Data analyticsproduct practices

454 views

Published on

Data Analytics Platform: Product Management Best Practices

Published in: Technology, Business
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
454
On SlideShare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
4
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • 1. Gather Use Case Scenarios: While this is a continuous as well as an iterative process through the life cycle, the product manager (PM) has to have an anchor to initiate the product development. The PM identifies key use cases of the product (possible to have some already identified in business case) that will define its essential features and functionality. A good practice is to have at least 3-5 such business applications for the product to start with the development. A rolling wave planning can be adopted as the product development progresses, and more clarity on the end product applications is obtained. Considering an example product for discussion here, if the overall intent of the data analytics platform being conceived is for personalization of content and/or contextualized search, the few use cases can be in the space of "conversion of customer intent to transaction", "personalized product/service recommendations", "delivering competency-based adaptive content", etc, depending on the client's industry/vertical application.
  • 2. Identify Key Features/Functions: This is the stage in which the product scope and its roadmap are defined with its essential features and functions identified from the primary use case applications and other stake holder requirements. The PM closely works primarily with the architect and the stakeholders, and if necessary the development team, through this process for an effective depiction of the product scope at the business and engineering architecture levels. For the example analytics platform discussed above, the capabilities can include product/service recommendations to customer based on her location or shopping/purchase history, recommendations based on interests/hobbies/habits, content access based on specific skills, etc.
  • 3. Gather Data Requirements, Specifications, and Formats: The applications and functionality of the product leads the PM and team to identify the required data (e.g., customer transactions, inventory, price logs, resource utilization data, customer traffic, etc.) and specifications (volume/size, variety, velocity/streaming, structured/unstructured, etc.), and formats (numerical, text, voice, video, image, etc.). Through this process, the PM also has to identify internal and external sources for the required data, the barriers and costs to acquire the data, the potential challenges in integrating the source APIs with the product platform input APIs, and all other related issues.
  • 4. Develop Data Warehousing Methods: The PM should have a good understanding of the capabilities available to warehouse, ingest, and manage the data as well as the extended capabilities to be built for the purpose. The warehousing of the data includes the extraction, transformation, and loading of the data for subsequent analyses. The ingestion of business/structured data is usually is done with the traditional enterprise data warehouse (EDW), while the unstructured data such as the customer activity (on webstore, social media, etc.) written into log files is warehoused in the Hadoop. A good knowledge of the star-schema for the traditional EDW helps PM to better handle issues during the architecture, design, and development phases. The best practice starts with domain modeling, contextual modeling, data modeling at logical and physical layers. Success of the platform also depends on choosing the right EDW tool for the right job: the PM and team usually have the choice among IBM's DB2 or IBM's Netezza appliance (or both combined) or MPP such as Teradata or a columnar database such as Vertica.Besides, a good grasp on the requirements for building a suitable Hadoop cluster (capacity, number of nodes, block replication, number of users, etc.) will help the PM work with the architect and the product development teams to build appropriate data warehouse systems. The PM's knowledge in capabilities required to facilitate the analyses of data across traditional EDW and Hadoop will further augment the team's expertise (for example, a cross analysis on a customer segment data in EDW and the customer activity in Hadoop cluster to derive a behavior pattern for a particular customer category requires such a capability, and is a common requirement now a days).
  • 5. Develop Data Mining Techniques: As the data warehousing systems and methods are established, the PM and team of data scientists can identify and develop necessary data mining techniques. For the example analytics platform discussed above, some of the data mining techniques include Frequency analysis, Collaborative filtering, Causal analysis, Matrix factorization, Association rule mining, Time series analysis, K-Means or Hierarchical clustering, Regression analysis, Bayesian networks, etc.
  • 6. Develop Reporting Layer: As the platform with analytics engines are built, the insights from analytics have to be displayed/reported and the interface for display depends on the users. In case the users are data scientists internal to the organization, usually the delivery ends at providing the platform, data, reporting & visualization tools (Tableau, Cognos, Datameer, etc.), and plugins/connectors to statistical analysis tools such as R and S, that are highly popular with data scientists. Data curation is also vital to deliver the right data and insights in the right format/structure at the right time to the right stakeholders, so that timely business action is taken. On the other hand, if the end users are individual consumers, then appropriate efforts are to be made to build a sleek interface to deliver the user specific content.The product development life cycle does not end here. Once the product is launched, information on its adoption, feature usage, and user experiences is gathered and fed back for further evolvement of the product. And so, the Plan-Do-Check-Act cycle continues.
  • Data analyticsproduct practices

