The Alteryx Designer solves this by delivering an intuitive workflow for data blending and advanced analytics that leads to deeper insights in hours, not the weeks typical of traditional approaches! The Alteryx Designer empowers data analysts by combining data blending, predictive analytics, spatial analytics, and reporting, visualization and analytic apps into one workflow.
Alteryx is a platform that allows companies to answer business questions quickly and efficiently. The platform can be used as a major building block in a digital transformation or automation initiative. Alteryx allows teams to build processes in a more efficient, repeatable, less error-prone, and less risky way.
Alteryx is a platform that allows companies to answer business questions quickly and efficiently. The platform can be used as a major building block in a digital transformation or automation initiative. Alteryx allows teams to build processes in a more efficient, repeatable, less error-prone, and less risky way.
An introduction to self-service data with Dremio. Dremio reimagines analytics for modern data. Created by veterans of open source and big data technologies, Dremio is a fundamentally new approach that dramatically simplifies and accelerates time to insight. Dremio empowers business users to curate precisely the data they need, from any data source, then accelerate analytical processing for BI tools, machine learning, data science, and SQL clients. Dremio starts to deliver value in minutes, and learns from your data and queries, making your data engineers, analysts, and data scientists more productive.
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
A dive into Microsoft Fabric/AI Solutions offering. For the event: AI, Data, and CRM: Shaping Business through Unique Experiences. By D. Koutsanastasis, Microsoft
In this new Accenture Finance & Risk presentation we explore an approach for implementing financial reporting robotics in order to automate processes across regulatory reporting capabilities and improve efficiency. View our presentation to learn more.
For more on regulatory reporting, see presentation on User Defined Tools: http://bit.ly/2rinORX
Visit our blog for latest Regulatory Insights: https://accntu.re/2qnXs1B
Organizations are grappling to manually classify and create an inventory for distributed and heterogeneous data assets to deliver value. However, the new Azure service for enterprises – Azure Synapse Analytics is poised to help organizations and fill the gap between data warehouses and data lakes.
Embarking on building a modern data warehouse in the cloud can be an overwhelming experience due to the sheer number of products that can be used, especially when the use cases for many products overlap others. In this talk I will cover the use cases of many of the Microsoft products that you can use when building a modern data warehouse, broken down into four areas: ingest, store, prep, and model & serve. It’s a complicated story that I will try to simplify, giving blunt opinions of when to use what products and the pros/cons of each.
Event: Passcamp, 07.12.2017
Speaker: Stefan Kirner
Mehr Tech-Vorträge: https://www.inovex.de/de/content-pool/vortraege/
Mehr Tech-Artikel: https://www.inovex.de/blog
Welcome to my post on ‘Architecting Modern Data Platforms’, here I will be discussing how to design cutting edge data analytics platforms which meet the ever-evolving data & analytics needs for the business.
https://www.ankitrathi.com
Delivering Data Democratization in the Cloud with SnowflakeKent Graziano
This is a brief introduction to Snowflake Cloud Data Platform and our revolutionary architecture. It contains a discussion of some of our unique features along with some real world metrics from our global customer base.
Delta Lake OSS: Create reliable and performant Data Lake by Quentin AmbardParis Data Engineers !
Delta Lake is an open source framework living on top of parquet in your data lake to provide Reliability and performances. It has been open-sourced by Databricks this year and is gaining traction to become the defacto delta lake format.
We’ll see all the goods Delta Lake can do to your data with ACID transactions, DDL operations, Schema enforcement, batch and stream support etc !
Alteryx Tutorial Step by Step Guide for BeginnersVishnuGone
Alteryx is perhaps the most well known BI stages that allows association to address business questions quickly and capably. The stage can be used as a critical construction block in an advanced change or computerization drive. Alteryx is utilized for information purifying, which has confounded characteristics between two data sources, NULL qualities, letters, or crude information and zeros in the information. Alteryx can likewise be utilized to investigate business open doors further develop independent direction. Alteryx permits us to rapidly get to, control, dissect, and yield information.
