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Articles by Adam Votava
Keeping Up With Data #108
Source: https://www.datasciencecentral.com/costs-of-being-an-analytics-laggard-and-path-to-becoming-a-leader/ There are many approaches to measuring data and analytics maturity. I like the ones focusing on the business impact of data, rather than technicalities like technology used, data types processed, or levels of analytics deployed. Because in the end, outcomes are way more important than outputs.
Keeping Up With Data #105
Disposable technology is one of the concepts I promote regularly. Bill Schmarzo wrote in 2019: "Technology architecture should serve two basic purposes: Facilitate the capture, refinement, curation, sharing, management, governance and analysis of the company's invaluable data assets. Build a technology architecture that doesn't get in the way of point #1." It's one of the concepts I'm regularly going back to.
Keeping Up With Data #104
5 minutes for 5 hours' worth of reading Automation is a great way to improve efficiency of operations. Why keep performing a repetitive task when we can have a code taking care of it? Typically, the candidates for automation come from situations that take too long, occur too often, or are simply too frustrating. But frequently, the idea behind the automation is way too complicated (and sometimes completely delusional). As with any decision, the benefits must outweigh the costs.
Keeping Up With Data #101
Measuring the value of data is becoming a standard agenda on many of my conversations. It's probably due to our focus on data due diligence and value creation that people often ask us about the value of data itself. My answer is typically anchored in three points: Data has economic value, which one realises through better decision making, improving efficiency of operations, or creating new revenue streams. All of which has value - realised, probable, or potential (see the image above).
Keeping Up With Data #100
5 minutes for 5 hours' worth of reading Data products, data as a product - these are big topics of the day. Data teams are getting inspiration from products, engineering, communication, marketing, or even whole companies. The role of data and analytics is not yet clearly defined - like for instance the role of finance or HR - and therefore every data team is different.
Keeping Up With Data #96
5 minutes for 5 hours' worth of reading The image from West Monroe's report made me chuckle. Given the growing importance of data and analytics for operational and business models of many organisations, it could be dangerous to rely solely on proclaimed capabilities. Don't get me wrong, many organisations actually have databases providing unique insights to customers, have ML embedded in the customer-facing platform, or have data science teams driving product enhancements.
Keeping Up With Data #69
5 minutes for 5 hours’ worth of reading Source: https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/the-data-driven-enterprise-of-2025 McKinsey recently published an interactive article talking to the characteristics of a data-driven enterprise of 2025. Among the seven characteristics we find the usual suspects like data being used for all decisions — essentially powering the organisation, or CDOs focusing on generating value.
Measuring Data Ecosystem Maturity
We consultants are obsessed with benchmarks and scales! Why? Well, for starters benchmarking plays into the competitive nature of the typical executive who doesn’t want to be lagging or unaware, doesn’t want to miss out, and who gets into ‘fighter mode’ when their business is benchmarked to competitors. But in reality, benchmarking is more than playing into behavioural science.
Measuring Data Ecosystem Maturity
We consultants are obsessed with benchmarks and scales! Why? Well, for starters benchmarking plays into the competitive nature of the typical executive who doesn’t want to be lagging or unaware, doesn’t want to miss out, and who gets into ‘fighter mode’ when their business is benchmarked to competitors. But in reality, benchmarking is more than playing into behavioural science.
Data is an asset. What is its value?
This article is a translation of my presentation given at Primetime for Big Data 2021 conference. Intuitively, we all understand that data has value. It’s why companies are investing heavily into data and why so many people have chosen data as their professional career path. Plus, data also fulfils the definition of an asset by IFRS (or even by OECD) standards.
Does empathy play a role in being data-driven?
It was demonstrated almost ten years ago, that data-driven decision making is superior to HiPPO. Since then, being data-driven has become an ambition for many companies and individuals alike.
Churn prediction model
No company likes to lose valuable customers. In the beginning, a company typically focuses on acquiring new clients, then grows by offering additional products to existing clients or trying to get them to use their products more. If all is going well, there comes a point when the company is large enough that it must also choose a slightly more defensive strategy and focus on retaining existing customers.
3 challenges of data adoption
Countless companies are embarking on a data journey. And increasingly they — correctly — start by designing a data strategy. But even with a great plan and positive intentions, success is not certain. One of the reasons why data initiatives are failing is the lack of data adoption across the organisation. Adoption needs to spread from the C-suite to individuals at all levels — all the way to the front-line. If adoption is low among the C-suite, the data strategy often fails at the first roadblock.
CoffeeFlow Analysis over Multiple Espresso Shots
Coffee Data Science I got a Acaia Pyxis scale a few months ago, and I started logging shots. I wanted one for two reasons: more accurately measure the output weight and examine flow rate. My Kim Express doesn’t have much room for a scale, so I needed something small. After pulling over 200 shots with this scale (232 to be exact), I’ve compiled some data to help understand espresso. However, I didn’t find any metric from the flow or continuous weight data that was a great predictor of performance.
Embracing ambiguity
Photo by Ryoji Iwata on Unsplash Data is increasingly important for business success. Companies are spending greatly on data infrastructure and data professionals alike. Yet, leading opinion-makers are warning that it might not be enough. Data literacy — the ability to read, work with, analyse and communicate with data — is lacking in the majority of organisations. As a consequence, these organisations are not able to fully use the vital business resource of data to their business advantage.
Chief data scientist as a conductor
Photo by Arindam Mahanta on Unsplash Chief data scientists are typically recruited from a pool of talented data scientists. And though the transition from talented to chief sounds like a small and logical step, the role is very different. Data scientists are solving problems in the front line. Every day they are sharpening their technical, coding, problem-solving and communication skills. Some aspire to become chief data scientists.
Setting up a green-field data science team
Building a team, not hiring individuals. Where to start, what to look for and how to decide. Photo by Chris Barbalis on Unsplash And here I am. Back at the beginning. Having to build a new data science team, from scratch. Again. But this time it feels different. Strangely, the number of mistakes I’ve made or witnessed in the past is giving me confidence. Building a team is not easy. You can’t simply order skills and personalities on Amazon. Individuals are exactly that: individual.
Engineering Practices for Machine Learning Lifecycle at Google and Microsoft
As demands for AI applications grow, we’ve seen a lot of effort put by companies to build their Machine Learning Engineering (MLE) tools tailored for their needs. There are just so many challenges faced by industries in regards to having a well-designed environment for their Machine Learning (ML) lifecycle: building, deploying, and managing ML models in production. This post will cover two papers, explaining MLE practices from two of the leading tech companies: Google and Microsoft.
Building Venue-Adjusted RAPM for Expected Goals: The Origin, the Process and the Results (Part 3)
The Results: How big a deal is scorekeeper bias? After writing over 7,000 total words for parts one and two of this article, I’m excited to finally share the results. If you’ve been reading along so far, I’m sure you are too, and I’d like to thank you for taking the time to read my work. I hope you’ve enjoyed it. If you haven’t been reading along, that’s okay.
Does your product need non-human intelligence?
Our vision at Typeform is to “make things a little more human.” replace unnatural moments of interaction between people and machines with the most innate form of communication: the conversation. By leveraging its beauty and simplicity, we allow real people to learn about each other — creating a context where conversational data collection can thrive. As the man in charge of analytics I wonder: what does this mean for our team? How can we data scientists make things a little more human?
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