What Is Data Analytics:
7 Must-Know Things About Your Next Career Choice


1. What Is Data Analytics?


Data analytics involves the examination of raw data sets to draw conclusions and discover trends/consistencies about the information derived.


Such insights allow businesses to create more informed decisions to optimize their processes and generate more value for the organization.


Businesses use various data analytics tools and technologies to get specific information. Therefore, data analysts work with business intelligence, statistical analysis, purpose programming languages, ETL, data science, and other industry-specific tools.


2. What Is A Data Analyst?


Data is only valuable when it is properly interpreted. Companies and organizations cannot interpret data accurately, which is why they require the services of professional data analysts who can transform collected data into value-driven information.


With much of our day-to-day activities dependent on data sharing, it requires a team of individuals trained specifically to handle large volumes of interpreted data which businesses can use for generating revenue, improving marketing campaigns, streamlining operations, and increasing customer satisfaction.


Trends are ever-changing, and businesses must keep up with fluctuating consumer demands to gain a competitive edge over their rivals.


If the idea of being influential in creating impactful business decisions appeals to you, then a data analyst career is worth considering.


3. What Does A Data Analyst Do?


Data analysts often need to produce agile business recommendations to improve systems within a company.


A data analyst job description may include, but should not also be limited to, the following tasks:


  • Interpret data and analyze results through statistical methods and techniques.
  • Develop and implement data analyses, collection systems, and other techniques for improving statistical quality and efficiency
  • Collect data from primary or secondary sources and maintain databases.
  • Identify trends and data anomalies.
  • Use predictive analysis to gather data on possible future scenarios and the likelihood of recurrence.
  • Extract data from unstructured sources, such as large blocks of text, complex databases, or raw sensor data
  • Convert raw data into an insightful and manageable format.
  • Implement and design databases to enable easier access to data mining results.
  • Present data to key stakeholders through data visualization.
  • Keep abreast of the latest data analytics trends and technologies.


4. Is Data Analytics A Good Career?


Moving closer and closer towards a fully digital and data-driven era, businesses will have to deal with complex decision-making. Data analysts are indispensable to any organization that uses agile and scalable technologies to meet customer satisfaction KPIs.


However, experienced data analysts are few and far between. This is why the demand for this role is always on the uptick.


According to Global News Wire, the market for big data analytics will exceed USD 549 billion in the next six years. The digital revolution is at work across different fields and industries, such as manufacturing, production, retail, healthcare, etc.


Many businesses need to adapt to changing consumer needs and expectations through accurate and reliable insights, which is why a career in data analytics is becoming more and more lucrative than ever.


Additionally, businesses offer attractive compensation packages to skilled and professional data analysts. Besides the base salary, bonuses, benefits, and profit shares are also rewarded for their performance.


According to Payscale, an entry-level data analyst in Singapore can potentially earn an average of $44,700 annually. Data analysts with several years of experience can expect to earn anywhere between SGD 52,000 to SGD 78,000.


The variation in salary earnings is due to several factors, such as skills, educational background, years of experience, and company.


To answer whether data analytics is a good career, yes. However, prospective data analysts must work towards making themselves more employable and desirable by companies through constant learning and application.


5. How Difficult Is Data Analytics?


Aspiring data analysts will almost always have some qualms about the role's technical demands, and understandably so.


Like any other field, data analytics can be extremely demanding. Data analysts face more challenges and requirements compared to other areas in technology, such as IT or full-stack development, as data analysis depends on the knowledge of specific programming languages and a mix of relevant soft skills.


Professionals with some background in writing scripts can transfer their skills towards data analysis in relatively short periods.


For example, basic SQL concepts and ideas can be learned in as little as three weeks. Beginners can easily pick up on this database language, even without prior experience, but with an affinity for learning new technical skills.


Programming languages like Python may be a bit more complex, but a beginner can learn its programming fundamentals in two months with ample drive and determination.


None of these skills is implemented by data analysts in a vacuum. Learning all of the above at a beginner level and a host of other data analytics skills can take over three months.


But the skills you can acquire and develop over that period can equip you to start working in a data analyst position immediately in your company of choice.


However, senior data analyst roles may have a steeper learning curve. Full proficiency in data analysis can take several years and is only possible while on the job.


Prospect data analysts must also understand that the industry is continuously evolving. Data analysis is ever-changing, and they must be committed to an unending process of learning different programming languages, data analysis tools, technologies, and trends.


6. Skills You Need To Be A Data Analyst

Data analysis or data science tools will vary depending on the role's demands, industry, or organization.


The average day in the life of a data analyst may involve scouring vast chunks of big data through systems analysis and automation of data retrieval processes.


They will also be preoccupied with preparing reports to show managers and stakeholders how collected data can be seamlessly integrated into the business model.


So, relevant data analysis skills are critical for success in the role. Below are some of the most vital skills that an average data analyst should possess:


Data Preparation and Cleaning

Data preparation accounts for about 60% of the role of data analysts. They spend most of their time retrieving data from multiple sources and preparing them for further analysis.


Data analysts will also have to recover missing and inconsistent data, which may skew analysis reports. While data preparation or cleaning isn't the most glamorous activity, analysts can add variety by treating it as a problem-solving task.


Additionally, they can equip themselves with knowledge of the best data cleaning and preparation practices by taking relevant courses.


Statistical Analysis

Statistical and predictive analytics tools are essential for all analysts. These are instrumental in solving common data science problems and preventing logical fallacies, inconsistencies, and other errors.


