There are several career paths that you can take on the road to becoming a data analyst. Sometimes it is an early career choice and you may have decided to specialise from day one. More often, data analyst is a role that you move into as part of your career development, perhaps because an opportunity presents itself, perhaps because you enjoy the challenge, perhaps because you become fascinated by what you can learn from data and how data can shape the way in which an organisation operates. This chapter looks at the ways you can become a data analyst, the opportunities and challenges of this career path and how you can continue to develop your career. It includes some real-world stories showcasing the range of backgrounds and career moves that can lead to you a career in data analysis.
The contributors to this book work in sectors including consultancy, industry, finance and higher education. We all regard ourselves as data analysts, but what we do varies, and we all took a different route to arrive at where we are. Your route to becoming a data analyst is likely to be just as interesting. If you decide that the data analyst role is for you, we hope you enjoy the journey and the role as much as we do.
Just a reminder – we are talking in this book about someone who works with data, analyses data, finds meaning in data, draws conclusions from data and shares that information and knowledge with the data stakeholders. We are not talking about being a business analyst or a business modeller, which are different roles, although there is sometimes an overlap between the role of the data analyst and the role of the business analyst.
THE CHANGING ROLE OF THE DATA ANALYST
This is an exciting time in the data world, particularly for data analysts. With the arrival of Big Data, data analysts face a whole range of new challenges and opportunities. Events are moving faster than ever before, and this is not a career where you can operate on autopilot! If you decide to become a data analyst, you will need to be committed to continually extending and developing your skills to stay current in a rapidly changing field. For that reason, there’s a lot of talk in this chapter about continuous professional development (CPD) and ways to develop and extend your skills.
The traditional view of a data analyst is to collect and analyse data to support business decisions. But a good data analyst does more. A good data analyst has the ability to communicate what story the data is telling so that their clients and partners make the right decisions.
(Duncan Watkins, Senior Consultant, Forrester)
CAREER OPPORTUNITIES
Chapter 2 looked at the kind of tasks data analysts carry out and the key industries where data analysts work. Here, we look at the roles and career paths open to data analysts.
Career paths for data analysts
As Chapter 2 explained, there are a wide range of possible careers for someone with data analyst skills. In this section we look in a bit more detail at some of the most common career paths for data analysts. Some roles, such as those in data analytics and data mining, tend to have a greater focus on mathematical and statistical analysis skills, while other roles might focus more on the business environment, visualisation and operational reporting.
Working with business intelligence
Business intelligence means providing managers at all levels in the company, but particularly at middle and senior management levels, with the information they need to manage and grow the organisation. Data analysts working in a business intelligence environment for a large company might be involved in working with data from data warehouses, analysing trends and presenting information. A data analyst working for a smaller company might cover several roles; for example, they may be involved in identifying, together with management, the key performance indicators (KPIs) of a company, helping to develop a dashboard to present these KPIs to managers, analysing and visualising the outcomes from the data, making predictions based on the data and presenting their findings to management. They might also find themselves involved in training end users to understand and interpret the data they share with them.
HOW I BECAME A DATA ANALYST (A)
My career in data analysis has been more of a gradual journey than a conscious career choice. I moved to the UK in my early 20s and started working in junior data entry positions in the legal and financial services sectors. At first, I wanted to study for a career in IT network security, but the temptation of quick salary increases combined with challenging analytical problems and the gratification of influencing the strategy of a very large UK insurance company persuaded me to stay in data analysis. I had started part-time distance-learning university studies in computing and amended my module choices to include more statistical courses. Formal training and self study taught me to use a number of the common software tools, while work-based courses and on-the-job training gave me an understanding of communication styles and project management. Nearly 20 years later, I now work as an independent analytics consultant in financial risk management – but I’m still on my journey towards being better at data analysis.
Working with data analytics and data mining
This role overlaps with the business intelligence role, but if you are working as a data analyst with a focus on data mining, you are likely to be working with large data sets and using data mining algorithms to find patterns in data. Data mining is linked to data warehouses, and you might find that you take on more than one role and become involved in the management or development of the data warehouse. Data analysts working in this role, or working with complex data, are sometimes referred to as data scientists. Data analysts working as data scientists are likely to be working for large companies or government organisations or may be involved in scientific or medical research.
