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Unified approach to analytics

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Unified approach to analytics

  1. 1. Unified Approach to Analytics
  2. 2. Objective 1 Current challenges with “Data Analytics” at Scale 2 Gaps to be addressed to meet future needs 3 Understand why we need: “Unified Approach to Analytics”
  3. 3. Problem Context Challenges of Data Analysis (around growing data)
  4. 4. Past Data analysis was achieved with Relational DB+RealTime+Spreadsheets.
  5. 5. Future Speed of innovation been more rapid. 01 The goal of business agility more attainable 02
  6. 6. Gaps/Challenges to meet the Future needs • Data is increasing in vertical trajectory, is becoming highly distributed, and can come in a variety of formats. • IDC Estimates ~ 180 ZB (by 2025) • Capturing and unlocking this growing data for innovation is becoming a big challenge. Growing data: • Move to cloud, setting up a big-data infrastructure, maintenance and over reliance on Dev-ops.Infrastructure Complexity: • Open source projects such as Hive, Presto, Kafka, MapReduce, Impala, and Apache Spark. All these technologies comes with different release cycles, lack institutional support mechanisms, and have varying performance deliverables Disparate Technologies: • A lack of automation between the various steps of data ingestion, ETL, exploration, modeling and presentation of data create massive inefficiencies. This greatly reduces the speed of innovation that is the promise of big data, data science, and a move to the cloud. Disjointed Analytics Workflows: • By viewing data through separate lenses, collaboration is very difficult, trust in the analytics can be difficult resulting in slower speed of innovation. Team Structure (Data engineers, scientists and analysts (within Data Organization)): • Challenges are around building a consolidated data security, legal compliance and financial liabilities.Protecting the data:
  7. 7. Key Challenges: Desperate Technologies and Growing Data
  8. 8. What is needed to succeed? Comprehensive, unified approach to analytics. Hosted Solution / Cloud Service Easier onboarding: Self-Serve (in minutes). End to End workflow (streamlined): • (Personas) Data Engineers: focused on data preparation and productionizing the models that the data scientists build through a common, unified framework. • (Personas) Data Scientists: can leverage the same platform to explore and visualize data real-time. Building machine learning models is simple with collaborative notebooks allows machine learning models at scale. Interactive dashboards enables publishing insights across the company to business analysts. Security + Operability Cost (Unified) (Secured)
  9. 9. Goal: • No longer have to wait for an infrastructure team to provision and configure hardware for them, Self-serve in minutes, and focused on building models and finding patterns in data that fuel innovation and accelerate go to market for transformative business outcomes. 1 Success Metrics: • Performing Data Analysis, Building a model and testing a prototype in hours, versus weeks or months. 2

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