How to Make Life Easier for Data Scientists

One of the most important factors for an organization’s growth is its ability to answer questions and adapt to change quickly. As a company’s data footprint and complexity grows, it gets more difficult to understand what’s really driving the business. It’s no surprise that data scientist has been one of the fastest growing roles in the last 6 years, as they are keenly equipped to make sense of data, answer questions, and communicate the results to the wider audience of a company.


Data scientists are tasked with viewing a problem through 3 lenses: analytical, technical, and business.

  1. Analytical

Data scientists need to think about problems in terms of their component parts and create models that can accurately answer questions. When wearing this hat, they consider where data originates, which transformations happen along the data journey, and how it fits in with their analysis.

  1. Technical

Data scientists combine data from different sources and transform it to be fit-for-purpose. They de-dupe, enrich, and cleanse. They write queries and apply algorithms. A data scientist frequently reaches for SQL, Python, R, and various data tools to get their workbenches in order.

  1. Business

Data scientists need to have a clear understanding of business operations, priorities, and initiatives. As they learn new information, they have to interpret it for business people. It’s not just about getting specific answers- a data scientist has to tell a story that is easy-to-understand and actionable.


Data scientists are a rare breed, and their work output is valuable to the business because it accelerates learning, lowers risks of decision-making, and requires a cross-disciplinary approach.


However, an unfortunate trend has surfaced: a significant portion of a data scientist’s job is not using these lenses or leveraging the results like businesses hope; instead, data scientists fight systems and tools to get the right data in one place and in the right format.


Things that slow down data scientists

If businesses want to maximize the ROI of data science, they need to eliminate the annoying challenges they face every day. Tell us if any of this sounds familiar:

  • “Adding new sources is difficult. Anytime I want to pull in data from a system or database, I have to write code or fight for another team to prioritize my needs.”
  • “I don’t have access to the right data. It’s locked in a black box or data silo, and I need to explore what’s available.”
  • “My analyses don’t stay up-to-date. I spend so much time building them, and then it has to be ported to a different system to be repeatable or run at scale.”
  • “Spreadsheets get slow as they grow, and I have to write code to replicate all of my logic whenever I graduate to a more robust system.”
  • “I can’t keep track of my data’s “story.” It’s hard to look at a spreadsheet and tell where each column comes from and when it was last updated.”
  • “I spend 80% of my time cleansing data.”
  • “Versioning is tough. Files are always flying around, and I can never tell which version I am working with.”


If you or your employees are nodding in agreement, your organization is wasting the time of some of your most valuable contributors.


How to make life easier for data scientists

The best way to support data scientists at your company is to make sure they can spend most of their time doing actual data science, instead of waiting for data, tracking down files, and struggling to keep things up-to-date. Here are some ways to get started:


  • Centralize your data. By having your data in one place, employees can analyze the information without worrying if they are missing anything.
  • Provide self-service. Simply stated: make it easy for people to search and download data from any database or application. (If it’s tagged and organized, that’s even better!)
  • Invest in a scalable infrastructure. When a data scientist wants to work on large amounts of data, they need the ability to see results quickly without worrying about infrastructure.
  • Make sure the best tools are available. Don’t handcuff data scientists with outdated software and old processes. If there’s a better tool available, let them try it out.


How Autodaas can help

Autodaas makes life easier for data scientists right out-of-the-box. We give them a place store all their data (databases, flat files, API connectors) and create the analyses they need. All the sources stay up-to-date automatically and Autodaas makes it easy to bring data in with a few simple clicks.


We automate the complicated tasks scaling infrastructure and keeping everything real-time. Sharing is simple too, so data scientists can distribute reports with a few clicks once they’re ready.


Autodaas also keeps data scientists in the tools they already know and use. We understand that data people like to have the flexibility to use the best tool for the job, so we connect to BI tools, Excel, Jupyter notebooks, and many other common data tools.

If you are interested in learning more about Autodaas, seeing a demo, or getting on the short-list to become a beta user, reach out to us here.