Reinventing the Modern Information Pipeline: Paxata and MapR

Editor's Notes

• #4: Deliver the only enterprise-grade data preparation platform that lets everyone transform raw, meaningless data into valuable, contextual and complete information
• #5: To seize the opportunity you must cross this data chasm. Why…Because its hard Traditional, legacy technologies and processes that companies currently leverage were NOT designed for the variety and volume of data that companies are working with today. Companies need to be more nimble We have many customers that have 10’s of Millions invested annual in traditional ETL processes, and they were still spending too much time preparing data and not on the value added tasks of analytics. They selected Paxata to help complement these technologies and fill the gaps with a more exploratory, interactive experience.
• #6: To seize the opportunity you must cross this data chasm. Why…Because its hard Traditional, legacy technologies and processes that companies currently leverage were NOT designed for the variety and volume of data that companies are working with today. Companies need to be more nimble We have many customers that have 10’s of Millions invested annual in traditional ETL processes, and they were still spending too much time preparing data and not on the value added tasks of analytics. They selected Paxata to help complement these technologies and fill the gaps with a more exploratory, interactive experience.
• #7: To seize the opportunity you must cross this data chasm. Why…Because its hard Traditional, legacy technologies and processes that companies currently leverage were NOT designed for the variety and volume of data that companies are working with today. Companies need to be more nimble We have many customers that have 10’s of Millions invested annual in traditional ETL processes, and they were still spending too much time preparing data and not on the value added tasks of analytics. They selected Paxata to help complement these technologies and fill the gaps with a more exploratory, interactive experience.
• #8: To seize the opportunity you must cross this data chasm. Why…Because its hard Traditional, legacy technologies and processes that companies currently leverage were NOT designed for the variety and volume of data that companies are working with today. Companies need to be more nimble We have many customers that have 10’s of Millions invested annual in traditional ETL processes, and they were still spending too much time preparing data and not on the value added tasks of analytics. They selected Paxata to help complement these technologies and fill the gaps with a more exploratory, interactive experience.
• #9: Deliver the only enterprise-grade data preparation platform that lets everyone transform raw, meaningless data into valuable, contextual and complete information
• #11: Visual Data Discovery Tools – people had a hunger to get at and dig into their data – traditional small spreadsheets or databases 1. Business teams funnel their data requirements to IT 2. IT runs requirements through linear ETL process, executed with manual scripting or coding 3. IT reviews with business, makes changes, fixes errors. Repeats this cycle. 4. By then, business teams make decisions long before data is available or they ask for changes and re-start the process Traditional Technologies Do Not Meet Today’s Needs Batch, Complicated, No Visibility, IT Only, Time Consuming, Error Prone, Expensive Legacy infrastructure for data preparation was never designed to scale to the orders of magnitude more data and the orders of magnitude more consumers of today’s information-driven world. A model in which a small set of highly skilled IT data scientists and data developers take business requirements and then execute a highly prescribed, lengthy, waterfall process for preparing data only to more often than not realize that they missed the mark as they lack the business context, is not a viable model.
• #12: Visual Data Discovery Tools – people had a hunger to get at and dig into their data – traditional small spreadsheets or databases 1. Business teams funnel their data requirements to IT 2. IT runs requirements through linear ETL process, executed with manual scripting or coding 3. IT reviews with business, makes changes, fixes errors. Repeats this cycle. 4. By then, business teams make decisions long before data is available or they ask for changes and re-start the process Traditional Technologies Do Not Meet Today’s Needs Batch, Complicated, No Visibility, IT Only, Time Consuming, Error Prone, Expensive Legacy infrastructure for data preparation was never designed to scale to the orders of magnitude more data and the orders of magnitude more consumers of today’s information-driven world. A model in which a small set of highly skilled IT data scientists and data developers take business requirements and then execute a highly prescribed, lengthy, waterfall process for preparing data only to more often than not realize that they missed the mark as they lack the business context, is not a viable model.
• #13: Visual Data Discovery Tools – people had a hunger to get at and dig into their data – traditional small spreadsheets or databases 1. Business teams funnel their data requirements to IT 2. IT runs requirements through linear ETL process, executed with manual scripting or coding 3. IT reviews with business, makes changes, fixes errors. Repeats this cycle. 4. By then, business teams make decisions long before data is available or they ask for changes and re-start the process Traditional Technologies Do Not Meet Today’s Needs Batch, Complicated, No Visibility, IT Only, Time Consuming, Error Prone, Expensive Legacy infrastructure for data preparation was never designed to scale to the orders of magnitude more data and the orders of magnitude more consumers of today’s information-driven world. A model in which a small set of highly skilled IT data scientists and data developers take business requirements and then execute a highly prescribed, lengthy, waterfall process for preparing data only to more often than not realize that they missed the mark as they lack the business context, is not a viable model.
• #14: Visual Data Discovery Tools – people had a hunger to get at and dig into their data – traditional small spreadsheets or databases 1. Business teams funnel their data requirements to IT 2. IT runs requirements through linear ETL process, executed with manual scripting or coding 3. IT reviews with business, makes changes, fixes errors. Repeats this cycle. 4. By then, business teams make decisions long before data is available or they ask for changes and re-start the process Traditional Technologies Do Not Meet Today’s Needs Batch, Complicated, No Visibility, IT Only, Time Consuming, Error Prone, Expensive Legacy infrastructure for data preparation was never designed to scale to the orders of magnitude more data and the orders of magnitude more consumers of today’s information-driven world. A model in which a small set of highly skilled IT data scientists and data developers take business requirements and then execute a highly prescribed, lengthy, waterfall process for preparing data only to more often than not realize that they missed the mark as they lack the business context, is not a viable model.
