Connecting Jethro to Hadoop

| March 10, 2017

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

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DataCamp

DataCamp offers entertaining online R tutorials & Data Analysis courses through fun videos and interactive coding challenges. DataCamp is an online data science school that teaches core R programming and data science skills through entertaining video lessons and interactive coding challenges. With over 100 hours of hands-on data analysis courses and R tutorials on Data Handling, Data Visualization, Reporting, Machine Learning, and Statistical Modelling, our members get the hands-on-experience they need need to begin a real-life project. Solve real data problems while receiving instant and personalized feedback that guides you to the solution. All our courses and tutorials are taught by instructors from leading companies such as RStudio and Revolution Analytics, and best-in-class academic institutions like Princeton and Duke University.

OTHER ARTICLES

A learning guide to accelerate data analysis with SPSS Statistics

Article | May 20, 2021

IBM SPSS Statistics provides a powerful suite of data analytics tools which allows you to quickly analyze your data with a simple point-and-click interface and enables you to extract critical insights with ease. During these times of rapid change that demand agility, it is imperative to embrace data driven decision-making to improve business outcomes. Organizations of all kinds have relied on IBM SPSS Statistics for decades to help solve a wide range of business and research problems.

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Machine Learning vs. Deep Learning. Which Does Your Business Need?

Article | February 17, 2020

In recent years, artificial intelligence research and applications have accelerated at a rapid speed. Simply saying your organization will incorporate AI isn’t as specific as it once was. There are diverse implementation options for AI, Machine Learning, and Deep Learning, and within each of them, a series of different algorithms you can leverage to improve operations and establish a competitive edge. Algorithms are utilized across almost every industry. For example, to power the recommendation engines in all media platforms, the chatbots that support customer service efforts at scale, and the self-driving vehicles being tested by the world’s largest automotive and technology companies. Because of how diverse AI has become and the many ways in which it works with data, companies must carefully evaluate what will work best for them.

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Taking a qualitative approach to a data-driven market

Article | February 18, 2021

While digital transformation is proving to have many benefits for businesses, what is perhaps the most significant, is the vast amount of data there is available. And now, with an increasing number of businesses turning their focus to online, there is even more to be collected on competitors and markets than ever before. Having all this information to hand may seem like any business owner’s dream, as they can now make insightful and informed commercial decisions based on what others are doing, what customers want and where markets are heading. But according to Nate Burke, CEO of Diginius, a propriety software and solutions provider for ecommerce businesses, data should not be all a company relies upon when making important decisions. Instead, there is a line to be drawn on where data is required and where human expertise and judgement can provide greater value. Undeniably, the power of data is unmatched. With an abundance of data collection opportunities available online, and with an increasing number of businesses taking them, the potential and value of such information is richer than ever before. And businesses are benefiting. Particularly where data concerns customer behaviour and market patterns. For instance, over the recent Christmas period, data was clearly suggesting a preference for ecommerce, with marketplaces such as Amazon leading the way due to greater convenience and price advantages. Businesses that recognised and understood the trend could better prepare for the digital shopping season, placing greater emphasis on their online marketing tactics to encourage purchases and allocating resources to ensure product availability and on-time delivery. While on the other hand, businesses who ignored, or simply did not utilise the information available to them, would have been left with overstocked shops and now, out of season items that would have to be heavily discounted or worse, disposed of. Similarly, search and sales data can be used to understand changing consumer needs, and consequently, what items businesses should be ordering, manufacturing, marketing and selling for the best returns. For instance, understandably, in 2020, DIY was at its peak, with increases in searches for “DIY facemasks”, “DIY decking” and “DIY garden ideas”. For those who had recognised the trend early on, they had the chance to shift their offerings and marketing in accordance, in turn really reaping the rewards. So, paying attention to data certainly does pay off. And thanks to smarter and more sophisticated ways of collecting data online, such as cookies, and through AI and machine learning technologies, the value and use of such information is only likely to increase. The future, therefore, looks bright. But even with all this potential at our fingertips, there are a number of issues businesses may face if their approach relies entirely on a data and insight-driven approach. Just like disregarding its power and potential can be damaging, so can using it as the sole basis upon which important decisions are based. Human error While the value of data for understanding the market and consumer patterns is undeniable, its value is only as rich as the quality of data being inputted. So, if businesses are collecting and analysing their data on their own activity, and then using this to draw meaningful insight, there should be strong focus on the data gathering phase, with attention given to what needs to be collected, why it should be collected, how it will be collected, and whether in fact this is an accurate representation of what it is you are trying to monitor or measure. Human error can become an issue when this is done by individuals or teams who do not completely understand the numbers and patterns they are seeing. There is also an obstacle presented when there are various channels and platforms which are generating leads or sales for the business. In this case, any omission can skew results and provide an inaccurate picture. So, when used in decision making, there is the possibility of ineffective and unsuccessful changes. But while data gathering becomes more and more autonomous, the possibility of human error is lessened. Although, this may add fuel to the next issue. Drawing a line The benefits of data and insights are clear, particularly as the tasks of collection and analysis become less of a burden for businesses and their people thanks to automation and AI advancements. But due to how effortless data collection and analysis is becoming, we can only expect more businesses to be doing it, meaning its ability to offer each individual company something unique is also being lessened. So, businesses need to look elsewhere for their edge. And interestingly, this is where a line should be drawn and human judgement should be used in order to set them apart from the competition and differentiate from what everyone else is doing. It makes perfect sense when you think about it. Your business is unique for a number of reasons, but mainly because of the brand, its values, reputation and perceptions of the services you are upheld by. And it’s usually these aspects that encourage consumers to choose your business rather than a competitor. But often, these intangible aspects are much more difficult to measure and monitor through data collection and analysis, especially in the autonomous, number-driven format that many platforms utilise. Here then, there is a great case for businesses to use their own judgements, expertise and experiences to determine what works well and what does not. For instance, you can begin to determine consumer perceptions towards a change in your product or services, which quantitative data may not be able to pick up until much later when sales figures begin to rise or fall. And while the data will eventually pick it up, it might not necessarily be able to help you decide on what an appropriate alternative solution may be, should the latter occur. Human judgement, however, can listen to and understand qualitative feedback and consumer sentiments which can often provide much more meaningful insights for businesses to base their decisions on. So, when it comes to competitor analysis, using insights generated from figure-based data sets and performance metrics is key to ensuring you are doing the same as the competition. But if you are looking to get ahead, you may want to consider taking a human approach too.