Top Big data Analytics tools: Emerging trends and Best practicesSpringPeople
For many IT experts, big data analytics tools and technologies are now a top priority. Let's find out the top big data analytics tools in this slide to initialize and advance the process of big data analysis.
An introduction to self-service data with Dremio. Dremio reimagines analytics for modern data. Created by veterans of open source and big data technologies, Dremio is a fundamentally new approach that dramatically simplifies and accelerates time to insight. Dremio empowers business users to curate precisely the data they need, from any data source, then accelerate analytical processing for BI tools, machine learning, data science, and SQL clients. Dremio starts to deliver value in minutes, and learns from your data and queries, making your data engineers, analysts, and data scientists more productive.
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
A dive into Microsoft Fabric/AI Solutions offering. For the event: AI, Data, and CRM: Shaping Business through Unique Experiences. By D. Koutsanastasis, Microsoft
In this new Accenture Finance & Risk presentation we explore an approach for implementing financial reporting robotics in order to automate processes across regulatory reporting capabilities and improve efficiency. View our presentation to learn more.
For more on regulatory reporting, see presentation on User Defined Tools: http://bit.ly/2rinORX
Visit our blog for latest Regulatory Insights: https://accntu.re/2qnXs1B
Organizations are grappling to manually classify and create an inventory for distributed and heterogeneous data assets to deliver value. However, the new Azure service for enterprises – Azure Synapse Analytics is poised to help organizations and fill the gap between data warehouses and data lakes.
Embarking on building a modern data warehouse in the cloud can be an overwhelming experience due to the sheer number of products that can be used, especially when the use cases for many products overlap others. In this talk I will cover the use cases of many of the Microsoft products that you can use when building a modern data warehouse, broken down into four areas: ingest, store, prep, and model & serve. It’s a complicated story that I will try to simplify, giving blunt opinions of when to use what products and the pros/cons of each.
Event: Passcamp, 07.12.2017
Speaker: Stefan Kirner
Mehr Tech-Vorträge: https://www.inovex.de/de/content-pool/vortraege/
Mehr Tech-Artikel: https://www.inovex.de/blog
Welcome to my post on ‘Architecting Modern Data Platforms’, here I will be discussing how to design cutting edge data analytics platforms which meet the ever-evolving data & analytics needs for the business.
https://www.ankitrathi.com
Delivering Data Democratization in the Cloud with SnowflakeKent Graziano
This is a brief introduction to Snowflake Cloud Data Platform and our revolutionary architecture. It contains a discussion of some of our unique features along with some real world metrics from our global customer base.
Delta Lake OSS: Create reliable and performant Data Lake by Quentin AmbardParis Data Engineers !
Delta Lake is an open source framework living on top of parquet in your data lake to provide Reliability and performances. It has been open-sourced by Databricks this year and is gaining traction to become the defacto delta lake format.
We’ll see all the goods Delta Lake can do to your data with ACID transactions, DDL operations, Schema enforcement, batch and stream support etc !
Alteryx Tutorial Step by Step Guide for BeginnersVishnuGone
Alteryx is perhaps the most well known BI stages that allows association to address business questions quickly and capably. The stage can be used as a critical construction block in an advanced change or computerization drive. Alteryx is utilized for information purifying, which has confounded characteristics between two data sources, NULL qualities, letters, or crude information and zeros in the information. Alteryx can likewise be utilized to investigate business open doors further develop independent direction. Alteryx permits us to rapidly get to, control, dissect, and yield information.
Top Big data Analytics tools: Emerging trends and Best practicesSpringPeople
For many IT experts, big data analytics tools and technologies are now a top priority. Let's find out the top big data analytics tools in this slide to initialize and advance the process of big data analysis.
Advanced Analytics and Machine Learning with Data Virtualization (India)Denodo
Watch full webinar here: https://bit.ly/3dMN503
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python, and Scala put advanced techniques at the fingertips of the data scientists. However, these data scientists spend most of their time looking for the right data and massaging it into a usable format. Data virtualization offers a new alternative to address these issues in a more efficient and agile way.
