To effectively leverage the power of rich visualizations in making data-driven decisions, you must significantly reduce front-end data preparation time.
In order to create visualizations that lead to answers quickly, you need to prepare your data in the right way. Together, Alteryx and Tableau can help. This paper will show you how.
Organizations today are both blessed and cursed by data. But to get insights, you need to unlock the data you have. If you are like most analysts, you are spending hours and hours struggling with “dirty data,” data that needs to be joined together, and data that is in the wrong shape for visualization. And each time the data changes, you have to redo your work. You’re stuck in the “gunk” of preparing data, and you never get out of it to do what you really need to be doing, which is analyzing and visualizing your data and realizing new, deeper insights! Learn more about alteryx tableau integration by checking out the presentation.
A New Analytics Paradigm in the Age of Big Data: How Behavioral Analytics Will Help You Understand Your Customers and Grow Your Business Regardless of Data Sizes
This presentation introduces big data and explains how to generate actionable insights using analytics techniques. The deck explains general steps involved in a typical analytics project and provides a brief overview of the most commonly used predictive analytics methods and their business applications.
Vijay Adamapure is a Data Science Enthusiast with extensive experience in the field of data mining, predictive modeling and machine learning. He has worked on numerous analytics projects ranging from healthcare, business analytics, renewable energy to IoT.
Vijay presented these slides during the Internet of Everything Meetup event 'Predictive Analytics - An Overview' that took place on Jan. 9, 2015 in Mumbai. To join the Meetup group, register here: http://bit.ly/1A7T0A1
Organizations today are both blessed and cursed by data. But to get insights, you need to unlock the data you have. If you are like most analysts, you are spending hours and hours struggling with “dirty data,” data that needs to be joined together, and data that is in the wrong shape for visualization. And each time the data changes, you have to redo your work. You’re stuck in the “gunk” of preparing data, and you never get out of it to do what you really need to be doing, which is analyzing and visualizing your data and realizing new, deeper insights! Learn more about alteryx tableau integration by checking out the presentation.
A New Analytics Paradigm in the Age of Big Data: How Behavioral Analytics Will Help You Understand Your Customers and Grow Your Business Regardless of Data Sizes
This presentation introduces big data and explains how to generate actionable insights using analytics techniques. The deck explains general steps involved in a typical analytics project and provides a brief overview of the most commonly used predictive analytics methods and their business applications.
Vijay Adamapure is a Data Science Enthusiast with extensive experience in the field of data mining, predictive modeling and machine learning. He has worked on numerous analytics projects ranging from healthcare, business analytics, renewable energy to IoT.
Vijay presented these slides during the Internet of Everything Meetup event 'Predictive Analytics - An Overview' that took place on Jan. 9, 2015 in Mumbai. To join the Meetup group, register here: http://bit.ly/1A7T0A1
every business needs a data analytics to get a detailed value of cost and profits. we will study the importance in detail in this particular presentation.
Is Your Company Braced Up for handling Big Datahimanshu13jun
Has your company recently launched new product or company is concerned with the poor sales figure or want to reach new prospects and also reduce the existing customers' attrition, then this thought evoking short hand guide is available for you to explore.
Evolution of Data Analytics: the past, the present and the futureVarun Nemmani
This paper delves into the topic of advanced analytics, the current industry demands to utilize and analyze huge/diverse amounts of data, how big data analytics is becoming a part of the decision making process and to anticipate trends. This paper takes the reader from Analytics era 1.0 to the current Analytics era 3.0; shows the future projections of big data analytics and also the current leaders of the Big Data Analytics market.
Impact of Data Analytics in Changing the Future of Business and Challenges Fa...IJSRP Journal
Data Analytics refers to a comprehensive approach that makes use of both Qualitative and Quantitative Information in order to draw valuable insights and arriving at conclusions based on the extensive usage of statistical tools accompanied by explanatory and predictive models running over the data. It tries to understand the behavior and dynamics of businesses thereby leading to improved productivity and enhancing business gains by helping with appropriate decision making. Considering the intensified disruption caused by recent revolution in the field of Data Analytics, this articles aims to cover the potential impacts that Data Analytics could have over the already existing businesses and how new entrants, especially across the emerging economies, could make the best use of Data Analytics in gaining an edge over their competitors. It also aims to deep dive into the challenges faced by businesses while adopting or moving to Data Analytics and how they can overcome those challenging barriers for a successful future. .
