By 2020 we had created 44 zettabytes of digital data. Just for context, that figure is around 40 times the number of stars in the observable universe. As AI, the Internet of Things (IoT) and other emerging technologies mature, the amount of data is only set to increase. By 2025, it is estimated that 463 exabytes of data will be created each day (an exabyte being a million trillion bytes). There is value in all this data, but it takes special skills and technologies to uncover it in the vast, ever-increasing sea of information. This is where data analysts come in.
Many organizations are now steering themselves with data-led decisions and using data analytics to improve business in all sorts of ways. A qualification like an MSc in Business Analytics from St. Bonaventure University can provide students of any background with the skills they need to succeed in increasingly high-demand roles working with big data.
But what are the core skills you will learn that will serve you well in a career in this ever-evolving field? There are many different skills required, and some may vary depending on the role and sector. From general skills like problem solving and communication, to specific technical skills like SQL and Python, these are some of the most important.
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Data analysts need to be able to see the big picture and think strategically but also to focus in on the fine details. They need to approach problems methodically and to communicate their findings to others who might not be so technically minded. Valuable soft skills for data analysts include:
Data analysis does not happen at the press of a button — if it did, we would not need data analysts at all. A huge part of any role involving data is the ability to find value and meaning in the numbers. This requires critical thinking and problem solving — two areas and skill sets that are very closely linked.
Critical thinking is all about looking for patterns and approaching things in a methodical, analytical but also creative way. This can help you to solve specific problems, which is a large part of data analysis. You might have a hyper-specific question to answer like: “Why are we selling more red dresses in this particular market?” Or it may be more general like: “What lessons can we learn from the past five years’ performance?” A critical thinking and problem-solving mindset will help you to find the right data and tools, and approach them in a way that allows you to extract valuable insights for others in the organization.
Finding that value often comes down to looking at small clues that point to a larger message or pattern. In some ways, it is akin to looking for that proverbial needle in a haystack, albeit with a powerful magnet (in the form of your technical skills and tools) to help. The actual process of assembling and analyzing data can sometimes be repetitive and tedious, so you will need to stay alert to the things you are looking for — even if you do not know what they are yet. Attention to detail is also important if you are involved in designing, building or coding systems used in the data capture and analysis pipeline.
Data analysts do not work in isolation. You may be working with colleagues performing similar roles with other datasets, liaising with data scientists who are working on new ways of capturing and analyzing the data you use, working with other departments to understand the questions you need to solve with your analysis, or collaborating with company web designers to ensure the traffic and data you need is being properly captured. All of this also requires the next key skill on the list — communication.
Communication might not be the first thing you think of when it comes to important skills in a data analysis role, but the ability to communicate well with a wide range of different people can actually be incredibly useful. It is not enough to be able to glean insights yourself from the masses of data you work with — you must also be able to communicate these insights and findings to others. You may sometimes need to communicate with people in other departments who do not have the same technical skills as you and need things explaining in a clear and understandable way. The same may go for people further up the management chain.
You may also have to explain the value of the work you do in order to emphasize the ongoing importance of data-led processes and decision making across the organization. Communication is not a one-way street, and you will also have to take information and instruction on board in terms of the problems that the business may face, which you may be involved in solving.
Data analysis involves a lot of number crunching, but all those numbers represent concepts, statistics and figures from the real world. Data analysts typically develop transferable skills to allow them to thrive in virtually any sector, but it is important to gain an understanding of the field you are working in.
If you are working in e-commerce, the data you are working with will represent something very different to the data collected in an engineering facility. Understanding how your industry works and the specific problems relating to it can help you to make sense of the data in front of you.
In order to achieve their goals, data analysts work with a wide range of specialist software and technologies. The exact mix of tools used may vary, but you will generally need to have specific knowledge in some of the following areas:
Some people are surprised that good old Microsoft Excel still gets a look in, in terms of data analysis. There are certainly much more powerful and specialist technologies available to data analysts, but the spreadsheet software is used by hundreds of millions of people worldwide and is an almost ubiquitous business tool. It also has more analysis power ‘under the hood’ than many people realize. It can be great for automating certain processes. Techniques like writing macros and using VBA lookups (Excel’s own programming language) are still commonly used for saving analysts time on frequently performed and repetitive processes like payroll or project management and the system remains a valuable one for light analysis and smaller datasets. However, it is limited in terms of larger datasets, and especially big data, so at least one statistical programming language is generally a must.
R and Python are currently the ‘big two’ of statistical programming, and allow you to go far beyond Excel’s capabilities. Both are open source and it is a good idea to learn at least one of them if you want to progress in your career — although there is debate in the analytics community and beyond about which is the most useful. R was designed specifically with analytics in mind, but Python is more popular, and is deployed in more organizations. It also has many specialist libraries and is particularly good in the fast-growing area of artificial intelligence (AI).
Both languages have benefits though, and enable similar data processing and analyzing tasks, helping you to work more efficiently at speed. There are other specialist analytical languages and tools out there, like SAS and SPSS, but Python and R are the industry standards and will generally have the capabilities you need.
