Data Analysis Company: Data Analyzing
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Data Analysis Reports: Research Data Analysis Help
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Research Data Analysis Help: Other Services
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Data Analysis Consultants: Data Analysts To Analyze The Data
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Data Analysis Consulting: Tips
The challenge of big datas and manual analytical methodologies continues to grow. With big datas enterprise adoption rates increasing from year to year, companies face the daunting task of integrating and making sense of all this new information. For companies looking for real business value in analyzing this datas, we’ve got a few tips to help them through the process.
Tip 1: Don’t Pre-Determine What Matters – Take ALL Datas into Account: Big datas comes from many disparate sources. Aggregating it is challenging. You need to normalize and analyze huge sets of figures. However, pre-determining what’s important creates a “silo effect”– observing only through a narrow lens. The solution is to understand that correlations and patterns from disparate, linked datas sources yield the greatest insights and transformative opportunities. Use a robust ETL system to take any possible datas into account and logically determine what is relevant. Avoid misconceptions due to lack of information. What you discover will often be surprising.
Tip 2: Filter Out Noise and Focus Only on What Matters: Big datas greatly increases the volume, velocity, and complexity of datas streaming into the organization. Datas from numerous disparate sources makes it harder to find correlations between seemingly unrelated phenomena. The solution is to collect input from 3rd party aggregators, blogs, and public sources, along with your traditional customer, market and operational datas, results in significantly greater datas noise. Whatever is irrelevant to your analytical goals must be filtered out early on. Analytics based on domain expertise, along with Time-Series analysis, will remove the noise while leaving in exceptions that might be important to enable early detection of breaking trends, market changes, performance issues and unrealized opportunities.
Tip 3: Verify Uniqueness: Now that you’ve gotten rid of irrelevant outliers and social media noise, how do you confirm if a trend is problematic or just part of the norm? For example, should an exceptional drop in sales of a given product in a specific region be reported as a separate problem? The solution is that the user must ask if the datas is unique in comparison with the behavior exhibited by all possible peer groups. In other words: Is this happening everywhere else? (e.g. other products, other stores, other geographies, competing products) Segmenting an opportunity or a business issue is essential for generating actionable datas.
Tip 4: Confirm Consistency: Once you’ve verified a trend as a problem or an opportunity, you must dig further to establish why the trend occurred. The solution is to go deeper into the weeds, look at all related datas, detect precisely what is unique here, and you will arrive at an informed assessment. The key to this process is Domain Expertise.
Tip 5: Stay Agile and Keep Your Solution Relevant: One of the key reasons for many failed BI projects is the inability to continuously morph the solution as the business and market change. Left unattended these variations create a gap between the business and where the breadth of the solution resides. The solution is to be mindful that analytics are only as good as the datas that comes in. If it’s not fresh, the results will be off. The solution must quickly adapt the analytics parameters as needed by the business. They therefore need to be kept constantly up-to-date with the latest internal and external datas. An introduction of a new packaging size for your drug; a promotion by your competitor; a new oncology specialist in your territory; a new payer contract… Any change in the business landscape influences the market. You must take it ALL into account to perform a meaningful assessment that will truly drive the business and affect the bottom line.
We shall give you few other quick tips for working effectively and efficiently with big datas.
Consider your work a search for buried treasure: You should view analyzing datas as a process similar to looking for buried treasure. In other words, datas mining resembles gold mining. You’re pounding your way through the datas or sifting through the granularity in search of valuable nuggets. This effort can be painstaking and tedious. However, with persistence and a bit of luck, you should often (will often?) be able to find valuable insights into both opportunities and threats you might otherwise have missed. You want and need to remember that.
Collect more datas: You should collect more datas . . . and then be good about storing and saving the datas you do collect. In order words, don't sloppily discard or carelessly lose or foolishly throw away the datas we already collect or have. That datas could be priceless. And if it isn't priceless today, who knows? It might be at some point in the future. Face it. The richer the datas set, the better the chances some cool insight will jump out at you.
Create more datas: Work to create more datas. Okay, that maybe sounds silly. But in some cases, useful datas can be created very economically. Here's a simple example: If you run a business, ask clients how they came to find you. You'll great insights into your marketing efforts as a result. You probably have other interesting ways to create more datas.
