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Data Analytics
51 posts about Data Analytics. Every summary links to the original.
Personalization Strategies for Media Companies
Media companies are expected to tailor content and messaging across channels, with personalization programs typically targeting lower churn, deeper sessions, and higher subscription revenue. The proposed foundation starts with an audit of CRM, email, warehouse, and customer data platform sources, followed by required-field definitions, ownership mapping, consent tagging, and ingestion-quality checks. Teams then map segment behaviors and conversion or retention paths, including negative signals, before selecting a CDP with real-time ingestion, identity resolution, unified profiles, segmentation, activation, and governance. Identity rules should connect anonymous and authenticated activity across web, mobile, and connected TV, while event-level streaming delivers messages through email, push, in-app, and advertising channels with minimal latency. The roadmap recommends an MVP comprising unified ingestion, a homepage recommendation surface, and triggered re-engagement email, with performance, churn, revenue lift, and model drift monitored through iteration.
Databricks StaffIntroducing Genie Agent Mode
Databricks introduces Agent mode in Genie spaces, an agentic process that plans, explores, and reasons over data to answer business questions. For each question, Genie can confirm an issue, test potential contributors through multiple queries, reflect on results, and decide what to investigate next, using Unity Catalog metadata and author-defined semantics from the Genie space. It produces a findings report with quantified results, identified contributors, visualizations, and references to the underlying SQL, and may include actionable recommendations. The agent scales its reasoning to task complexity, using validation for straightforward questions and more planning for multi-step investigations; in AI/BI Dashboards, Genie uses Agent mode by default. Workspace admins enable the feature in Workspace Previews, after which users turn on the Agent toggle in Genie spaces; API support and unstructured document analysis are planned additions.
Hanlin SunWhat is operational analytics?
Operational analytics uses real-time or near-real-time data to monitor day-to-day operations and support immediate decisions, unlike traditional analytics, which generally relies on batch data to explain past performance. Its workflow collects logs, events, clickstreams, telemetry, transactions, and other signals; streams them into a unified platform for cleaning, transformation, and enrichment; analyzes incoming data with rules, models, or anomaly detection; and sends results to dashboards, alerts, or operational applications. This can help teams detect issues earlier, reduce mean time to detect (MTTD) and mean time to respond (MTTR), improve forecasting, coordinate across departments, and act on inventory, customer, or system changes. The approach also requires integrating heterogeneous systems, maintaining data quality and schemas, embedding insights into existing workflows, and building reliable low-latency pipelines, with tools such as Lakeflow, Databricks SQL, and AI and machine learning capabilities presented as examples.
Databricks StaffRamp ·
Learnings from Building and Scaling Ramp’s Growth Engine
Ramp’s growth team explains the principles and systems it used to scale customer acquisition since launching its first product in February 2020. By February 2021, annualized run-rate revenue reached $12 million, crossed $100 million a year later, and was far beyond that figure at the end of 2023. The approach combines first-principles thinking and prioritization with fast, data-driven MVP experiments, then productionizes ideas that show significant impact; it also relies on full-stack capabilities across business operations, engineering, and sales. An AI email overlay that classified and prioritized sales messages helped representatives handle more prospects and increase conversion rates. The team attributes tens to hundreds of millions of dollars in sales pipeline and real revenue to these practices, while noting that sustainable growth requires a solid product and real addressable market.
Hima TammineediShopify ·
A Five-Step Guide for Conducting Exploratory Data Analysis
Exploratory data analysis (EDA) is presented as a practical way to describe a dataset, uncover patterns such as missing data, correlations, and outliers, and generate hypotheses that inform business questions and later data products. The guide recommends first clarifying the intended task with leadership and product, then checking dataset size, sample rows, and the unique row identifier; its synthetic merchant-behaviour example has 1,500 samples, 13 features, and one row per shop per day. It then advises examining missingness by feature, investigating why values are absent before deleting or imputing them, and classifying features as continuous, discrete, or categorical to guide analysis. Visualizing distributions and relationships helps reveal changes across samples and time, while box plots and percentile thresholds support systematic outlier review. The process is framed as a starting point whose findings can support regression, classification, or KPI-dashboard decisions.
