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Know the Difference between Alteryx and Tableau

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Know the Difference between Alteryx and Tableau

Alteryx and Tableau are two leading software products in the realm of data science with broad application. Both are pivotal in the analysis of data and obtaining important insights. But what is the specific role of each product in an enterprise? Are the products complementary or incompatible? Will using them jointly enhance performance? Read on to find out.

Function of Alteryx:

In general, data preparation takes up a major portion of an analyst’s time. Before the data can be analyzed, it needs to be prepped, which involves many humdrum tasks such as combining data sources together and changing the format of data. This process of readying the data in a format appropriate for analysis is called ETL, short for Extract, Transform and Load.

Alteryx is a top software product for simplifying the ETL process. It offers a large array of tools for handling data. From importing data from various sources to getting it ready for analysis work, Alteryx provides a tool for every task. After modifying the data, you can use Alteryx tools with advanced statistical capabilities to perform sophisticated analyses, like predictive analytics and time series forecasting.

For top rated Alteryx certification training in Delhi, get in touch with DexLab Analytics.

Function of Tableau:

Tableau is renowned as a useful data visualization tool, performing a distinct role in the world of data analytics.  Tableau helps transform data into charts and dashboards, revealing useful insights contained in the data. High-quality charts can be created with Tableau, which in turn leave an impactful impression on the audience.

Combination of the Two Tools:

Tableau and Alteryx complement each other really well. Alteryx comes in very handy in converting data into operable format, but it has limited capacity to display data before an audience. Tableau effectively fills this void. On the other hand, Tableau is unparalleled at data visualization, but is lacking in the field of data preparation, especially areas requiring advanced analytics. Hence, the combination of the two tools adds value to the overall task.

In a nutshell, Alteryx simplifies the tedious preparation process of data analysis and Tableau makes the explanatory part of analysis more enjoyable.

Who works with these products?

A good thing about Alteryx and Tableau is that they are user-friendly and hence, don’t demand advanced technical expertise. They contain easy-to-use drag and drop interfaces. So, data analysts with different levels of skill and experience are comfortable working with these tools.

However, these two software products are market leaders and that reflects in their cost. They are quite pricey and can be afforded only by companies greater than the average ones. It makes sense; because these companies generally work huge volumes of data and have the financial power to afford high-level analytics tools like these.

For companies with smaller budgets, Power BI can be an alternative to Tableau, but its functionality will not be at par with Tableau. In case of Alteryx, there isn’t a clear alternative. R and Python are typically used by businesses, but these are coding languages demanding high levels of skill.

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Concluding Note:

Alteryx and Tableau are both leading products in the analytics industry, the former deals with data preparation whereas the latter deals with visualization. Together they boost up business operations. Affordability might be an issue for many organizations, but if you’re looking for the best products in the market, you cannot go wrong with Alteryx and Tableau.

Looking for ways to upskill and get a hefty pay hike in your analytics career? Check out the courses offered by DexLab Analytics. The faculty, comprising industry experts, provides professional certification courses in a number of key areas, like Tableau, credit risk modeling and more!

Reference: blog.kubicle.com/what-is-the-difference-between-alteryx-and-tableau

 

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Customer Analytics: A Basic Introduction

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Customer Analytics: A Basic Introduction

Customer Analytics is today’s hottest kid on the block, especially for executives. In simple terms, customer analytics is the process of analyzing and evaluating a flood of data that is being collected every day from every possible and probable customer standpoint. This customer data is then used in building superior predictive models to ascertain who the best customers for the retailer are, where he can find this kind of customer base and the value-potential these customers possess – either in terms of visits or dollars.

Customer data provides valuable and actionable insights that help retailers in executing their future marketing and real estate strategies. Put simply, it basically uses the past to predict the future.

Inadequate Customer Data: The Problem

No wonder, Customer Analytics is indeed a wonderful tool yet it’s not as simple as it sounds. Basically, collecting and determining data is an expensive affair as well as time-consuming. However, it is an absolute necessity. If not this, the retailers won’t be able to realize the potentials of customer analytics to the fullest.

However, most of the retailers, at least 60% of the lot don’t have access to data or they possess unreliable data. Generally speaking, an average company’s data is nearly 55% accurate and 14 months old, which makes the data fundamentally useless.

Faulty data skews customer profiles – resulting in lost opportunities, escalating costs, poor use of analytic solutions, dwindling numbers of customers – effectively costing retailers $700 billion annually.