Regularly run experiments: Datas creation methods such as experimenting via AB testing and pilot studies can economically provide datas of extraordinary value. For example, author Timothy Ferris in his bestselling book, The Four Hour Workweek, describes using pay-per-click advertisements to gauge product feasibility. That’s a great idea, and one that probably in many cases results in way more accurate analytical conclusions than a focus group.
Go big (with your datasets and your samples): If you learned about statistics in the age before computers and their large datasets were widely available and easy to use, you may have a tendency to make judgments and decisions based on small datasets. Today, that's really pretty inexcusable. Nowadays, you should be working with huge datasets. Whenever possible, “go big” and use big or bigger datasets and samples.
Don't delegate assessment of datas: From the perspective of many managers or business owners, having some young tech-savvy intern might seem like the best approach to getting really good analyzing of datas performed. But if you talk with the people doing lots of assessment of datas, you’re quite likely to hear that what you really want to do is assign the smartest, most experienced team member you can to work on this project. In other words, the people you really want doing this work are the people who probably don’t have time to do it. Maybe, in fact, you should just do the assessment of datas yourself if you’re the grand Pooh-Bah. Again, think about this work as akin to mining for buried treasure. The insights you might uncover could be enormously valuable. As good as some young buck or young doe might be, you for darn sure don’t want them to miss some outstanding opportunity or a potentially catastrophic threat because they lack experience or don’t yet have fully developed strategic thinking skills.
Waste time pouring over meaningless datas: Here's a silly idea. Maybe you should occasionally waste time pouring over seemingly meaningless datas: cross-tabulations of time-stamped sales receipts, analytics datas from your website, third-party transaction logs, and so forth. You never know what you’ll find. And sometimes the best insights can come from the most surprising places.
Inventory internal datas sources: A housekeeping item: You probably want to keep an inventory of internal datas sources. And the list should probably include more than just the accounting system and your web servers’ analytics files. All sorts of interesting datas exist, when you start thinking about it. And some of this stuff will get lost or get forgotten if you aren't careful.
Build a library of external raw datas sources: A quick reminder? Some of your raw datas sources aren’t internal but external. Don’t forget about those. Even the smallest businesses may have access to third-party payment processing files and transaction lists created by outside web services.
Protect proprietary datas sources: Because any proprietary datas sources potentially have enormous value, you of course want to carefully protect the asset. Now of course this means that you want to safely store and regularly back up the datas, but that's not all. Protecting your proprietary datas means you want to make sure that the datas stays proprietary and (maybe even more so) that any insights contained in the datas stay internal. Something to think about.
Further we share some key pieces of advice that could prove invaluable to your organization’s future.
1. Have a datas strategy: The way to business success through datas comes down to four strategic steps: Organize, analyze, act, and optimize. Organizing datas can be done using a succinct list of useful questions such as: How will you use it? How will you secure it? What people and resources do you have, and what do you need to find? What processes do you need to follow to protect the integrity of the datas? Which technology do you need? Assessment, meanwhile, is not only about gathering a vast array of datas to draw insights from, but also requires reporting and integration of assets to drive intelligence. The ‘holy grail’ of analysis is to go from attribution through to contribution and then prediction. But datas gathering and analysis are not end goals, nor are either useful unless you then act and optimize your findings by constantly testing and measuring the effectiveness of every action. Achieving the desired business outcome is the ultimate objective.
2. Close the customer feedback loop: The best way of achieving the ‘3Rs’ of customer-led marketing – recognize, relate and retain – is by commercializing the datas in your organization. Doing this requires not only a 360-degree customer view, but also engaging in an ongoing dialogue with customers using datas. Brands need to start acting across the customer journey and pick up all the crumbs of datas around that, in order to take people across that journey. Every interaction should improve the next interaction.
3. Be a datas analytics connoisseur: With disruption the rule rather than the exception in business today, dataset analytics is the tool every organization needs to rely on to remain competitive. We’re going to see increasing sophistication of in-house analytics. Competitors are hitting your saturated market and they’re using analytics to do it. You need to invest in analytics not to win, but to simply not die or lose your customer base.” But it’s not enough to launch a big datas initiative, put in some predictive modeling and tick the completion box, nor is setting up a specialist team and hoping for the best. You have to continually innovate right across the business. And that means knowing what you’re doing with datas and analytics. You can’t play politics with analytics or pretend to know what you’re doing if you don’t. If your analytics efforts aren’t working well, you’re dead; if they’re working, you might just be OK. Analytics is central to your business and it affects everybody. You have to have analytics literacy. You don’t necessarily have to be able to do the maths, but you have to understand what a predictive model is for, and what makes one work versus not. “Be connoisseurs, if not chefs.”
