What is Analytics?
Analytics in simple terms and the power of data
Analytics is the systematic computational analysis of data or statistics for the discovery, interpretation, and communication of meaningful patterns in data. It entails applying data patterns toward effective decision-making. A foundational understanding of analytics can be extremely useful to businesses and professionals across industries and fields.
Working with analytics benefits organizations through the ability to make data-driven decisions based on facts. Analyzing data can increase operational efficiency by identifying bottlenecks or redistribution of resources. Analytics can help you identify, mitigate, and manage risks.
Organizations that look at market analytics can understand trends and competition. Customers benefit when companies examine analytics and make decisions based on data around customer preferences. Analytics also enables personalization which helps make better product recommendations, improve customer service, and enhance marketing communications.
The secret to driving true insights is marrying data with analytics. A combination of data, analytics, and the necessary data skills enables companies to maximize their technology investments and uncover opportunities that drive business strategy and strengthen customer trust.
The four types of analytics
The basis of analytics can be broken down into four key types: descriptive, diagnostic, predictive, and prescriptive. Each has their own benefits but they can also be used in tandem. By looking at information through all four lenses, you’ll be able to glean the most information and see how each piece fits together.
Descriptive analytics
Descriptive analytics answers the question, “What happened?” It allows you to pull trends from raw data and describe what happened or what is happening. Descriptive analytics can be used to:
- Create reports, financial statements, and dashboards
- Identify trends in consumer preference and behavior
- Track and analyze progress toward a goal
Diagnostic analytics
Diagnostic analytics answers the question, “Why did this happen?” It is the process of using data to determine the causes of trends and correlations between variables. It also includes regression analysis and hypothesis testing. It can be done manually, using an algorithm, or with statistical software. Diagnostic analytics can be used to:
- Examine market demand for a product
- Explain customer behavior to improve user experience or product quality
- Improve company culture through surveys and interviews
Predictive analytics
Predictive analytics answers the question, “What might happen in the future?” It helps you determine the likelihood of future outcomes using techniques like data mining, statistics, data modeling, artificial intelligence (AI), and machine learning. Predictive analytics can be used to:
- Interpret an organization’s historical data
- Discovers patterns to make predictions about the future
- Identify upcoming risks and opportunities
Prescriptive analytics
Prescriptive analytics answers the question, “What should we do next?” It is the process of using data to determine the best course of action. Prescriptive analytics can be used to:
- Increase sales through lead scoring
- Create algorithmic recommendations
- Automate marketing processes
Modern analytics technologies
Data has become increasingly useful as modern analytics technologies develop. The use of machine learning and AI, for example, are constantly improving analytics processes. These technologies save time and reduce human error.
Big data analytics
Organizations commonly use big data analytics to uncover trends, patterns, and correlations in large amounts of raw data. Big data is a large volume of data and datasets that come in diverse forms and from multiple sources, it’s the aggregate of information about interactions, for example, when consumers open marketing emails, message customer service, and make purchases. To operationalize big data, large datasets must be collected, processed, cleaned, and analyzed.
Use cases of big data analytics include:
- Helping organizations identify ways to do business more efficiently and cut costs
- Providing a better understanding of customer needs and improve product development
- Tracking purchase behavior and market trends through insights
Augmented analytics
Augmented analytics is a class of analytics that uses artificial intelligence (AI) and machine learning that helps us better interact with data at a contextual level. It includes tools and software that assist with data preparation, insight generation and explanation, recommendations, and guidance. Machine learning, which powers augmented analytics, helps by reducing or eliminating repetitive tasks like cleaning, shaping, and filtering data. This helps people reach insights in real-time and make decisions more quickly.
Use cases of augmented analytics include:
- Supply chain management can understand why certain locations aren’t delivering products at the expected rate
- Travel and hospitality organizations can find the optimal, personalized offers to upsell or cross-sell customers
- Marketing and communications agencies can explore the effectiveness of ad campaigns and uncover variables
Real-life analytics examples
United Nations World Food Programme
The United Nations World Food Programme built an organization-wide data literacy program focused on collaboration, peer leadership, and attracting talent. They helped “their staff make leaps forward in integrating visual analytics into vulnerability assessments and other work areas to improve impact at every level of the organization.” Their attention to analytics paid off. By closer analyzing their data they had access to were able to save enough money to feed an extra 2 million people in one year.
When we have the right data in front of us, we can read it, communicate with it, argue with it, and interpret it. Data has become a common language within our organization to accelerate our mission.
Whole Foods Market
Whole Foods Market created a single source of truth, merging their financial and operations data. Through a series of dashboards, they were able to get a better view of staff performance nationwide. A sales dashboard, for example, helped team leaders see where they needed to increase staffing and at what time of the day. The terminal dashboard helps them see which registers need to open or close. Using analytics, they were able to make decisions that improved daily operations and customer experience.
Tableau brings analytics to life
Tableau was the catalyst to help employees have that ‘aha data moment.’ That flicker of data cognition turning into deeper understanding was what Nissan needed for success in a digital world.
Tableau can help anyone see and understand complex data sets. It offers data visualization through interactive dashboards and data storytelling for presentations. Users are able to combine multiple data sources, providing a holistic view of the business. Ad-hoc analysis allows users to quickly explore data, identify trends, and discover insights.
Tableau Prep helps users clean, transform, and shape their data before it’s analyzed. Tableau’s advanced analytics software offers statistical analytics and integration with programming languages. The platform offers collaboration between teams and online publication and sharing.
Anyone can take control of their data with Tableau, it’s customizable and easy to use for non-technical team members. Tableau turns trusted data into actionable insights. Make better decisions every time with an intuitive, AI-powered analytics platform.