Watch this session to learn how companies can use data virtualization to:
- Create a logical architecture to make all enterprise data available for advanced analytics exercise
- Accelerate data acquisition and massaging, providing the data scientist with a powerful tool to complement their practice
- Integrate popular tools from the data science ecosystem: Spark, Python, Zeppelin, Jupyter, etc
When and How Data Lakes Fit into a Modern Data ArchitectureDATAVERSITY
Whether to take data ingestion cycles off the ETL tool and the data warehouse or to facilitate competitive Data Science and building algorithms in the organization, the data lake – a place for unmodeled and vast data – will be provisioned widely in 2020.
Though it doesn’t have to be complicated, the data lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the data swamp, but not the data lake! The tool ecosystem is building up around the data lake and soon many will have a robust lake and data warehouse. We will discuss policy to keep them straight, send data to its best platform, and keep users’ confidence up in their data platforms.
Data lakes will be built in cloud object storage. We’ll discuss the options there as well.
Get this data point for your data lake journey.
It is a fascinating, explosive time for enterprise analytics.
It is from the position of analytics leadership that the mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics.
The coming years will be full of big changes in enterprise analytics and Data Architecture. William will kick off the fourth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.
Introducing Trillium DQ for Big Data: Powerful Profiling and Data Quality for...Precisely
The advanced analytics and AI that run today’s businesses rely on a larger volume, and greater variety, of data. This data needs to be of the highest quality to ensure the best possible outcomes, but traditional data quality tools weren’t designed for today’s modern data environments.
That’s why we’ve developed Trillium DQ for Big Data -- an integrated product that delivers industry-leading data profiling and data quality at scale, in the cloud or on premises.
In this on-demand webcast, you will learn how Trillium DQ:
• Empowers data analysts to easily profile large, diverse data sources to discover new insights, uncover issues, and report on their findings – all without involving IT.
• Delivers best-in-class entity resolution to support mission-critical applications such as Customer 360, fraud detection, AML, and predictive analytics.
• Supports Cloud and hybrid architectures by providing consistent high-performance processing within critical time windows on all platforms.
• Keeps enterprise data lakes validated, clean, and trusted with the highest quality data – without technical expertise in big data or distributed architectures.
• Enables data quality monitoring based on targeted business rules for data governance and business insight
Why Your Data Science Architecture Should Include a Data Virtualization Tool ...Denodo
Watch full webinar here: https://bit.ly/35FUn32
Presented at CDAO New Zealand
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python, and Scala put advanced techniques at the fingertips of the data scientists.
However, most architecture laid out to enable data scientists miss two key challenges:
- Data scientists spend most of their time looking for the right data and massaging it into a usable format
- Results and algorithms created by data scientists often stay out of the reach of regular data analysts and business users
Watch this session on-demand to understand how data virtualization offers an alternative to address these issues and can accelerate data acquisition and massaging. And a customer story on the use of Machine Learning with data virtualization.
How Data Virtualization Puts Enterprise Machine Learning Programs into Produc...Denodo
Watch full webinar here: https://bit.ly/3offv7G
Presented at AI Live APAC
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python and Scala put advanced techniques at the fingertips of the data scientists. However, these data scientists spend most of their time looking for the right data and massaging it into a usable format. Data virtualization offers a new alternative to address these issues in a more efficient and agile way.
Watch this on-demand session to learn how companies can use data virtualization to:
- Create a logical architecture to make all enterprise data available for advanced analytics exercise
- Accelerate data acquisition and massaging, providing the data scientist with a powerful tool to complement their practice
- Integrate popular tools from the data science ecosystem: Spark, Python, Zeppelin, Jupyter, etc.
Microsoft Fabric is the next version of Azure Data Factory, Azure Data Explorer, Azure Synapse Analytics, and Power BI. It brings all of these capabilities together into a single unified analytics platform that goes from the data lake to the business user in a SaaS-like environment. Therefore, the vision of Fabric is to be a one-stop shop for all the analytical needs for every enterprise and one platform for everyone from a citizen developer to a data engineer. Fabric will cover the complete spectrum of services including data movement, data lake, data engineering, data integration and data science, observational analytics, and business intelligence. With Fabric, there is no need to stitch together different services from multiple vendors. Instead, the customer enjoys end-to-end, highly integrated, single offering that is easy to understand, onboard, create and operate.
