Word of the Week: Hadoop cluster

| November 7, 2016

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A Hadoop cluster is a special type of computational cluster designed specifically for storing and analyzing huge amounts of unstructured data in a distributed computing environment.Such clusters run Hadoop’s open source distributed processing software on low-cost commodity computers. Typically one machine in the cluster is designated as the NameNode and another machine the as JobTracker; these are the masters. The rest of the machines in the cluster act as both DataNode and TaskTracker; these are the slaves. Hadoop clusters are often referred to as “shared nothing” systems because the only thing that is shared between nodes is the network that connects 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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Choosing External Data Sources: 4 Characteristics to Look For

Article | May 12, 2021

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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CISA Keep Customer Focus in AI Adoption

Article | February 27, 2020

When it comes to adopting artificial intelligence (AI) and machine learning (ML) capabilities, it’s important to look at its range of effects from many different viewpoints.According to Senior Advisor for AI at the Cybersecurity and Infrastructure Security Agency (CISA) Martin Stanley, his agency wanted to look at adoption through three different perspectives: how CISA was going to use AI, how stakeholders will use AI, and how U.S. adversaries are going to use AI.You have to understand the needs of your stakeholders, but you also have to do it fast,” Stanley said at a Feb. 26 ServiceNow Federal Forum, adding that it’s a challenge to take in all the necessary information and deliver an outcome. AI and ML can help streamline this process. Stanley spoke about how a big percentage of the AI implementation is being purposeful in how the government’s data is managed and taking care of the data and technology is a key part to the adoption process. He also added that helping people by making work more efficient is key to why AI adoption is important saying: At the end of the day, this is all about helping people.

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5 Predictive Data Analytics Applications

