Blog Posts for "Thought Leadership"

February 9, 2023

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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December 27, 2022

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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November 10, 2022

Data Science Teams are Doing it Wrong: Putting Technology Ahead of People

Despite $200+ billion spent on ML tools, data science teams still struggle to productionize their data and ML models. We decided to do a deep dive and find out why.  Back in 1991, former US Air Force pilot and noted strategist John Boyd called for U.S. Military reforms after Operation Desert Storm. He noted that […]

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October 4, 2022

Map Business Context as an input to Build and Outcome Focused Data Strategy

Machine learning and data science today are in a unique position where access to capital is often not the biggest barrier to success. Companies globally are continuing to invest into artificial intelligence to the tune of $140 billion, either to develop AI-native products or solutions or as a way to solve business problems and improve […]

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September 27, 2022

2023: The Brave New World of Data Privacy and Accountability

The data privacy and compliance landscape continues to significantly change in 2022, and it is necessary to understand these changes as soon as possible so you can chart your path, and that of your organization, over the next few years. EMERGING MEGATRENDS IN THE WORLD OF DATA​ 01. Increased regulatory activity. In the last couple […]

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April 5, 2022

Why Feature Stores Need to be Designed for Sub-ML Use Cases

In our last article, we introduced Sub-ML use cases and discussed how their number is growing. In this article, we’ll try and understand how purpose built feature stores for solving Sub-ML use cases can help drive more value with data. Data Science as a discipline has seen the kind of evolution that only few others […]

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January 11, 2022

Welcome to the age of Sub-ML use cases

Let’s say you work at a modern data-driven company and you want to find a way to enhance one of your processes, like partner management. It makes sense considering you have limited resources to invest in partner development, but it ranks high on your growth goals for the year. The first step would be to […]

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October 1, 2018

Should Data Scientists Be Excited Or Worried About The New Privacy Laws?

The General Data Protection Regulation (GDPR) legislated and passed by the European Union has sent ripples around the world, and depending on who you ask, this could either spell apocalypse, the workings of a nanny state, or a very positive step towards consumer privacy. The direct objective of such a ruling is to give control […]

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September 5, 2018

Why your business doesn’t have to wait, to start giving back

“If you’re in the luckiest 1% of humanity, you owe it to the rest of humanity to think about the other 99%.” — Warren Buffett W.B. has given away more than he has left. In fact, he has pledged to give 99% of his wealth. It gives us pause. When talk of CSR and philanthropy are […]

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September 3, 2018

How to get the most out of your organization’s data: The mindset

Every business is a data business And while this aphorism has been around for some time, what does this actually mean to enterprise stakeholders? What should key decision makers be valuing and excited about as they start to invest in analytics tools and ML/AI? Here’s what we think are the most important aspects to embrace […]

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June 11, 2018

Reducing Organizational Data Infrastructure Costs

We speak to a number of organizations who are in the process of building and deploying data infrastructure and analytical processes. Organizations face a number of challenges that prevent them from meeting their analytical business objectives. The idea of this note is to share our thoughts on one specific challenge – high cost. Specifically: Cost […]

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September 14, 2017

How to Turn Your Startup Into a Data Informed Business

This post is a useful way to think about how to start on a data journey if you’re a young startup that’s just pushed data to the back burner (say until you had ‘enough’ traction) or even if you’re part of a more mature company that’s used to making decisions more on instinct and experience, […]

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July 17, 2017

The Pitfalls of Data Science and how you can avoid them

[Update]: This article is getting a good bit of engagement. If it resonates with you, I’d love it if you could answer a short 2 minute survey on your data journey here. I will add the same survey link at the end of this post as well. Depending on who you ask, you’re going to hear data […]

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July 12, 2017

How to Architect for Data Consumption

This is my pet peeve – technical architects are building systems and applications that make data analysis complicated, error-prone, and inefficient. We need enablement of data consumption as a first-class requirement of any system that is built. I explain here how we could architect differently to improve data consumption. Technical systems architects, including myself until […]

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February 20, 2017

Data Shifts Power Within Organizations

A major challenge in going more data-driven in organization has less to do with data itself, and more to do with the ability to manage the dynamics that emerge as decision makers look at data as an input to decision process. I have a particular kind of power shift in mind. I am not referring […]

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September 15, 2016

Available But Unusable Data – Part II – Semantic Gaps

At Scribble Data we are thinking deeply about why decision makers are not able to get to the data when they need even when relevant data is available in their own databases. The reason this question matters is because we find that decision makers routinely make high risk decisions involving products, marketing, and operations with […]

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