This is a hugely important new product from Microsoft and I will simplify your understanding of it via a presentation and demo.
Agenda:
What is Microsoft Fabric?
Workspaces and capacities
OneLake
Lakehouse
Data Warehouse
ADF
Power BI / DirectLake
Resources
With Enterprise data growing rapidly year over year, traditional analytics approaches have proven to be expensive and unyielding. The result is that a growing proportion of our data is unused “dark data”. How can we create the basis for a data driven organization? Enter the "perfect storm" of cloud data analytics tools and approaches.
AnalytiX DS specializes in the development of ‘agile tools’ for the data integration industry which automate manual data mapping and ETL conversion processes.
Bizview Performance Management for Qlikview UsersTridant
Bizview is a mid-market performance management product that enables customers to increase operational efficiency, improve staff productivity and generate more revenue
Leveraging Telecom Network Data with AlteryxTridant
Alteryx allows you to blend massive volumes of business and engineering data from your Business Support Systems (BSS) and Operational Support Systems (OSS) with third-party demographic, firmographic, and geospatial data in a single, intuitive workflow.
Powerful analytics transform disparate data into actionable insight with geographic significance, so you can make strategic decisions about network expansion, customer acquisition and retention, proactive maintenance, and other critical improvements. Plus, results can be easily shared across your company to enable agile decisions that improve network performance, increase customer satisfaction, and drive new revenue opportunities.
Turn network and customer data into actionable insight
Whether you are a wireless, wireline, or cable network operator, the customer is king. From retaining existing customers to acquiring new subscribers from your competitors, competitive advantage in the fast-moving communications market is all about customer satisfaction and network modernization.
Alteryx Strategic Analytics allows you to combine massive volumes of business and engineering data from your Business Support Systems (BSS) and Operational Support Systems (OSS) with third-party demographic, firmagraphic, and industry-specific data in single, integrated environment. Powerful analytics transform disparate data into actionable insight with geographic significance, so you can make strategic decisions about network expansion, customer acquisition and retention, proactive maintenance, and other critical improvements.
Plus, results can be easily shared across your company to enable agile decisions that improve network performance, increase customer satisfaction, and drive new revenue opportunities.
Tridant - IBM Solutions Partner of the YearTridant
Tridant is one of Asia's most experienced analytics and performance management companies. With over 300 projects last year and a highly experienced and skilled team, we provide exceptional business value to clients
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.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
1. Data Sheet
As a business analyst, you may be all too familiar with the complicated and painful
nature of the analytic process. You often need one set of software tools to gather,
cleanse and blend information from the ever-growing number of data sources,
a different set to build and publish analytic models, and still more applications
to put your time-critical information into the hands of business decision-makers.
And that’s if you even attempt these steps yourself without relying on a centralized
analytics department staffed by data scientists with other priorities.
Alteryx changes everything you know about the convoluted and time-consuming
analytic process, making it simple, fast, intuitive, and cost-effective. Without any
programming, Alteryx Designer lets you blend data, create advanced predictive and
spatial analytics, generate powerful reports, and output results in a single, intuitive
workflow. The result? Deeper insights in hours rather than the weeks of traditional
approaches, allowing you to spend less time preparing your data and more time
getting insight from it.
Blend Internal, Third-Party, and Cloud-Based Data in a Single Tool
To do your job as a business analyst, you must access and blend data from many
sources: internal data from spreadsheets and data warehouses, third-party data
from external data providers, and cloud-based data from social media applications,
Big Data stores, and other SaaS platforms. Typically, this means leveraging multiple
tools—and even multiple people—to pull together all the relevant data you
need for your analytics.
Not anymore. Alteryx eliminates the inefficiencies inherent in data blending
with a single, intuitive workflow. Using Alteryx Designer, you can access, prepare,
cleanse, blend, and enrich your data up to 100X faster than with other approaches.
