At CompassRed we help companies navigate the data landscape. It’s a challenging environment, with change being one of the few constants. On behalf of our clients we use billions of data points and artificial intelligence to uncover insights, automate internal processes, and predict future outcomes. Often, we’re asked how we operate our team. First, here are a few of our beliefs and goals.
There is nothing more exciting to our team at CompassRed than adding new colleagues. Whether we’re bringing in a team of interns or adding some well seasoned colleagues - we know we’re adding people that make us smarter. And making ourselves smarter is one of our key values. Here’s what we’re wondering about when we have a chance to connect with you:
At CompassRed, we know that there are a lot of answers to this question. We’re going to concentrate on 4 of them. Most importantly, we’ll discuss how to successfully solve each of these. In no particular order, they are:
At CompassRed, we work with diverse companies and organizations in their journey of embracing Data Science, and Data Analytics, and the architectural requirements to support it. Everyone at our company works at the intersection of math and technology. We are, at our core, a creative science company searching for insight that will affect change in the world on behalf of ourselves and our clients.
At CompassRed, we work with many data sources and platforms on behalf of our clients.
Web and mobile properties are very common and important data sources.
Most often those properties are tracked with Google Analytics. (Datanyze shows GA with 62.12% market share in the US.) . Our clients mirror that statistic at minimum.
What we watch, read, listen to, and who we converse with shape our beliefs and ultimately who we are. At CompassRed we are filled with voracious thinkers, readers, and consumers of … Podcasts. We actually have a few who create them. It’s always interesting to see what’s on other’s desks — but it’s also interesting to see what’s in their mind.
One of the most exciting parts about being a data scientist is the pace of advancements in the field. There are constantly new innovations. New algorithms. New techniques. New stories. It is a battle to stay up-to-date with all of the changes in the field, but always worth it. While those new innovations rarely apply immediately to my day-to-day work, they spark ideas and allow me to be a better data scientist in the long-run.
In 2019, there are a plethora of options when it comes to Business Intelligence (BI) platforms. There are many factors to consider when selecting the platform that will work best for you and your team, which can quickly become overwhelming. Luckily for you, we have gone through the trouble of evaluating three of the most popular products on the market: Tableau, Microsoft Power BI, and Google Data Studio. We evaluated these products across five major feature categories: connecting to data, cleaning data, analyzing data, visualizing data, and sharing data. We also include an overall score at the bottom of this post if you would prefer skipping through the detail.
Any trip to your job board of choice will quickly tell you one thing: a lot of companies are hiring data scientists of all skill levels¹. There are a lot of common themes across these roles, which have been covered in plenty ofplaces. From a technical perspective, you’ll need skills in the most popular open source languages — R or Python and SQL at a minimum. If you have experience with a variety of big data toolsets and scripting languages you’re even better off. That’s to say nothing of the large variety of other technical skills a data scientist might need. From the soft skills perspective, every company wants data scientists to be story tellers and business experts.
CompassRed is a Data Science and Analytics Lab. We bring really hard data problems inside our walls and try to find answers. Our analysts sit right next to our data scientists and we surround them with the most advanced technology to make it all work. While one is asking “What happened?”, the other is asking “What will happen?”.
I am constantly fielding questions from our clients that ask:
How can I tell if our customer experience needs improvement?
Where are the friction points that cause our current or potential customers to leave or drop off?
What is the most common path to conversion?
The Google Marketing Platform team quietly made a huge update to Google Data Studio over the past week. Analysts now have the ability to choose “Extract Data” directly from the data source selection screen.
Data science is a big concept, and diving head first into the foreign world of data science can look intimidating at first. On our third day as interns at CompassRed Data Labs
CompassRed is delighted to announce it will further strengthen its digital analytics and data storytelling capabilities with the appointment of Patrick Strickler as Lead Data Analyst.
On June 13th, 2018, we put on an event in Philadelphia, PA, to celebrate and explore the world of data science. It was called the Data Jawn, and it featured a roster of speakers working in as many sectors in data science in Philly as we could muster.
To take advantage of data science, an organization needs to consider their data quality and accessibility, and the willingness of their staff to use the results of data analysis results. Most importantly, an organization must have a clear understanding of how it expects to benefit from data science.
At CompassRed, we love Delaware Innovation Week. We're particularly excited about tonight's Innovation Celebration at the Mill.
Ever wondered what the Twitterverse looks like during a major event like that? We did too. In order to take a look, we quickly prototyped a dashboard using Power BI to get a snapshot of what's happening. Curious? Check it out for yourself!
Strive is a Wilmington-based nonprofit that was founded in 1996 as a Sports Challenge Leadership Academy. They work with students to develop research backed qualities of leadership that will serve as cornerstones for life-long success.
The team at Strive believes in making informed decisions based on assessment and research when it comes to the development and implementation of their programming, and that assessment requires data analysis.