Working with Big Data
Big Data analytics is one of the fastest growing and most rapidly changing areas of data analysis and is likely to be a key sector in the future. Data analysts working with Big Data need to be able to deal with high volumes of data in a variety of formats, and also need to be able to work with sophisticated analysis tools. Big Data analytics applies to social media data and uses techniques such as sentiment analysis and text mining, but also covers sensor data and other forms of Big Data. It often involves real-time analysis and reporting, and some people might argue that the role of a Big Data analyst is very different from data analysts working with traditional data analysis. Tools and techniques change rapidly in this field, and data analysts will find that they are constantly extending and developing their skill sets.
Working with data assurance and data quality
Data analysis requires an understanding of the quality of the data that is being used. As a data analyst working in the field of data quality, you might use sampling techniques to examine the data, to verify what data is being collected and where and how the data is collected. You might have a role in evaluating the data and deciding how it can be used as input to support further analysis. This might be a specialist role, but often these tasks will form part of the work of the data analyst. Data analysts tend to feel an obligation to verify the quality of the data they are working with – on the rubbish in, rubbish out principle.
Mixed analytics
The changing role of the data analyst means that you may work in a variety of roles, perhaps all at the same time or at different stages of your career. Some data analysts specialise, for example, in data mining analytics, but many data analysts deal with different sorts of data and different types of analysis. Some people work in a role that combines data analyst skills with other skills and responsibilities, and some data analysts have a number of roles during their careers; have a look at the ‘How I became a data analyst’ boxes in this chapter. Some people move into roles that are not directly related to data analysis, but where their experience of data analysis provides them with a useful skill set. You may decide to specialise, but things are changing so rapidly that it is a good idea to have a range of skills and abilities in order to allow you to adapt to wherever the data industry goes next. Whichever career path you take, you will need to continually update and extend your skills.
What kind of jobs are there for someone with data analyst skills?
Data analyst skills open up a range of job opportunities and it is important to recognise that roles overlap. Even if you are primarily a data scientist, you might find that part of your role includes elements of the data architect role. You might find that a role one company calls operational research is called data analysis in another company, or that a role you would describe as a data scientist has a different job title. These are just some examples, and you will find that roles and the way roles are defined varies between companies. The important thing is to check the job specification and see what the role involves.
To give you an idea of the kind of careers that might be available, these are some examples of jobs directly related to the data analyst role:
• Data analyst/data scientist/Big Data analyst: the kind of thing just discussed, working in fields such as business intelligence, data analysis, data mining, Big Data analytics. For example, a data analyst working for a large retail company might analyse purchasing patterns looking at the associations between customer purchases and customer characteristics and identifying which items are usually purchased in combination with other items. Next time you look for an item at a supermarket and find it on the bottom shelf of the display, a data analyst is probably responsible for ensuring that this particular item is displayed in this particular location.
• Operational research: this can cover a range of activities, but usually relates to supporting decision-making through analysis. In some companies, this role may be seen as different from the data analyst role, but in other companies, it may be regarded as very similar.
• Analytical officer/chief analytical officer: this role is pretty much what the name suggests, someone who is responsible for the data analysis needed by a company. Sometimes this role includes other analytical responsibilities and may be wider than data analysis. The chief analytical officer is usually a senior role in a company.
These are some examples of jobs that might include a data analyst role – where you might be expected to have good data analysis skills, but data analysis is not the main focus of the job:
• Data consultant: acting as a data consultant requires the ability to handle a range of different tasks, which may include systems analysis, data management and data analytics. Data analysis skills are likely to be an important part of this role, but not its main focus.
• Data architect: someone who is responsible for analysing the data that a company holds and how the company works with that data. The data architect is responsible for developing strategies for managing data, integrating data where necessary and ensuring that data is available to the end users and other stakeholders. The data architect role focuses more on the nature and distribution of data as opposed to the more statistical and mathematical focus of a data scientist. A data architect will usually have a track record of working with and understanding data, and data architect is often a more senior role.
• Data engineer: this is similar to the data architect role, but is usually more technical. The data engineer role focuses more on working with data and databases, and implementing data policies, rather than on making strategic decisions about data.
