Augmented analytics facilitates this process faster to derive insights from large amounts of structured and unstructured data to gain ML-based recommendations. In addition, it helps to find patterns in the data that usually go unnoticed, removes human bias, and allows predictive capabilities to inform an organization of what to do next. Artificial intelligence has brought about an augmented analytics trend, and there has been a significant increase in the demand for augmented analytics. Benefits of Augmented Analytics Organizations now understand the benefits of augmented analytics which has led them to adopt it to deal with the increasing volume of structured and unstructured data. Oracle identified top four reasons organizations are opting for augmented analytics: Data Democratization Augmented data science availability to everyone has become a possibility thanks to augmented analytics. Augmented analytics solutions come prebuilt with models and algorithms, so data scientists are not needed to do this work. In addition, these augmented analytics models have user-friendly interfaces, making it easier for business users and executives to use them. Quicker Decision-making You will receive suggestions and recommendations through augmented analytics about which datasets to incorporate in analyses, alert users with dataset upgrades, and recommend new datasets when the results are not what the users expect. With just one click, augmented analytics provides precise forecasts and predictions on historical data. Programmed Recommendations Natural language processing (NLP) is featured on the augmented analytics platforms enabling non-technical users to question the source data easily. Interpreting the complex data into text with intelligent recommendations is automated by natural language generation (NLG), thus speeding up the analytic insights. Anyone using the tools can find out hidden patterns and predict trends to optimize the time it takes to go from data to insights to decisions using automated recommendations for data improvement and visualization. Non-expert users can use NLP technology to make sense of large amounts of data. Users can ask doubts about data using typical business terms. The software will find and question the correct data, making the results easy to digest using visualization tools or natural language output. Grow into a Data-driven Company It is more significant to understand data and business while organizations are rapidly adjusting to changes. Analytics has become more critical to doing everything from understanding sales trends, to segment customers, based on their online behaviors, and predicting how much inventory to hold to strategizing marketing campaigns. Analytics is what makes data a valuable asset. Essential Capabilities of Augmented Analytics Augmented analytics reduces the repetitive processes data analysts need to do every time they work with new datasets. It helps to decrease the time it takes to clean data through the ETL process. Augmented analytics allows more time to think about the data implications, discover patterns, auto-generated code, create visualizations, and propose recommendations from the insights it derives. Augmented analytics considers intents and behaviors and turns them into contextual insights. It presents new directions to look at data and identify patterns and insights companies would have otherwise missed out on completely- thus altering the way analytics is used. The ability to highlight the most relevant hidden insights is a powerful capability. Augmented analytics, for example, can help users manage the context at the explanatory process stage. It understands the values of data that are associated with or unrelated to that context, which results in powerful and relevant suggestions that are context-aware. Modern self-service BI tools have a friendly user interface that enables business users with low to no technical skills to derive insights from data in real-time. In addition, these tools can easily handle large datasets from various sources in a quickly and competently. The insights from augmented analytics tools can tell you what, why, and how something happened. In addition, it can reveal important insights, recommendations, and relationships between data points in real-time and present it to the user in the form of reports in conversational language. Users can have data queries to get insights through the augmented analytics tools. For example, business users can ask, “How was the company’s performance last year?” or “What was the most profitable quarter of the year?” The systems provide in-depth explanations and recommendations around data insights, clearly understanding the “what” and the “why” of the data. It enhances efficiency, decision-making, and collaboration between users and encourages data literacy and data democracy throughout an organization. Augmented Analytics: What’s Next? Augmented analytics is going to change the way people understand and examine data. It has become a necessity for businesses to survive. It will simplify and speed up the augmented data preparation, cleansing, and standardization of data, thus assist businesses to focus all their efforts on data analysis. BI and analytics will become an immersive environment with integrations allowing users to interact with their data. New insights and data will be easier to access through various devices and interfaces like mobile phones, virtual assistants, or chatbots. In addition, it will help decision-making by notifying the users of alerts that need immediate attention. This will help businesses to stay updated about any changes happening in real-time. Frequently Asked Questions What are the benefits of augmented analytics? Augmented analytics helps companies become more agile, gain access to analytics, helps users make better, faster, and data-driven decisions, and reduces costs. How important is augmented analytics? Augmented analytics build efficiency into the data analysis process, equips businesses and people with tools that can answer data-based questions within seconds, and assist companies in getting ahead of their competitors. What are the examples of augmented analytics? Augmented analytics can help retain existing customers, capitalize on customer needs, drive revenue through optimized pricing, and optimize operations in the healthcare sector for better patient outcomes. These are some of the examples of the use of augmented analytics. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What are the benefits of augmented analytics?", "acceptedAnswer": { "@type": "Answer", "text": "Augmented analytics helps companies become more agile, gain access to analytics, helps users make better, faster, and data-driven decisions, and reduces costs." } },{ "@type": "Question", "name": "How important is augmented analytics?", "acceptedAnswer": { "@type": "Answer", "text": "Augmented analytics build efficiency into the data analysis process, equips businesses and people with tools that can answer data-based questions within seconds, and assist companies in getting ahead of their competitors." } },{ "@type": "Question", "name": "What are the examples of augmented analytics?", "acceptedAnswer": { "@type": "Answer", "text": "Augmented analytics can help retain existing customers, capitalize on customer needs, drive revenue through optimized pricing, and optimize operations in the healthcare sector for better patient outcomes. These are some of the examples of the use of augmented analytics." } }] }

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Data Analytics vs Data Science Comparison

Article | September 13, 2021

The terms data science and data analytics are not unfamiliar with individuals who function within the technology field. Indeed, these two terms seem the same and most people use them as synonyms for each other. However, a large proportion of individuals are not aware that there is actually a difference between data science and data analytics.It is pertinent that individuals whose work revolves around these terms or the information and technology industries, should know how to use these terms in the appropriate contexts. The reason for this is quite simple: the right usage of these terms has significant impacts on the management and productivity of a business, especially in today’s rapidly data-dependent world.

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Spotlight

CrowdFlower Inc.

CrowdFlower is the essential human-in-the-loop platform for data science teams. CrowdFlower helps customers generate high quality customized training data for their machine learning initiatives, or automate a business process with easy-to-deploy models and integrated human-in-the-loop workflows. The CrowdFlower platform supports a wide range of use cases including self-driving cars, intelligent personal assistants, medical image labeling, content categorization, customer support ticket classification, social data insight, CRM data enrichment, product categorization, and search relevance.

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