Interestingly, the companies that have mastered the art of Customer Analytics are 7.4 times more likely to outdo their rivals in terms of sales, 6.5 times more likely to retain existing customers and approximately 19 times more likely to hit above-average profitability.

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Why Use Customer Analytics?

While there are retailers who have just grazed the layers of customer insights, you will find another set of retailers who are successfully utilizing the treasure trove of customer data merging analytics into it and identifying crucial information that leads to streamlining operations, accelerating productivity, personalizing marketing initiatives in accordance to both current and potential customers. This yields better profitability and detects locations where retailers can open new shops and target new customers.

With such intense market competition, retailers need to outnumber their tailing rivals and for that, they have to leverage the power of customer analytics. Instead of being an option, it has now become a necessity. So, say thanks to Customer Analytics, because of it, retailers are in a position to greatly enhance their potentials to target the right customers at the right time in the right place and in the most effective way.

If you are interested in customer marketing analytics courses in Delhi, feel free to reach us at DexLab Analytics. We offer excellent marketing analytics certification courses to the interested candidates at amazing prices! Contact us now.

 

The blog has been sourced from ―  www.buxtonco.com/blog/what-is-customer-analytics

 

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Demand for Data Analysts is Skyrocketing – Explained

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Demand for Data Analysts is Skyrocketing - Explained

The salary of analytics professionals outnumbers that of software engineers by more than 26%. The wave of big data analytics is taking the world by storm. If you follow the latest studies, you will discover that there has been a prominent growth in median salary over several experience levels in the past three years (2016 to 2018). In 2019, the average analytics salary has been capped at 12.6 lakh per annum.

The key takeaway is that the salary structure of analytics professionals continues to beat other tech-related job roles. In fact, data analysts are found out-earning their Java correspondents by nearly 50% in India alone. A latest survey provides an encompassing view of base and compensation salaries in data science along with median salaries followed across diverse job categories, regions, education profiles, experience, tools and skills.

In this regard, a spokesperson of a prominent data analytics learning institute was found saying, “The demand for AI skills is expected to increase rapidly, which is also reflected by the fact that AI engineers command a higher salary than peers.” She further added, “Many of our clients have realized that investing in data-driven skills at the leadership level is a determining factor for the success of digital and AI initiatives in the organization. With the increasing adoption of digital technologies, we expect an enduring growth of Data Science and AI initiatives to offer exciting and lucrative career options to new age professionals,”

Over time, we are witnessing how markets are evolving while the demand for skilled data scientists is following an upward trend. It is not only the technology firms that are posting job offers, but the change is also evident across industries, like retail, medical, retail and CPG amongst others. These sectors are enhancing their analytical capabilities implying an automatic increase in the number of data-centric jobs and recruitment of data scientists.

Points to Consider:

  • In the beginning, nearly 76% of data analysts earn 6-lakh figure per annum.
  • The average analytics salary observed in 2018-19 is 12.6 lakh.
  • In terms of analytics career, Mumbai offers the highest compensation of 13.7 lakh yearly, followed by Bangalore at 13 lakh.
  • Mid-level professionals proficient in data analytics are more in demand.
  • Knowing Python is an added advantage; Python Programming training will help you earn more. Expect a package of 15.1 lakh.
  • Nevertheless, we often see a pay disparity for female data scientists against their male counterparts. While women’s take-home salary is 9.2 lakh, male from the same designation and profession earns 13.7 lakh per annum.

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As endnotes, the demand for data science skills is skyrocketing. If you want to enter into this flourishing job market, this is the best time! Enroll in a good data analyst course in Delhi and mould your career in the shape of success! DexLab Analytics is a top-notch data analyst training institute that offers a plethora of in-demand skill training courses. Reach us for more.

 

This article has been sourced from — www.tribuneindia.com/news/jobs-careers/data-analytics-professionals-ride-the-big-data-wave/759602.html

 

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Bayesian Thinking & Its Underlying Principles

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Bayesian Thinking & Its Underlying Principles

In the previous blog on Bayes’ Theorem, we left off at an interesting junction where we just touched upon the ideas on prior odds ratio, likelihood ratio and the resulting Posterior Odds Ratio. However, we didn’t go into much detail of what it means in real life scenarios and how should we use them.

In this blog, we will introduce the powerful concept of “Bayesian Thinking” and explain why it is so important. Bayesian Thinking is a practical application of the Bayes’ Theorem which can be used as a powerful decision-making tool too!

