Data Analytics

This guide provides definitions, examples and practical advice to help you understand and execute modern data analytics.

What is Data Analytics?

Data analytics is the use of tools and processes to combine and examine datasets to identify patterns and develop actionable insights. The goal of analyzing data is to answer specific questions, discover new insights, and help you make better, data-driven decisions.

Why is Data Analytics Important?

Data analytics is crucial because it empowers you to extract hidden insights from big data, enabling informed decision-making, optimizing processes, and driving innovation. It's like having a superpower to see patterns and predict the future, giving your businesses a significant edge.

Data Analytics Process

Traditionally, performing big data analytics meant a lot of heavy lifting in terms of coding and manual analysis. Modern tools and analysis techniques incorporate AI to enhance every aspect of analytics by automating processes, enabling advanced techniques, improving accuracy and efficiency, and generating insights and recommended actions.

Diagram depicting the workstream from data sources to integration options to repository to analysis options to insights and actions to take

1) Define your business objectives for analytics, such as identifying promising new markets or weak production processes.

2) Identify the data sources you’ll need – systems such as transactional, supply chain, social media, and CRM applications. This can be historical data or real-time streaming data.

3) Integrate your data into a repository such as a data warehouse or data lake, typically in the cloud. This data integration process of extracting, transforming, and aggregating raw, unstructured data gives you a comprehensive, unified view of your business and facilitates efficient data retrieval and analysis. AI algorithms used by data engineers and found in modern tools improve the data quality and efficiency of data collection and preparation by cleaning up errors such as duplications, redundancies, and formatting issues. There are five different approaches:

  • An ETL pipeline is a traditional type of data pipeline which converts raw data to match the target system via three steps: extract, transform and load. Data is transformed in a staging area before it is loaded into the target repository (typically a data warehouse). This allows for fast and accurate data analysis in the target system and is most appropriate for small datasets which require complex transformations.

  • The more modern ELT pipeline has the data loaded immediately and then transformed within the target system, typically a cloud-based data lake, data warehouse or data lakehouse. This approach is more appropriate when datasets are large and timeliness is important, since loading is often quicker. ELT operates either on a micro-batch or change data capture (CDC) timescale. 

  • Data streaming moves data continuously in real-time from source to target. Modern data integration platforms can deliver analytics-ready data into streaming and cloud platforms, data warehouses, and data lakes.

  • Application integration (API) allows separate applications to work together by moving and syncing data between them. Various applications usually have unique APIs for giving and taking data so SaaS application automation tools can help you create and maintain native API integrations efficiently and at scale.

  • Data virtualization also delivers data in real time, but only when it is requested by a user or application. Still, this can create a unified view of data and makes data available on demand by virtually combining data from different systems. Virtualization and streaming are well suited for transactional systems built for high performance queries.

4) Perform data analysis to find hidden patterns, trends, and valuable insights from large datasets. Your goal here is to not only answer specific hypotheses but discover new questions and unanticipated insights by exploring the data.

5) Gain insights and trigger actions in other systems by integrating your analytics software into other applications. For example, when churn risk spikes, an automated email campaign is automatically triggered in your marketing platform, offering personalized retention incentives. Your analytics tool can also be set to trigger real-time alerts to help you stay on top of your business and take timely action.

Types of Data Analytics

Data analytics is typically categorized into four types: descriptive, diagnostic, predictive, and prescriptive.

  • Descriptive analytics helps you know what has happened. It focuses on summarizing KPIs and metrics and visualizing key trends in historical data through analytical dashboards and visualizations (charts, graphs, etc.).

  • Diagnostic analytics helps you know why something happened. It uses sophisticated data mining and statistical techniques such as drill-down analysis, cohort analysis, and anomaly detection to sift through massive datasets for hidden connections and correlations among data points.

  • Predictive analytics helps you know what might happen. It uses powerful statistical models such as regression analysis and machine learning and deep learning algorithms to analyze historical and even real-time data to predict potential outcomes, like customer churn, market shifts, or equipment failures, empowering you to proactively take action. 

  • Prescriptive analytics helps you know what to do next. It builds upon the insights of predictive models and uses advanced algorithms like simulation and optimization to recommend the best course of action based on various potential scenarios. 