Top 9 Search-Driven Analytics Evaluation CriteriaJames Capurro
In our personal lives, search has transformed how access information. Google, Facebook and Amazon have raised our expectations for how we want to access data at work. Finally, after a decade of failed promises and misguided approaches in the enterprise, search is making a comeback.
In this book, we present nine different criteria that you can use to evaluate search-driven analytics products - everything from training time to search intelligence, data modeling, and total cost of ownership.
Leveraging Data Science in the Automotive IndustryDomino Data Lab
Cars.com Inc. is a decision engine for car buyers and a growth engine for our partners. Data Science is the bread and butter of any decision engine and Cars is no different. In this talk, I will discuss how we quantify various parameters of a car and plan to make use of all the data in hand to put predictive models at various stages of a users’ automobile lifecycle. This talk will also cater to students looking to gain knowledge on how data science is utilized at scale while still following certain processes and leading the way for business and product partners.
IBM Watson Analytics sets powerful analytics capabilities free so practically anyone can use them. Automated data preparation, predictive analytics, reporting, dashboards, visualization and collaboration capabilities, enable you to take control of your own analysis. You can then take the appropriate action to address a problem or seize an opportunity, all without asking IT or a data expert for help.
CIO Applications Magazine Names Bardess One of the Top 25 ML Solution Providerschrishems1
The investment Bardess has made in Tangent Works InstantML is critical to the industry to address what we see as a current weakness with all other AutoML technologies which is they rely on brute force (heavy compute effort) to select the best algorithm and hyper parameters, at the expense of rapid results.
every business needs a data analytics to get a detailed value of cost and profits. we will study the importance in detail in this particular presentation.
Is Your Company Braced Up for handling Big Datahimanshu13jun
Has your company recently launched new product or company is concerned with the poor sales figure or want to reach new prospects and also reduce the existing customers' attrition, then this thought evoking short hand guide is available for you to explore.
Evolution of Data Analytics: the past, the present and the futureVarun Nemmani
This paper delves into the topic of advanced analytics, the current industry demands to utilize and analyze huge/diverse amounts of data, how big data analytics is becoming a part of the decision making process and to anticipate trends. This paper takes the reader from Analytics era 1.0 to the current Analytics era 3.0; shows the future projections of big data analytics and also the current leaders of the Big Data Analytics market.
Impact of Data Analytics in Changing the Future of Business and Challenges Fa...IJSRP Journal
Data Analytics refers to a comprehensive approach that makes use of both Qualitative and Quantitative Information in order to draw valuable insights and arriving at conclusions based on the extensive usage of statistical tools accompanied by explanatory and predictive models running over the data. It tries to understand the behavior and dynamics of businesses thereby leading to improved productivity and enhancing business gains by helping with appropriate decision making. Considering the intensified disruption caused by recent revolution in the field of Data Analytics, this articles aims to cover the potential impacts that Data Analytics could have over the already existing businesses and how new entrants, especially across the emerging economies, could make the best use of Data Analytics in gaining an edge over their competitors. It also aims to deep dive into the challenges faced by businesses while adopting or moving to Data Analytics and how they can overcome those challenging barriers for a successful future. .
Top 9 Search-Driven Analytics Evaluation CriteriaJames Capurro
In our personal lives, search has transformed how access information. Google, Facebook and Amazon have raised our expectations for how we want to access data at work. Finally, after a decade of failed promises and misguided approaches in the enterprise, search is making a comeback.
In this book, we present nine different criteria that you can use to evaluate search-driven analytics products - everything from training time to search intelligence, data modeling, and total cost of ownership.
Leveraging Data Science in the Automotive IndustryDomino Data Lab
Cars.com Inc. is a decision engine for car buyers and a growth engine for our partners. Data Science is the bread and butter of any decision engine and Cars is no different. In this talk, I will discuss how we quantify various parameters of a car and plan to make use of all the data in hand to put predictive models at various stages of a users’ automobile lifecycle. This talk will also cater to students looking to gain knowledge on how data science is utilized at scale while still following certain processes and leading the way for business and product partners.
IBM Watson Analytics sets powerful analytics capabilities free so practically anyone can use them. Automated data preparation, predictive analytics, reporting, dashboards, visualization and collaboration capabilities, enable you to take control of your own analysis. You can then take the appropriate action to address a problem or seize an opportunity, all without asking IT or a data expert for help.
CIO Applications Magazine Names Bardess One of the Top 25 ML Solution Providerschrishems1
The investment Bardess has made in Tangent Works InstantML is critical to the industry to address what we see as a current weakness with all other AutoML technologies which is they rely on brute force (heavy compute effort) to select the best algorithm and hyper parameters, at the expense of rapid results.