We’ll consider an example to understand how Bayesian Thinking is used to make sound decisions.

For the sake of simplicity, let’s imagine a management consultation firm hires only two types of employees. Let’s say, IT professionals and business consultants. You come across an employee of this firm, let’s call him Raj. You notice something about Raj instantly. Raj is shy. Now if you were asked to guess which type of employee Raj is what would be your guess?

If your guess is that Raj is an IT guy based on shyness as an attribute, then you have already fallen for one of the inherent cognitive biases. We’ll talk more about it later. But what if it can be proved Raj is actually twice as likely to be a Business Consultant?!

This is where Bayesian Thinking allows us to keep account of priors and likelihood information to predict a posterior probability.

The inherent cognitive bias you fell for is actually called – Base Rate Neglect. Base Rate Neglect occurs when we do not take into account the underlying proportion of a group in the population. Put it simply, what is the proportion of IT professionals to Business consultants in a business management firm? It would be fair to assume for every 1 IT professional, the firm hires 10 business consultants.

Another assumption could be made about shyness as an attribute. It would be fair to assume shyness is more common in IT professionals as compared to business consultants. Let’s assume, 75% of IT professionals are in fact shy corresponding to about 15% of business consultants.

Think of the proportion of employees in the firm as the prior odds. Now, think of the shyness as an attribute as the Likelihood. The figure below demonstrates when we take a product of the two, we get posterior odds.

Plugging in the values shows us that Raj is actually twice as likely to be a Business consultant. This proves to us that by applying Bayesian Thinking we can eliminate bias and make a sound judgment.

Now, it would be unrealistic for you to try drawing a diagram or quantifying assumptions in most of the cases. So, how do we learn to apply Bayesian Thinking without quantifying our assumptions? Turns out we could, if we understood what are the underlying principles of Bayesian Thinking are.

Principles of Bayesian Thinking

Rule 1 – Remember your priors!

As we saw earlier how easy it is to fall for the base rate neglect trap. The underlying proportion in the population is often times neglected and we as human beings have a tendency to just focus on just the attribute. Think of priors as the underlying or the background knowledge which is essentially an additional bit of information in addition to the likelihood. A product of the priors together with likelihood determines the posterior odds/probability.

Rule 2 – Question your existing belief

This is somewhat tricky and counter-intuitive to grasp but question your priors. Present yourself with a hypothesis what if your priors were irrelevant or even wrong? How will that affect your posterior probability? Would the new posterior probability be any different than the existing one if your priors are irrelevant or even wrong?

Rule 3 – Update incrementally

We live in a dynamic world where evidence and attributes are constantly shifting. While it is okay to believe in well-tested priors and likelihoods in the present moment. However, always question does my priors & likelihood still hold true today? In other words, update your beliefs incrementally as new information or evidence surfaces. A good example of this would be the shifting sentiments of the financial markets. What holds true today, may not tomorrow? Hence, the priors and likelihoods must also be incrementally updated.

Conclusion

In conclusion, Bayesian Thinking is a powerful tool to hone your judgment skills. Developing Bayesian Thinking essentially tells us what to believe in and how much confident you are about that belief. It also allows us to shift our existing beliefs in light of new information or as the evidence unfolds. Hopefully, you now have a better understanding of Bayesian Thinking and why is it so important.

On that note, we would like to say DexLab Analytics is a premium data analytics training institute located in the heart of Delhi NCR. We provide intensive training on a plethora of data-centric subjects, including data science, Python and credit risk analytics. Stay tuned for more such interesting blogs and updates!

About the Author: Nish Lau Bakshi is a professional data scientist with an actuarial background and a passion to use the power of statistics to tackle various pressing, daily life problems.

 

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The Almighty Central Limit Theorem

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The Almighty Central Limit Theorem

The Central Limit Theorem (CLT) is perhaps one of the most important results in all of the statistics. In this blog, we will take a glance at why CLT is so special and how it works out in practice. Intuitive examples will be used to explain the underlying concepts of CLT.

First, let us take a look at why CLT is so significant. Firstly, CLT affords us the flexibility of not knowing the underlying distribution of any data set provided if the sample is large enough. Secondly, it enables us to make “Large sample inference” about the population parameters such as its mean and standard deviation.

The obvious question anybody would be asking themselves is why it is useful not to know the underlying distribution of a given data set?

