A combination of ex-programmers, mathematicians, and technology specialists make for the ultimate collection of data scientists for organizations looking to break down data barriers and accelerate time to insight.
Data science is not, as the saying goes, an exact science. Given the infinite varieties of data, technology stacks, business strategies, and the myriad industries now relying upon data to drive key decisions, there’s no one-size-fits-all solution to data science challenges. And as a result, there is a need for different types of data scientists.
Why? From a data scientist’s perspective, the data science requirements of every company represent a unique challenge. Transferrable skills are unlikely to tick every box, no matter your experience.
For organizations where high-quality data science is imperative, this means developing a team that can bring different bespoke skill sets together to tackle any challenge from multiple angles. You need professionals with a range of skills developed across diverse backgrounds.
While every data scientist is unique, too, there are three archetypes that help us understand how organizations can go about building a robust team. We have:
- The ‘expert’ data scientist, most knowledgeable with the technologies that house and integrate data.
- The programmer-turned-data scientist, an expert in programming and the language of data to control and organize it.
- The mathematician-turned-data scientist, with the understanding of statistical methods required to methodically analyze quantitative data.
We can think of these three personas as three sides of the same triangle. It’s extremely unlikely that one data scientist can accommodate all three specialties and have in-depth experience in each of them, but by combining these skill sets, it’s possible to develop a future-proof data science team that can democratically attack data science challenges.
Achieving high performance
A data-driven organization must have the right tech stack to perform at its best. Data is only meaningful if the right systems are implemented for organizations to make the best use of it. Data scientists with a technical mindset will strive to build the most high-performing, scalable, future-proof solution with specific capabilities for their organization, e.g., in-memory analytics, massive parallel processing, and columnar storage.
Such technologically specialist data scientists are imperative for building and integrating not just solutions for data management, visualizations, and machine learning (ML) training models but also the likes of Business Intelligence (BI) further down the line.
With all this in mind, organizations need to fully evaluate whether on-premises, cloud, or hybrid cloud is the right option for what they want to achieve, and technical data scientists will have experience of every deployment model to fully assess the right option. Often, flexibility is the priority, which means a hybrid cloud approach can be the most efficient, managing sensitive workloads on-premises and using the cloud for remote employee access to datasets.
Added to this, technology-minded data scientists can ensure data turns into value faster than ever before by extending comprehensible data to every business department and employee for a truly data-driven business.
At its heart, data science is the art of extracting knowledge and insights from large amounts of data and accelerating its use with applications such as ML, which can’t be achieved without robust coding skills. It’s no mean feat, as there are multiple programming languages and libraries required to execute myriad data-related tasks, making ex-programmers another essential side of the data science team tripod.
While many data scientists will be familiar with Python – a widely-used, industry-standard language with relatively simple building blocks and important libraries (e.g., Matplotlib to facilitate the visualization of data; Scikit-learn for ML; and Pandas for data analysis) – there are other languages like Julia that have joined the scene that accelerate the speed of data analysis.
Beyond this, programmers are also ideally placed to be able to code User Defined Functions (UDFs) for versatility. These scripts enable companies to execute their own data analytics operations and address problems that can’t rely on SQL (Structured Query Language) alone.
Making it count
With all that said, the value of data needs to be extracted and quantified based on facts too. Interpreting masses of quantitative data is yet another challenge. Ex-mathematicians turned data scientists have a perfect grounding in methods and processes that enable them to reach analytically sound decisions on repeat.
These mathematically minded data scientists’ skills lie in identifying patterns, trends, correlations, and anomalies and whether the data interrogated actually addresses the original query or question. Applying such statistical methods then provides organizations with insights based on transparent, repeatable techniques and facts to make strategic business decisions. This is particularly beneficial for organizations that are looking to interpret and apply data to revenue challenges, global sales figures, and international inventory.
Data-driven decision making is imperative for every business, and with the growing volume and complexity of information every day, it is crucial that data science teams are built with long-term success in mind. A combination of ex-programmers, mathematicians, and technology specialists make for the ultimate data science tripod for organizations looking to break down data barriers and accelerate time to insight.
Make 2023 the year to unlock new questions, fresh possibilities, and previously unimaginable observations across everything from shifts in customer behavior to financial modeling.