Using a Semantic and Graph-based Data Catalog in a Modern Data FabricCambridge Semantics
Watch this webinar to learn about the benefits of using semantic and graph database technology to create a Data Catalog of all of an enterprise's data, regardless of source or format, as part of a modern IT or data management stack and an important step toward building an Enterprise Data Fabric.
Operational Analytics: Best Software For Sourcing Actionable Insights 2013Newton Day Uploads
Actionable Insights are those views of data that cause managers to ask new questions about how processes work and take action. They differ from traditional key performance measures and daily operating reports that focus on delivering a picture of progress against a strategic objective, operating budget or forecast. What software is best for your business to source these game-changing perspectives of your enterprise?
Now companies are in the middle of a renovation that forces them to be analytics-driven to
continue being competitive. Data analysis provides a complete insight about their business. It
also gives noteworthy advantages over their competitors. Analytics-driven insights compel
businesses to take action on service innovation, enhance client experience, detect irregularities in
process and provide extra time for product or service marketing. To work on analytics driven
activities, companies require to gather, analyse and store information from all possible sources.
Companies should bring appropriate tools and workflows in practice to analyse data rapidly and
unceasingly. They should obtain insight from data analysis result and make changes in their
business process and practice on the basis of gained result. It would help to be more agile than
their previous process and function.
This essay presents a new framework to analyze the impact of AI and ML on work. Its premise is that AI and ML have already been adopted in many firms. Now, efforts are underway to simplify the next stage of adoption by removing the complex requirement to create well-formulated algorithms.
This innovation is automating the deployment of ML ecosystems. Early adopters report substantial gains in new revenues, additional efficiencies in operations and a changed mindset for employees. One example of the latter is LinkedIn’s efforts to establish a “culture of data,” where data serves as the foundation for corporate strategy and data analytics-based operations. This essay contends that by lifting earlier roadblocks to adoption, growth of ML and AI systems will increase, greater attention will be paid to obtaining and structuring data resources, and more ML systems can be applied to evaluating strategic and financial decisions.
Semantic 'Radar' Steers Users to Insights in the Data LakeCognizant
By infusing information with intelligence, users can discover meaning in the digital data that envelops people, organizations, processes, products and things.
5 Things You Should Know About Data ProductsScribble Data
Humans are generating and collecting close to 3.5 quintillion bytes of data every day!
This has given rise to data products–from BI dashboards to derived datasets, ML models, and more- bringing data closer to business users.
Our Co-founder COO shares his thoughts around data products, and how each data product adds intelligence and efficiency to advanced analytics problems, speeding up analytical throughput by 10x.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
1. White Paper
A Guide to Preparing Your
Data for Tableau
Written in collaboration with Chris Love, Alteryx Grand Prix Champion
2. 2
Introduction
If you’re like most data analysts today, creating rich visualizations of your data
is a critical step in the analytic process. Visualizations let you pinpoint the
trends, discover the risks, and uncover the opportunities hidden in your data
so you can find the answers you need—and make the right data-driven decisions
for your business.
But creating beautiful and elegant data visualizations that wow business users
requires a significant amount of behind-the-scenes work to prepare the data—
work that can take longer than creating the visualizations themselves.
First, to get the most complete and accurate view of your business problem
in context, you need to gather, blend, and cleanse data from an ever-growing
number of disparate sources. Next, even when you’ve wrapped your arms
around your internal data sources, you don’t have the whole story. That
requires enriching your internal data with third-party data sources—such as
demographic or location information—and then running sophisticated spatial
or predictive analytics. And finally, once you have generated your analytic
application, the output may not be easily consumable for visualization and
extra work may be required to get it ready for “prime time.”
As a data analyst, you’re familiar with all of these challenges, and until now
you’ve probably overcome them with manual workarounds. But those
workarounds are no longer enough, thanks to the rapidly changing nature of
data as well as the speed at which business users ask questions and expect
answers. You’re still feeling overworked and scrambling to meet deadlines.
In order to create visualizations that lead to answers quickly, you need to
prepare your data in the right way. Together, Alteryx and Tableau can help.
This paper will show you how.
1. Gather and
blend disparate
data sources
3. Shape data
for visualization
4. Explore
and enable
discovery
in Tableau
2. Enrich internal
data with third-
party data and
run analytics
To effectively leverage
the power of rich
visualizations in
making data-driven
decisions, you must
significantly reduce
front-end data
preparation time
Consumer Reports, which runs
more than 1.8 million surveys
annually, saved thousands
of hours of manual labor by
automating its survey preparation
and data reshaping process
in Alteryx.
