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DATA ANALYSIS
A Definition of Data Analysis Data analysis is a primary component of data mining and Business Intelligence (BI) and is key to gaining the insight that drives business decisions. Organizations and enterprises analyze data from a multitude of sources using Big Data management solutions and customer experience management solutions that utilize data analysis to transform data into actionable insights. Dennis Junk, a HubSpot certified inbound marketer with Aptera, aptly explains data analysis in his blog post: data analysis is “all the ways you can break down the data, assess trends over time, and compare one sector or measurement to another. It can also include the various ways the data is visualized to make the trends and relationships intuitive at a glance.” Data analysis involves asking questions about what happened, what is happening, and what will happen (predictive analytics). As Junk puts it, “analytics is generally the data crunching, question-answering phase leading up to the decision-making phase in the overall Business Intelligence process.” Data Analysis Model Gwen Shapira, a solutions architect at Cloudera and an Oracle ACE Director, outlines seven key steps of data analysisfor Oracle’s Profit magazine. Shapira explains that while each company has its own data requirements and goals, there are seven steps that remain consistent across organizations and their data analysis processes: Decide on the objectives – Determine objectives for data science teams to develop a quantifiable way to determine whether the business is progressing toward its goals; identify metrics or performance indicators early Identify business levers – Identify goals, metrics, and levers early in data analysis projects to give scope and focus to data analysis; this means the business should be willing to make changes to improve its key metrics and reach its goals as well Data collection – Gather as much data from diverse sources as possible in order to build better models and gain more actionable insights Data cleaning – Improve data quality to generate the right results and avoid making incorrect conclusions; automate the process but involve employees to oversee the data cleaning and ensure accuracy Grow a data science team – Include on your science team individuals with advanced degrees in statistics who will focus on data modeling and predictions, as well as infrastructure engineers, software developers, and ETL experts; then, give the team the large-scale data analysis platforms they need to automate data collection and analysis Optimize and repeat – Perfect your data analysis model so you can repeat the process to generate accurate predictions, reach goals, and monitor and report consistently Benefits and Challenges of Data Analysis Data analysis is a proven way for organizations and enterprises to gain the information they need to make better decisions, serve their customers, and increase productivity and revenue. The benefits of data analysis are almost too numerous to count, and some of the most rewarding benefits include getting the right information for your business, getting more value out of IT departments, creating more effective marketing campaigns, gaining a better understanding of customers, and so on. But, there is so much data available today that data analysis is a challenge. Namely, handling and presenting all of the data are two of the most challenging aspects of data analysis. Traditional architectures and infrastructures are not able to handle the sheer amount of data that is being generated today, and decision makers find it takes longer than anticipated to get actionable insight from the data. Fortunately, data management solutions and customer experience management solutions give enterprises the ability to listen to customer interactions, learn from behavior and contextual information, create more effective actionable insights, and execute more intelligently on insights in order to optimize and engage targets and improve business practices.
Pankaj Adhikari
Agriculture
Agriculture is the cultivation of land and breeding of animals and plants to provide food, fiber, medicinal plants, and other products to sustain and enhance life.[1] Agriculture was the key development in the rise of sedentary human civilization, whereby farming of domesticated species created food surpluses that enabled people to live in cities. The study of agriculture is known as agricultural science. The history of agriculture dates back thousands of years; people gathered wild grains at least 105,000 years ago and began to plant them around 11,500 years ago before they became domesticated. Pigs, sheep, and cattle were domesticated over 10,000 years ago. Crops originate from at least 11 regions of the world. Industrial agriculture based on large-scale monoculture has in the past century come to dominate agricultural output, though about 2 billion people worldwide still depend on subsistence agriculture. Modern agronomy, plant breeding, agrochemicals such as pesticides and fertilizers, and technological developments have sharply increased yields from cultivation, but at the same time have caused widespread ecological and environmental damage. Selective breeding and modern practices in animal husbandry have similarly increased the output of meat, but have raised concerns about animal welfare and environmental damage through contributions to global warming, depletion of aquifers, deforestation, antibiotic resistance, and growth hormones in industrially produced meat. Genetically modified organisms are widely used, although they are banned in several countries. The major agricultural products can be broadly grouped into foods, fibers, fuels, and raw materials (such as rubber). Classes of foods include cereals (grains), vegetables, fruits, oils, meat, milk, fungi, and eggs. Over one-third of the world's workers are employed in agriculture, second only to the service sector, although the number of agricultural workers in developed countries has decreased significantly over the past several centuries.