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Advanced Data and Analytics Can Add Value in Private Equity Industry!

Article | January 6, 2021

As the organizations go digital the amount of data generated whether in-house or from outside is humongous. In fact, this data keeps increasing with every tick of the clock. There is no doubt about the fact that most of this data can be junk, however, at the same time this is also the data set from where an organization can get a whole lot of insight about itself. It is a given that organizations that don’t use this generated data to build value to their organization are prone to speed up their obsolescence or might be at the edge of losing the competitive edge in the market. Interestingly it is not just the larger firms that can harness this data and analytics to improve their overall performance while achieving operational excellence. Even the small size private equity firms can also leverage this data to create value and develop competitive edge. Thus private equity firms can achieve a high return on an initial investment that is low. Private Equity industry is skeptical about using data and analytics citing the reason that it is meant for larger firms or the firms that have deep pockets, which can afford the revamping cost or can replace their technology infrastructure. While there are few private equity investment professionals who may want to use this advanced data and analytics but are not able to do so for the lack of required knowledge. US Private Equity Firms are trying to understand the importance of advanced data and analytics and are thus seeking professionals with the expertise in dealing with data and advanced analytics. For private equity firms it is imperative to comprehend that data and analytics’ ability is to select the various use cases, which will offer the huge promise for creating value. Top Private Equity firms all over the world can utilize those use cases and create quick wins, which will in turn build momentum for wider transformation of businesses. Pinpointing the right use cases needs strategic thinking by private equity investment professionals, as they work on filling the relevant gaps or even address vulnerabilities. Private Equity professionals most of the time are also found thinking operationally to recognize where can they find the available data. Top private equity firms in the US have to realize that the insights which Big data and advanced analytics offer can result in an incredible opportunity for the growth of private equity industry. As Private Equity firms realize the potential and the power of big data and analytics they will understand the invaluableness of the insights offered by big data and analytics. Private Equity firms can use the analytics insights to study any target organization including its competitive position in the market and plan their next move that may include aggressive bidding for organizations that have shown promise for growth or leaving the organization that is stuffed with loads of underlying issues. But for all these and also to build careers in private equity it is important to have reputed qualification as well. A qualified private equity investment professional will be able to devise information-backed strategies in no time at all. In addition, with Big Data and analytics in place, private equity firms can let go of numerous tasks that are done manually and let the technology do the dirty work. There have been various studies that show how big data and analytics can help a private Equity firm.

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Spotlight

DataCamp

DataCamp offers entertaining online R tutorials & Data Analysis courses through fun videos and interactive coding challenges. DataCamp is an online data science school that teaches core R programming and data science skills through entertaining video lessons and interactive coding challenges. With over 100 hours of hands-on data analysis courses and R tutorials on Data Handling, Data Visualization, Reporting, Machine Learning, and Statistical Modelling, our members get the hands-on-experience they need need to begin a real-life project. Solve real data problems while receiving instant and personalized feedback that guides you to the solution. All our courses and tutorials are taught by instructors from leading companies such as RStudio and Revolution Analytics, and best-in-class academic institutions like Princeton and Duke University.

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