Focus on Your Analysis, Not Your SQL Code

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This document discusses the challenges of using SQL for data analysis and introduces Alteryx as an alternative. It notes that SQL can be difficult to understand and repeat, while Alteryx allows users to see the full data workflow, perform transformations without coding, and access different data sources flexibly. The presentation includes an agenda, overview of Alteryx's benefits, and demonstration of its capabilities.

Technologie

Focus onYour Analysis, NotYour SQL Code
Beth Narrish, Senior Product Marketing Manager
Dan Hilton, Solutions Architect
Mar 7, 2017

Download a FREETrial: alteryx.com/trial© 2017 Alteryx, Inc. | Confidential
Agenda
• SQL and SQL
Challenges
• Who is Alteryx
• Benefits ofWorkflow
• Demo
• Q&A
2

© 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial
The legacy approach to analytics is often challenging for
organizations
3
Data Blending
Predictive &
Spatial Analytics
Production &
Output Creation
Data Access Prep
& Cleansing
Data integration tool Analytic coding App creationData quality tool
Slow, long process Multiple tools and
process steps
Expensive software
and labor
IT Staff Data Scientists Line-of-Business

© 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial
And while many organizations are relying on spreadsheets for
data analysis, they are also using low-level SQL queries
4
Only
Harvard Business Review: Data Blending:A Powerful Method For Faster, Easier Decisions

© 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial
• SQL is the standard language for relational
database management systems and has been for
40 years
• Source: American National Standards
Institute
• While NoSQL has gained popularity with
Hadoop, MongoDB and other sources, SQL is still
king
• Impala and Spark can utilize SQL
Why SQL?
5

© 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial
But SQL can pose challenges for analysts and their colleagues
• Colleagues often cannot pick up SQL code
and understand how data was pulled
• Documenting steps is needed to repeat the
process when data changes
• Troubleshooting each step in a complex
query can be tedious
6

Download a FREETrial: alteryx.com/trial© 2017 Alteryx, Inc. | Confidential
A Leading Platform for Self-Service DataAnalytics
7
Prep, blend, and analyze
all your data using a
repeatable workflow
Deliver deeper insights
in hours, not weeks
Deploy and share
analytics at scale

© 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial
A Leading Platform for Self-Service DataAnalytics
8
Enrich
Prep & Blend Analyze
Input All Relevant Data
Share
OutputAll Popular Formats

© 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial
WhyWorkflows?
Transparency
See data flow through the process, and
the actions performed at every step
10

© 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial
WhyWorkflows?
SeamlessTransformations
Extract, transform and load data
without writing any code
Efficiency In Debugging,Testing
and Prototyping
Stop spending time writing code only
to get a null data set; understand
exactly what is happening at each
stage of your analytic process
11

© 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial
WhyWorkflows?
Accessibility
Access structured, unstructured or
semi-structured data from virtually
any environment: SQL Server,
NoSQL databases, desktop, cloud,
Big Data environments, and more
Flexibility
Leverage existing SQL statements,
and develop new procedures
12

© 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial
Conclusion
14

© 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial
Conclusion
15

See whatAlteryx can do for you!
http://www.alteryx.com/alteryx-for-sql
http://www.alteryx.com/product-training
16

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Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a comprehensive platform designed to address multi-faceted needs by offering multi-function data management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion. In this research-based session, I’ll discuss what the components are in multiple modern enterprise analytics stacks (i.e., dedicated compute, storage, data integration, streaming, etc.) and focus on total cost of ownership. A complete machine learning infrastructure cost for the first modern use case at a midsize to large enterprise will be anywhere from $3 million to $22 million. Get this data point as you take the next steps on your journey into the highest spend and return item for most companies in the next several years.

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Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace – from digital transformation, to marketing, to customer centricity, to population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.

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Uncover how your business can save money and find new revenue streams. Driving profitability is a top priority for companies globally, especially in uncertain economic times. It's imperative that companies reimagine growth strategies and improve process efficiencies to help cut costs and drive revenue – but how? By leveraging data-driven strategies layered with artificial intelligence, companies can achieve untapped potential and help their businesses save money and drive profitability. In this webinar, you'll learn: - How your company can leverage data and AI to reduce spending and costs - Ways you can monetize data and AI and uncover new growth strategies - How different companies have implemented these strategies to achieve cost optimization benefits

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Data Mesh is a trending approach to building a decentralized data architecture by leveraging a domain-oriented, self-service design. However, the pure definition of Data Mesh lacks a center of excellence or central data team and doesn’t address the need for a common approach for sharing data products across teams. The semantic layer is emerging as a key component to supporting a Hub and Spoke style of organizing data teams by introducing data model sharing, collaboration, and distributed ownership controls. This session will explain how data teams can define common models and definitions with a semantic layer to decentralize analytics product creation using a Hub and Spoke architecture. Attend this session to learn about: - The role of a Data Mesh in the modern cloud architecture. - How a semantic layer can serve as the binding agent to support decentralization. - How to drive self service with consistency and control.