4. Don’t use datas as a crutch: Too many organizations only look at datas as a way of reporting on existing decisions and actions. Instead, they should see it as an opportunity to drive innovation.
Do note that these above given tips are provided only as a general guideline and would vary based on the requirement of the project. Inspite of the above tips; it is not possible for most of you to do your own projects. This is where you need expertise of our company. What makes us different? We are unique because all of our recommendations are based on reliable science. We put the evidence at the forefront and are very critical of where that evidence came from. We take a three phase approach when building a solution plan for you. First, we identify the question. Most think you need to focus on the problem, but you can't actually know the problem until you ask the right question. We spend a lot of time teasing out information in order to determine which questions to ask. Some examples might be: What attracts my current customers? When should I open a second location and where? When should I buy my supplies? Is my advertising working? How do we boost our occupancy? Am I maximizing product yield? Are there bottlenecks in my business? Who should I be targeting? Do I have efficiency problems? If so, where? Is my website effective and how should I utilize it? Next, we test. We collect datasets and use a variety of tools to test the questions we decide to ask. The datasets can vary greatly ranging from paper surveys to web metrics. The tools we use are a variety of statistical assessment and research methods. We utilize our experience from the academic research fields and apply them to your solution plan. Finally, we build recommendations. Now that we know what questions to test and have solid datas, we can start to translate the outcome into suggestions for you. We look at what is feasible and create an actionable task list. Tasks are based on solid evidence and sound statistics, so you know the changes you make will have positive impacts.
Datas can, broadly, be analyzed in two ways- Quantitative and Qualitative method. The former technique of datas assessment includes interpretation of datasets such that it can be organized to provide quantifiable results. This is the stage where researchers tend to panic since the quantitative datas assessment involves the use of statistical tools like, mean averages, correlations, finding deviations, trend analysis, multiple regression analysis, ANOVA, factor analysis and many more. They find it difficult to select the type of assessment, suitable to their collected datasets, and to interpret it on their own.
There are a number of ways to analyze and digest datas. When crafting your report, we provide the following options: 1. Cleansing: Inaccurate datas can occur and if not properly cleansed, provide inaccurate results that could falsely impact important business decisions. We will inspect your datas, remove errors and ensure it is useful for assessment. 2. Quality of Datas: Datas quality as a whole can be measured to minimize errors and ensure its effectiveness. This is one of the first steps in assessment and is assessed with tools like frequency counts, mean, median, standard deviation, normality and associations. If decisions made by a single person have affected how datas was obtained or how variables were distributed, datas may need to be cleaned or even recollected. 3. Assessment of Missing Observations: We check for missing values and whether those missing values are missing at random. We also check to make sure that no more than 25% of values are missing, and we take action if imputation is needed in any way. 4. Assessment of Extreme Observations: If outliers cause a disruption in the datas, we analyze them to determine what actions must be taken to rectify them. 5. Assessment of Homogeneity: We analyze how consistent the datas is as a whole and, therefore, how reliable it is in our assessment. If an item is deleted from the scale, we measure its effects and its implications for your datas.
We offer comprehensive services for small, medium and large corporations across a wide range of industries. Our thorough research services will enable you to better understand the key points within your industry and make informed decisions based on the hard facts gathered by our research professionals. With a wealth of experience and knowledge in visualizing and understanding large, complex datas, we provide a variety of services tailored to your project’s needs. We provide comprehensive services, from datas validation to the creation of presentation materials. Our advanced capabilities include: Cleaned datas file based on test statements, recodes, and assignments and computed variables; Real-time datas editing; Multivariate analysis; A very wide array of statistical testing, including t-test, z-test, ANOVA and Chi-Square; Unlimited tables and number of respondents; Create marginals, frequencies, counts and percentages; Weighting by factors, targets, percentages or margins; Means, medians, standard deviations, etc; Produce complete variable distribution showing percentages, cumulative percentages and numerous descriptive statistics.
