Although there has been a growing demand for data analysts globally, there has also been wide debate about whether it is better to be a data analytics generalist or specialist.
Without a doubt, data analysts and data scientists are in demand. As more organisations seek to leverage big data, or just the abundance of data that can be generated from a variety of sources – within and outside organisations – which can help them be more strategic and make informed decisions, the access to expertise to manipulate and analyse that data has been growing.
In its 2018 The Future of Jobs Report, the World Economic Forum found that 85% of respondents to its survey are likely to expand their adoption big data analytics by 2022. Additionally, and by 2022, roles for which increased demand is projected include data analysts and scientists, AI (artificial intelligence) and machine learning specialists and big data specialists. Correspondingly, 96% of survey respondents were definitely planning or likely to hire new permanent staff with skills relevant to new technologies.
Historically, the Caribbean region has been plagued with a relatively high unemployment rate (usually somewhere in the teens), and has been considerably higher (up to three times higher) among the youth demographic. However, thanks to the pandemic, and the virtual collapse of the region’s tourism and hospitality industry, unemployment has surged, and to vary degrees, it has become critical for jobs to come back on stream.
In fostering digital transformation and our continued transition to Information Societies, Caribbean governments have also been advocating for more digital jobs. One of the areas that has been mooted is analytics, with the focus of incentivising school leavers and university students to study data analytics, in order to build and increase skills availability, which in turn could be used to attract investors and generate more jobs. Further and in today’s workplace, with data analytics is being touted as an in-demand skill, there seems to be an expectation that individuals will retool to become data analysts or scientists, to avoid becoming redundant or less relevant.
Core skills of data analyst
Generally, there is no consensus on the skills a data analysts should possess, and so they may differ by industry, or even from employer to employer. However, and with respect to core skills, it is expected that, among other things, a data analyst would be:
- good with numbers, mathematics and statistics
- proficient in the use of common data analysis tools, such as SQL and Microsoft Excel, and is able to build queries using them
- able to use visualisation tools, such as Tableau, SAS, Cognos and Python and R visualisation libraries, and
- proficient in the use of at least one programming language, such as R, SAS and/or Python.
Increasingly, colleges and universities are offering data analysis programmes as pure fields of study and/or as courses within larger programmes. There has thus been a growing debate about whether it is better to be a data analytics generalist or specialist. A data analytics generalist would be an individual who is knowledge and expertise lies almost exclusively in data analytics. On the other hand, a data analytics specialist would be an experienced worker who has added data analytics skills to their existing stable of expertise.
Generalists tend to have more solid data analytics grounding
Individuals who are considered generalists may have more detailed data analytics/data science knowledge, and so are likely to have developed a broader skills base, to better address the various stages of a data science or data analytics project. In having a solid grounding, and being able to be seen as a ‘Jack of all trades’, can offer generalists greater flexibility in the roles or fields to which they can be assigned.
Although there is growing demand for data analysts, frequently, finding the right fit for one’s qualifications and experience can be a challenge. Being a generalist may be more marketable, allowing candidates to better adapt to differing fields, depending on the opportunity.
Specialists can produce more relevant analysis
On the flipside, and in already possessing domain knowledge and expertise in a particular field, data analytics specialists are able to capitalise on that domain expertise to add more value to the organisation or industry in which they work. Typically, data analytics is being produced within some context – be it for an organisation’s finance department, or based on sales, customer care, human resource, marketing, etc. In being versed or possessing some expertise in the areas for which the analysis is being conducted, tends to produce better insights and richer results overall.
The application of domain expertise should not be underestimated. Moreover, one of the reasons why there is really no standard set of qualifications or requirements for data analysts is due to the fact that increasingly, organisations appreciate that being able to technically manipulate the data is just a first step. The real value is the subsequent analysis, which invariably ought to be underpinned by domain knowledge in the area for which the analysis is being produced.
Understanding the job market is important
In determining whether it is better to be a data analytics generalist or a specialist, an understanding of the job market is essential. In working locally or internationally it is important to get a sense of what organisations are looking for. Smaller organisations might prefer generalists, who can work across multiple areas or stages of the data analytics process, which could include data acquisition, data cleaning, and modelling, to name a few. Larger organisations might prefer having data analytics generalists, but have them work exclusively in just one stage of the analytics process. On the other hand, organisations may prefer hiring data analytics specialists, who will support specific departments or functional areas, and already have a background in those areas, thus enriching what they bring to the table.
Almost regardless of the market, there will still be some uncertainty. Although generalists might potentially be more marketable, specialists, when hired, may be able to command higher salaries, but are unlikely to be the right fit for all opportunities. To decide how best to proceed, it is best to be guided by one’s current situation:
- Do you already possess domain expertise in an specific area?
- Would upskilling your data analytics game make you more marketable in your current organisation or industry?
- What are the requirements in the job postings you have been viewing?
- How far are my current knowledge, skills and expertise from what the market is asking for?
The above are just a few of the questions one should ask. There might not necessarily be a wrong answer, but depending on the path chosen and market circumstances, it may take you a bit longer to get to your final destination.
Image credit: Lukas (Pexels)
Those of us who have been out of the job market of anything for a little long while now, may find it tricky to offer well-informed insight on this topic. But it’s an important topic because this area, along with Cyber Security, is becoming more and more relevant and is sadly appears be overlooked.
If I were looking to have a data analytics join my team, I would probably settle for a generalist. This is more so because in the actual work environment there is likely to be a variety of changing situations dictated by the realities in the marketplace of the business. A generalist will adjust reasonably well along with those adjustments in response to shifting business strategy.