• Business analyst: the business analyst role is not the same as the data analyst role, but a successful business analyst requires data analysis skills and people sometimes move between this role and the data analyst role.
These are some examples of jobs which at first sight might not seem to be linked to the data analyst role, but often require a data analysis skill set:
• Business innovator/business transformation manager: this might not seem to be a data analyst role, but look at the ‘How I became a data analyst’ boxes in this chapter for more insight. Data analysis skills are an important part of many roles.
• Project manager: managing a large project requires good analysis skills, but does not usually require the specialist analytical skills of, for example, a data scientist.
• Logistics manager: a logistics manager is usually involved in managing the flow of goods and services, handling complex data and analysing and resolving problems. Understanding and analysing data are an important part of the logistics manager’s skill set, but are only part of the role.
BUILDING A CAREER AS A DATA ANALYST: GETTING STARTED
In Chapter 2, you read about the role, key tasks and skills that a data analyst requires. In this section we look at the sort of qualifications that can help you to build a career as a data analyst. Later on in this chapter, we will look at CPD and how you can combine formal academic qualifications with the soft skills that a data analyst needs to succeed.
The Skills Framework for the Information Age
If you are involved in information systems management at any level, you have probably heard of the SFIA. SFIA is an international skills framework that describes the professional skills and competencies that individuals and organisations need to have or need to develop to succeed in the information age. Qualifications such as the BCS Data Analyst Apprenticeship Scheme are designed with reference to SFIA. If you are thinking of doing a specialist data analyst qualification, it is worth checking the skills and accreditation offered against the professional skills and competencies described in SFIA.
Getting started
There are a number of ways to start your career as a data analyst. As you can see from the career stories in this chapter, a lot of data analysts do not set out to become data analysts and only specialise later in their careers. You may go, or have gone, straight into a data analyst role on leaving education, or you may find that your job means that you are working as an entry level data analyst and are learning data analyst skills on the job. The nature of the data analyst role means that if you want to progress in your chosen career, you would be expected to have some formal qualifications to demonstrate your skills and competence. One way to achieve this is to take additional qualifications part-time, but you could also decide to complete an on-the-job professional qualification, which might then allow you to continue on and achieve further qualifications. BCS, The Chartered Institute for IT offers training within in the workplace through the BCS Data Analyst Apprenticeship Scheme.
HOW I BECAME A DATA ANALYST (B)
When I started my IT career in IT infrastructure support, I had no understanding or appreciation of data analysis. Over time I became an IT instructor, and this provided me with my first opportunity to help people develop small IT database systems, thus expanding my own data skills. As my career progressed into systems development, IT consultancy and IT management, my skills and use of data grew, but purely within the realms of design and reporting rather than analysis. It wasn’t until I started to work in business improvement that I began to develop my analytical data skills in order to help businesses get more value from their data. After completing my MBA dissertation, on the use of data modelling to predict customers at risk of falling into debt, I cemented my reputation as a data analyst. I now work as a business transformation manager and use a variety of business improvement, IT and data analysis skills to help my organisation improve, and, while data analysis is an important part of my role, it is very much used alongside other skills and disciplines to support my work.
What is the Data Analyst Apprenticeship Scheme?
The Data Analyst Apprenticeship Scheme is an apprenticeship qualification. It is recognised as a Level 4 qualification equivalent to a Level 4 National Vocational Qualification (NVQ) or to a Higher National Certificate (HNC). The Data Analyst Apprenticeship Scheme is designed for people who want to get a qualification while they are working.
How does the Data Analyst Apprenticeship Scheme work?
The Data Analyst Apprenticeship Scheme is run by BCS in conjunction with employers. What this means is that you study for the apprenticeship mainly in the workplace and you would need to have the support of your employer to join the scheme. The qualification is intended to take 24 months to complete. During that time, you will complete the Certificate in Data Analysis Tools and the Diploma in Data Analysis Concepts. You must complete both the Certificate and the Diploma to complete the apprenticeship, although it may be possible to gain an exemption from some elements. The full specification is available from the BCS website at www.bcs.org, but briefly, you will learn about data integration and data preparation, industry standard tools and the data life cycle, the different types of data and data architectures, data analysis tasks, data quality issues and compliance and ethical issues.
