One data analytics definition is: “qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyze behavioral data and patterns, and techniques vary according to organizational requirements.”
Although some data analysts may look at large and complex data, also known as “big data,” a lot of work also involves smaller data, such as internal data sets and company records.
Data analytics is often confused with data science. Although similar in nature, data analytics is more concerned with solving problems through defined data sets, whereas data science requires the development of new models and algorithms through coding and programming.
What Is a Data Analyst?
As more industries and companies develop in-house data functions, data analytics is becoming an increasingly sought-after career. A data analyst may be responsible for:
- Data mining and setting up the infrastructure and systems to process, manage, and analyze data. This may include the ETL (extract, transform, and load) data function.
- Data warehousing to build databases (storage systems) for easy data retrieval and access.
- Running queries to answer specific questions and extract useful insights.
- Cleansing data and making the necessary adjustments to ensure quality.
- Creating reports to present information to business stakeholders.
Types of Data Analysis
There are a few different types of data analysis:
- Descriptive analytics: This analysis seeks to explain what happened with variables, such as revenue, sales, cost, demographic data, etc. With descriptive analytics, the organization is looking for patterns and trends. For example, what happened with women’s clothing sales in the month of July?
- Diagnostic analytics: Diagnostic analytics explains the “why” and “how” between a particular data set. For example, why did sales go up in some retail outlets and not others?
- Predictive analytics: As the name suggests, predictive analytics is about predicting the future and what actions to take based on how variables are likely to behave. For example, will a promotion work at a store in X city based on the city’s characteristics and performance in similar geographic regions? Predictive analytics is the basis of machine learning and artificial intelligence.
- Prescriptive analytics: Prescriptive analytics determines which action to take to improve a situation or solve a problem. For example, since the promotion did not work as well as anticipated with families, what can we do to drive growth in the coming weeks?
Techniques for Data Analytics
Depending on objectives and use cases, data analysts can choose from a variety of data analysis methods to validate hypotheses and check for patterns.
- Regression analysis: Regression analysis is a statistical method that models relationships and correlations between different variables.
- Factor analysis: The goal of factor analysis is to find a defensible structure underlying the data.
- Cohort analysis: Cohort analysis requires the grouping of data by specific characteristics.
- Cluster analysis: Cluster analysis ensures that the objects in one group are more similar to each other than objects in other groups.
- Time series analysis: When data can be organized over time (for example, stock market prices), time series analysis predicts future behavior.
- Text analytics: When analyzing emails, open-ended survey responses, or websites, text mining involves searching for patterns and summary information.
How Can Data Analytics Be Used?
Data analytics serves multiple purposes depending on the industry, but these are some of the most common problems businesses address through data analytics.
- Sales forecasting: Based on sales growth, historical performance, and future market trends, companies can predict future sales numbers with greater accuracy.
- Price optimization: Data analysis helps companies determine the price elasticity of different customer groups to different products and services.
- Fraud prevention: Credit card issuers have historically used rules to identify suspicious transactions. With more advanced big data analytics and machine learning tools, it’s easier to detect and predict criminal behavior. This also applies to insurance, banking, and other industries concerned with high security.
- Marketing segmentation, targeting, and positioning: Data analytics can show whether certain marketing techniques, such as a promotional campaign or social media infographic, are leading to the desired response. With CRM systems and demographic data, companies can acquire a 360-degree view of the customer and better understand their purchasing behavior, which can lead to custom recommendations and more personalized outreach.
- Operational efficiency: By analyzing the different steps of a protocol, it is possible to identify bottlenecks and enact improvements. For example, adding an extra person at a particular shift could increase the speed of production or changing procurement orders could rebalance inventory supply and demand at retail locations.
The Future of Data Analytics
Data analytics has come a long way, and there are many more advances on the horizon.
With real-time analytics, data can be reported and acted upon in the moment—for example, by adjusting posted wait times at a restaurant or seats available at a concert. Automated analytics removes many of the tedious, manual tasks associated with inputting data. More data and software tools are becoming open source, providing a way for organizations of any size and budget to invest in data capabilities.
Machine learning, deep learning, natural language processing, and artificial intelligence are now enabling machines to learn from data and make decisions autonomously. Large technology companies are working on quantum computers that will revolutionize the speed of processing big data.