3. 3
Gather and Prepare Your Data
Alteryx makes it simple to gather all your relevant data sources into a single
workflow so you can analyze your business problem in context. Whether your
data is in a data warehouse, Excel spreadsheets, social media apps, Big Data
platform, cloud apps, or any combination of these, you can quickly and easily
create the data set you need in the intuitive drag-and-drop workflow of
Alteryx, without needing any special tools or programming skills.
Once you have all the relevant data that’s required to answer the business
question at hand and you have your data set in front of you, you’re ready to
go, right? Wrong! You still need to make sure your data set is free of duplicates,
extraneous characters, trailing zeroes, and other data noise. How can you
determine how much of your main product you have really sold if you have
10 or 15 different product names that represent the same SKU? And how
will you know who your best customers are if your data has three different
entries for a single company’s name, for example, one with “Inc.,” one without,
and one that looks like another company but is really the subsidiary of your
largest customer?
With Alteryx, creating standardized product and company name taxonomies,
for example, is a snap. Thanks to built-in tools that automatically deduplicate,
parse, and eliminate extraneous data, you can easily create the cleanest
possible data set.
Plus, with Alteryx, you only have to define your cleansing process once.
Every time you introduce or update your data, Alteryx automatically cleanses
the data set according to the rules you defined the first time. The result?
A truly automated data preparation process.
You can also use Alteryx in this step to ensure the data fits your specific
purpose, whether you plan to run predictive models or generate rich data
visualizations. For example, predictive modeling requires a narrow data set
with just a few specific variables to establish causality and then use this
data to predict future outcomes. In contrast, when creating a visualization,
you want as much data at hand as possible so you can recombine, compare,
and view the data from different perspectives: by gender, by income level,
by geography, by purchase level, and more.
Using Alteryx to blend and analyze data and then visualize analytic results
in Tableau, Discovery Communications monitors its video editing processes,
tracking its edit bays and which film clips are edited on which machine and
by whom. Thanks to Alteryx and Tableau, operators can pinpoint the exact
machines or bays in which problems may have occurred, quickly recalibrating
machines and remediating errors. What’s more, by blending error information
with editing information, the company can also determine if a particular editor
generates an unacceptable number of errors, and then provide retraining on
editing processes.
For more information, please see http://bit.ly/DiscoveryComm.
“Analysts spend a significant
amount of time, over two-thirds
(69%), in data-related tasks in the
analytics process, compared
to the analytic ones where their
time should be spent.”
Mark Smith, CEO and Chief Research
Officer, Ventana Research
4. 4
Enrich Your Data and Run Analytics
In some cases, you might not have all the information you need; your internal
data just needs more. More what? More context. With third-party data that’s
available with Alteryx, you can get the big picture in minutes.
Need to determine how your customers stand out from the rest of the
population? No problem. Just use Experian Mosaic categories in Alteryx to
segment your customers. Then match your customers to these categories
so you can target prospects with similar attributes.
Which potential store locations will be the most profitable? Using the
Experian data in Alteryx, you can answer that question by discovering how
many customers live within a certain distance or drive time from the store.
Run Powerful Predictive Analytics
Conventional wisdom says that only highly trained data scientists and
programmers with Ph.D.s can run predictive analytics, creating complex
algorithms to predict, for example, which product a customer will purchase
next and when. Conventional wisdom has changed, thanks to Alteryx.
Using Alteryx, business analysts can harness the power of predictive analytics
by merely dragging and dropping pre-built predictive analytics macros and
configuring those macros with a few items in a dialog box. Market-basket
analysis, linear regression, A/B testing, decision trees, and forest models are all
just a few keystrokes away for every business user, making them self-reliant
and able to iterate quickly to answer new business questions.
Based on previous data from other similar customers, you can use Alteryx
predictive analytics to determine the most likely next purchase, along with a
degree of certainty for that prediction. You can also use predictive capabilities
to determine which offers to give to which customers and whether to offer
them by mail or live on your website.
What’s more, any predictive analytic model can be easily represented and
visualized in Tableau. You can use bar charts to represent treatment vs. non-
treatment items in A/B tests. Or use bubble charts to represent market-basket
analysis and show how a customer’s purchase of one item can influence his
purchase of others.