• #15: Visual Data Discovery Tools – people had a hunger to get at and dig into their data – traditional small spreadsheets or databases 1. Business teams funnel their data requirements to IT 2. IT runs requirements through linear ETL process, executed with manual scripting or coding 3. IT reviews with business, makes changes, fixes errors. Repeats this cycle. 4. By then, business teams make decisions long before data is available or they ask for changes and re-start the process Traditional Technologies Do Not Meet Today’s Needs Batch, Complicated, No Visibility, IT Only, Time Consuming, Error Prone, Expensive Legacy infrastructure for data preparation was never designed to scale to the orders of magnitude more data and the orders of magnitude more consumers of today’s information-driven world. A model in which a small set of highly skilled IT data scientists and data developers take business requirements and then execute a highly prescribed, lengthy, waterfall process for preparing data only to more often than not realize that they missed the mark as they lack the business context, is not a viable model.
• #16: Visual Data Discovery Tools – people had a hunger to get at and dig into their data – traditional small spreadsheets or databases 1. Business teams funnel their data requirements to IT 2. IT runs requirements through linear ETL process, executed with manual scripting or coding 3. IT reviews with business, makes changes, fixes errors. Repeats this cycle. 4. By then, business teams make decisions long before data is available or they ask for changes and re-start the process Traditional Technologies Do Not Meet Today’s Needs Batch, Complicated, No Visibility, IT Only, Time Consuming, Error Prone, Expensive Legacy infrastructure for data preparation was never designed to scale to the orders of magnitude more data and the orders of magnitude more consumers of today’s information-driven world. A model in which a small set of highly skilled IT data scientists and data developers take business requirements and then execute a highly prescribed, lengthy, waterfall process for preparing data only to more often than not realize that they missed the mark as they lack the business context, is not a viable model.
• #19: Slide use: problem of data (option 4) This is a five-part slide. Use this along with the 4 slides before it. Talking Points: Big Data and self-service analytics necessitate a fundamental transformation from an IT-centric data preparation process to a self-service data preparation model. In the self-service model, the steps that make of data preparation – data integration, quality, cleansing, enrichment and shaping don’t go away, they need to be re-imagined in a way that enables the business or data analyst to accomplish these tasks on their own which in turn empowers them to work with vertical slices of relevant data and get the results they want, when they need them. However, it’s important that the self-service model also provide the governance and traceability that IT requires to maintain trust in data and analytic results. In this new model, IT’s role changes to collection and centralization of access to raw data and to providing the right infrastructure to the business that drive self-service data preparation and analytics, while maintaining full governance.
• #20: Slide use: problem of data (option 4) This is a five-part slide. Use this along with the 4 slides before it. Talking Points: Big Data and self-service analytics necessitate a fundamental transformation from an IT-centric data preparation process to a self-service data preparation model. In the self-service model, the steps that make of data preparation – data integration, quality, cleansing, enrichment and shaping don’t go away, they need to be re-imagined in a way that enables the business or data analyst to accomplish these tasks on their own which in turn empowers them to work with vertical slices of relevant data and get the results they want, when they need them. However, it’s important that the self-service model also provide the governance and traceability that IT requires to maintain trust in data and analytic results. In this new model, IT’s role changes to collection and centralization of access to raw data and to providing the right infrastructure to the business that drive self-service data preparation and analytics, while maintaining full governance.
• #23: Slide use: Who are the data analysts Talking points: This pyramid describes the typical information work roles in today’s enterprises and highlights the dramatic scale that self-service data preparation can bring. Legacy and many Big Data tools target the Data Scientist and the Data Developer, but as you can see there are hugely more data analysts our there, and self-service data prep empowers them to drive their own data destiny, breaking the logjam of traditional IT-constrained ETL and data preparation. By Data Analysts, we are referring to Power Excel users or Tableau users who understand data and analytics, but don’t write code or scripts. For self-service data prep to truly transform an organization, it must empower the data analyst; however, self-service data prep simplifies many traditionally complex and time-consuming preparation operations and the work of data scientists and data developers can be dramatically accelerated by self-service data prep. Source: Prakash VC deck
• #24: Slide use: Who are the data analysts Talking points: This pyramid describes the typical information work roles in today’s enterprises and highlights the dramatic scale that self-service data preparation can bring. Legacy and many Big Data tools target the Data Scientist and the Data Developer, but as you can see there are hugely more data analysts our there, and self-service data prep empowers them to drive their own data destiny, breaking the logjam of traditional IT-constrained ETL and data preparation. By Data Analysts, we are referring to Power Excel users or Tableau users who understand data and analytics, but don’t write code or scripts. For self-service data prep to truly transform an organization, it must empower the data analyst; however, self-service data prep simplifies many traditionally complex and time-consuming preparation operations and the work of data scientists and data developers can be dramatically accelerated by self-service data prep.
• #25: Deliver the only enterprise-grade data preparation platform that lets everyone transform raw, meaningless data into valuable, contextual and complete information