What is Data Analytics? How to Use it in Your Career?
In this digital world, Data is the backbone of all businesses. With such large-scale data production, it is essential to have a field that focuses on deriving insights from it. What is data analytics? What tools help in data analytics? How can data analytics be applied to various industries? We will be answering all these questions here!
Table of contents
- What is Data Analytics?
- Types of Data Analytics
- How Data Analytics Works?
- Applications of Data Analytics
- Tools and Technologies Used in Data Analytics
- Challenges and Limitations of Data Analytics
- Final Verdict
What is Data Analytics?
The excess data generated from variable sources holds no meaning unless analyzed. The process of raw data analysis to convert it into an interpretable, usable form with the capability of actionable insights is data analytics. The analyzed data finds application in deciding the company’s future strategies, taking an overview of progress and analyzing successful and unsuccessful programs worthy of continuing and stopping.
Types of Data Analytics
1. Descriptive Analytics
Descriptive analytics includes data aggregation and mining to take an overview analysis of past actions. The data is gathered, analyzed for patterns and presented to various audiences. It focuses on changes in numbers in a specific time period, and the insights answer the ‘what’ of the process.
2. Diagnostic Analytics
The reasoning behind dynamic or status data is diagnostic analytics. It answers the ‘why’ by identifying relevant data sources appropriate for finding the answer. Commonly used methods include regression analysis, time-series data analytics, probability theory and filtering.
3. Predictive Analytics
Predictive Analytics aims to provide predictions and data-based insights to decide the company’s future steps. It analyzes previously available data and performance to anticipate possibilities with logic. It helps companies make researched decisions compared to random or chance-based events. Some of the techniques involved in predictive analytics include neural networks, regression and decision tree.
4. Prescriptive Analytics
Prescriptive Analytics is based on predictive analytics and helps find the best possibility or circumstance among the numerous other possible methods. It helps take advantage of the predictive outcomes through machine learning, algorithms and computational modeling.
Also Read: This is How Experts Predict the Future of AI
How Data Analytics Works?
Step 1: Data Collection
Data analytics begins with identifying the required information for specific analysis. The data is collected, assembled through integration, and converted into the desired format. The next step is to load in the analytics system. Alternatively, data collection can be performed by separating the flowing data from the analytics system, and partitioning the data, followed by analysis.
Step 2: Data Cleaning and Preprocessing
Data processing removes contaminants or unwanted information capable of affecting result accuracy. The data cleaning and preprocessing involve profiling to ensure data consistency and eliminate redundant data. The data is further prepared and introduced with data governance policies.
Step 3: Data Modeling
Data modeling involves building analytics, validating its accuracy, and optimizing it for the desired usage. The process, known as model training, involves using predictive modeling tools, various programming languages, and analysis software. It also triggers business actions automatically.
Step 4: Data Visualization
The last step is to communicate the results to business executives. They are further intriguing and easily understandable by incorporating infographics, graphs, and charts. Using real-time updated tools is the preferred option to avoid missing significant details.
Applications of Data Analytics
The large volume of data generated helps in business analytics by personalizing the customer experience and helping make informed and logical business decisions. It also helps to enhance operational efficiency, promote risk mitigation, alleviate setbacks’ effects, and increase security.
The healthcare industry requires data analytics for better resource availability preparation, meeting abrupt treatment facility requirements and increasing checkup efficiency. Moreover, it aids in risk mitigation, delivers quick outcomes and brings sustainable changes.
Social Media Analytics
Data analytics benefit social media by analyzing the audience specifications, preferences and behaviors. It helps optimize the content per the customer requirement, provides insight into campaign performance and optimizes influencer marketing by acting on suitable targets.
Marketing analysis is market segmentation into different customer understanding criteria. It eases competitor analysis by focusing on their strengths and weaknesses and providing improvement strategies. Data analytics helps in sales forecasting, product development and pricing strategy.
The pre and post-game data generated during different games and activities helps analyze players’ performance, devise the in-game strategy, identify hidden talents, and interpret the sports lovers’ preferences and requirements.
Tools and Technologies Used in Data Analytics
1. Statistical Programming Languages (R, Python)
The statistical programming languages R and Python provide platforms comprising statistical and machine learning tools for hypothesis testing, time series, regression analysis and deploying machine learning models. They also contribute through visualization and mining tools for data extraction, and python additionally provides web scraping and natural language processing.
2. Data Visualization Tools (Tableau, Power BI)
Data visualization tools such as Tableau and Power BI provide platforms to create interactive dashboards for better presentation. It also allows the creation of graphs, infographics, maps, charts, and predictive analytics applications. Power BI enables visualization through different representation formats, data modeling, team collaboration, and integration with other tools.
3. Big Data Technologies (Hadoop, Spark)
Hadoop is specifically involved in complex analytics, storage and management, data processing and node editing, thus allowing scalability. Big data technology like Spark also enables data processing, aids in building machine learning models, in-memory processing imparting speed, provides real-time analytics, and easily integrates with other big data tools.
4. Cloud Computing Platforms (AWS, Azure)
Cloud computing platforms allow real-time operability by different members on the same team or different teams. The benefits available while performing data analytics are the accurate updates, storage, processing and transformation tools, and potential to deploy machine learning models and scalability.
Challenges and Limitations of Data Analytics
Data Privacy and Security Concerns
Data analytics pose a risk through privacy breaches, leading to embarrassment for organizations comprising confidential information. It compromises anonymity, along with the easy revelation of masked data. Data analytics can also influence behaviors leading to the suggestion of unethical actions. Similarly, the protection of data legally in situations where data revelation is required can also pose a risk to the safety of individuals.
Data Quality and Availability Issues
Multiple times the data analytics isn’t found to be accurate. It occurs due to inaccurate gathered data or wrong algorithms. Adding data to previously inaccurate information further increases the risk of negatively impacting the organization’s decision and future. The inaccurate data analytics in companies serving customers can lead to denial of service, wrong diagnosis, false accusations and inappropriate treatments.
Bias and Interpretation Challenges
There are numerous biases encountered in data analytics, such as selection, sampling, interpretation, confirmation, algorithm and overfitting bias. These biases occur due to confirming and sticking to old beliefs, the sample needs to be more specific or represent the overall population in consideration, misinterpretation of data, high complexity of data and multiple other reasons. You can eliminate this bias by recognizing the specific type encountered.
Data analytics is crucial in today’s era, where tons of data is generated by and for companies. These hold key information to decide the brand’s next steps and growth. It helps understand patterns and relationships between services or products and customers and analyze trends. Having multiple job prospects, gaining knowledge, skills and expertise in data analytics is necessary. We provide all the mentioned aspects through our Data Science Blackbelt program. So what’s the wait for? Enroll in the course and begin the new journey.