Building a Business Practice in Data Science
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Building a Business Practice in Data Science

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My thoughts on what data science is, what skills data scientists have, what are the current issues in the Business Intelligence pipeline, how can machine learning automate a part of the BI chain, why ...

My thoughts on what data science is, what skills data scientists have, what are the current issues in the Business Intelligence pipeline, how can machine learning automate a part of the BI chain, why and how data science should be democratized and made available to every one including decision makers (business users), how business analyst should build complex data models and how data scientists should be freed up from the mundane tasks of rinse and repeat before building models that provide input for decision making, how companies can build a business practice around data science. big-data is all data and the big-data apps offer the ability to combine all data (public + private) and expand the horizon to discover more meaningful insights.

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Building a Business Practice in Data Science Building a Business Practice in Data Science Presentation Transcript

  • Data Science Building a Business or a Business Practice in Data Science
  • Data Science is… • An art of mining large quantities of data • An art of combining disparate data sources and blending public data with corporate data • Forming hypothesis to solve hard problems • Building models to solve current problems and provide forecast • Anticipate future events (based on historical data) and provide correcting actions (yield curve in finance, fraud detection in banking, storms effect on travel, operational downtime) • Automating the analytics processes to reduce time to solve future problems
  • A Data Scientists has following minimum set of core skills… • Problem solver • Creative and can form an hypothesis • Is able to program with large quantities of data • Can think of bringing data from appropriate data source and can bring and blend data • Stats/math/analytics background to build models and write algorithms • Can quickly develop domain knowledge to understand key factors which influence the performance of a business problem
  • Roles data scientists play… • Problem description • Hypothesis formation • Data assembly, ETL and data integration role • Model development (pattern recognition or any other model to provide answers) and training • Data visualization • AB Testing • Propose solutions and/or new business ideas
  • The balance between human vs. machines… • Current: humans play a significant role in the process – ETL, joins, models, visualization, machine- learning and then repeating and recycling this process as the problem changes • Tomorrow: a big portion of the food-chain can be automated via machine learning so machines can take over and data-scientists can be freed up to build more algorithms/models • The process can be automated so repeating/recycling can be cheaper and less time consuming
  • The Data Science pipeline currently looks like… • From Data to Insights – this entire process requires mundane skills (IT), specialized skills (data-scientist) and elements of human psychology to present the right information at the right time • The data needs to be discovered, assembled, semantically enriched and anchored to a business logic – this task can be be automated through machine learning (a set of harmonized tools with AI) to free up scarce resources
  • The Data Science pipeline currently looks like (cont’d)… • Specialized skills today get addressed by open source technologies such as R and expensive solutions like Matlab and SPSS. • Very few software solution carefully introduce human interface to make their application consumable without requiring customer training (i.e. not Google easy)
  • The pipeline needs complete rethinking… • Automate mundane tasks that IT gets tagged with • Discover data automatically • Detach business logic from data models • Make blending public data with corporate data a second nature • Free up data-scientists so that they can build analytics micro-apps for a domain or a sub-domain • data-science need not be a niche (or a specialized category), it should appeal to the masses (democratization of data and brining insights to everyone without needed specialized skills)
  • Opportunity in Data Science… • Understand the value chain (IT + Business Analyst + Data Scientists + Business Users) • Provide something for everyone - a single integrated platform (ETL + Data Integration + Predictive modeling + in-memory computing + storage) for data scientists so that they can build standard analytical apps and move away from proprietary models and standardize (which also helps IT) • Analytical apps on this platform (think of them as rapid deployment solutions) for business users
  • Opportunity in Data Science (cont’d)… • Help business analysts write basic models (churn, segmentation, correlation etc.) without requiring advanced skills • Work with consulting companies so that they can consult and build apps on your platform for companies that do not have data scientists on their pay-roll (like Mu-Sigma and Opera Solutions) • Partner with public data provider (to help clients), consulting companies (for rapid solutions), R/Python/ML communities (to grab mind-share and show thought-leadership)