This Week’s Most Viewed Story (22th November 2020)
My Most Viewed Stories Of 2020
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Companies of all sizes are looking into implementing data science and machine learning into their products, strategies, and reporting.
However, as companies start managing data science teams, they quickly realize there are a lot of challenges and inefficiencies that said teams face.
Although it’s been nearly a decade since the over-referenced “Data Scientist: The Sexiest Job of the 21st Century” article, there are still a lot of inefficiencies that slow data scientists down.
Data scientists still struggle to collaborate and communicate with their fellow peers across departments. Also, the explosion of data sources inside companies has only made it more difficult to manage data governance. …
The rise in self-service analytics is a significant selling point for data warehousing, automatic data integrations, and drag and drop dashboards. In fact, in 2020, the largest software IPO this year was a data warehousing company called Snowflake.
The question is how do you get your data from external application data sources into a data warehouse like Snowflake?
The answer is ETLs and ELTs.
ETLs (Extract, Transform, Load) are far from new but they remain a vital aspect of Business Intelligence (BI). …
Since then the concept has evolved and taken on a life of its own. Increasing challenges and complexities of business have forced data warehousing to become a distinct discipline. Over the years this has led to best business practices, improved technologies, and hundreds of books being published on the topic.
Image Source: Seattle Data Guy
Companies of all sizes have embraced using data to make decisions. However, according to a 2019 report from Goldman Sachs, it’s actually quite difficult for businesses to use data to build a sustainable competitive advantage.
Our team has worked with and for companies across industries. We’ve seen the good, the bad, and the ugly of data strategy. We’ve seen teams implement successful data lifecycles, dashboards, machine learning models, and metrics. We’ve also had to come in and untangle, delete, migrate, and upgrade entire data systems.
Throughout these projects, we’ve seen several issues that pop up repeatedly: alack of data governance; bad data; complex Excel documents; a lack of alignment between data teams and the businesses; and an over abundance of dashboards, leading to confused decisions. …
Data scientists and data analysts are constantly required to answer questions for the business. This could result in a more ad-hoc analysis or some form of model that will be implemented into a company’s workflows.
But to perform data science and analytics, teams first need access to quality data from multiple applications and business processes. …
Did you know that $28.5 billion were spent on investing in machine learning projects, tools, and employees in 2019?
Machine learning has taken over pretty much every industry, owing to the automation and flexibility it is bringing to work. Companies of all sizes are using tools and cloud services like AWS Comprehend and other similar services to improve their business workflows and create new products.
However, one of the concepts that is slightly newer and proving to be helpful in complex machine learning deployment environments is MLOps.
Is the suffix “Ops” a little overused? Yes.
But MLOps has its place in the technology world. It’s actually a sign of a maturing discipline. As best practices on machine learning model management and deployment become more crystalized, it becomes much easier to develop automated platforms that can manage many of the mundane and error-prone steps in your machine learning workflow. …
Parsing and processing documents can provide a lot of value for almost every department in a company. This is one of the many use cases where natural language processing (or NLP) can come in handy.
NLP is not just for chatbots and Gmail predicting what you are going to write in your email. NLP can also be used to help break down, categorize, and analyze documents automatically. For example, perhaps your company is looking to find relationships through all of your contracts or you’re trying to categorize what blog posts or movie scripts are about.
This is where using some form of NLP processing could come in very handy. It can help break down subjects, repetitive themes, pronouns, and more from a document. …
There is a lot of talk about how data, data science, and machine learning can all be applied to help make critical business decisions.
As our team works with various companies across the U.S. and in various industries, we have had a lot of opportunities to do more than just talk. We have helped many companies take their data and turn it into valuable decisions.
Not every data project has required complex models or machine learning. …
It’s always been the key to making good decisions. Whether you are a commander on a battlefield in 1000 BC or a CEO in a boardroom in 2000.
Having critical information at the right time can make help make million and billion dollar decisions.
But, just having the data is not enough.
I could give you random stock prices, the temperature it will be for the next 3 weeks in Vegas and the number of kids being born tomorrow. …