Knowledge of statistics is also critical in validating your analysis. But the depth of required statistical knowledge will depend on the demands of your role and the particulars of the data you're assessing.


For instance, if the organization you work for relies primarily on trends and probabilities, you'll need a robust background in probabilistic analysis.


Expect to conduct multiple sampling techniques so you can successfully examine the potential outcomes of a controlled study or experiment.


Data Exploration And Processing

The first step in every data analysis project, data exploration, involves the assessment of different dataset categories (size, accuracy, quantity, etc.) in the hopes of better apprehending the nature of the data.


A data analyst will generally take up a business problem or requirement. They will attempt to provide solutions to this problem through exploration, processing, and analysis of relevant data.


Data exploration allows analysts to determine connections, patterns, and relationships across the data they've gathered. These discoveries can be useful for the company when it comes to minimizing costs or improving ROI.


Building Data Visualizations

Another essential data analyst skill is data visualization. This refers to your ability to create visual representations from raw data, such as charts, maps, graphs, tables, infographics, etc.


The purpose of data visualization is to allow others to understand your work. In a business setting, you're the subject matter expert. Managers and stakeholders can only understand your data if you present it in digestible visual representations without diluting the information.


Visualizations are significant in every data analyst's exploration, as there may be aspects of the data better represented visually than through a numbers-only analysis.


Data Reporting

Many technical barriers involving data analytics prevent business owners from fully understanding how to make critical decisions based on the available data.


This is why they need a data analyst that can create accessible reports after analyzing raw data. They may use simple tables and graphs or other more complex dashboards.


For example, a data report can be a financial dashboard which gives business owners a glimpse into their financial operations. The report can represent the business's overall financial health through structured information such as:


  • Cash conversion cycles
  • Current assets
  • Current liabilities
  • Vendor payment error rate



Data analysts must also be able to communicate on multiple platforms. Achieving success in the role means being comfortable conversing, explaining, writing, and listening to others.


Communication is always key since you'll be collaborating with your colleagues. For instance, listening intently to everyone in a project kickoff meeting can help you understand the client's analysis requirements.


You'll also need to explain complex data analytics concepts to non-technical colleagues. Communication may be a soft skill, but it shouldn't be underestimated in data analytics.


You may be using all complex data tools and machinations. But convincing key managers and stakeholders to act on critical business decisions can be challenging if you're unable to explain what the data means.


Industry Knowledge

A successful data analyst must also understand concepts and topics specific to the industry and company they work for.


For instance, if you're working for a company involved in logistics, you'll need to understand supply chain management concepts.


Industry knowledge can definitely be learned while on the job. However, if you want to be a data analyst in a specific industry, building a solid understanding of its nuances will greatly help.


Having a tailored set of skills and knowledge upfront will make you a more attractive job candidate and an efficient employee down the line.


Problem Solving

Data analytics is 90% critical thinking and understanding of which questions to ask. You'll often face many problems and challenges in your line of work.


As such, you must be capable of apprehending statistical data and market research to produce solutions for these problems.


For example, you may be working with a company facing financial constraints. They may not have adequate resources to invest in statistical tools and software you'll need for the job.


As a data analyst, the challenge is to breed innovation in approaching a myriad of problems in your line of work. Problem-solving will be a significant asset as you approach different situations.


7. Career Opportunities For Data Analysts

Analyzing and answering data-related queries are part and parcel of any professional data analyst. But your career path may diverge or converge with your peers, depending on your chosen field.


Here are career some data analyst jobs and career opportunities:


  • Data Consultant - Interprets analyzed data and generates action plans accordingly.
  • Data Scientist - Determine the right data sets and variables to provide solutions that offer the best outcomes for businesses.
  • Data Manager - Supervises, organizes and secures databases and data systems for an organization.
  • Data Specialist - Manages database development, monitors database performance, and transforms raw data into digestible information.
  • Data Engineer - Gathers, validates and prepares high-quality data, which a data scientist uses to make business-oriented decisions.


Apply As A Data Analyst With Luxoft!

Becoming a full-fledged data analyst is no easy feat, requiring years of on-the-job experience and constant learning outside work.


But those who have pursued this career enjoy some of the most lucrative salary perks in the industry.


What better way to kickstart your data analyst career than with one of the world's biggest transformative digital solutions providers?


Luxoft empowers businesses towards digital transformation by adapting to ever-changing digital demands. Join our team of over 15,000 specialists worldwide and enjoy unmatched professional growth in your chosen career.


Explore job opportunities with Luxoft Singapore or contact us via email (luxoftcareers-sg@dxc.com) to experience the Luxoft difference.


Frequently Asked Questions About Data Analytics


What Is The Main Purpose Of Data Analytics?


The primary purpose of data analytics is to apply statistical skills and technologies to data to discover trends and come up with solutions to problems. Data analytics has been influential in enterprise and business management by improving decision-making.


What Is Data Analytics For Beginners?


Data analytics is a strategic and scientific method of drawing conclusions from big data through analysis and trend identification.


What's The Difference Between Data Analytics And Data Science?


Data analytics and data science primarily differ in scope. Data scientists deal with a much broader range of discovering substantial correlations between large datasets. Meanwhile, data analytics deals with uncovering actionable insights from extracted data.


What Are The Different Types Of Data Analysis?


The four main types of data analysis are:


  • Descriptive Analytics - Examines what has happened in the past.
  • Diagnostic Analytics - Assess and solve anomalies within the data.
  • Predictive Analytics - Predicts the likelihood of an event happening in the future.
  • Prescriptive Analytics - Examines the best possible course of action after determining what has happened, why it happened, and what might happen in the future.