And because Alteryx Designer enables you to complete the full range of data
preparation tasks in a single, drag-and-drop workflow and without any programming,
you’ll speed through the previously time-consuming data preparation process,
leaving you with more time for value-added analysis.
Alteryx Designer
Delivering an Intuitive Workflow for
Data Blending and Advanced Analytics
The intuitive workflow
inherent in Alteryx Designer
allows you to:
• Blend internal, third-party,
and cloud-based data
• Build powerful R-based
predictive and spatial analytic
applications without any
programming
• Share deep data insight with
business decision-makers
in hours, not weeks
2. 2
Alteryx Designer enables you to access
virtually any data source, including:
• Data warehouses and databases,
such as Oracle, SAP, Teradata,
HP Vertica, and Pivotal Greenplum
• ERP and cloud-based applications,
such as Salesforce.com, Marketo,
and Google Analytics
• Hadoop data stores, including
Cloudera and MapR distributions
• NoSQL Databases, such as MongoDB
• Flat files and Office applications,
such as Microsoft Excel and Access
• Social data and sentiment analysis
from Twitter, Facebook, Foursquare,
and other DataSift-based sources
• Third-party data from Experian, D&B,
TomTom, and the US Census Bureau
• Other analytics platforms, such as
SPSS and SAS
Make Better Business
Decisions with Powerful
R-Based Predictive Analytics
You know that the most accurate
business decisions are based on
forward-looking, predictive analytics
rather than on past performance
or simple guesswork. Unfortunately,
most predictive analytics tools
require statistical experts with
specialized training to code complex
algorithms and complicated models.
That means another bottleneck and
level of indirection while you wait
for someone else to create your
analytic application.
Now, Alteryx puts the power of
predictive analytics in your own hands.
With Alteryx Designer, you can turn
your raw data into actionable insight
with drag-and-drop tools that let you
create and run your own predictive
analytics with no programming. Using
the Alteryx visual interface and more
than 30 predictive tools based on the
R open source statistical language,
you can quickly and easily predict
customer behavior, determine future
inventory requirements, identify new
retail store locations, and more.
Alteryx Designer lets you easily include
any of the following predictive analytics
without any programming:
• Predictive modeling techniques,
such as logical regression or
decision trees
• Clustering techniques, such as
K-centroid clustering and principle
component analysis
• Data investigation techniques,
such as scatter plots and
association analysis
The Alteryx intuitive workflow for
data blending and advanced analytics
leads to deeper insights in hours
instead of weeks
3. 3
Let Location Matter by Including
Spatial Analysis in Your Applications
Thanks to the explosive growth
of mobile computing and social
media, most consumer and business
interactions now include a location
data-point, making spatial analysis an
increasingly significant component
in data analysis. Who is buying your
product, where are they located,
how far away do they live from and
how long does it take them to reach
the nearest store—these questions all
factor into the decision-making process.
But, as with predictive analytics,
geospatial and location intelligence
has been the domain of a small number
of experts who cannot scale to meet
the needs of growing businesses
with an increasing dependence
on analytics.
Alteryx Designer enables you to easily
append pre-packaged location data
from TomTom to the rest of your data
set using an intuitive, drag-and-drop
interface, so you can not only visualize
where events are taking place but
also understand their impact on
your business. Now you can conduct
advanced location-based calculations,
such as drive-time, trade area, and
spatial matching and point creation
analyses, in your same analytic
workflow—and make location-based
business decisions that make sense
for your business.
With Alteryx Designer, you can include
advanced analytics with spatial context,
such as:
• Geocoding and standardization
of addresses
• Data blending on spatial aspects
• Trade area creation and analysis
• Drive-time analytics
• Mapping and geographic
visualization
Share Insights with Business
Decision-Makers
Your job is not done after you run
your analysis: now you need to get the
insight ready for and into the hands
of other business users and decision-
makers. Often, you spend as much or
even more time packaging the results
for consumption by others as you
do running the analyses themselves.
