What Is Data Analytics? A Complete Guide

Having a strong data analytics strategy can have transformative effects on your business, giving your organization better visibility into what’s going well and what needs to be improved. It bridges the gap between knowing and doing, empowering your teams to optimize for efficiency and customer satisfaction. 
 

1. Informed decision-making

No guessing, no assumptions. Make data-driven decisions based on evidence and patterns found in historical data sets for more accurate and reliable results.
 

2. Improved operational efficiency

Knowing is half the battle, as they say, and data analytics provides actionable insights that can spotlight operational inefficiencies, bottlenecks and opportunities for improvement across the organization. This gives businesses the information they need to streamline processes, lower costs, enhance productivity and eliminate or refine strategies that aren’t working.
 

3. Risk mitigation

As fraud continues to evolve and increase in frequency, organizations need to remain vigilant against bad actors. Data analytics enables proactive threat and anomaly identification, making it easier for organizations to develop targeted risk-mitigation strategies with the use of AI and machine learning.
 

4. Enhanced customer understanding

Data analytics provides businesses with the critical customer data and insights needed to personalize customer experiences, improve their products and services, and increase customer satisfaction and loyalty. This is a huge competitive advantage that improves not just customer retention, but also new customer acquisition, and overall company growth.

While having a wealth of data is great, there are steps you have to take first to move it from being in a state of raw data to being able to provide you with meaningful, actionable insights. A typical data analytics project will involve working through the data analytics lifecycle: defining the objective, collecting the data, cleaning and preparing the data, and analyzing it. 
 

1. Define the objective

What exactly is your team hoping to glean from the data you’re collecting? What process or operation are you all trying to optimize? What campaign are you supporting? Spending time and resources gathering a mass of data isn’t helpful unless you have clear, defined goals. Outline what you’re aiming to achieve to determine data requirements and how the data should be grouped and separated.
 

2. Collect the data

Now that you know exactly what you’re looking for, your data analysts can collect relevant data to support your goals. This could be anything from traffic to your site over a period of time, customer surveys, social media engagement statistics, transactional data, risk scoring and so much more.
 

3. Clean and prepare the data

Scrub data and examine it for errors, inconsistencies and outliers which could impact the reliability and usability of the data set. Then organize it for easy analysis. Spreadsheets are ideal for smaller data sets, but become slow and unwieldy when dealing with larger data sets. Business intelligence (BI) tools are ideal for data visualization and dashboards, while databases and data warehouses are necessary for very large data sets.
 

4. Analyze the data

So you have your clean, prepped data set. Now’s the time to dive in to find patterns, relationships and trends using logic and statistical techniques. Remember the types of data analytics that we discussed earlier? This is where your data analysts utilize those methods to understand the what and why of your data and plan for next steps. 

Spreadsheets and free versions of BI tools are common resources for analyzing small data sets, and are typically used by smaller teams who may not even have a dedicated data analyst on staff. At the enterprise level, cloud services and big data processing frameworks and cloud services handle intermediate to very large data sets, often in conjunction with full-featured BI platforms, to produce comprehensive reporting and visualization.

Broadly speaking, data analytics techniques are characterized into the categories of supervised and unsupervised learning. Supervised techniques learn from labeled data to make predictions, while unsupervised techniques find patterns in unlabeled data.
 

Regression analysis

This is a supervised learning technique used to predict a continuous numerical value based on one or more independent variables. Put another way, it’s a statistical method for understanding the relationship between a dependent variable and one or more independent variables by fitting a line or curve to the data. It helps determine which variables have an impact.

A key question regression analysis answers is, “How much or how many?” and helps businesses determine which factors matter the most and the least.
 

Classification analysis

Classification analysis answers the question, “What class or category does this belong to?” and is a powerful tool for making sense of information and making informed decisions.

This supervised learning technique is used to sort and categorize data into groups by training a classification model and then giving it new data to predict which category it belongs to. Essentially, classification analysis is all about using past, labeled data to teach a model how to categorize new, unlabeled data correctly.
 

Clustering analysis

Unlike the previous two techniques, clustering analysis is an unsupervised learning technique used to group similar data points into clusters. There are no predefined labels or categories; the algorithm discovers the groupings within the data. Clustering analysis answers the question, “How can we group this data?”

As we mentioned at the start of this guide, every industry can benefit from data analytics, and companies of all sizes are giving themselves a competitive edge by utilizing their own data to optimize, evolve and grow. Let’s take a look at three industries — retail, healthcare and finance — to see how they commonly incorporate data analytics into their workflows and processes.
 

Retail
 

• Ad personalization: Retailers can leverage data gathered on each customer, like purchase histories, demographics and behavioral data, to personalize the email and social media promotions they send to those customers. Creating a tailored shopping experience helps build brand loyalty and wallet share.

• Online retail inventory management: To ensure inventory levels can meet customer demand, online retailers analyze past customer purchasing behaviors, stock levels and third-party and public data. Keeping digital shelves stocked increases customer retention and prevents them from having to click away to get what they want.

• Brick-and-mortar safety: It’s crucial that physical stores eliminate hazards that could potentially injure customers or employees. Analyzing a variety of data points, such as incident reports and safety training records, and utilizing the four types of data analytics, can help leadership develop improved employee safety training to reduce the occurrence of incidents.
   

Healthcare
 

• Predicting disease: Predictive analytics uses patient data, such as lifestyle, genetics and medical history, to identify patients who are at high risk of developing certain conditions. This helps doctors develop personalized treatment plans and start preventative care to get ahead of the disease.

• Resource allocation: Hospitals can use data, such as seasonal emergency room admissions, to forecast patient admission levels to optimize staffing and supplies to meet patient needs, reducing wait time and increasing patient satisfaction.

• Fraud detection: Data analytics can help hospitals, pharmacies and insurance companies prevent fraud by analyzing billing patterns, prescription usage and insurance claims to identify anomalies and unauthorized transactions.
   

Finance
 

• Risk management: Banks can analyze a range of customer data, including spending habits, income stability and payment history to determine someone’s creditworthiness.

• Customer churn prediction: If certain data (like no recent banking activity or high fees) suggests that a customer may close their account, the bank can proactively offer services or incentives to retain them, like a specialized savings rate.

• Algorithmic trading and market analysis: High-frequency trading firms use complex algorithms to analyze market data, identify market opportunities and trends, and execute trades in seconds.

• Spreadsheets: Microsoft Excel, Google Sheets
• Business intelligence (BI) tools: Tableau, Microsoft Power BI
• Programming languages: Python, R, SQL

Data analytics is essential to competitive advantage in virtually any industry. Data-driven decision-making creates meaningful efficiencies by optimizing internal processes, upgrading customer experience, increasing brand loyalty — and, in the case of healthcare, improving patient wellness. 

Data continues to grow at staggering rates, increasing the complexity of analytics challenges. At the same time, tools and techniques, including advanced applications of AI, provide new ways to interact with data to draw high-value insights. Expect the evolving field of data analytics to remain an essential part of success in the digital age.