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Too often I hear the question “Can you help me with our data strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component: the data strategy itself. A more useful request is: “Can you help me apply data strategically?” Yes, at early maturity phases the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (must less perfect) data strategy on the first attempt is generally not productive –particularly given the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” This program refocuses efforts on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. It also contributes to three primary organizational data goals. Learn how to improve the following: - Your organization’s data - The way your people use data - The way your people use data to achieve your organizational strategy This will help in ways never imagined. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs as organizations identify prioritized areas where better assets, literacy, and support (data strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including: - A cohesive argument for why data strategy is necessary for effective data governance - An overview of prerequisites for effective strategic use of data strategy, as well as common pitfalls - A repeatable process for identifying and removing data constraints - The importance of balancing business operation and innovation

Data Strategy Best Practices

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The question is asked all the time: “What part of the organization should own your Data Governance program?” The typical answers are “the business” and “IT (information technology).” Another answer to that question is “Yes.” The program must be owned and reside somewhere in the organization. You may ask yourself if there is a correct answer to the question. Join this new RWDG webinar with Bob Seiner where Bob will answer the question that is the title of this webinar. Determining ownership of Data Governance is a vital first step. Figuring out the appropriate part of the organization to manage the program is an important second step. This webinar will help you address these questions and more. In this session Bob will share: - What is meant by “the business” when it comes to owning Data Governance - Why some people say that Data Governance in IT is destined to fail - Examples of IT positioned Data Governance success - Considerations for answering the question in your organization - The final answer to the question of who should own Data Governance

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It is clear that Data Management best practices exist and so does a useful process for improving existing Data Management practices. The question arises: Since we understand the goal, how does one design a process for Data Management goal achievement? This program describes what must be done at the programmatic level to achieve better data use and a way to implement this as part of your data program. The approach combines DMBoK content and CMMI/DMM processes – permitting organizations with the opportunity to benefit from the best of both. It also permits organizations to understand: - Their current Data Management practices - Strengths that should be leveraged - Remediation opportunities

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MLOps is a practice for collaboration between Data Science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of “machine learning” and “operations,” MLOps applies DevOps principles to ML delivery, enabling the delivery of ML-based innovation at scale to result in: Faster time to market of ML-based solutions More rapid rate of experimentation, driving innovation Assurance of quality, trustworthiness, and ethical AI MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.

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Focus on Your Analysis, Not Your SQL Code

1. Focus onYour Analysis, NotYour SQL Code Beth Narrish, Senior Product Marketing Manager Dan Hilton, Solutions Architect Mar 7, 2017

3. © 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial The legacy approach to analytics is often challenging for organizations 3 Data Blending Predictive & Spatial Analytics Production & Output Creation Data Access Prep & Cleansing Data integration tool Analytic coding App creationData quality tool Slow, long process Multiple tools and process steps Expensive software and labor IT Staff Data Scientists Line-of-Business

4. © 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial And while many organizations are relying on spreadsheets for data analysis, they are also using low-level SQL queries 4 Only Harvard Business Review: Data Blending:A Powerful Method For Faster, Easier Decisions

5. © 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial • SQL is the standard language for relational database management systems and has been for 40 years • Source: American National Standards Institute • While NoSQL has gained popularity with Hadoop, MongoDB and other sources, SQL is still king • Impala and Spark can utilize SQL Why SQL? 5

6. © 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial But SQL can pose challenges for analysts and their colleagues • Colleagues often cannot pick up SQL code and understand how data was pulled • Documenting steps is needed to repeat the process when data changes • Troubleshooting each step in a complex query can be tedious 6

7. Download a FREETrial: alteryx.com/trial© 2017 Alteryx, Inc. | Confidential A Leading Platform for Self-Service DataAnalytics 7 Prep, blend, and analyze all your data using a repeatable workflow Deliver deeper insights in hours, not weeks Deploy and share analytics at scale

8. © 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial A Leading Platform for Self-Service DataAnalytics 8 Enrich Prep & Blend Analyze Input All Relevant Data Share OutputAll Popular Formats

9. How Alteryx Helps SQL Coders 9

11. © 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial WhyWorkflows? SeamlessTransformations Extract, transform and load data without writing any code Efficiency In Debugging,Testing and Prototyping Stop spending time writing code only to get a null data set; understand exactly what is happening at each stage of your analytic process 11

12. © 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial WhyWorkflows? Accessibility Access structured, unstructured or semi-structured data from virtually any environment: SQL Server, NoSQL databases, desktop, cloud, Big Data environments, and more Flexibility Leverage existing SQL statements, and develop new procedures 12

13. Demonstration

16. See whatAlteryx can do for you! http://www.alteryx.com/alteryx-for-sql http://www.alteryx.com/product-training 16

Focus on Your Analysis, Not Your SQL Code

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Focus on Your Analysis, Not Your SQL Code