Data is omnipresent and now even more accessible to organisations than ever before. In fact, according to Statista, in 2021, the global data analytics market was valued at over $240 billion. In addition to that, it is forecasted to further peak at a market value of over 650 billion dollars by 2029.
These growth predictions are fuelled by the acceleration of digital technologies continually generating substantial amounts of data. This influx of data has led companies to consider new approaches beyond traditional data analysis methods to derive actionable business insights.
There has been a dire need for individuals with data analytics competencies. These include data visualisation, database management, statistical modelling, etc., to help companies keep pace with today’s dynamic business landscape.
Who is a data analyst?
Before we describe a data analyst, let’s first define data science.
Fundamentally, data science is a relatively nascent multi-disciplinary field that reconciles statistics, business domain knowledge, and computer science to proficiently leverage Big Data.
A subset discipline of data science is data analytics.
Data analytics revolves around the extensive analysis of data to extract meaningful insights and identify consistent trends.
One can consider data science as a precursor to data analysis as it focuses on restructuring, untangling, and cleaning data to draw accurate and insightful conclusions.
As such, a data analyst is a technical professional specialist who essentially examines data to find key insights and solve business problems.
In essence, data analysts spend most of their time translating and aggregating business data points into usable graphs, charts, tables, and reports. Usually, they may be required to have an undergraduate degree or master’s degree in advanced analytics, computer modelling, or mathematics.
However, understanding the basics of data analytics and applying it within your professional field may not always require a professional degree.
Data analytics workshops like those available at Accord Training can be taken up by non-professionals for a quick introduction to the world of analytics through examples, stories, and case studies.
Data analysts can work across different sectors like finance, manufacturing, pharmaceuticals, government, and even education. As gatekeepers of organisational data who enable stakeholders to exploit it to make strategic business decisions, data analysts principally:
- Produce and track key performance indicators.
- Design reporting processes.
- Monitor and audit data quality.
- Develop records management processes and policies.
- Collate, understand and document detailed business requirements employing appropriate tools and techniques.
- Identify areas to increase the efficiency of data processes.
- Set up and maintain automated data processes.
- Evaluate and implement external services and tools to support data validation and cleansing.
- Prepare reports for all stakeholders using business analytics reporting tools.
- Create custom data dashboards and visualisations for stakeholders.
- Liaise with clients to fully understand data content.
- Carry out surveys and analyse survey data.
- Manipulate and interpret sophisticated data sets.
Why are data analytics skills important?
Some quick reasons why these skills may be advantageous for your organisation:
- These skills can come in handy when aiming to budget and forecast within a business whilst leveraging historical revenue, sales, and cost data. A data analyst can easily identify necessary cost cuttings or investment strategies to make a company more profitable.
- They help with understanding the likelihood of specific business risks occurring in order to make cost-effective steps to help mitigate them.
- It helps with understanding key metrics, like conversion rates, to fill the sales pipeline during sales and marketing activities.
- Data analytics skills are valuable to guide the direction of product research and development, design, and user experience.
Important data analytics skills to acquire in 2023
1. Machine learning
As a branch of artificial intelligence, machine learning (ML) in data analytics focuses on the design of custom algorithms to find patterns in data sets to derive accurate predictions of future outcomes.
ML activities revolve around training computers to automatically learn and perform repetitive tasks— without the need for additional programming. In practice, the more data an ML algorithm processes, the more “intelligent” it becomes.
While data analysts aren’t expected to be masters of ML, being comfortable with this discipline can give you a competitive edge and set you on a course for an advanced career as a data scientist.
2. Artificial intelligence
As alluded to earlier when discussing machine learning, artificial intelligence (AI) is a discipline that should be a key component of a data analyst’s toolkit. AI concepts are important to know, especially when exploring predictive analytics.
AI-powered systems can sift through data from thousands of sources to forecast what works and what does not when dealing with customer preferences, product development, and marketing channels.
3. Data visualisation
Data visualisation and storytelling skills are important to facilitate a better understanding of data-driven insights. This helps business decision makers (who may lack advanced analytical training) to understand intricate data patterns and insights at a glance.
Generally, this capability allows a data analyst to convey useful insights to stakeholders to shape a company’s decision-making for the better.
4. MS Excel & BI Tools like Tableau and Power BI
While competencies in R or Python are desirable when handling large datasets, advanced Excel skills can be utilised for lighter and quick analytics.
Generally, MS Excel has remained a mainstay for businesses in most industries, with many free resources online for those seeking to get into structured data analytics.