The following are the top three benefits that your company can leverage by partnering with us: 1. We will inspect, clean and transform your datas to create models that will highlight the important information within your business and provide you with insights that can give you a competitive edge over other companies in your industry. 2. With our advanced services, you can make key decisions while receiving important conclusions that might otherwise have been hidden within massive or disorganized datas sets. 3. By using cutting edge statistical tools and working with only the best trained statisticians and datas management experts in the research field, we can ensure that your analysis needs are met for every major component of your business, whether you need your financial datas to be analyzed and presented or are interested in key decision points based on competitor datas. To ensure that you receive only the best services, partner with our company which is trusted by executives and research firms around the globe.
We have extensive experience of working on big datas projects. In order to make your business a success, you need to understand your market. This is where we can help you. Our customers often come to us with datas that is in need of analysis and a desire to make an impact with their business. We take the information they give us and apply the latest technologies to help them understand trends and ideas that can be found in their datas, enabling them to find value in the information they have. For example, we do work for companies in the advertising industry. In such a competitive market, it is vital that our clients really understand the people they are targeting and know how to get the best results. We look at different aspects of their marketing campaigns to work out how they can achieve this. How many people view their ads, which ads generated the biggest number of clicks and which ad generated the most revenue are examples of things we look for to help our clients understand what they have achieved – and where they can improve. To do all of this, we use the latest technology to generate the most accurate, effective results possible. We make use of techniques such as cloud computing, datas mining, machine learning and web analytics to produce top quality results. These are all terms that can sound complicated, but they have a simple aim: to allow us to analyze datas from a range of perspectives so that we can condense it down into useful information. This information is then used to help businesses generate more revenue, cut costs and generally learn more about their market. The first step in any project is for us to sit down with you and learn about your business and its needs. We’ll take a look at your existing datas and other resources to get a better idea of how we can transform your ideas into useful, practical business solutions. Once we have a good idea of what you do and what you are looking for, we apply datas mining and other techniques to your datas. When we have finished analyzing datas, we will transform this into a product that will add value to your business and break down the results very clearly for you. In our work, we focus on quality and simplicity to make sure your resulting product will be easy to extend and update as your business grows and you acquire new datas.
Let our experienced team handle your research project and save your time for you. Our team constantly delivers the most complex projects in a fraction of the time clients are accustomed to. We have a faculty of trainers & consultants, who are professionals in their respective subject areas, such as statistics, mathematics, operations research, and econometrics. These are instructors and consultants who have a thorough knowledge of working in industries and academic bodies such as Manufacturing, information technology, automotives, FMCG, and pharmaceuticals handling projects like Brand tracking studies, Churn analysis, Customer satisfaction studies, Concept & Product testing, Ad hoc studies etc Our staff is equally qualified to analyzing results using any of a number of techniques, including Conjoint Analysis (Full Profile Conjoint; Self Explicated Conjoint; Choice Based Conjoint Analysis); Monte Carlo Simulations; Multidimensional Scaling Analysis; Correspondence Analysis; Cluster Analysis; Factor Analysis; Datas Mining with CHAID; Classification and Regression Trees, and Neural Network Analysis; Multiple Regression; Discriminant Analysis; Forecasting Analysis. Our cross functional team brings experience from a variety of domains and specialties, including GIS, as well as reporting and presentation.
Our business is founded on the core principles of creativity, excellence and inventiveness. This means that when you use our services, you can be assured of a high quality service and fantastic solutions tailored exclusively for your needs. As our business has been built on enduring relationships with our customers, we also provide an utmost level of customer service in addition to reducing your research time. Read further below.
Data Analysis Projects: Data Analysis Papers--Hire
In almost any type of industry, datas are an immensely valuable tool that can provide you with an upper hand in developing, presenting and marketing your new products or services to your target audience. Datas analysis, being the most critical stage of a research, often poses a threat to the researchers since this step involves the task of “making sense” of the separate pieces of accumulated datas. All the datas, which you have collected, is termed as the “raw” datas before it is evaluated to produce meaningful reports. Datas Analysis is, thus, a systematic method to transform the collected raw datas to a condensed form making it logically descriptive. It is an integral part of your dissertation since it enables you to present your research findings in such a way that they form the answer to your research questions. The important point, to be considered here, is that the technique adopted for datas analysis should be appropriate for your research. However, the process of analyzing datas cannot be carried out by anyone and requires the expertise of trained research analysts who can gather, analyze and extrapolate valuable information from your datas. This is where our company can assist you.
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