    1. 1. © Ram Sangireddy. All rights reserved. Not to be reproduced without prior written consent. Data Analytics Platform: Product Management Best Practices Ram Sangireddy “Knowledge Sharing for Empowerment” Read the discussion in detail at http://ramsangireddy.com/ Disclaimer: The author takes the sole responsibility for the opinions expressed in this deck, and the thoughts/opinions may not be attributed to his current or past employers.
    2. 2. 2 © Ram Sangireddy. All rights reserved. Not to be reproduced without prior written consent. Product Management Overview Product Development Has Potential? Ideation Primary/ Secondary Research Business Case Testing & Validation Launch Market Need? Make or Buy? MakeYes Yes Post-Launch Review This deck focuses on various stages in the Development of a data analytics platform product
    3. 3. 3 © Ram Sangireddy. All rights reserved. Not to be reproduced without prior written consent. Product development processes are not discrete events with well- defined interfaces, but in fact overlap & interact through life cycle Product Development Overview Gather Use Case Scenarios Identify Key Features/ Functions Gather Data Requirements, Specs, Formats Develop Data Warehousing Methods Develop Data Mining Techniques Develop Reporting Layer End Begin Process Interaction P1 P2 P3 P4 P5 P6 ProductDevelopmentLifeCycle P1 P2 P3 P4 P5 P6 * Illustrative only
    4. 4. 4 © Ram Sangireddy. All rights reserved. Not to be reproduced without prior written consent. Gather Use Case Scenarios – process of developing documents that formally constructs the end product applications of value to the user Product Development Overview Gather Use Case Scenarios Identify Key Features/ Functions Gather Data Requirements, Specs, Formats Develop Data Warehousing Methods Develop Data Mining Techniques Develop Reporting Layer  Market research  Customer insights  Client interviews  Brainstorming  Business case  Product statement of work  Agreements Inputs Tools & Techniques Key benefits of the process:  A well-defined start for product development  Creation of formal applications for the product  Defined way for all stakeholders to envision product’s market use  Use case scenarios Outputs  Clients/Users  Product sponsor  Marketing team Stakeholders Various kinds of scenarios can be:  Persona defined context-based  Key path scenarios  Validation scenarios For example, if a data analytics platform is for personalization of content and/or contextualized search, the use cases can be in the space of  Conversion of customer intent to transaction  Personalized product/service recommendations  Delivering competency-based adaptive content
    5. 5. 5 © Ram Sangireddy. All rights reserved. Not to be reproduced without prior written consent. Identify Key Features/Functions – process of defining product scope and its roadmap with its essential features and functions Product Development Overview Gather Use Case Scenarios Identify Key Features/ Functions Gather Data Requirements, Specs, Formats Develop Data Warehousing Methods Develop Data Mining Techniques Develop Reporting Layer  Scrum meetings  Brainstorming  Roadmapping  Use case scenarios  Business case  Product statement of work Inputs Tools & Techniques Key benefits of the process:  Well-defined product scope  Creation of formal roadmap  Platform functionality  Platform capabilities Outputs  Tech Architect  Development team  Clients/Users  Product sponsor Stakeholders For the data analytics platform intended for personalization of content and/or contextualized search, the capabilities can include:  Product/service recommendations based on location or shopping/purchase history,  Recommendations based on interests/habits  Content access based on specific skills
