Data analytics is more than just analyzing statistics and crunching numbers. It uncovers trends and patterns in all the data that flows through business systems, revealing insights that help decision-makers understand their enterprise and its audience. Without it, data is useless and has no meaning or context.
This guide takes a deep dive into data analytics and why it matters.
Before we explore the different types of data analytics and their benefits, let's define this often-misunderstood term.
Data analytics is the discipline of analyzing raw data to find context and meaning in that data. For example, an e-commerce retailer might analyze sales data to discover its most popular products.
As big data sets have gotten bigger, it's almost impossible to perform data analytics manually, so digital tools facilitate this process.
One of the most popular methods for analyzing data is to move it to a central repository like a data warehouse before running it through business intelligence (BI) tools that produce reports, dashboards, heat maps, and data visualizations.
Data owners typically extract data from business systems, transform it into the correct format for analytics, and then load it to a warehouse such as Amazon Redshift or Google BigQuery, which serves as a single source of truth (SSOT) for all data analytics activities. Businesses might move unstructured data to a data lake.
Generally, this data warehousing process involves coding, data mining, and programming languages like Python and SQL, so a data engineer will step in and take over.
There are various bottlenecks involved in data analytics. Most data-driven companies house data in multiple systems that don't communicate with one another, potentially causing data silos.
Data warehousing and other data integration methods can solve this issue. However, businesses should be wary of data loss and quality problems when transferring sensitive information to a third-party repository.
There are also data governance concerns when moving information between data sources and a target system for statistical analysis. Non-compliance with legislation like GDPR and HIPAA can result in excessive financial penalties, depending on an organization's jurisdiction and niche.
There are four data analytics types:
Descriptive analytics involves the analysis of historical data to uncover relationships in that data.
Examples of this type of data analytics include key performance indicators (KPIs) such as return on investment (ROI), revenue per customer, and year-on-year sales growth.
By using his method, businesses can learn how well they have performed in the past and make more informed decisions.
While descriptive analytics focuses on the past, diagnostic analytics examines the current state of affairs in a business. Data owners draw from the conclusions of descriptive analytics and learn why something in the present day has happened.
Say a business has experienced a sudden sales slump. That business might use diagnostic analytics to find the cause of the slump and share these insights with stakeholders.
Statistical techniques can help decision-makers during this process.
Predictive analytics looks toward the future. It involves historical data to forecast future outcomes, helping decision-makers avoid worst-case scenarios and identify potential business opportunities.
Predictive analytics includes various statistical algorithms and machine learning methods such as decision trees and neural networks.
These methods help businesses avoid risk, discover cost-cutting measures, and predict future sales outcomes.
Prescriptive analytics uses data to find the best course of action for a specific scenario. Like predictive analytics, it uses machine learning techniques to discover data trends, allowing decision-makers to optimize business practices and answer the question, "What should our business do next?"
Here are just some of the advantages of data analytics:
Identifying patterns and trends in large data sets improves workflow management significantly.
Moving data from an inventory management system, for example, to a centralized repository primes data for analytics in BI tools, which provide real-time insights about manufacturing, production, and logistics.
Data analysts and data scientists can visualize this data through interactive heat maps and reports and learn how to make operations more efficient. That might motivate an enterprise to hire more staff or invest in new inventory equipment.
Data analytics can improve productivity in a workplace. By performing diagnostic analytics, retailers, for example, can learn more about employee productivity and find out which workers need additional training or resources.
Retailers might also use predictive modeling and analytics to forecast future staffing needs and hire extra employees for the upcoming holiday season.
BI analytics tools make it easy to visualize potential future outcomes, providing retailers with actionable insights into their business they can't find anywhere else.
Most data-driven companies can use data modeling and advanced analytics to learn more about customers who purchase their products and services, whether that's in a B2B or B2C context.
Running data through BI tools like Looker and Tableau enables these companies to view metrics, such as customer lifetime value (CLV) and cost per acquisition (CAC), enabling decision-makers to discover the most lucrative customers and prospects.
Marketers can also use insights from business analytics to move customers through their pipelines and update customer relationship management (CRM) systems with demographic insights.
These marketers can use data insights to learn more about a customer's location, interests, purchasing decisions, and behavior and improve decision-making.
Companies can reduce operational costs and learn more about budgets by analyzing data sets.
Moving data from a transactional database to a warehouse such as Microsoft Azure or Amazon Redshift, for example, can help decision-makers in the retail sector discover whether purchased products and services generate significant profits.
Companies can also identify ways to save money in the future by using predictive analytics and statistical models.
Data analytics can provide accurate fraud analyses for companies in almost any niche, helping financial managers forecast potential risks that might jeopardize their business.
An online retailer might use descriptive analytics to learn more about fraud events in the past and predictive analytics to predict the likelihood of these events happening again.
Running data through a BI tool can help businesses save thousands of dollars in fraud losses and provide ongoing peace of mind.
Data analytics is a complex process that involves analyzing raw data to find context and meaning in that data.
Using methods like data warehousing can automate this process and provide data-driven companies with unparalleled business intelligence. Four main data analytics techniques help decision-makers better understand the past, present, and plan the future steps of their business.
Using a combination of these techniques can streamline workflows, improve productivity, analyze customer trends, cut operational costs, and reduce risk and fraud.
If you want to learn more about how data analytics will benefit business decisions, Adservio can help.
We unleash the power of data, artificial intelligence, and machine learning to support your data initiatives and help you get more value from distributed data environments.
Contact one of our professionals today to learn more.