Data Analytics Tools

You have many tools to choose from. The best tool for you is the one that fits your needs and skillset.

Microsoft Excel and Google Sheets spreadsheets are great for small datasets and quick insights. You’re probably familiar with these simple tools and know that they’re versatile for basic data cleaning, analysis, and visualization. 

SQL (Structured Query Language) provides a standardized language for querying and manipulating relational databases, enabling you to extract, transform, and analyze data efficiently through commands that facilitate tasks such as filtering, sorting, and aggregating data.

Self-service analytics tools such as Qlik, Power BI, and Tableau enable you to easily analyze and visualize large data sets without the need for writing code. The best tools include augmented analytics that uses artificial intelligence (AI) and machine learning to enhance human intuition with suggested insights and analyses, automation of tasks, search & natural language interaction, and advanced analytics calculation.

Python is a versatile and widely-used programming language that has become a popular tool for data analysis, offering extensive libraries such as Pandas, NumPy, and Matplotlib that enable you to efficiently manipulate, analyze, and visualize data, making it a robust choice for a wide range of data-driven tasks. 

R is a programming language and software environment specifically designed for statistical computing and graphics.

SAS (Statistical Analysis System) software suite is used for advanced analytics, business intelligence, and data management.

Apache Spark is an open-source, distributed computing system that can process large-scale data and perform advanced analytics.

Jupyter Notebooks is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text.

KNIME is an open-source data analysis, reporting, and integration platform that allows users to visually design data workflows.

MATLAB is a programming and numeric computing environment widely used in academia and industry for tasks such as data analysis, simulation, and data modeling.

D3.js is a JavaScript library for producing dynamic, interactive data visualizations in web browsers.

Examples and Use Cases

A wide range of industries and job roles leverage business analytics techniques. Here are some common example use cases:

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Career Opportunities

Everyone likes top-10 lists, so here we give you lists for why you should consider career paths in this field, the skills you’ll need, and the steps to take.

Top-10 Reasons to Choose Data Analytics as a Career:

  1. High Demand: This field enjoys a consistently high demand for skilled professionals as organizations increasingly rely on data-driven decision-making.

  2. Diverse Applications: A career in this field provides you the opportunity to work across various industries, from finance and healthcare to e-commerce and technology, contributing to diverse and impactful projects.

  3. Competitive Salaries: Business analysts and data scientists often enjoy competitive salaries and benefits, reflecting the value placed on their skills in extracting meaningful insights.

  4. Continuous Learning: The dynamic nature of a career path in analytics ensures a continuous learning curve, with ongoing opportunities to acquire new tools, technologies, and methodologies.

  5. Global Relevance: With data being a universal language, a career in this field can offer you global relevance and the chance to work on projects with international impact.

  6. Innovation and Problem Solving: This field allows you to innovate and solve complex problems by uncovering patterns, trends, and valuable insights from large datasets.

  7. Career Advancement: The field provides numerous opportunities for career advancement, allowing you to progress from entry-level roles to leadership positions as you gain experience and expertise.

  8. Impactful Decision-Making: You’ll play a crucial role in empowering your organizations to make informed, data-driven decisions, contributing to improved efficiency and strategic planning.

  9. Flexibility and Remote Work: Many analytics and data science roles offer flexibility and the possibility of remote work, providing a work-life balance that suits individual preferences.

  10. Contribution to Business Growth: You’ll contribute directly to business growth by enabling your organization to identify opportunities, mitigate risks, and stay ahead of market trends through data insights.

Top-10 Skills Needed To Be Successful in Analytics:

  1. Statistical Analysis: Proficiency in statistical techniques is crucial for interpreting data and drawing meaningful conclusions.

  2. Programming Skills: Knowledge of programming languages such as Python or R is essential for data manipulation and analysis.

  3. Data Visualization: The ability to create clear and compelling visualizations helps communicate findings effectively to non-technical stakeholders.

  4. Database Management: A solid understanding of databases and SQL is necessary for accessing and retrieving relevant data.

  5. Machine Learning: Familiarity with machine learning concepts and algorithms enhances the ability to develop predictive models for data analysis.

