A data analyst’s job is the most convincing job in the market right now – it's challenging and rewarding! As great as the salary package is, the job comes with varied responsibilities too. There is a lot of data collected by companies on a daily basis to achieve different business goals, and data analysts play the most important role in identifying the best solutions for a business problem.
The crazy statistics of school days….
I used to wonder during school days – what is the use of so many formulae? Mean, median, standard deviation – why do we even need these? Statistics involved having to work with numbers extensively, without knowing much about their practical applications of it.
That’s not true anymore…
It seems cool now that we had statistics as a mandatory subject in school because everything is the same – just as we learnt it…! As a data analyst, one can see great things happening using all that was learnt during childhood!
Starting your journey as a data analyst is fruitful, even if your ultimate goal is to become a data scientist. Because of its wide scope, it would be a great way to start or resume your career as a data analyst to gain both the technical expertise and domain experience.
Wait…. what’s the difference?
The designations data analyst, data scientist and business analyst…. all are related but different!
Who is a Data Analyst?
Analyse huge sets of data by compiling, filtering and interpreting them to arrive at useful business insights and make business decisions. It requires sound technical knowledge and analytical skills. You should be skilled in computers, science and maths.
Who is a Data Scientist?
The visualizations created by data analysts are used by a data scientist to identify trends and patterns, weak points of business and formulate strategies to fill the gaps. IT requires both technical know-how and understanding of human behaviour, other than the ability to perceive things differently and make best predictions.
Who is a Business Analyst?
Business analyst uses the information presented by a data analyst to identify business problems and propose feasible solutions. They are adept in finance, business administration and economics.
Why Become a Data Analyst?
If you are from a programming background, you can easily enter the field of data analysis. If you are not, you can still enter into this field by doing some additional courses and certifications. There is a lot to learn and explore than you would think. A data analyst has to be adept in both technical and analytical skills, have excellent communication skills and a business mindset. This means that even after you do certifications and learn all the technical skills like data modelling, statistics, mining and segmentation techniques, writing database queries, you will still be challenging yourself on lot of other skills, be it your attention to detail, accuracy or the way you gather and organize the huge datasets.
Do you know why all of the above is worth?
That’s because you will always grow up in the career ladder through vivid experiences and finding practical solutions to business problems. Seeing your analysis going live and having a positive impact on the whole business will give immense job satisfaction and a sense of achievement.
And yes, you get a fat salary package! Want to know how much a data analyst can earn? Click here .
Analysis or Analytics?
Many times, both the above terms are used interchangeably, though there is a subtle difference. Data analysis is a bigger term, it is a practice that uses data analytics tools and techniques to achieve different business objectives.
Data Analysis Process
There is so much data available with different agencies that often they do not know what is useful and what is not. To find out what is the right set of data for a particular business problem, drawing accurate inferences and making the right business decisions is the most important part of a data analyst’s job.
Through this process lifecycle, we will be able to understand the overall picture of a data analyst’s roles and responsibilities. The lifecycle includes six main steps or phases.
Each of the stages requires different kinds of knowledge and skills and a data analyst has to adapt to different roles accordingly. For example, understanding the problem and setting the measurement parameters needs a whole lot of segmentation, organization and reasoning skills; collection, preparation and analysis needs data mining skills, analytical skills and technical knowledge of various tools and techniques; interpreting requires good business thinking skills; publishing and monitoring requires good reporting and analytical skills.
Types of Data Analysis
The core part of the entire process is analysing the data. It is one thing to collect, clean and organize data and totally another to go deeper and apply the tools and techniques to analyse the dataset. There are different ways to analyse data and the specific roles and responsibilities depend on business needs as each type has its own benefits and purposes.
1. Descriptive Analysis
The simplest type of analysis, it tells you about “what happened” of a particular situation by summing up the past data, usually in the form of dashboards. This is the most common analysis type and is mostly used for tracking key performance indicators (or KPI’s) to know about a business’s performance based on multiple benchmarks. It can be a monthly revenue report, employee performance, KPI dashboards, monthly sales report etc... Descriptive analysis shows detailed data in numerical form, like mean, median, mode, average, standard deviation, percentage or frequency or in graphical form like histograms, scatter plots, sociograms and geographic information systems.
Statistical tools, both numerical and graphical are extremely useful for descriptive analysis. Thus, you should have the knowledge of at least some of the tools like Microsoft Excel, SQL, SPSS, R/Python, SAS, MATLAB, Minitab etc…
2. Diagnostic Analysis
If something good did not happen in the descriptive analysis, then it is important to know “why it happened” that way from a business perspective. This type of detailed ‘diagnosis’ is important to analyse the main causes of the problem and make necessary changes. This type of data analysis connects all important points and finds patterns and behaviour. Some examples could be –
- A logistics company trying to figure out why there are delays in a particular shipment route
- A product company investigating why the sales of their product reduced in a particular quarter
- A digital marketing company getting details of why their website is no longer listed in the top 3.