To put it simply in real life, often times than not the population size of anything will be unknown. Population size here refers to the entire collection of something, like the exact number of cars in Gurgaon, NCR at any given day. It would be very cumbersome and expensive to get a true estimate of the population size. If the population size is unknown its underlying distribution will be known too and so will be its standard deviation. Here, CLT is used to approximate the underlying unknown distribution to a normal distribution. In a nutshell, we don’t have to worry about knowing the size of the population or its distribution. If the sample sizes are large enough, i.e. – we have a lot of observed data, it takes the shape of a symmetric bell-shaped curve. 

Now let’s talk about what we mean by “Large sample inference”. Imagine slicing up the data into ‘n’ number of samples as below:

Now, each of these samples will have a mean of their own.

Therefore, effectively the mean of each sample is a random variable which follows the below distribution:

Imagine, plotting each of the sample mean on a line plot, and as “n”, i.e. the number of samples goes to infinity or a large number the distribution takes a perfect bell-shaped curve, i.e – it tends to a normal distribution.

Large sample inferences could be drawn about the population from the above distribution of x̅. Say, if you’d like to know the probability that any given sample mean will not exceed quantity or limit.

The Central Limit Theorem has vast application in statistics which makes analyzing very large quantities easy through a large enough sample. Some of these we will meet in the subsequent blogs.

Try this for yourself: Imagine the average number of cars transiting from Gurgaon in any given week is normally distributed with the following parameter . A study was conducted which observed weekly car transition through Gurgaon for 4 weeks. What is the probability that in the 5th week number of cars transiting through Gurgaon will not exceed 113,000?

If you liked this blog, then do please leave a comment or suggestions below.

About the Author: Nish Lau Bakshi is a professional data scientist with an actuarial background and a passion to use the power of statistics to tackle various pressing, daily life problems.

About the Institute: DexLab Analytics is a premier data analytics training institute headquartered in Gurgaon. The expert consultants working here craft the most industry-relevant courses for interested candidates. Our technology-driven classrooms enhance the learning experience.

 

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Upskill and Upgrade: The Mantra for Budding Data Scientists

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Upskill and Upgrade: The Mantra for Budding Data Scientists

Have the right skills? Then the hottest jobs of the millennium might be waiting for you! The job profiles of data analysts, data scientists, data managers and statisticians harbour great potentials.

However, the biggest challenge in today’s age lies in preparing novice graduates for Industry 4.0 jobs. Although no one has yet cleared which roles will cease to exist and which new roles will be created, the consultants have started advising students to imbibe necessary skills and up-skill in domains that are likely to influence and carve the future jobs. Becoming adaptive is the best way to sail high in the looming technology-dominated future.

Data Science and Future

In this context, data science has proved to be one of the most promising fields of technology and science that exhibits a wide gap between demand and supply yet an absolute imperative across disciplines. “Today there is no shortage of data or computing abilities but there is a shortage of workforce equipped with the right skill set that can interpret data and get valuable insights,” revealed James Abdey, assistant professorial lecturer Statistics, London School of Economics and Political Science (LSE).

He further added that data science is a multidisciplinary field – drawing collectives from Economics, Mathematics, Finance, Statistics and more.

As a matter of fact, anyone, who has the right skill and expertise, can become a data scientist. The required skills are analytical thinking, problem-solving and decision-making aptitude. “As everything becomes data-driven, acquiring analytical and statistical skill sets will soon be imperative for all students, including those pursuing Social Sciences or Liberal Arts and also for professionals,” said Jitin Chadha, founder and director, Indian School of Business and Finance (ISBF).

DexLab Analytics is one of the most prominent deep learning training institutes seated in the heart of Delhi. We offer state-of-the-art in-demand skill training courses to all the interested candidates.

The Challenges Ahead

The dearth of expert training faculty and obsolete curriculum acts as major roadblocks to the success of data science training. Such hindrances cause difficulty in preparing graduates for Industry 4.0. In this regard, Chiraag Mehta from ISBF shared that by increasing international collaborations and intensifying industry-academia connect, they can formulate an effective solution and bring forth the best practices to the classrooms. “With international collaborations, higher education institutes can bring in the latest curriculum while a deeper industry-academia connect including, guest lecturers from industry players and internships will help students relate the theory to real-world applications, ” shared Mehta during an interview with Education Times.

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Industry 4.0: A Brief Overview

The concept Industry 4.0 encompasses the potential of a new industrial revolution – where gathering and analyzing data across machines will become the order of the day. The rise of this new digital industrial revolution is expected to facilitate faster, more flexible and efficient processes to manufacture high-quality products at reduced costs – thus, increasing productivity, switch economies, stimulate industrial growth and reform workforce profile.