2023-10-18Shopify ·
4 Tips for Shipping Data Products Fast
Shipping data products under tight deadlines requires teams to stay focused while avoiding overengineering and building something users will not use. Shopify’s Data Science team recommends four practices: run a timeboxed design sprint with a small team and a designated Champion, prototype quickly—often with spreadsheets—and gather feedback throughout development. For first iterations, the post advises using simple approaches instead of machine learning when complexity would delay integration, polish, and user conversations; a sales forecasting prototype used linear regression with two input variables before a more complex model was built. User research should use specific questions and a diverse set of users, as feedback changed a confusing 0–100 scoring display to easier-to-interpret letter grades. Together, these practices provide a clearer path to launching useful products quickly.
2023-10-18Shopify ·
How to Make Dashboards Using a Product Thinking Approach
The guide presents dashboards as automatically updated collections of visualisations or metrics for monitoring business questions and argues they should be built with a product-thinking approach. It first recommends deciding whether a dashboard is appropriate, then defining one clear goal, a specific audience, and metrics that reflect their needs before sketching the layout with stakeholders. Implementation guidance covers readable and efficient queries, version control, peer review, visual hierarchy, restrained content, business and data context, refresh frequency, technical reviews, and user sign-off. After launch, teams should market the dashboard, make it discoverable, monitor usage and outcomes, iterate when it falls short, and assign ownership for maintenance. The intended outcome is a dashboard that stays useful and delivers business impact by keeping users front and centre.
2023-10-18Shopify ·
How to Use Quasi-experiments and Counterfactuals to Build Great Products
Correlation does not establish causation, so the post presents causal inference as a way to determine why product metrics change and to validate or invalidate business strategies. The proposed evidence ladder places randomized A/B tests first, quasi-experiments such as fixed-effects regression and difference-in-differences second, and counterfactual estimation third, with descriptive statistics providing no direct causal evidence. It explains that quasi-experiments use non-random treatment and control divisions, while difference-in-differences depends on a parallel-trends assumption and fixed-effects regression assumes relevant dividing factors were collected. For a security update released to everyone, the team trained a time-series model on unaffected feature usage and global activity trends, then found no lift versus actual usage, indicating no negative effect on usage. Because observational methods bring greater uncertainty and frequent false positives, the post recommends stating assumptions, relaxing them through robustness checks, and using DAGs and Dagitty to test causal hypotheses.
2023-10-18Shopify ·
How to Track State with Type 2 Dimensional Models
Application databases often retain only current values in Type 1 dimensions, limiting analysis of historical settings such as feature adoption, retention, and switching behavior. The post explains Type 2 dimensional models through Shopify’s need to track users’ admin languages over time, contrasting application-model changes, scheduled database snapshots, and event logging. Its selected implementation uses Rails after_commit callbacks to send created or updated records to Kafka, then transforms that event history into records with valid_from, valid_to, and is_current fields using ETL recipes involving PySpark and dbt. The approach provides the required granularity but can miss changes or events, requires delete handling and data-quality checks, and is presented as an iterative solution; MySQL binlogs are described as a more reliable future source.
2023-10-18Shopify ·
Shopify's Data Science & Engineering Foundations
Shopify’s Data Science & Engineering team describes the foundations it uses to provide daily insights to internal teams, merchants, and partners across a rapidly changing commerce ecosystem. Its data warehouse follows a shared dimensional-modelling philosophy, with modelled data built on Spark in a single GitHub repository and made broadly queryable through Presto, while ETL jobs are unit tested and prevented from failing silently. Centralized dashboards, reproducible vetted data points, peer review, product-aligned sub-teams, accessible communication, and cross-team collaboration extend those foundations into analysis and decision-making. Together, these practices let team members reuse existing work, produce trusted and understandable metrics, address the specialist gap through collaboration, and derive new metrics with fewer than 50 lines of SQL.
2023-10-18Shopify ·
Bug Bounty Year in Review 2019
Shopify’s 2019 bug bounty review describes experiments and process improvements intended to increase program speed, while reporting changes in response, resolution, disclosure, and bounty metrics. Shopify-Experiments, a private program launched in mid-2019 for high-signal, high-impact hackers, tested expanded scope, full payment after triage, disclosure requirements, self-closing false positives, and collaboration with third-party developers. Using registration data, HackerOne API exports, and a Slack chatbot, the team improved program analytics and automated report-state changes, assignments, comments, bounty suggestions, and common invalid-report closures. Average first response fell to 16 hours, triage to 2 days and 13 hours, bounty payment to 7 days and 1 hour, and post-triage resolution to 20 days and 3 hours; disclosures rose to 74 bugs. For 2020, Shopify announced full bounty payment within seven days of triage, a $50,000 maximum bounty, higher payouts for several vulnerability classes, and added visibility for duplicate reports.
2023-10-18