What’s involved? Cutting and pasting
into reports, changing data formats
for visualization tools, and updating
files to feed into other systems and
processes. All of which wastes time
and introduces errors, negating
the power and timeliness of the
insight itself.
Advanced spatial analytics tools in
Alteryx Designer enable drive-time
calculations, trade area analysis,
spatial matching, and more
4. Alteryx is a registered trademark of Alteryx, Inc. 9/14
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 500 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 Designer streamlines the sharing
of data insights throughout your
organization by integrating powerful
reporting and file output capabilities
into the same intuitive workflow you
use for data blending and advanced
analytics. Once you’ve completed your
analysis, you can create custom reports
featuring tables, charts, and maps
that can be refreshed and emailed on
demand. Or you can easily save your
results in a wide variety of formats
used by downstream systems and
business processes, including native
Tableau or QlikView file formats for
data visualization. You can also share
workflows from Alteryx Designer
as analytic applications using the
Alteryx Analytics Gallery or Alteryx Server.
With just a few simple clicks—and
without altering the integrity of your
analytic results—you can bring the
power of Alteryx analytics to everyone
in your organization.
Desktop System Requirements
Minimum
• Microsoft Windows 7 or later (32-bit)
• Dual Core
• 4G RAM
• 500 GB free disk space
Recommended
• Microsoft Windows 7 or later (64-bit)
• Quad Core i7 (single chip)
• 3GHz or faster processor
• 8G RAM
• > 1 TB free disk space
High Performance
• Microsoft Windows 7 or later (64-bit)
• Quad Core i7 (single chip)
• 3GHz or faster processor
• 16G RAM
• > 1 TB Disk free disk space
Supported File Formats
Flat Files
• ASCII
• CSV – Comma Separated Value
• MDB/ACCDB – Microsoft®
Access database
• DBF – dBASE Database File format
• XLS/XLSX – Microsoft Excel®
spreadsheet
• HTM/HTML/XML – Hyper Text/Extensible
Markup Language
• QVX – QlikView data eXchange
• SAV – IBM SPSS file format
• SAS7BDAT – SAS binary file format
• TDE – Tableau
Relational Database Files
• ODBC – Open Database Connectivity
• OLE-DB – Object Linking and
Embedding Database
• OCI – Oracle®
Spatial Database
Spatial Files
• GRD/GRC – Grid and Classified Grid
• KML – Google Keyhole Markup Language
• MDB/PGDB – ESRI Personal Geodatabase®
• MID/MIF – MapInfo Professional®
Interchange Format
• SDF – Autodesk®
Spatial Data Files
• SHP – ESRI®
ArcMap®
Shape
(includes .SHP,* .DBF,* .SHX,* .PRJ*)
• SZ – Alteryx Spatial Zip
• TAB – MapInfo Professional Table
(includes .TAB,* .DAT,* .MAP,* .ID,* .IND*)
Supported Databases
• Apache Hive (read-only support)
• Amazon Redshift
• Amazon S3**
• Cassandra***
• Cloudera Impala
• ESRI GeoDatabase (read-only support)*
• Google BigQuery
• Hortonworks
• HP Vertica**
• IBM DB2
• IBM Netezza®
*
• Microsoft SQL Server®
*
• MongoDB
• MySQL®
• Oracle*
• Pivotal Greenplum Database
• SQLite
• Sybase®
• Teradata and SQL server**
• Teradata®
Reporting Formats
• PNG – Portable Network Graphics
• HTML – Hyper Text Markup Language
• PCXML – Alteryx Markup Language
• PDF – Adobe®
Portable Document Format
• RTF – Rich Text Format
• DOC/DOCX – Microsoft Word
• XLS/XLSX – Microsoft Excel
• PPT/PPTX – Microsoft PowerPoint
Tools/Macros
• Amazon S3
• Datasift
• Foursquare
• Gnip
• Google Analytics
• Hadoop Distributed File System (HDFS)
• Marketo
• Salesforce.com
• Sharepoint Lists
• Twitter
* Includes support for vendor-specific spatial functionality
** Now includes bulk load support
*** ODBC Driver Available upon Request