Diagnostic analysis is mostly done by employing machine learning techniques as they are efficient in building a model to find patterns, identifying unusual events and detecting anomalies. Some popular algorithms are Linear regression, logistic regression, K means clustering algorithms, Decision trees, Naïve Bayes classifier algorithm and many more. Although machine learning algorithms are useful in finding weak points of a business, the analysis still have to be governed by data analysts to make relevant decisions based on the output of the machine model and through their own domain expertise.
3. Predictive Analysis
So, we figured the “why did it happen?” clause. The next step is to predict “what” is going to happen. It is forecasting the events that are more likely to happen in future. This type of analysis is done by using past and present data, to predict the future. Think of it as weather forecast. This analysis requires skilled manpower as well as technology, as it relies on statistical modelling and lot of parameters and conditions are involved. The more detailed the data is, the more accurate predictions can be made. Common tools for predictive analysis are business intelligence tools like TIBCO Spotfire, Minitab, SAS Advanced analytics, Microsoft R, Sisense, RapidMiner and many more. Using these tools, predictions can be made about sales, risk assessment, company performance, product conversion rates etc….
4. Prescriptive Analysis
Here is where the real power of data analysis and data science lies! This is where all the other 3 stages combine together to give insights of data and important business decisions are taken to solve the business problem. AI (Artificial Intelligence) systems use prescriptive analysis to train their model based on all the previous data and improve upon the model based on their learnings. These systems work just like the brain of a human, who learns by experience. Netflix, Amazon and Facebook are some examples where this type of analysis is used. Some tools and techniques used by data analysts for prescriptive analysis are NLP (Natural Language Processing), Image processing, Applied statistics, optimization models and heuristics.
Here is a nice diagram from Gartner to show how the role of a data analyst evolves with each type of analysis–
Tools and Techniques for Various Types of Data Analysis
Look at the below diagram to summarize the different tools and techniques used in each type of data analysis, that will give you a fair understanding of what to expect at different levels of being a data analyst.
Most Valuable Skills for a Data Analyst
A data analyst must possess the following skills –
- Computer science
- Business knowledge (Economics)
The job of a data analyst involves taking up technical, business and leadership roles.
On the technical front, knowledge of SQL, R/Python, Microsoft Excel/Google spreadsheet, Tableau/Qlik, statistics, and mathematics is a must.
From a business point of view, analytical skills, problem-solving skills, keen observation and precision, good domain knowledge, and strategic thinking are the keys to be a good data analyst.
From a leadership point of view, it depends on whether you are an entry-level data analyst or a senior level. If you have a few years of experience, you can correctly correlate the job of a data analyst and a project manager. Other than the technical and business skills, you will have to make data-driven business decisions, have good communication skills – both verbal and written, and good team management skills and if you want to take it to the next level, you should be able to diagnose potential problems early in the data analysis process lifecycle.
Data Analyst Roles and Responsibilities
By now, you must have a fair idea of what is expected from a data analyst – this is common to any industry you work in, except that the domain knowledge will be different. To summarise, the key roles and responsibilities of a data analyst are –
- Design and maintain databases and other data systems; ensuring the data systems are always up to date and free of errors, including removal or old, duplicate, or corrupt records
- Mine structured or unstructured data from internal and external sources, through various means like surveys, interviews, etc.
- Interpret sets of data using different statistical tools and studying patterns, behaviors and trends that can help with diagnostic and predictive analysis
- Work on the bigger picture – how can the analyzed data help solve complex business problems across the organization
As you move up the ladder, the following additional responsibilities will be handed over –
- Prepare relevant reports with numbers and graphical illustrations of the trends, patterns, and interpretations using the data
- Collaborate with different teams, developers, system engineers, top leaders, and stakeholders to convert data insights into potential process improvements, business use cases or better policies for governing data
- Have a detailed and structured document listing out the processes, tools, and techniques used to conduct the data analysis and be able to demonstrate the entire process to the stakeholders if necessary
- Able to work out a logical and methodical approach to solve problems, meet deadlines and communicate any gaps to the stakeholders
Data analyst jobs are most popular in banks, financial institutions, telecommunication companies, digital marketing, the pharmaceutical industry, the logistics and manufacturing industry, public sector organizations, consultancies, and even in colleges and universities. That makes it predominant in almost every sector that is possible today. Data analysis is generating a lot of jobs and is a promising niche to take up in the future, and that’s why it is important to know the right roles and responsibilities so that you are prepared to take up the job of your dreams.
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