    6. 6. 6 © Ram Sangireddy. All rights reserved. Not to be reproduced without prior written consent. Gather Data Reqs/Specs/Types – process of identifying required data and the sources, costs/barriers, and integration challenges Product Development Overview Gather Use Case Scenarios Identify Key Features/ Functions Gather Data Requirements, Specs, Formats Develop Data Warehousing Methods Develop Data Mining Techniques Develop Reporting Layer  Scrum meetings  Brainstorming  Market research  Product scope (functions/capabilities)  Use case scenarios Inputs Tools & Techniques Key benefits of the process:  Clarity in platform requirements of data for analytics  Foundation for data extraction and transformation  Data specifications  Data sources and APIs Outputs  Tech Architect  Development team  Clients/Users Stakeholders  Data required to fulfill capabilities (e.g., customer transactions, inventory, price logs, resource utilization data, customer traffic, etc.)  Specifications (volume/size, variety, velocity/ streaming, structured/unstructured, etc.)  Formats (numerical, text, voice, video, image, etc.)
    7. 7. 7 © Ram Sangireddy. All rights reserved. Not to be reproduced without prior written consent. Develop Data Warehousing Methods - process of building suitable capabilities to warehouse, ingest, and manage all the data Product Development Overview Gather Use Case Scenarios Identify Key Features/ Functions Gather Data Requirements, Specs, Formats Develop Data Warehousing Methods Develop Data Mining Techniques Develop Reporting Layer  Data modeling  ETL development  Scrum meetings  Resource estimations  Data requirements  Product scope (functions/capabilities) Inputs Tools & Techniques Key benefits of the process:  Developed data warehouse systems for loading, analyses  Defined management of structured & unstructured data  Enterprise Data Warehouse (EDW)  Hadoop cluster Outputs  Tech Architect  Development team Stakeholders Information Integration & Governance ETL Engine HadoopHadoop ClusterEnterprise Data Warehouse (EDW)
    8. 8. 8 © Ram Sangireddy. All rights reserved. Not to be reproduced without prior written consent. Develop Data Mining Techniques - process of building suitable mining techniques for necessary insights from analytics Product Development Overview Gather Use Case Scenarios Identify Key Features/ Functions Gather Data Requirements, Specs, Formats Develop Data Warehousing Methods Develop Data Mining Techniques Develop Reporting Layer  Scrum meetings  Prototyping  White boarding  Capability development  EDW + Hadoop  Product scope (functions/capabilities) Inputs Tools & Techniques Key benefit of the process:  Defined mining tools and applications for analytics  Data Mining Applications Outputs  Development team  Data scientists Stakeholders Information Integration & Governance ETL Engine HadoopHadoop ClusterEnterprise Data Warehouse (EDW) Data Mining Applications BI / Reporting Exploration Predictive Analytics Content Analytics
    9. 9. 9 © Ram Sangireddy. All rights reserved. Not to be reproduced without prior written consent. Develop Reporting Layer - process of curating data and building suitable interfaces for delivering insights Product Development Overview Gather Use Case Scenarios Identify Key Features/ Functions Gather Data Requirements, Specs, Formats Develop Data Warehousing Methods Develop Data Mining Techniques Develop Reporting Layer  Scrum meetings  Security  Visualization  Data mining outcomes  Product scope (functions/capabilities) Inputs Tools & Techniques Key benefit of the process:  Delivery of actionable insights to business users for timely action  ACTIONABLE INSIGHTS Outputs  Development team  Client/User Stakeholders
    10. 10. 10 © Ram Sangireddy. All rights reserved. Not to be reproduced without prior written consent. About Author: Ram believes in building endurance for long distance running Ram has over 10 years of experience in technology innovation and business strategy for growth in hardware, software and services. Since July 2011, he has been with IBM developing strategy, vision and roadmap to build innovative capabilities that successfully integrate with IBM Smarter Commerce products (WebSphere Commerce, Sterling Commerce, Unica, Coremetrics, DemandTec, Tealeaf, etc.). Previously, he was in a career of research focused on high-performance computing, distributed computing, network processing, and related areas. He brings unique capabilities to work with and build consensus among technologists and business leaders possessing varied responsibilities and market objectives. Ram earned his MBA degree from Kellogg School of Management (Northwestern University) and Ph.D. degree in Computer Engineering. Ram Sangireddy Read the discussion in detail at http://ramsangireddy.com/

    ×