The apprenticeship is assessed in several ways. You will complete a number of tasks, including preparing a portfolio of evidence, completing a project, having an interview with an assessor and completing a final, timed multiple choice question (MCQ) test. You will also need a reference from your employer. Before you start the apprenticeship, you should discuss with your employer exactly what you will be required to do.
What are the entry requirements?
The entry requirements vary depending on the agreement with your employer, but the apprenticeship usually requires five GCSEs or equivalent and a relevant Level 3 qualification or successful completion of an aptitude test. An A Level is a Level 3 qualification and so is a Level 3 NVQ. For suitable applicants who do not have a Level 3 qualification, success in the aptitude test could be accepted in its place. Again, you will need to discuss the entry qualifications with your employer.
How will the Data Analyst Apprenticeship Scheme help my career?
Completing the Data Analyst Apprenticeship Scheme will give you a recognised Level 4 qualification that is mapped to the internationally recognised SFIA. The material you study will give you a solid foundation in data analysis and can provide you with the basis to continue on to get further qualifications.
Higher education qualifications for the data analyst role
A higher education qualification does not necessarily mean a BSc or BA degree. There are a range of international qualifications, and there are also UK qualifications such as the HNC and Higher National Diploma (HND). In this section, however, we are looking at honours degrees such as a BSc Hons or BA Hons or a Masters level degree. Some data analysts will go on to achieve further qualifications, such as a PhD.
Which is the best degree subject for a data analyst?
Do not chose a course because of its title. Look at what is taught on the degree and what modules you will study. There is a wide range of possible degree titles, but some degrees that are most likely to be relevant are:
• Data science: data science degrees are offered by a number of UK universities, but are not yet as widely available as more traditional qualifications such as the BSc in computer science. Data science degrees usually emphasise the mathematical and statistical elements and the use of analysis tools. As data science degrees are not yet very common, and as you may not make the decision to specialise until towards the end of your degree studies, you might choose to take a traditional computing degree and then go on to specialise at Masters level, perhaps doing an MSc in data science.
• Mathematics and statistics: this may be offered as a separate statistics course or as a combined mathematics and statistics course. If you study a mathematics degree and you know you want to be a data analyst, make sure you are also covering statistics. Some universities may offer degrees that combine computing with another subject, such as mathematics and computing or statistics and computing.
• Business information systems or business computing or similar: these degrees cover a range of topics and the content of the degree varies between universities, so make sure you read the degree specification. To become a data analyst, you want to be confident that you are covering topics which will give you the analytical skills you will need.
• Computer science or computing science or another computing-related degree qualification: these awards will give you a grounding in computing concepts, but some topics will be more relevant than others. If you already know that you want to be a data analyst, check the specification to make sure the course is covering the topics you want to study. Topics such as database design and development, data mining, data management, statistical analysis, operational research, mathematics, Big Data analytics, project management and data visualisation are all useful skills for a data analyst. Most degree courses also include modules that will help you to develop the soft skills you will need to succeed, such as communication and team working.
There are a number of other degree courses that might be relevant, such as economics, and some courses will include teaching on topics relevant to the data analyst, such as Big Data and Big Data analytics. Check what you will be studying on the degree course and talk to the admissions tutors to make sure you fully understand what your course will cover.
HOW I BECAME A DATA ANALYST (C)
When I started out, I hadn’t heard much about analytics or the role of a data analyst. I had studied market research in my MBA and was quite keen to pursue primary research. I wasn’t a believer in secondary research which most data analysts deal with. I got headhunted by a couple of PhD fellows who wanted to up-skill a few people as there was a lack of experienced data analysts in the city. From a small team, I soon moved into a 1100 employee analytics centre of excellence. I started my data analyst role in pricing. Later on, I moved to banking and used my data skills to solve problems that our customers face. Gradually, I moved into consulting and now have worked as an analyst, project manager and modeller. It’s been 15 years and as I look back, I wonder how a person who did an advertising internship ended up as an analyst.
