Blog posts

How Advanced Analytics Can Fuel Data-Driven Decision-Making

4 Advanced Analytics Techniques to Improve Decision-Making

In today’s data-driven business landscape, organizations are constantly pressured to make faster, more informed decisions that drive better outcomes.  According to Forbes, 53% of companies use big data analytics to take inform business decisions.  An HBR study points out that companies that use data-driven decision-making are 6% more profitable than those that don’t. However, with […]

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What are data products

What are Data Products?

“There were 5 exabytes of information created between the dawn of civilization through 2003, but that much information is now created every two days.” – Eric Schmidt, Google Human beings are now, on a semi-daily basis, generating and collecting data that equals the volume of the total collective knowledge of our species till around the […]

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Advanced Analytics Benefits For Organization

5 Advanced Analytics Benefits For Your Organization

Advanced data analytics is a powerful tool for businesses that want to gain insights from their data. Advanced data analytics can provide unprecedented visibility into customer trends and preferences through sophisticated algorithms and technologies. Organizations can use these insights to identify new opportunities or better understand customer behavior. According to a McKinsey study, organizations that […]

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Power of Big Data and Advanced Analytics

Harnessing the Power of Big Data and Advanced Analytics

International Data Corporation (IDC) predicts that by 2025, the amount of data generated worldwide will reach 163 zettabytes, growing at a CAGR of 44%. Not just that, Gartner predicts that by 2025, AI-driven automation will reduce data preparation time by 95%, enabling organizations to analyze vast amounts of data in real-time. Walmart, the world’s largest […]

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The Path To Ubiquitous Machine Learning

The Path To Ubiquitous Machine Learning

Imagine a world where a confluence of intelligent systems anticipate and cater to every want and need, seamlessly enhancing your day-to-day existence. A world where machine learning trickles into every cog that makes our world work, making it as essential and widespread as electricity. There is a lot of optimism about machine learning (ML) in […]

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Understanding the advanced data analytics lifecycle

Understanding the Advanced Data Analytics Lifecycle

Businesses around the world generate massive quantities of data daily in the form of server logs, web analytics, transactional information, and customer data. To effectively process this much information and derive actual value from it, businesses need to consider advanced analytics techniques for decision-making. We already discussed its applications across industries in our previous article. […]

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Unlocking the potential of advanced analytics with Scribble Data

Advanced Analytics: Techniques, Examples, and Benefits

Data is the most important asset for any modern organization, backing most business-critical decisions today. However, fully capturing the potential of the company’s data sources, so that they start yielding impactful business insights, is not a straightforward task and the traditional BI and analytics stack is just not at the level to handle the complex […]

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Scribble Data's plans and predictions for 2023

2023: A Critical Year for ML’s Rapid Growth

As 2022 draws to a close, it is time to reflect on the year gone by and welcome 2023! I’d like to take this opportunity to talk about some of the highs, the lows, the opportunities and learnings in 2022, how we’ve seen the market evolving, how it’s impacted some of the choices we’ve made […]

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Security in machine learning through feature stores

Security in ML Systems using Feature Stores

With the transformational early successes in value creation, AI/ML is set to become ubiquitous. By 2030, AI could potentially contribute up to $15.7tr to the global economy. As more and more organizations are depending on data and Machine Learning (ML) models for their crucial decision-making, the security of data and these ML systems is business […]

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Case Studies

Ecommerce case study by Scribble Data

Accelerated ML Engineering for a Leading E-Commerce Brand

Learn how a leading e-commerce brand selling children’s apparel built their data intelligence platform on Scribble Data that supported the rapid development and deployment of use cases such as Product Listing Optimization and Re-ordering.

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Understanding Shopping Paths at a National Mall Chain

Learn how a nationwide mall chain used Scribble Data’s Enrich platform to identify patterns of shopper footfalls, determine the timing and location of ads, and achieve a significant M-o-M increase in revenue.

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A National Level Retail Store Chain

A national level retail chain in India leverages Scribble Data Enrich for developing an accurate understanding of their buyer personas, their distribution, demand and context at a fine granularity to address multiple operational use cases.

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Terrapay logo for its case study with Scribble Data

Streamlined data insights and agile data preparation for Terrapay

Learn how Terrapay, a leading cross-border payment infrastructure solution provider built the Terrapay Intelligence Platform (TIP) with Enrich to achieve operational efficiency through use cases such as Forecasting, Partner Performance Analytics, Customer Journey Analytics, and more.

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Mars logo for its case study with Scribble Data

How Mars Took Steps to Evaluate the Potential Impact of the “Great Resignation”

Learn how Mars, a US-based multinational CPG manufacturer of confectionery, pet food, and other food products and a provider of animal care services collaborated with Scribble Data to assign a “probability of attrition” based on which employees were quitting and their motivating factors through data, and ML modeling.

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Customer Testimonial: Cloudphysician

Dileep Raman, Cloudphysician’s Co-founder and Chief of Healthcare, talks about how Scribble Data enabled them to rapidly build pipelines to transform their data and get daily updated feature sets as well as trustworthy models – all in less than 4 weeks!

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Customer Testimonial: Mars, Inc.

Dr. Vidyotham Reddi of Mars, Incorporated–a leading US-based multinational CPG manufacturer of confectionery, pet food, and other food products and a provider of animal care services, talks about his experience of working with Scribble Data. Learn how his team at Mars was able to assign a “probability of attrition” to employees, calculated based on which […]

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What’s the deal with sentient AI? – Achint Thomas

Sentience in AI has always been the holy grail for computer science. What qualifies as AI sentience, and what is just another case of a model mimicking the data it’s trained on?

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Anatomy of a production ML feature engineering platform – Venkata Pingali

This talk draws upon the Scribble’s experience in building and evolving a production feature engineering platform, and the many conversations we have had with user data scientists. The talk will focus on the learnings, and not on the Scribble product itself, and expand on the talk from Fifth Elephant Mumbai in Jan 2019 on reducing […]

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Accelerating ML using Production Feature Engineering Platform by Venkata Pingali

Anecdotally, only 2% of the models developed are productionized, i.e., used day to day to improve business outcomes. Part of the reason is the high cost and complexity of productionization of models. It is estimated to be anywhere from 40 to 80% of the overall work.

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Global Feature Store Meetup #13 – Scribble Data

Feature stores have been traditionally designed for complex ML applications (Big-ML) that normally assume clear and high value propositions, long lead times, skilled staff, and advanced methods. Sub-ML is a space of mid-complexity ML applications where there is higher uncertainty in terms of value, methods used, available staffing, and speed is critical. Sub-ML is interesting […]

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Operationalizing responsible Machine Learning

ML models have to be both economically viable and FAccT (Fair, Accountable, Transparent). The terminology is new but not the need to defend models or to attest they can be trusted. Such requirements were present from the 70s for credit scoring models. What has changed is the scale and scope.

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Experimentation in Data Science

The ‘science’ in Data Science refers to the process of developing systematic understanding of the world through observations and experimentation. This science is happening in the context of fast moving organizations, in near realtime, and by folks who have varied backgrounds. The most familiar version of the experimentation is the A/B testing.

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DevOps for Machine Learning Projects

In this session, Dmitry Pretrov and Ivan Shcheklein – co-founders of DVC – discuss: How software engineering principles apply to Machine Learning development and deployment. How ML systems are different from traditional applications. Importance of data versioning.

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