Leverage Spatial Analytics
While retailers, supply chain managers, and transportation managers have
long known and leveraged spatial analytics, any organization that uses
mobile technology is waking up to the power of spatial. With spatial analytics,
you can track how traffic flows inside a store, the physical location of a buyer
when she makes an online purchase, and even where the customer is physically
located when she expresses a sentiment about your product.
What’s more, if you are planning to create any type of map in Tableau,
Alteryx spatial analytics can help you create the relevant data set for your
business decisions. Using Alteryx, you can create polygons that capture data
within a specific area and then output that data directly to Tableau for visual
representation in a drive-time map, cell tower coverage area, disease outbreak
area, flood zone, and more.
“All levels of my team, from data
scientists to business analysts,
found they could use Alteryx
within a couple of hours, to produce
results that would have taken
days or weeks to get from IT.
We can automate jobs in seconds,
and spend more time analyzing
data instead of getting it.”
Kim Carrico, Director of Marketing Planning
and Analysis, Optimum Lightpath
5. 5
Visualizing blended data in Tableau
Experian Mosaic Data ERP Sales System
ERP Sales DatabaseDrivetime Analysis
CRM System
TomTom Geospatial Data
Shape Your Data and Visualize in Tableau
Even though you’ve blended and enriched a clean data set and performed the
right spatial and predictive analytics in Alteryx, your data might not be in the
right format for visualization. Maybe you need to transpose rows and columns
or regroup the data in a different way that makes sense.
Using Alteryx, you can reshape your data to make it easily consumable in
Tableau. For example, many companies work with survey data, which is
generally stored in long rows of responses per respondent. If you try to use
survey data in this format as the underlying data for a visualization, you’ll end
up with gibberish. Instead, visualizing survey data requires a reshaping of the
data into columns that relate to each question. Alteryx makes this type of
reshaping easy and automatic, eliminating hundreds and even thousands
of hours of manual effort to reformat the data for Tableau.
And, with the ability to output TDE files directly to Tableau, Alteryx enables
a near-instant iteration process. Just add a new data source, run Alteryx,
and view the results in Tableau. Or change your analytics model, run it in
Alteryx, and visualize and discover the results in Tableau. It’s that simple.
No need to re-cleanse and reshape your data every time you update—
it’s all automated and repeatable.
What’s more, Alteryx can launch Tableau Workbooks, which also use Tableau
TDE files. This means you can not only launch your Tableau file when you
update your content, but you can also launch it in the context of your
Tableau Workbook and see how it impacts existing formatted visualizations.
“Alteryx significantly enhances our
ability to help people make sense
of data. When used in combination
with Tableau, the ease with which
complex data sets can first be
prepared in Alteryx and then
visualized in Tableau is incredible.”
Craig Bloodworth, Tableau Zen Master
and CTO, The Information Lab
6. Alteryx is a registered trademark of Alteryx, Inc. 6/14
230 Commerce, Ste. 250, Irvine, CA 92602
+1 714 516 2400
www.alteryx.com
About Alteryx
Alteryx is the leader in data blending
and advanced analytics software.
Alteryx Analytics provides analysts
with an intuitive workflow for data
blending and advanced analytics
that leads to deeper insights in hours,
not the weeks typical of traditional
approaches. Analysts love the Alteryx
analytics platform because they can
deliver deeper insights by seamlessly
blending internal, third-party, and
cloud data, and then analyze it using
spatial and predictive drag-and-
drop tools. This is all done in a single
workflow, with no programming
required. More than 400 customers,
including Experian, Kaiser, Ford,
and McDonald’s, and 200,000+ users
worldwide rely on Alteryx daily. Visit
www.alteryx.com or call 1-888-836-4274.
Alteryx is a registered trademark of
Alteryx, Inc.
Conclusion
Creating visualizations are a big part of your job as a data analyst—and it is
only becoming more important. But if you are spending 50 to 75 percent of your
time preparing your data for visualization instead of creating the visualization
itself, you’re not giving business users what they need to pinpoint trends,
discover risks, and take advantage of time-critical opportunities when they
need it.
To create the rich visualizations that lead to answers quickly, you need Alteryx
and Tableau. Pull together all the relevant data for the most complete, accurate
view of your business problem in Alteryx. Use Alteryx to enrich your internal
data with third-party data source and run powerful predictive or spatial
analytics to get the big picture. Shape your analytic results in Alteryx for easy
output to and consumption in Tableau. And do all the heavy lifting once and
automate the process for future content updates. The result? Less time spent
preparing data for visualization and more time actually exploring the data and
enabling discovery in Tableau.
For more information, please go to alteryx.com/tableau