Article | May 31, 2021

According to Google trends, predictive data analytics has gained a significant amount of popularity over the last few years. Many businesses have implemented predictive analytics applications to increase their business reach, gain new customers, forecast sales, and more. Predictive Analytics is a type of data analytics technology that makes predictions with the help of data sets, statistical modeling, and machine learning. Predictive analytics uses historical data. This historical data is fed into a mathematical model that recognizes patterns and trends that are then applied to current data to forecast trends, practices, and behaviors from milliseconds to days and even years. Based on the parameters supplied to them, organizations find patterns within that data to detect risks, opportunities, forecast conditions, and events that would occur at a particular time. At its heart, the use of predictive analytics answers a simple question, “What would happen based on my current data and what can be done to change the outcome.” In the current times, businesses have multiple products offerings at their disposal to choose from vendors of big data predictive analytics in different industries. They can help these businesses leverage historical data discovering complex data correlation, recognizing patterns, and forecasting. Organizations are turning to predictive analytics to increase their bottom line and gain advantages against their competition. Some of those reasons are listed below: • With the growing amount and types of data, there is more interest in utilizing it to produce valuable insights • Better computers • An abundance of easy to use software • Need of competitive differentiation due to tougher economic conditions As more and more easy-to-use software have been introduced, businesses no longer need statisticians and mathematicians for predictive analytics and forecasting. Benefits of Predictive Analytics Competitive edge over other businesses The most common reason why multiple companies picked up predictive analytics was to gain an advantage over their competitors. Customer trends and buying patterns keep changing from time to time. The ones who can identify it first will go ahead in the game. Embracing predictive analytics is how you will stay ahead of your competition. Predictive analytics will aid in qualified lead generation and give you an insight into the present and potential customers. Business growth Businesses opt for predictive analytics to predict customer behavior, preferences, and responses. Using this information, they attract their target audience and entice them into becoming loyal customers. Predictive analytics gives valuable information about your customers such as which of them are likely to lapse, how to retain them, whether you should market directly at them, etc. The more you know about them, the stronger your marketing will become. Your business will become the leader in predicting your customer’s exact needs. Customer satisfaction Retaining existing customers is almost five times more difficult than acquiring new ones. The most successful company is the one that invests money in retaining those customers as much as acquiring new ones. Predictive analytics helps in directing marketing strategies towards your existing customers and get them to return frequently. The analytics tool will make sure your marketing strategy caters to the diverse requirements of your customers. Personalized services Earlier marketing strategies revolved around the ‘one size fits all’ approach, but gone are those days. If you want to retain and acquire new customers, you have to create personalized marketing campaigns to attract customers. Predictive analytics and data management help you to get new information about customer expectations, previous purchases, buying behaviors, and patterns. Using this data, you can create these personalized marketing strategies that will help keep up the engagement and acquire new customers.   Application of Predictive Analytics Customer targeting Customer targeting divides the customer base into different demographic groups according to age, gender, interests, buying, and spending habits. It helps companies to create tailored marketing communications specifically to the customers who are likely to buy their products. Traditional techniques do not even come close to identifying potential customers as well as predictive analytics does. The major constituents that create these customer groups are: • Socio-demographic factors: age, gender, education, and marital status • Engagement factors: recent interaction, frequency, spending habits, etc. • Past campaign response: contact response, type, day, month, etc. The customer-specific targeting for the company is highly advantageous. They can: • Better communicate with the customers • Save money on marketing • Increase profits Customer churn prevention Customer churn prevention creates major hurdles in a company’s growth. Although it has been proven that retaining customers is cheaper than gaining new ones, it can become a problem. Detecting a client’s dissatisfaction is not an easy task as they can abruptly stop using your services without any warning. Here, churn prevention comes into the picture. Churn prevention aims to predict who will end their relationship with the company, when, and why. The existing data sets can help develop predictive models so companies can be proactive to prevent the fallout. Factors that can influence the churn are as follows: • Customer variables • Service use • Engagement • Technicalities • Competitor variables Using these variables, companies can then take necessary steps to avoid the churn by offering customers personalized services or products. Risk management Risk assessment and management processes in many companies are antiquated. Even though customer information is abundantly available for evaluation, it is still antiquated. With advanced analytics, this data can be quickly and accurately analyzed while maintaining customer privacy and boundaries. Risk assessment thus allows companies to analyze problems with any business. Predictive analytics can approximate with certainty which operations are profitable and which are not. Risk assessment analyzes the following data types: • Socio-demographic factors • Product details • Customer behavior • Risk metrics Forecast sales Evaluating the previous history, seasonality, and market-affecting events make revenue predicting vital for a company’s planning and result in a company’s demand for a product or a service. This can be applied to short-term, medium-term, and long-term forecasting. Predictive models help in anticipating a customer’s reaction to the factors that affect sales. Following factors can be used in sales forecasting: • Calendar data • Weather data • Company data • Social data • Demand data Sales forecasting allows revenue prediction and optimal resource allocation. Healthcare Healthcare organizations have begun to use predictive analytics as this technology is helping them save money. They are using predictive analytics in several different ways. With the help of this technology, based on past trends they can now allocate facility resources, optimize staff schedules, identify patients at risk, adding intelligence to pharmaceutical and supply acquisition management. Using predictive analytics in the health domain has also helped in preventing cases and risks of developing health complications like diabetes, asthma, and other life-threatening problems. The application of predictive analytics in health care can lead to making better clinical decisions for patients. Predictive analytics is being used across different industries and is good way to advance your company’s growth and forecast future events to act accordingly. It has gained support from many different organizations at a global scale and will continue to grow rapidly. Frequently Asked Questions What is predictive analytics? Predictive analytics uses historical data to predict future events. The historical data is used to build mathematical model that captures essential trends. That predictive model is based on current data that predicts what will happen next or suggest steps to take for optimal outcomes. How to do predictive analytics? • Define business objectives • Collect relevant data available from resources • Improve on collected data by data cleaning methods • Choose a model or build your own to test data • Evaluate and validate the predictive model to ensure How does predictive analytics work for business? Predictive analytics helps businesses attract, retain, and grow their profitable customers. It also helps them in improving their operations. What tools are used for predictive analytics? Some tools used for predictive analytics are: • SAS Advanced Analytics • Oracle DataScience • IBM SPSS Statistics • SAP Predictive Analytics • Q Research { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What is predictive analytics?", "acceptedAnswer": { "@type": "Answer", "text": "Predictive analytics uses historical data to predict future events. The historical data is used to build a mathematical model that captures essential trends. That predictive model is based on current data that predicts what will happen next or suggest steps to take for optimal outcomes." } },{ "@type": "Question", "name": "How to do predictive analytics?", "acceptedAnswer": { "@type": "Answer", "text": "Define business objectives Collect relevant data available from resources Improve on collected data by data cleaning methods Choose a model or build your own to test data Evaluate and validate the predictive model to ensure " } },{ "@type": "Question", "name": "How does predictive analytics work for business?", "acceptedAnswer": { "@type": "Answer", "text": "Predictive analytics helps businesses attract, retain, and grow their profitable customers. It also helps them in improving their operations." } },{ "@type": "Question", "name": "What tools are used for predictive analytics?", "acceptedAnswer": { "@type": "Answer", "text": "Some tools used for predictive analytics are: SAS Advanced Analytics Oracle DataScience IBM SPSS Statistics SAP Predictive Analytics Q Research" } }] }

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Spotlight

Valorem

Valorem Reply is a digital transformation firm focused on driving change with hyper-scale and agile delivery of unique digital business services, strategic business models and design-led user experiences. Through the expertise of our people and the power of Microsoft technologies, our innovative strategies and solutions securely and rapidly transform the way our clients do business.

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