As mentioned earlier in this chapter, a lot of data analysts don’t start out as data analysts. If you are considering a career as a data analyst, but have not completely made up your mind, or want to keep your options open, consider a degree such as computer science or mathematics and then take a specialist qualification afterwards.
I did a degree in a different subject; can I still be a data analyst?
Of course. Having a degree in a different subject does not stop you becoming a data analyst. One of the authors of this book has a first degree in English. A degree shows an ability to study at a certain level and you can acquire the specialist skills you will need through further study and professional development. If you read the career stories in this chapter, you will see that many professional data analysts never set out to be data analysts.
Should I study a Masters degree?
An MSc can be a useful way of acquiring specialist skills, particularly if your first degree was not relevant to your role as data analyst. Some data analysts have Masters level qualifications, usually in a relevant subject such as Big Data or data science or statistical analysis, and some data analysts go on to achieve PhDs, often while working on a data analysis project. Most Masters degrees can be studied part-time. On the other hand, a lot of data analysts prefer to get professional or technical qualifications rather than academic qualifications; you will need to decide what would be most helpful for you, and we suggest that you look at the later section of this chapter, ‘Is this role for me?’, to decide which skills and qualifications will most help you in your role.
Graduate training schemes
As the name suggests, a graduate training scheme is a scheme where graduates are recruited by companies to train for a particular role – in this case, that of data analyst. Terms and conditions differ between organisations, but, generally, you can expect to earn a graduate salary and to receive on-the-job training with the expectation that you are likely to be offered a permanent post once you have successfully completed the training and probation period. Some companies will also support you in gaining further job-related qualifications. Graduate training schemes are offered by a wide range of organisations, including government bodies. On the whole, it tends to be the larger companies who offer graduate training schemes and you can look on company websites for information. University careers centres will have information about the different types of companies and schemes that are on offer, and will point you in the direction of recruitment agencies that specialise in this area.
BUILDING A CAREER AS A DATA ANALYST: DEVELOPING YOUR ROLE
In this section we look at the ways in which you can develop in your career as a data analyst and acquire additional skills and qualifications.
What is continuous professional development?
CPD is a key part of building your career profile. It is the process of learning, reflecting on your learning and continuing to update your knowledge and skills. Chapter 2 talked about the need for data analysts to deal with new challenges and to be prepared to adapt to developments in the data world. In a changing field such as data analysis, qualifications and experience can quickly become out of date. The reason that professional development is described as ‘continuous’ is because it is expected to be a process that continues throughout your career.
• The first step in the CPD process is an audit of your skills and knowledge. People are often surprised to recognise how much they already know.
• Once you have established what you know and what skills and qualifications you have, you can move on to identifying any gaps in your knowledge and areas that you wish to develop further, perhaps related to what you currently do, perhaps related to what you hope to do in the future.
• The next stage is to plan your professional development activities, identifying what you need to do to achieve the goals you have set yourself.
• This is followed by completion of the professional development activities.
• Followed by the final step, where you review what you have learnt and reflect on what you have achieved.
• This leads to starting the process again: auditing your skills and knowledge to help you to identify other areas for development.
CPD is something you should expect to do throughout your professional career. One common theme in all the ‘How I became a data analyst’ stories in this chapter is that the learning process never stops.
CPD includes documenting what you have learnt, usually by creating a portfolio of CPD activities. There are two good reasons to document your professional development. The first is that this helps you to reflect on what you have learnt, helps you with the process of identifying gaps or limitations in your skills and knowledge and encourages you to continue to develop. The second is that, in terms of career progression, being able to demonstrate a record of CPD helps you to show that you have a professional, career-focused approach and that you are continually developing and extending your skills.
HOW I BECAME A DATA ANALYST (D)
I never set out to work with data analytics. I started my career working as a hospital administrator in the NHS, but gravitated towards the systems management side of things. I have always enjoyed working with data; it is very satisfying to see patterns emerge and to know that somewhere, buried in the files and the spreadsheets, is the question which holds the answer to solving a problem. For me, data analysis is about first identifying the questions to ask and then looking for the answers. I’ve worked in a number of fields, getting formal qualifications along the way and I am now researching in Big Data and Big Data analytics. It is a challenging field because nothing stands still. I constantly have to learn new skills and new approaches. I love it.
