Want to get data right? When you first decide to “do data better”, it often leads to a second decision – build vs buy. This results in the consideration of hiring a data scientist and/or data engineer to help get you there. If you decide to go down the build route you’ll need data professionals to create, implement and manage your data infrastructure.
A complete data team in a large organisation will likely feature Data Analysts, Data Engineers and Data Scientists. Unsure of the difference between the roles? Before you start the recruitment process, make sure you understand the roles and responsibilities of data jobs.
Too many businesses attempt to cheat the system with ‘Do Not Pass Go: Proceed Directly to Data Science’; however, once you’ve established a single version of the truth and core analysis, data science is a fantastic way to empower major decisions.
What does a Data Scientist do?
Data Scientists gather and analyse large sets of data, usually in the data lake or file store, rather than data warehouse, specialising in building machine learning and Artificial Intelligence models. A Data Scientist’s role combines computer science, statistics and mathematics with business knowledge.
They use advanced techniques to build data products, algorithms that add value to stakeholders in a tangible manner. Often they are creating the data foundation to enable analytics.
5 Data Scientist tasks
- Acquiring and cleaning data – Data scientists are responsible for ensuring that the dataset is clean and complete. Without this step, data may be incorrect and the outcomes and algorithms will be unreliable. This can take up a lot of expensive time and effort and it is estimated to be a surprising 80% of the time in getting to value.
- Building and/or training models – This involves establishing ways of gathering and manipulating data and having a clear understanding of what is important in the data, in order to answer the questions you are asking. Scientists will build a statistical, mathematical or a simulation model to gain understanding and make predictions.
- Put models into production – Once a model has been built, it must be tested and evaluated before going into production. The deployment of the models is just as important as the model building. Frameworks and tooling are necessary to ensure that models are reliable, scalable, repeatable and discoverable.
- Work with stakeholders to understand how data science adds value in their category or market place – Data Scientists need to work with stakeholders to ensure that they produce models that add value for customers, solving a real business problem.
- Research opportunities – This is particularly true of Data Scientists in startups, where you want the customer value proposition to be derived from the data the customer shares with you. Ask yourself, do I want the customer to buy my product because of the data value or is this ancillary? Is my business creating genuine differentiation and competitive advantage in this area? Or do I need to catch up to industry best practice? Companies tend to hire Data Scientists to do research when they need fast implementation of well understood best practices. This may be for a recommendation engine or churn prediction.
The alternative solution to building data infrastructure
kleene’s SaaS software makes this easy, so you won’t need to make expensive Data Engineer hires. You can hopefully save on some Data Science headcount too, by cutting down significantly on data cleaning and preparation time. We provide scale-ups with the data infrastructure necessary to build their single source of truth and empower Analysts to deliver value (without the need for data engineering!)
Our end-to-end automation and analysis removes the headache of data. Don’t worry if there isn’t in-house data expertise, kleene can handle your strategy, report building and much more!
Book a demo today to start solving your data problem!