The utility industry is experiencing a paradigm shift in utilizing technology for data-driven decision-making and problem-solving. With the exponential growth in the amount of data, the role of data scientists has become indispensable. This reliance on data engineers, scientists, and analysts comes with challenges and considerations. We will explore the job description of data scientists, their importance in the utility industry, and challenges and strategies for optimizing resource allocation and retention.
Let’s start with the basics – What is a Data Scientist?
They are highly skilled professionals with unique technical skills, statistical knowledge, and domain-specific insights. They utilize advanced analytical techniques to extract valuable insights from large and complex datasets. In the utility industry, data scientists leverage data to improve operational efficiency, enhance customer satisfaction, and drive strategic decision-making for planning.
Most importantly, perhaps, a data scientist can help provide business value in three key ways:
- Testing hypotheses using the scientific method
- Uncovering trends in the data and determining what questions should be asked
- Providing answers to these questions and hypotheses
Data Science – a role that’s here to stay
“Data is the new oil.” You’ve heard this quote many times. With the grid changing rapidly, Data Scientists and those with data science skills are some of the most valuable people to help answer questions. Questions that come up during public hearings, from the board of directors, from regulatory, and generally support guide strategy. A data scientist can help you understand data you probably already have and help answer these questions about what and how you should plan in one, five, ten, or more years. Data science expertise can come from many disciplines. Data scientists can be trained directly in data science or come from a mathematics or statistics background, computer science, physics, and other natural or social sciences, or engineers keen on the subject. There’s no right path to becoming a data scientist or perfect course. Data scientists then need to understand the utility domain, and the ones that do will be in high demand for many years.
Suboptimal conditions create a high turnover rate.
The challenge of working with utility data is that it is often difficult to access, and even once retrieved, it can be error-prone and difficult to structure. Data analysts face the daunting task of working with large volumes of raw and unstructured data, which almost always contain errors, inconsistencies, and missing values – all without being provided the use of adequate tools to address these challenges. Insufficient or outdated software within a utility can severely hinder the work of employed data scientists. These limitations can result in inefficiencies, increased manual effort, and reduced productivity. It can and does also result in frustration and turnover. Losing experienced data scientists can significantly impact ongoing projects and institutional knowledge, leading to delays and increased costs.
Investing in your people pays off – a guide to hiring & retaining data scientists.
So we have determined that we need data scientists – but how do we create a situation where employees and employers are happy and thus increase retention? The solution is a mix of approaches, including human-centric and technological.
On the latter front, data projects can benefit from investing in data cleansing and data structuring solutions to help alleviate the burden of manually performing this task by the data science team. If you want your data science team to thrive, flip the script on the adage that “80% of data science is data cleansing, and 20% is actual data science”. Using advanced AI-driven tools for data cleansing enables your data science team to focus on getting results.
Overall, here is a recipe for data science success in organizations:
- Foster collaboration between data scientists, domain experts, and utility professionals to enhance data understanding and maximize the value of insights. This approach reduces the need for many data scientists while achieving impactful results.
- Provide an environment for continuous learning and skill development. Invest in training programs to upskill existing employees and cultivate a data-driven mindset within the organization. This promotes employee retention and career progression, thus reducing the need for frequent re-hiring.
- Leverage new technology such as automation and AI! Utilize automated data cleansing and preprocessing tools and AI-driven analytics platforms to streamline the data scientists’ workflow. This improves efficiency, reduces manual effort, and enables faster insights generation.
- Consider strategic partnerships or outsourcing data science tasks to specialized service providers. This allows companies in the utility industry to tap into external expertise and resources, optimizing costs while maintaining high-quality results.
A data science team is an investment that can pay dividends for your future success, especially given the changes the electricity grid faces. Enabling data-driven decision-making is the way forward, and data scientists will help lead us there. However, to make the investment worthwhile, we need to increase retention rates by reducing the challenges they face with workloads. By implementing simple strategies like collaboration and learning combined with investments in data cleansing in advance of projects, utility companies can save resources when hiring and retaining data scientists, ensuring their organizations thrive in the era of data-driven transformation.