Planning your continuous professional development
You will spend a lot of time and effort developing your career. To try and help you make sure that you are identifying the right CPD activities, and that these activities will help you to achieve your career aims, spend some time completing the checklists at the end of this chapter. In the rest of this section, we discuss CPD under three headings: professional activities, technical skills development and soft skills development.
Professional activities
Professional development may involve membership of a professional body and also professional certification. BCS, the Chartered Institute for IT is the recognised professional body for people working in IT in the UK. Membership of BCS gives you access to BCS resources and publications. You will be able to attend meetings and take part in BCS branch activities, such as talks, discussions and visits. You will also be able to join up to five BCS special interest groups. The special interest groups are what the name suggests: the groups are made up of members who meet several times a year to discuss specialist interests. For example, there is a BCS special interest group for data management.
There are five different categories of BCS membership, which match the different stages in your career development. You can join as a Student or Apprentice Member, provided you meet the entry requirements. This will allow you to develop your understanding of the IT industry while you are studying. If you are at the start of your career and do not qualify for student or apprentice membership, you may be able to join as an Associate Member. If you have more experience in the IT industry or have a relevant honours degree, you may be eligible to join as a Professional Member. When you have a track record of professional excellence and contribution to the profession, you can apply to become a Fellow of the British Computer Society. If you do not meet the criteria for membership, but have an interest in the work of BCS, the Chartered Institute for IT, you can apply to become an affiliate member.
Professional membership helps you to network, to share information and experiences with fellow professionals and to develop your understanding of the IT industry in general and your own specialist area in particular. Depending on the field in which you work, you might also join other professional organisations, to help you develop domain knowledge and expert understanding of your field.
Technical skills development
The data analyst role requires good technical skills in a changing data environment. You will need to build on and extend your existing skills and learn new ones.
The audit that you carry out of your skills will allow you to identify the areas that you wish to develop. The skills you need will vary, depending on the area in which you are working and the tools you are using. You might decide to take specialist courses to improve your data analysis skills, such as becoming a specialist in the R programming language, or you might want to take a course in data mining algorithms or statistical analysis. There are a wide range of providers offering training, which ranges from intensive short courses studied in your own time to full-time courses. There are also massively open online courses (MOOCs), which are designed for open access distance learning. MOOCs are usually free and cover a very wide range of subjects.
Participation in specialist groups and forums can also help you to develop your skills. You can post queries and receive replies and as you become more experienced, you can respond to questions from other users and take part in expert discussions.
Professional certification allows you to ‘badge’ your technical skills and to ensure that your skills remain up to date and relevant in a changing technical environment. Professional certification is provided by most of the major vendors, such as Oracle, Microsoft and Cloudera, and by organisations such as BCS, the Chartered Institute for IT. For example, Microsoft offers a professional programme for data science and BCS offers a professional certificate in data analysis.
You might decide to get additional academic qualifications, for example by taking courses in your own time. Further education colleges, universities and organisations such as the Open University offer a range of courses, many of which can be studied by distance learning.
Before you make a decision about your training needs, you will need to do some research. If you decide that what you need is formal certification, check out the cost of this, what the course will cover and whether the certificate that is awarded is recognised in the workplace. Formal qualifications will have industry and/or government approval. If you decide that you do not need formal certification but want to extend your skills, then make sure that the training course you choose will meet your needs. Suppose, for example, that you want to extend your skills to include expertise with the R programming language: identify what you want to learn about R, check out the course specification, have a look at online reviews to see what other people have said about the different courses and ask around. Sometimes you can do a trial lesson, which will let you check that the course is pitched at the level you need. Many of the online skill courses are free, but some are on a paid-for basis, so always check the small print.
Soft skills development
Chapter 2 discussed the contribution that soft skills can make to the role of the data analyst. CPD for the data analyst is not just about developing technical skills; it also covers the development of soft skills.
Soft skills development is often done in the workplace. Larger companies sometimes run workshops on things such as how to develop team working or communication skills. Smaller companies may not provide this training, or you may wish to organise your own training. Check out what industry and professional training bodies can offer. For example, BCS provides resources and workshops on career development.
