Which is More Useful for My Business – Data Analytics or Data Science Employees?
Clued up business owners today know that information is power, and data is the new currency. They will also be painfully aware that failing to become a digital-first, data-driven company will sound their business’s death knell. They just won’t cut the mustard and stay relevant in the post-pandemic recovery stages or thereafter. Businesses eyeing up the road to recovery must judiciously leverage real-time data to become more responsive and able to make accurate, timely decisions.
In a survey done by alteryx.com, 73% of respondents claimed that data was the key to driving their business growth e.g., having a data-savvy workforce able to leverage data use, will bring you significant business benefits, such as, being able to: capitalize on new business opportunities, generate more revenue, reduce costs, forecast market trends more accurately, improve the efficiency of existing operational processes, and create actionable insights- all through data-driven decision making. The list of benefits is limitless with data in the right hands. Today, data analytics is used increasingly to drive innovation and create value. Gartner Data and Analytics claim data will, “have an even bigger impact on society in the next 20 years than the internet did in the last twenty.”. Compelling, right? On the flip side however, shifting to this new digital-first, data-driven business ecosystem may be a confusing, self-defeating transition if you don’t know what you’re needing or how to harness these skills. On top of that, talent in data analysis and data science is scarce since they’re hotly pursued by large established organizations. As well, they’re expensive to hire as a consequence, especially for small businesses and startups. So, how can you become one of those data leveraging, highly productive, data-driven businesses? Crucially, get to know what each of these roles offer your business in terms of relevant data skills and evaluate whether you need experts in these fields or just people who possess certain relevant data and analysis skills, or some type of combination.
What value is data analytics to my company?
Data analysts examine massive data sets to identify trends, develop charts and create visual presentations to help businesses make more strategic decisions. Although what they do differs across industries and companies, they fundamentally make use of data to draw meaningful insights and solve problems. They analyze well-defined, historical data sets using a comprehensive toolkit to answer specific business needs e.g., why sales dropped over a particular quarter or why a marketing campaign did better in particular regions etc. The best data analysts can combine their technical expertise with an ability to communicate quantitative findings to non-tech colleagues or clients.
What can a data scientist offer?
Many people use these terms, data analytics and data science interchangeably, however, they’re not the same although related. Data science is an umbrella term of which data analytics is a subset. A data scientist designs and constructs new processes for data modeling and production prototypes, algorithms, predictive models etc. They estimate the unknown by asking questions, writing algorithms, and building statistical models. They’re usually tasked with designing data modelling processes, algorithms, and predictive models to extract information needed by a business organization to solve complex problems. Often, they’re charged with maintaining data systems and databases, using statistical tools to interpret data sets, and preparing reports that effectively communicate trends, patterns, and predictions based on relevant findings. They are typically more highly qualified and experienced than data analysts so command higher salaries.
How can I afford to cover these roles in my business’s future strategic plan without employing high-paid experts in these fields?
It’s a fact that data analysts can earn on average per annum USD83,750 to USD142,500 depending on their skills and experience while data scientists who usually have a graduate degree and more experience can earn an average salary per annum between USD105,750 and USD180,250 (Robert Half Salary Guide 2020) - prohibitive payouts for new startup ventures and the like.
The www.alteryx.com’s survey reported 69% of respondents believe empowering people is critical to digital transformation which in turn is crucial to maintaining a competitive edge. Therefore, the best place to start is with training your own employees with the data skills you need. It’s a no-brainer in terms of the safest investment for your company’s future growth. Affordable ways of acquiring the expert data skill sets you require, could include recruiting newly graduated data scientists in junior positions and nurturing their talent. Alternatively, recruit data and analytics talent based on part-time, temporary, and full time contracts when your business requires their critical skills, and use these quick talent hires to upskill, train, and participate in skills transfer programmes to tackle immediate challenges and overcome potential skills shortages in the future. To help your business succeed as this pandemic continues to drag its feet, you’ll need to do a regular data skills’ audit of your employees with data recruitment in mind so that you can fill gaps as they evolve.
Another pathway to foster critical data skills for business among your staff is to send them on professional training courses or encourage them to do relevant courses off their own bat, like learning programming languages, such as Python, SQL (Structured Query Language) and VBA (Excel’s Visual Basic for Applications).
Key takeout: Whether a data analyst or a data scientist serves your business best is perhaps not the right question. What you need to harness without a doubt in today’s, and the future’s, business arena to be successful and enduring as a company, is a workforce equipped with competencies in data skills, cognitive skills, and innovation. One which is risk aware and has a ‘fail early, learn fast’ attitude to digitization so that they’ll continually improve their quality of insights and more easily convert them into actions that boost the company’s bottom line. By becoming literate in the language of data and by learning to understand where the data is coming from and what to do with it, your business will have, and keep, that competitive advantage.