  6. Critical Thinking: Analytical skills and the ability to think critically are essential for approaching complex data problems.

  7. Problem-Solving: Data analysts should excel in problem-solving, identifying issues and devising effective solutions.

  8. Attention to Detail: Precision in handling data is crucial to ensure accuracy and reliability in analysis outcomes.

  9. Communication Skills: Strong communication skills, both written and verbal, are important for presenting findings and collaborating with team members.

  10. Domain Knowledge: Understanding the specific industry or domain in which one works enhances the ability to derive actionable insights from data in context.

The below Venn diagram, adapted from Stephan Kolassa, shows how data analysts and data scientists (near the heart of the diagram) must combine skills in communication, statistics and programming with a deep understanding of the business.

10 Steps to Getting a Job in Data Analytics:

  1. Educational Foundation: Obtain a relevant degree in a field like statistics, mathematics, computer science, or analytics to learn the fundamentals.

  2. Learn Key Tools and Languages: Acquire proficiency in essential tools and programming languages such as Python, R, SQL, and data visualization tools like Tableau.

  3. Build a Strong Portfolio: Develop a portfolio showcasing projects that demonstrate your analysis skills, including real-world problem-solving and data visualization.

  4. Gain Hands On Experience: Seek internships, participate in online challenges, or contribute to open-source projects to gain practical experience and enhance your resume.

  5. Network: Attend industry events, join online communities, and connect with professionals to expand your network and stay updated on industry trends.

  6. Continuously Learn: Stay abreast of emerging technologies and methodologies through continuous learning, including online courses, workshops, and certifications.

  7. Create a LinkedIn Profile: Develop a professional LinkedIn profile highlighting your skills, projects, and education to make yourself more visible to potential employers.

  8. Customize Resumes and Cover Letters: Tailor your resumes and cover letters to highlight relevant skills and experiences for each job application.

  9. Prepare for Interviews: Practice common interview questions, be ready to discuss your projects, and demonstrate problem-solving skills.

  10. Showcase Soft Skills: Emphasize soft skills such as communication, critical thinking, and teamwork in interviews to demonstrate your overall suitability for the role.

Modern Analytics Demo Video

Modern Analytics Demo Videos

See how to explore information and quickly gain insights.

  • Combine data from all your sources

  • Dig into KPI visualizations and dashboards

  • Get AI-generated insights

FAQs

What do you mean by data analytics?

Data analytics refers to the process of examining raw data to uncover patterns, draw conclusions, and make informed decisions using various techniques, tools, and methodologies. It involves extracting meaningful insights from data to support business intelligence, strategy, and decision-making processes.

What are the types of data analytics?

Data analytics can be categorized into four main types:

  1. Descriptive analytics focuses on summarizing and interpreting historical data.

  2. Diagnostic analytics aims to identify the reasons behind past outcomes and performance.

  3. Predictive analytics involves forecasting future trends and outcomes based on historical data.

  4. Prescriptive analytics recommends actions to optimize outcomes by analyzing various possible scenarios.

Is data analytics a good career?

Yes, big data analytics is considered a promising career with high demand, competitive salaries, and opportunities for career advancement, given the increasing reliance on data-driven decision-making across various industries. The diverse applications of analytics and its global relevance further contribute to its attractiveness as a career choice.

Is data analytics good money?

Yes, data analysis can offer competitive salaries and good earning potential due to the high demand for skilled professionals who can extract valuable insights from data to inform business decisions. The ability to contribute to data-driven decision-making in various industries enhances the financial prospects for individuals pursuing a career in this field.

What do data analysts do?

Data analysts collect, process, and analyze data to extract meaningful insights, identify trends, and present findings to inform business decision-making. They use statistical methods, programming languages, and data visualization tools to interpret complex datasets and help organizations make informed choices based on the results.

What is the difference between data analytics and data science?

While both data analytics and data science involve extracting insights from data, the former primarily focuses on analyzing historical data to identify trends and inform decision-making, whereas data science encompasses a broader spectrum, including predictive modeling, machine learning, and the development of algorithms to derive actionable insights and solve complex problems. Data science often involves a more comprehensive approach to data exploration and experimentation, incorporating elements of computer science and statistical analysis beyond traditional analytics.

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