Manoj rajvanshi
DATA-DRIVEN MARKETING
Definition of Data-Driven Marketing Data-driven marketing is the process by which marketers glean insights and trends by analyzing company-generated or market data, then translating these insights into actionable decisions informed by the numbers. The goal of data-driven marketing is to optimize marketing processes and strategies to cater to changing trends and the unique demands of audiences and consumers by leveraging data to gain deeper insight into what customers want. When brands fully understand the who, what, where, when and why of how consumers are engaging with their marketing efforts, they are able to make better decisions surrounding everything from the timing of advertising in a given medium to the customization of marketing copy to cater to specific audience segments. How Data-Driven Marketing Works The process of data-driven marketing relies on the use of information (in the form of data) in order to drive marketing efforts. Data is collected on every aspect of a user’s engagement from demographics to market-wide metrics and individual interactions, and is then analyzed in order to determine markers of success. These insights are then used to help decide where and how to focus marketing resources, the types of creative that are most effective at maximizing ROI, which customers are most likely to churn, and many other crucial details that can aid marketers in shaping brand growth. Data-driven marketing is gaining in popularity in large part due to its proven ability to maximize ROI. It also helps to maximize the efficiency of marketing efforts by reducing wasteful spend and improving resource allocation, and ultimately empowers brands to deliver a more customer-centric approach to marketing. Gaining a better understanding of a brand’s prospects and their preferences is one of the most effective methods for increasing conversion rates. Examples of Data-Driven Marketing There are thousands of examples of data-driven marketing in use every day if you know where to look. In fact, the majority of the marketing messages we’re exposed to on any given day are driven by data-derived insights. Data-driven marketing has become the new norm thanks to the widespread accessibility of data and analytics tools. One company that has used data-driven marketing to its advantage is Hootsuite. When they noticed both free and paid signups were dropping off, they looked deep into their data pool to determine the features different segments of their user base were relying on most. They then utilized this data to create different usage tiers for the service based on what their customers were actually using the product for. The result? An increase in both free and paid service signups. Another example of successful data-driven marketing in action is Status Page. This company used data to increase conversion rates by 311%. After identifying which stage in the process prospects were being lost, and why, they implemented site changes to make the signup process more appealing and saw tremendous success as a result. Of course, these are just two of the many examples of businesses successfully leveraging data-driven marketing to influence results. Benefits of Data-Driven Marketing Data-driven marketing offers widespread benefits, including not only effectiveness but also ease of implementation thanks to the availability of user-friendly tools that do much of the heavy analytics lifting. Many marketers turn to data-driven marketing to improve audience targeting. With the right data, brands can know exactly who is engaging with their marketing efforts through which channels, and even at what time of day they are active. This can aid brands in laser-focusing their marketing efforts to the right mediums at the most effective times. Data-driven marketing also allows marketers to analyze the types of messaging and offers that consumers are most responsive to, often analyzed through A/B testing. This lets marketers focus their efforts into projects that will offer superior ROI and are of the highest value to the consumer. Conversely, it also allows marketing teams to see where they may be going wrong and how to correct the issue — as with the above example from Status Page. Data-driven marketing can also be used to optimize customer experience. If a marketer sees a bounce rate at a certain point of their campaign, they can evaluate the why and adjust as necessary so that the customer experience is optimized. This is the kind of initiative that has a powerful impact on growth and retention. Challenges of Data-Driven Marketing One of the biggest cons of data-driven marketing is also its biggest advantage — the intense focus on using insights in the decision-making process. This hyperfocus on using data to make marketing decisions can come at the expense of creativity, depending on how marketers choose to leverage data-derived insights. When metrics tunnel vision takes over, creativity is no longer driving the message and the ‘magic’ of connecting with consumers no longer happens. Savvy marketers implement the right blend of creativity and numbers-driven factors into every marketing campaign and tactic. Additionally, data can also indicate a course of action that goes against a brand’s values. While this is rare, marketers should maintain a sense of brand integrity and use data to inform, but not dictate, decision-making when it comes to sacrificing values. Brand guidelines are helpful to ensure that brand identity standards and values are maintained. Best Practices for Data-Driven Marketing The key to obtaining success with data-driven marketing efforts is to plan, test, analyze, iterate, and then redeploy and scale accordingly once you’ve gleaned enough insights to inform your course of action. Decide what you’re measuring and how you’ll be successful. Utilize A/B testing and other measures in order to see what works best and then compare results to your original KPIs. Then, implement and invest accordingly, taking care to analyze new data and adjusting your marketing efforts continuously. Data drives the majority of marketing decisions in today’s highly competitive world. If you’re not yet using data to derive marketing insights and inform decision-making, you’re already behind the curve.
History of india
History of indian.
Ashish Nayak
Distance in Civil Engineering
Distance in Civil Engineering
Simran Kaur Sidhu
RES
RES
Anuja Bhadra
Blasing
BLASING IN ROCK
Rishi Sood
Recapitalization of Indian Banks
The government of India has decided to infuse Rs.2.11 trillion in the national Public Sector Banks (PSBs) over a period of 2 years to strengthen their capital base. This came at a time when GDP growth rate of India had hit a record low of 5.7 % in the 2nd quarter of the current financial year, while the NPAs (Non-Performing Assets ) grew to 10 % of the loans advanced by the banks. The ever-increasing NPAs have reduced the money lending capability of the banks. Banks have become apprehensive of lending loans to Corporate, translating into lesser investment in infrastructure and their cascading effect leading to a slower growth. Infusing such a massive amount will help the banks to write-off its bad loans, and subsequently, increase their lending capacity. A better lending capacity will increase the flow of capital in the economy, which is expected to kick the GDP growth rate. Recapitalization is also expected to curb the infamous twin balance sheet problem of India. According to Arvind Subramaniam, the Chief Economic Adviser to the government of India, given its fiscal position, recapitalization is the best the government could have done to revive the debt-laden banks of India. Recapitalization is only the first step to address the rot in the banking sector. To put India back on the track of high growth the government must follow it with structural reforms and better monitoring should be done to ensure NPAs do not pile up.
Siddhant Kashyap
PSE
PSE
Solved problems on Transformational fluid-chemical
Solved problems on Transformational fluid-chemical
Ramakrishna Chilamakurthi
kargil
kargil