Access to the internet and social media, wide use of smartphones and tablets, and the Internet of Things are some notable key drivers of the data explosion that we are witnessing today. Data has many uses. Whether it is to discover and explore new markets, new product lines, understand customer behavior, or uncover inefficiencies in processes, the role that data plays in decision-making cannot be underestimated. Data is core to critical decision-making and business growth, yet without working up data and unearthing information and insights within it, data will not be useful. Data analytics and data analysis are two processes used to convert raw data into information that is useful for informing business decisions and driving business value.
However, much as these terms have been used interchangeably, data analytics is different from data analysis. Data analytics is a broader term that refers to the overarching discipline or science that encompasses the systematic management of data, including data collection, processing, analysis, and interpretation of information from the data, as well as the tools and techniques used to do so. Usually, data analytics focuses on the future. Data analysis, in simple terms, is the process of investigating the structure and components of a given dataset in detail to understand what happened and why they happened. Data analysis is a component of data analytics whose focus is on the past. Thus a data analytics certification course typically includes data analysis as one of the foundational modules.
Both data analytics and data analysis refer to the examination of data and are important for businesses. Let us dig further to understand data analytics and data analysis approaches, their roles, and their importance in business.
Data analytics overview
Data analytics is a broad discipline that entails the end-to-end management of data alongside the tools, technologies, and techniques used in managing data. This includes data collection, preprocessing, storage, analysis, interpretation, and the tools and techniques used to do so.
The goal of data analytics is to discover hidden trends and patterns in data to draw inferences that will be useful for data-driven decision-making. This field draws an inference from historical data and/or new input data processed in real-time or batch form from multiple sources.
Data analytics has been widely used across industries by large and small organizations alike to facilitate informed decision-making. Data analytics has been employed:
- When businesses seek to increase revenue by tapping into new markets or product lines
- To optimize marketing by creating appropriate personalized campaigns for specific market segments
- To improve operational efficiency by discovering and addressing system inefficiencies
- Understand customer behavior and deliver value
- Discover and respond to the latest market trends and patterns to gain a competitive advantage
The data analytics process
The data analytics process, as we have already seen, comprises all aspects and tools of data management from the time data is gathered until when it is analyzed, interpreted, and insights drawn from it for decision-making within a data pipeline. A typical data analytics process involves the following steps:
- Business case evaluation
- Data collection and ingestion
- Categorization and organization of data in data lakes, databases, or data warehouses
- Data storage in hot, warm, or cold storage
- Extract, Load, and Transform (ETL) process in which data cleaning is a crucial aspect
- Data analysis and interpretation to discover and apply hidden patterns, trends, and insights to decision-making
- Data visualization and presentation
Types of data analytics
There are four main types of data analytics which are:
- Descriptive analytics uses current and historical data to identify and describe trends and relationships to better understand events that have happened over a period of time. Businesses use descriptive analytics to generate reports and establish relationships between different metrics. For instance, current month or year-to-year sales, price-to-earnings ratio, return on investment, social media engagement, etc. These are often communicated visually as that makes it far easier for users to understand the data. For example a dashboard in Power BI which is very easy to learn to use.
- Diagnostic analytics uses current and historical data to identify the cause of events that may have been identified through descriptive analytics. This is through techniques like data mining, correlations, drill-downs, data discovery, probability theory, and statistical analysis.
- Predictive analytics aims to predict future events using historical data and take the necessary action in anticipation. These may include such events as equipment downtime, customer churn, future cash flow, and predicting staffing needs.
- Prescriptive analytics uses data to determine the most appropriate course of action to take to deliver the desired outcomes. For instance, recommend the best course of action to reduce operational costs, reduce downtimes, reduce time-to-market for products, and increase revenue.
Data analytics tools
Data analytics is systematic and makes use of various systems and software to manage datasets. These include:
- Business intelligence tools like Microsoft Excel, SAS, SAP, ORACLE, Power BI, and Tableau.
- Online Analytical Processing (OLAP) tools like Oracle OLAP and IBM Cognos
- Reporting tools.
- Advance analytics tools such as data mining, text mining, machine learning, statistical analysis, and other computer-based data analytics models.
- Big data analytics tools.
The applications of data analytics are numerous. Whether it is the banks analyzing credit card transactions to detect fraud and assess risk, E-commerce businesses analyzing social media posts to discover what influences their audience purchase decisions, healthcare institutions analyzing patient data to predict treatment outcomes, or enterprises analyzing CRM data to find avenues of strengthening customer relationships, data analytics plays an integral role in business growth and performance.
Data analysis overview
Unlike data analytics, data analysis is a process and not a discipline. It refers to the process of investigating, transforming, and organizing data to study individual parts or structures with the goal of extracting useful information and drawing inferences. For this reason, data analysis is just one component of data analytics. The core aim of data analysis is to discover useful information from a given dataset.
The data analysis process
The data analysis process involves
- Cleaning data
- Extract, Transform, Load (ETL) process
- Modeling data
- Querying data
Data collection, data storage, and data visualization activities are considered to be separate processes from data analysis. This is because the data analysis process is limited to a specific dataset that has already been cleaned and prepared for analysis.
Types of data analysis
The two broad categories of data analysis methods are quantitative analysis and qualitative analysis.
- Quantitative data analysis refers to the analysis of numerical data based on quantifiable variables that can be compared statistically.
- Qualitative data analysis takes an interpretive approach as it analyses non-numerical data such as text, images, video, and audio.
These data analysis methods can be used in combination or independently to gain insights that will inform decision-making in business. The choice of technique will be determined by the types and formats of data being used.
Data analysis tools
Data analysis tools are simply tools used to collect, organize, process, and analyze datasets. Popular examples of data analysis tools include:
- Google fusion
- Tableau Public
As the world generates more data, the uses of data are innumerable. At the very basic, this data is analyzed to discover hidden patterns, trends, and insights that inform decision-making. Data analytics is the field that encompasses the processes, tools, and technology used to manage data and draw insights from it. On the other hand, data analysis is a component of data analytics that involves cleaning, transforming, modeling, and visualizing data to spot patterns and insights that are useful for driving business strategy and performance.