Want to know more about data science courses in Gurgaon? Feel free to reach us at DexLab Analytics.

 

The blog has been sourced from ― timesofindia.indiatimes.com/home/education/news/learn-to-upskill-and-be-adaptive/articleshow/68989949.cms

 

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Bayes’ Theorem: A Brief Explanation

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Bayes’ Theorem: A Brief Explanation

(This is in continuation of the previous blog, which was published on 22nd April, 2019 – www.dexlabanalytics.com/blog/a-beginners-guide-to-learning-data-science-fundamentals )

In this blog, we’ll try to get a hands-on understanding of the Bayes’ Theorem. While doing so, hopefully we’ll be able to grasp a basic understanding of concepts such as Prior odds ratio, Likelihood ratio and Posterior odds ratio.

Arguably, a lot of classification problems have their root in Bayes’ Theorem. Reverend T. Bayes came up with this superior logical function, which mathematically deducts the probability of an event occurring from a larger set by “flipping” the conditional probabilities.

 


 

Consider,  E1, E2, E3,……..En to be a partition a larger set “S” and now define an Event – A, such that A is a subset of S.

Let the square be the larger set “S” containing mutually exclusive events Ei’s.  Now, let the yellow ring passing through all Ei’s be an event – A.

Using conditional probabilities, we know,

Also, the relationship:

Law of total probability states:

Rearranging the values of  &  gives us the Bayes Theorem:

The values of  are also known as prior probabilities, the event A is some event, which is known to have occurred and the conditional probability   is known as the posterior probability.

Now that, you’ve got the maths behind it, it’s time to visualise its practical application. Bayesian thinking is a method of applying Bayes’ Theorem into a practical scenario to make sound judgements.

The next blog will be dedicated to Bayesian Thinking and its principles.

For now, imagine, there have been news headlines about builders snooping around houses they work in. You’ve got a builder in to work on something in your house. There is room for all sorts of bias to influence you into believing that the builder in your house is also an opportunistic thief.

However, if you were to apply Bayesian thinking, you can deduce that only a small fraction of the population are builders and of that population, a very tiny proportion is opportunistic thieves. Therefore, the probability of the builder in your house being an opportunistic thief is actually a product of the two proportions, which is indeed very-very small.

Technically speaking, we call the resulting posterior odds ratio as a product of prior odds ratio and likelihood ratio. More on applying Bayesian Thinking coming up in the next blog.

In the meantime try this exercise and leave your comments below in the comments section.

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In the above example on “snooping builders”, what are your:

  • Ei’s
  • Event – A
  • “S”

About the Author: Nish Lau Bakshi is a professional data scientist with an actuarial background and a passion to use the power of statistics to tackle various pressing, daily life problems.

About the Institute: DexLab Analytics is a premier data analyst training institute in Gurgaon specializing in an enriching array of in-demand skill training courses for interested candidates. Skilled industry consultants craft state-of-the-art big data courses and excellent placement assistance ensures job guarantee.

For more from the tech series, stay tuned!

 

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Study: The Demand for Data Scientists is Likely to Rise Sharply

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Study: The Demand for Data Scientists is Likely to Rise Sharply

Data is like the new oil. A large number of companies are leveraging artificial intelligence and big data to mine these vast volumes of data in today’s time. Data science is a promising landmine of job opportunities – and it’s high time to consider it as a successful career avenue.

The prospect of data science is skyrocketing. Today, it is estimated that more than 50000 data science and machine learning jobs are lying vacant. Plus, nearly 40000 new jobs are to be generated in India alone by 2020. If you follow the global trends, the role of data scientist has expanded over 650% since 2012 yet only 35000 people in the US are skilled enough.

Data scientists are like the platform that connects the dots between programming and implementation of data to solve challenging business intricacies – says Pankaj Muthe, Academic Program Manager (APAC), Company Spokesperson, QlikTech. The company delivers intuitive platform solutions for embedded analytics, self-service data visualizations and guided analytics and reporting across the globe.

According to a pool of experts, data science is the hottest job trend of this century and is the second most popular degree to have at the master level next to MBA. No wonder, this new breed of science and technology is believed to be driving a new wave of innovation! Data scientists and front-end developers attracted the highest remuneration across Indian startups throughout 2017.