Sometimes it is easier to do an audit of technical skills than an audit of softer skills, but as Chapter 2 showed, both sets of skills are needed to succeed as a data analyst. The checklists at the end of the chapter will help you to assess your soft skills.
A data analyst should be able to understand and articulate the impact of data on their organisation. They should be proficient with data management processes and tooling required to ensure that the data an organisation relies on to make decisions is complete, accurate and fit for purpose.
(Vicki Leigh-MacKenzie, Data Governance Expert, Nordea)
CAREER PROGRESSION: WHAT NEXT AS A DATA ANALYST?
Once you have become established as a data analyst you might choose to stay in that field and aim to progress eventually to a role such as chief analytical officer or chief data scientist. Alternatively, you might choose to move into a different role, using your analytical skills to enter a field such as business analysis or data management.
Some data analysts follow a very clear career path, moving from junior data analyst, to data analyst, to senior data analyst and then perhaps a role equivalent to chief data analyst. However, as the ‘How I become a data analyst’ stories in this chapter show, other data analysts often follow different career paths with a variety of routes to becoming a data analyst and progression in the career.
A useful approach is to think about what you want to achieve and when you want to achieve it by so that you can set your own career goals. If you have decided that the data analyst role is for you, then you need to think about how to develop your career. Consider these questions:
• What kind of data analyst do you want to be?
• Where do you see yourself in 5 years’ time?
• Where do you see yourself in 10 years’ time?
• What do you want to achieve by the end of your career?
CAREER PLANNING
This section will help you to identify the skills and qualifications you will need to become a data analyst and achieve your career goals.
Becoming a data analyst
Now that you have read through this book on the role of the data analyst, you might need to ask yourself some questions. These are covered in this section.
Is this role for me?
A data analyst might work in several arenas. Which part of the data analyst role most appeals to you?
If you have decided that you want to become a data analyst, you need to ask yourself: what qualifications and skills do I already have and what qualifications and skills will I need to develop to make a career in this field?
The best way to answer these questions is carry out a personal audit of your qualifications, skills and aptitudes. By aptitudes, we mean: what do you like and what do you see yourself as being good at? Take the time to think about what you enjoy. If you don’t like working with data, for example, you probably shouldn’t become a data analyst.
We’ve provided checklists in Tables 5.1–5.4 to help you carry out your own personal audit; you can use these as they are, or adapt them to provide the basis for your own checklist. We’ve completed some sections in italics as examples to show you how they work.
These checklists are designed to help you identify your qualifications, skills and aptitudes and also to identify any gaps and areas for future development. When you have completed them, you should have a picture of where you are, what professional development work you need to undertake and which aspects of the data analysis role interest you.
Be realistic, but don’t sell yourself short. You need to acknowledge your strengths and the knowledge and experience you already have as well as the areas where you need to develop your skills.
Table 5.1 What qualifications do you have or are about to achieve?
Table 5.2 What experience do you have that might be relevant to a data analyst career?
Table 5.3 What technical skills do you have that might be relevant to the data analyst role?
What personal skills and abilities do you have? Which areas do you feel you need to develop further? These questions are based on the discussion in Chapter 2. If you are not sure what something means, please refer back to Chapter 2.
Table 5.4 What personal skills and abilities do you have?
Taking this further
Now that you have completed the checklists, you should have a good idea of your strengths and weaknesses, so try completing a SWOT analysis. SWOT stands for strengths (the things you know you are good at), weaknesses (limitations or challenges), opportunities (what opportunities do you see for yourself?) and threats (what things might hold you back or prevent you from succeeding?). Use the diagram in Figure 5.1 to help you to identify your strengths, your weaknesses, the opportunities that are available to you and the things that might hold you back on your journey to becoming a data analyst.
SUMMARY
In this chapter we have looked at career paths and opportunities for data analysts and at ways in which you can develop your career and gain the skills you need to succeed. You can use the checklists provided in this chapter to carry out an audit of your qualifications, skills and experience and to help you set your career goals. Good luck in your career journey, but be prepared to be flexible about your career plans. As the case studies we have included in this chapter show, things don’t always go to plan; sometimes they work out much better than expected.