Today’s BI tools like Tableau and Power BI are intuitive and user-friendly, allowing people to analyse data quickly and efficiently without the knowledge of programming or complex formulae.
5. Data management
This is about collecting, aggregating, and cleaning of data. Particularly, data cleaning activities within data management focus on fixing duplicated, corrupted, and incorrectly formatted data.
In essence, data management focuses on ensuring the accuracy of data, as inaccurate datasets often result in erroneous analysis.
6. Statistical programming
Statistical programming skills are important for data analysts when describing dataset characteristics like size, quantity, and accuracy to dissect the nature of the data.
Fundamentally, this programming paradigm should always support the stakeholder to act upon their data through a life cycle of activities like: pre-processing, visualisation, analysis, prediction, and preservation.
Coding is a discipline that revolves around transforming ideas and instructions into digital language that a computer can understand. Competent data analysts must be able to code to programmatically leverage programs like MATLAB for quick data pre-processing, organisation, and cleaning.
Coding competencies can help data analysts easily understand the syntax and structures of statistical computing languages like R when handling complex or large quantities of data.
Econometrics is the branch of economics that involves the exploitation of mathematical methods for statistical purposes.
These skills are desirable for data analysts to help forecast future trends based on historical data, especially in the financial sector (for instance, at investment banks and hedge funds).
9. Programming languages (MATLAB, R, PYTHON, SQL, etc)
Experience with statistical programming languages like Python and R can allow a data analyst to perform more advanced visualisation and analysis tasks beyond the typical spreadsheet.
For example, R skills can be specifically helpful for statistical data analysis. Fortunately, both R and Python are open-source and have tons of resources and communities available for guidance.
10. Algebra and calculus
Advanced mathematical skills are advantageous to thrive at any data analytics task. Particularly, linear algebra and calculus skills have applications in machine and deep learning.
For example, calculus skills can be employed to build cost/loss functions when training algorithms to achieve their objectives.
Soft skills needed by data analysts
Data analysts should be able to communicate in multiple formats. This communication capacity should encompass writing, speaking, listening, and explaining.
For example, when meeting with business stakeholders, a data analyst may be required to explain a sophisticated topic to non-technical teammates.
By the same token, they may be required to write a comprehensible summary of their data analysis, or to explain a specific discovery in their data exploration.
Data analysts require presentation skills when sharing their data visualisations with an audience of stakeholders. It helps them to focus on the audience rather than themselves to communicate a message effectively.
Critical thinking skills
Critical thinking skills help data analysts to easily identify patterns and extract actionable insights whilst going above and beyond their basic work processes. As such, they are able to add value to their companies by identifying data aspects that aren’t obvious to the naked eye.
Data analysts are inherently problem solvers as their daily work involves overcoming hurdles, bugs, and roadblocks.
Problem-solving skills, therefore, come in handy in different scenarios, like when dealing with incomplete data or overcoming resource constraints that might require inventive approaches to a problem.
Can non-analytics professionals learn data analytics?
Yes, non-analytics professionals can learn it too. While data analytics is often made to sound complicated and like a niche discipline, it has vast applications in the real world.
Data is a powerful tool that is critical to driving business decisions and solving problems. Therefore, data analytics skills are recommended for every professional as they equip one to think analytically and approach problems the right way.
With deductive reasoning skills and the ability to make informed decisions, any employee can become an indispensable team member.
Fortunately, if you’re a non-analytics professional, there is an introductory data analytics course workshop with Accord Training that helps to lay the foundation for a basic level of data literacy. With this workshop, you can delve into the multifaceted data landscape, and acquire foundational data analysis skills.
For corporate employees, it can help them gain new strengths and skills to make strategic business decisions, predict business trends, and improve business productivity.
Data analytics is growing in importance in the modern business sphere.
Whether you want to become acquainted with data analytics basics as a non-analytics professional, or to expand your knowledge, having a data analytics certification on a resume is a big plus.
Remember, the value of data-competent individuals is going up, thus creating even better job opportunities and career progression options. This makes it the perfect time to start a journey into the world of data analytics.
Over the years, in her capacity as a trainer, Amrita has developed and delivered courses on data analytics, project finance and risk and compliance in Singapore and in Myanmar. Since 2018, she has been an empaneled trainer for analytics workshops at the Civil Service College (CSC), where she conducts public run workshops on analytics and data visualization, and in-house workshops for agencies like IRAS and MHA. In May 2020, the CSC selected Amrita to brief 70+ members of the training community of the civil service to allay their concerns about technical topics like data analytics being delivered virtually.
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