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Eligibility Criteria

To become a professional data scientist, a degree in computer science/engineering or mathematics is a must. Most of the data scientists have a knack for intricate tasks and aptitude to learn challenging programming languages. Any good organization seeks interested and intelligent candidates with the zeal to learn more. The subjects in which they need to be proficient are mathematics, statistics and programming. Moreover, data science jobs need a very sound base in machine learning algorithms, statistical modeling and neural networks as well as incredible communication skills.

Today, a lot of institutes offer state-of-the-art data science online courses that prove extremely beneficial for career growth and expansion. Combining theoretical knowledge and technical aspects of data science training, these institutes provide skill and assistance to develop real-world applications. DexLab Analytics is one such institute that is located in the heart of Delhi NCR. For more, feel free to reach us at <www.dexlabanalytics.com>

Future Prospects

After land, labour and capital, data ranks as the fourth factor of production. According to the US Department of Statistics, the demand for data engineers is likely to grow by 40% by 2020. If you are looking for a flourishing career option, this is the place to be: an entry-level engineer begins their career as a business analyst and then proceeds towards becoming a project manager. Later, after years of experience, these virgin business analysts further get promoted to become chief data officers.

 

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Alteryx and Its Popular Use Cases

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Alteryx and Its Popular Use Cases

Alteryx, a quick-to-implement data analytics tool, allows data analysts and scientists to work out business problems with unprecedented speed. This end-to-end platform enables data professionals to carry out diverse activities. With Alteryx, it’s possible to create a repeatable workflow in order to automate data tasks done manually. It offers intuitive interfaces, both code-friendly and code-free, for analytics modeling. While some data analysts value the predictive modeling, applications development and geospatial tools offered by Alteryx, others depend on it for data enrichment. The Alteryx Designer allows one to organize, blend, match and analyze data from a variety of sources that include flat files, APIs and much more.

Alteryx is very popular among business users as it provides a unified analytics experience for the entire enterprise. Below are two popular use cases where organizations have implemented Alteryx and achieved measurable results.

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Schneider Electric improved sales operations efficiency with Alteryx

Schneider Electric is an energy management consultant involving over 150,000 employees and operations in over 100 nations. They were seeking out newer and improved ways to identify top customers with excellent revenue potential and deploy sales resources proficiently. Managing manual processes and plenty of sources of dissimilar sales data, customer data, market data, company financials, etc., is quite a task. Naturally, this overwhelmed the Business Analytics team, slowing down the process of obtaining valuable sales-oriented insights and delaying decision-making too.

As a way out for the account selection process, they resorted to Alteryx and came up with an integrated analytics solution. This hugely sped up the generation of the high-revenue customer list. Alteryx automatically gathered and blended third-party and in-house data, and provided insights in half the time as required by the sales team previously.

Better Insights: Alteryx enriched data quality and included a higher number of data sources. Hence, sales managers got the necessary inputs to optimize deployment of sales resources on main customer profiles.

Hours vs. Weeks: Alteryx helped deploy resources to the right customers with great speed. The analysis time was brought down to three weeks from eight weeks.

Intuitive Workflow: Productivity as well as time-to-insight improved because Alteryx permits analytical applications to be shared between analysts.

AAA National used Alteryx for analyzing customer profiles, resulting in improved servicing

AAA National, a reputed alliance of motor clubs all over North America, wanted a better strategy so that member clubs can sell extra products to active members. Moreover, new members had to be located in each territory and brick-and-mortar offices were to be optimally placed depending on the demographics and drive-times of customers. However, each club handled their own data and aggregating all the information was quite a challenge for Database Administrators. Often, the data would be incomplete or erroneous.

Alteryx Analytics with integrated spatial analysis was implemented to generate self-explanatory drive-time maps along with reports. This enabled member clubs to form a clearer understanding of their membership base and also improve response times and staffing pertaining to the enterprise’s emergency roadside service.

Better Insights: Facilitated a clearer understanding of purchase history and demographics of club members, improving branch location decisions and marketing campaigns of the clubs.

Hours vs. Weeks: Previously, the time taken to generate a precise data set for analysis work took over three days; this time came down to two minutes.

Intuitive Workflow: There was no longer any need to use four kinds of tools to check membership requests. A single tool was sufficient for data blending, improvement and spatial analysis, reducing errors and saving money and time.

Feeling inspired to employ Alteryx concepts like insightful dashboards, data blending and intuitive workflows? Look up Alteryx Training Institutes in Delhi NCR. DexLab Analytics offers advanced Alteryx certification courses best suited for data professionals looking to build a lucrative career in this field.

 

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