A large part of any data professional’s working life will be spent working with and querying databases. Structured Query Language, more commonly known as SQL, is the industry standard here and has been since it appeared in the 1970s. It is a must for anyone working in statistical or data analysis and pretty much every business and organization over a certain size requires their employees to be familiar with SQL, even if they are not particularly data-driven or do not utilize big data. Every organization still needs to manage and store data to some degree, and SQL remains useful for small to quite large datasets.
It is not the only system in play though, and ‘NoSQL’ structures generally refers to any of a number of database models that do not organize along SQL lines. There is no one standard NoSQL framework but it can still be useful to familiarize yourself with one or more database language outside the norm of SQL.
Remember how we said that communication was a vital (if often overlooked) skill for most data analysts? Well, data visualization communicates your findings and insights in a way that is easy to understand. The human brain is very adept at processing images, so translating data into visual cues such as charts, graphs, maps and infographics can be a great way to get information across to people with different levels of understanding and technical aptitudes. This can allow decision-makers to quickly grasp the most salient points uncovered in your analysis, while features that allow you to ‘drill down’ into the graphics can provide a more granular level of detail for those who require it.
Data visualization is not the only skill you need that relates to effective communication, but it often crops up in other areas. The ability to deliver a presentation can be a valuable skill, for example, and visualization may play a big part in any presentation you give, helping you to tell the story successfully. The same is true of written reports and white papers, which can benefit from the clarity of visual representation rather than walls of text, which can be off-putting and difficult to digest. The ability to create dashboards that contain hundreds of interactive data points is also a useful communication skill to have.
Machine learning is not a skill per se, but rather a specialist area that is becoming increasingly more important in the realms of big data and data analytics. As such, it is useful to have at least a basic knowledge of machine learning and AI, and how they pertain to data analytics in particular. Not every data analyst will work with machine learning, but it is set to have a greater and greater impact on the field.
A number of factors have driven the rise of machine learning in data analytics including the ever-increasing volumes and varieties of data on which machine learning models can train, more affordable data storage and improvements in computational processing power. This makes it easier and cheaper for organizations to build or deploy machine learning models that can analyze bigger and more complex sets of data and deliver faster, more accurate results.
Large organizations will often have specialist roles to take care of all the different aspects of data management, such as data architects, data engineers, information security specialists and database administrators. Together, they help to ensure that the collection, organization and storage of data is carried out efficiently, securely and in the most cost-effective way. There can be a certain amount of overlap though, and data analysts will often find themselves involved in the management of data to some degree.
Another, more specialist part of data management is data cleaning, especially in an organization that utilizes machine learning. Cleaning datasets can allow even relatively simple algorithms to perform data analytics tasks faster and more accurately, where an uncleaned set can slow the process and even throw up misleading patterns and insights. Data preparation is not always the most glamours part of a data analyst’s job, but it can be an important one that saves time and allows for better results.
MATLAB is not yet in the position of being an industry-required skill, but it is becoming more popular with businesses using big data. MATLAB is a high-performance language for technical computing that has a number of applications including the creation of user interfaces, interfacing with programs written in other languages and — most pertinently to data analytics — the plotting of functions and data and the implementation of algorithms. It is increasingly popular with data analysts as it enables major time savings in data management tasks such as the pre-processing of data, data cleaning and organization, as well as visualization. Notably, its properties allow any machine learning model built within its environment to be executed across multiple platforms.
It is important to be able to use all the technology needed to collect, store, organize and analyze big datasets, but you also need a good understanding of the math that underpins analytical practice out in the real world. It seems obvious but you will need a good grasp of how probability and statistics can help you to more easily spot patterns and trends in the data you analyze, and avoid and mitigate biases and logical errors, resulting in more accurate and trustworthy results. Some other mathematical principles that may be valuable for a career working with data include linear algebra and calculus, which have applications in machine learning, deep learning and many other areas.
The soft skills developed on a course or when following a career in data analysis tend to be extremely transferable. Skills like problem solving, teamwork and communication are applicable in a wide range of roles and careers. The technical skills you learn will tend to be more role specific, but data analysts are in great demand across every sector, from finance and consulting, to manufacturing and pharmaceuticals, government, education, healthcare, science and pretty much all organizations in the public sector. There are also a wide range of potential employers, ranging from the ‘big four’ consultancies to smaller financial services, marketing companies, manufacturers, the media and research organizations.
As we continue to create ever-increasing amounts of data, and more and more organizations see the value they can extract from it, the role of data analysis is set to become increasingly prominent. It seems certain to be the case that AI and machine learning will also become more important in the field, as datasets get even bigger and more complex, and the algorithms and models involved continue to evolve. That does not mean that skilled human data analysts are on the verge of being replaced, however. Human analysis is still required to make real-world sense of patterns spotted by machine systems, as well as in the very development and operation of those systems.
The collection, management and analysis of data will no doubt continue to change as technologies mature. Some of the specific skills required might also change over time, but a grounding in the core soft and technical skills provided on a data analytics course can help you to keep up with developments and play a valuable (and lucrative) role in this exciting field.
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