What Is Data Analytics?
Data analytics is the process of evaluating data to draw conclusions and identify ways to improve business operations. This process helps organizations uncover trends, find problems, optimize performance, and improve decision-making.
Data analytics uses statistical analysis, AI, ML, and deep learning to describe, predict, and recommend actions for improvement.
The Benefits of Data Analytics
According to a 2022 survey, data analytics benefit businesses by:
- Boosting efficiency and productivity
- Fostering more effective decision-making
- Leading to better financial performance
- Creating a competitive advantage
- Improving customer experience
Data analytics also reduce human error by using automated systems to process and evaluate data. This leads to more accurate, actionable results and ultimately, a better experience for customers and internal teams alike.
The History of Data Analytics
We can trace the beginnings of modern-day data analytics to car factory lines in the early 1900s.Henry Ford measured assembly line speeds, then analyzed these metrics to see their impact on overall production.
Over the past several years, businesses have used ledgers and spreadsheets to track data. Analytics was a manual process, guided by experience. With the rise of big data, analytics software, and data warehouses/lakes/lakehouses, data analytics became something entirely different. Now, it’s often an automated process, performed at scale.
Today, nearly every organization is using some form of data analytics. In fact, businesses spent more than $215 billion in 2021 on big data and business analytics — a 10% increase from previous years.
Companies are also investing heavily in self-service data analysis and low-code/no-code tools. These emerging solutions facilitate data democratization, giving frontline employees access to data tools, dashboards, and visualizations.
The Four Main Types of Data Analytics
There are four main types of data analytics:
- Descriptive
- Diagnostic
- Predictive
- Prescriptive
Descriptive Data Analytics: What Happened?
Descriptive data analytics focuses on looking backward. It answers the question “what happened”, giving measurable insights into past performance. For example:
- How much revenue did we earn last month/quarter?
- How much customer churn did we experience?
- What is our profit margin?
Diagnostic Data Analytics: Why Did It Happen?
Diagnostic data analytics also uses historical data, but goes deeper into why something happened. For example:
- Why was our revenue up or down?
- Why did we see more churn?
- Why was our profit margin lower?
Predictive Data Analytics: What Will Happen?
Predictive data analytics is forward-looking. It uses historical data and trends to logically predict possible outcomes.t AI, ML, and data modeling can be used to provide these insights into future events. For example:
- What is our future cash flow?
- When are machines most likely to break down or require maintenance?
- What are the optimal inventory levels to meet future consumer demand?
Prescriptive Data Analytics: What Should We Do Next?
Prescriptive data analytics goes beyond the other types of analysis by recommending action steps. It uses AI/ML to search for causation and correlation then provide actionable insights. For example:
- Adjusting lead scoring models to increase conversions
- Delivering optimal content for the buyer’s journey
- Recommending upsells or cross-sells
Amazon leverages prescriptive analytics with its recommendation engine, which is responsible for more than a third of its total sales.
Delivering Business-Driven Insights
Each type of data analytics offers a different perspective that can be used to monitor, measure, or improve business operations. Business leaders can leverage these insights to assess market trends, respond to changes, and make smarter, more informed decisions.
Data-driven organizations are three times more likely to significantly improve decision-making, according to PwC research. On average, businesses that act on their analytics findings earn 8% higher EBIT (Earnings Before Interest and Taxes) than those that don’t.
But companies that don’t put data analytics at the center of their operations will fall behind quickly.Gartner predicts that organizations lacking a sustainable data analytics framework by 2024 will be as much as two years behind their competitors.
What to Consider for Your Data Analytics Solution
Implementing strong, actionable data analytics into your business’s operations requires strong solutions. To create the best possible approach to data analytics, look for tools that can: .
A Comprehensively Handle Data Analysis at Scale
Organizations should look for an end-to-end platform that can handle data collection and analysis at scale. The platform needs to seamlessly integrate with other platforms and legacy systems, compiling a variety of data into a single source. You shouldn’t need to switch between platforms in order to conduct analysis with AI, machine learning, and model building/training. AtScale facilitates this level of flexibility by embedding analytics into your current tech stack.
embedded data analytics platform accelerates and automates tasks so your teams can spend less time on manual tasks and more time intelligently analyzing the data.
Eliminate Data Silos
More than 60% of data collected by companies never gets used. This is because it’s often trapped in data silos, making it inaccessible across the organization. The right data analytics solutions facilitate a better data storage strategy and create a unified data architecture, providing a“single source of truth.”
Create a Universal Semantic Layer
A universal semantic layer helps organizations establish a strong data analytics process by bridging the gap between data sources and data users. It eliminates the complexity of application data, accelerating workloads and enabling better data governance and consistency.
Leverage Cloud Resources
Cloud resources need to play a role in your data analytics toolbox. They enable the entire organization to access and use data and analytics. Cloud resources ultimately foster an environment of data accessibility and decentralization, regardless of where each team is located.
Developing a Data-Driven Culture
Along with having the right tools, a business also needs to adopt a data-driven culture across the entire organization.
In order to do this, the leadership team needs to embrace the data analysis process. They must also set a precedence for data-driven decision-making.
A data-driven culture also requires company-wide data literacy. Rather than delegating analysis to data scientists, organizations need to make data accessible and usable to frontline employees. Data visualizations help facilitate this by producing dashboards that make it easy for employees to see what’s happening in real-time.
To properly use this data, team members must be able to understand and appreciate it, then work with tools to extract insights and achieve their goals.
AtScale Accelerates Data Value
AtScale helps companies create greater value from data and accelerate the flow of data-driven insights with a semantic layer platform.We provide a common language that simplifies and extends BI and data science across your entire organization.
AtScale maximizes the potential of your data by optimizing your analytics infrastructure, enabling self-service data analysis, and accelerating the adoption of data science and enterprise AI.
Additional Resources:
- Data & Analytics Maturity Workshop
- The Practical Guide to Using a Semantic Layer for Data & Analytics
- Improving Data Analytics: Key Insights from Fifth Third Bank, Stanley Black and Decker, and Snap Inc.
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