“Consumer data will be the biggest differentiator in the next two to three years. Whoever unlocks the reams of data and uses it strategically will win.” - Angela Ahrendts, Senior VP of Apple. Over the past decade, big data has become the fuel, while analytics became the engine to pioneer exceptional ventures. Big data is useless without the tools and people available to analyze it. Imagine having all of the best ingredients to make a dish, yet no chef to prepare it.
It’s no wonder that companies worldwide are invested in scaling their data analytics to stay competitive within the tech industry and looking towards AI to help solve problems. According to the IDC, AI spending will reach $110 billion worldwide. Successfully scaling your data analytics can result in cost reductions, time savings, intelligent decision making, and optimized products in the long run, but it may come with growing pains. So you might be asking yourself, how and when do I decide to take the leap with AI? Let’s walk through the steps required to build, scale, and retain your data team.
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If you’re on the road to building a data team, your business has grown enough to start thinking about a dedicated team for data analytics or your business is taking a shift towards implementing more data-driven decision-making. Regardless of where your company is, building a data team starts with defining your business’s objectives before deciding who to place on the team. From there, you can move on to determining who your key players will be. Every team will look different, and titles will fluctuate as roles evolve. Nevertheless, it’s helpful to get a brief overview of the roles that should make up your data science team. Feel free to disregard the titles, but pay close attention to the role they play.
Data Engineers’ primary responsibilities are to improve data quality and accessibility. This entails data collection, storage, and sanitation to streamline people’s access to data. Some technical experiences that may be required include working with BigQuery, SQL, Python, or Spark. They should also have experience in database design and data processing techniques.
Data Scientists’ primary responsibilities are to identify the data to use, apply experiments to help the company make decisions, and build new algorithms and models to predict the future of a product or their customers. Some common technical skills include database languages such as SQL and programming languages such as Python, R, and MATLAB. They are also skilled at machine learning and create new processes for data modeling.
Data Analysts’ primary responsibilities are to interpret and communicate findings from existing data. They should present their conclusions with compelling visualizations to ensure employers and stakeholders will better understand the product. Some common technical skills include database tools such as SQL and programming languages such as Python and R to handle large data sets.
As your data team starts to grow, it may be time to start thinking about ways to diversify and create more specializations. When looking to hire new data scientists, develop a standardized analysis procedure that is scalable and easily reproduced in your new hires.
Work with your engineering team to find ways to upskill or reskill them to create a more efficient ETL process. Consider opting into an enterprise data warehouse, like BigQuery, to give your back-end engineers the responsibility of creating data collection pipelines. Your data scientists can spend more time working on new things and autonomously conduct the whole analytical workflow without the reliance of other teams to produce a usable dataset. Enterprise data warehouses, like BigQuery can reduce SQL query times and saves time in the extraction and loading of data.
ETL stands for Extract, Transform and Load. In short, it’s the process of moving data from one database to another.
ML jobs have increased by 75% in the past four years and are estimated to continue growing in demand. If you’re a smaller company, it’ll be challenging to attract data scientists experienced in ML. Consider upskilling your data analysts or software engineers by providing ML training.
Here are a couple of courses to get you started:
To benefit the whole data team, normalize the procedure to spend more time converting individual ad-hoc data analysis into a reproducible data model for others to use. Prioritizing company gain over individual efficiency will help solve business problems faster and encourage a culture of collaboration between your data scientists. Consider using business intelligence and analytic products such as:
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Building and scaling your data team is one thing, but now it’s time to keep them in your company. Data scientists are in short supply with high demand, and they know it. According to a recent report by Deloitte, these new data scientists are typically millennials looking to create technology that can change the world. Here are some ways to keep job satisfaction and retention rate high within your data team.
A global survey conducted by Kaggle showed office politics as the highest predicting factor for job satisfaction. Managers should pay special attention to incoming data scientists and keep in mind their company work culture. It may also benefit you to know that removing data scientists to a remote position only further exacerbates underlying issues if the company culture holds political tension.
Like with most engineers, compensation stands as a high priority when considering job satisfaction. With data scientists, they are most concerned with a gradual increase in their salary. Keep your team confident with their career trajectory by rewarding their work with a steady raise to their compensation and recognizing their work. As their work evolves with new responsibilities, consider changing their job titles to represent their work better.
Studies show that your programmers’ job satisfaction improves with an internal learning platform. Regarding your data team, listen to their interests in any new analytics packages and find a way for them to start using them. Using new technology and learning new skills help your data scientists build their experience and improve their job satisfaction. By providing new tools and learnings, your data scientists will remain motivated to stay with your company and deliver good work.
Scaling your data analytics starts with investing in the growth of your data team. While there are several job titles to represent different roles to play for successful data analytics, you can scale your business by streamlining your data science and data construction processes. From there, look to develop your team’s culture in prioritizing accessibility to your data analytics for your whole company. As your team grows, continue to invest in them by shielding them from office politics, supporting their career trajectory, and providing them with new learning opportunities.
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