Everything you need to know about data analytics
Harnessing the power of data through analysis is the backbone of any modern business. However, it can be an overwhelming discipline to learn. If you’re in a position where you’re uncertain about getting started in data analysis for your business, here’s a thorough overview.
By the end of this post, you’ll know what data analytics is and how it can benefit your business.
This post will discuss the following topics:
- What is data analytics?
- The importance of data analytics
- What is the process of analyzing data?
- Techniques for data analytics
- Types of data analytics
- Data analytics tools
What is data analytics?
Data analytics is the process of examining large amounts of quantitative and qualitative data. It’s useful for companies looking to understand and predict business trends. If companies can understand these trends in advance, they can draw out meaningful, actionable insights to make smarter business decisions.
The goal of data analytics is to pull out important business insights from the various information that you’ve collected about your customers. It’s accomplished through data collection, data cleaning and preprocessing, exploratory data analysis, data visualization, and predictive modeling. By analyzing data from multiple sources — such as databases, spreadsheets, and social media — companies can gain a deeper understanding of their operations, customers, and market trends.
Data analytics is now more accessible and scalable thanks to new technologies and software, allowing businesses to make data-driven decisions at every level of the organization.
The importance of data analytics
Data analytics can be a key player on your business team that slam dunks success and growth. It can empower organizations to make smarter, data-driven decisions about the future of their business. By analyzing historical data and identifying patterns, businesses can strategically plan, accurately forecast, and quickly adapt to a volatile economy to give them a competitive edge.
Data analytics can also reduce spending and increase return on investment (ROI) by finding bottlenecks, streamlining operations, and optimizing resource allocation. It can even identify waste or fraud.
What is the process of analyzing data?
Analyzing data requires several key steps, such as collecting, storing, cleaning, and extracting meaningful insights.
Collect the data
Start by auditing which data you need for the analysis. This could include customer information, UX interviews, or website impressions. Look for the data in your CRM system, previous marketing campaigns, or analytics software. Collecting data should be viewed as a constantly evolving and dynamic cycle happening in real-time.
Store the data
Once you collect the data, it needs to be stored somewhere. Often, it will require a transformation to make it useful in the future. This transformation is referred to as Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT), depending on your process. “Extract” focuses on retrieving data from the source systems, “Transform” involves cleaning and formatting the data, and “Load” transfers the transformed data into a storage area such as a data lake. Ultimately, what you're doing is giving that data shape and meaning. You're turning the raw data into a usable format.
On a side not, one of the biggest mistakes you can make is storing the data and forgetting about it. Instead of using data analytics to drive cost savings, storing unused data is actually wasting resources.
Clean the data
Like any big project, it’s important to check for inconsistencies, duplicates, or missing information. Raw data is usually messy, and it can take a long time to properly clean. However, it’s an essential step for accurate and reliable results.
Analyze the data
Now it’s time to analyze. This step involves running the data through software tools that will provide valuable insights from the data collected. We’ll look deeper into the different techniques for analyzing data next.
Techniques for data analytics
Data analysts unlock the potential of data with several different methods, allowing them to process data and extract valuable information. Below are some of the most popular techniques used today.
Regression analysis estimates and models the relationships between sets of variables. One example is examining the number of Facebook friends for an author and the number of hardcovers her last book sold to predict a different variable, such as the success of her forthcoming trilogy.
Be careful about confusing relationships between variables with insights into causation. Regression analysis can determine if there is a relationship, but not the cause and effect behind it.
Also known as dimension reduction, factor analysis condenses the data of many variables into a “super-variable.” Take, for example, your health. A doctor might measure your height, weight, and blood pressure. They’ll then take all those factors and condense it into one all-encompassing review of your health. Factor analysis helps data analysts uncover variables that drive choices and behavior.
A cohort refers to group of people who share a characteristic. Cohort analysis involves segmenting customer data into smaller groups of people, or cohorts. This helps businesses watch trends and patterns that are specific to those people. Getting into the nitty gritty of how your customers think can be priceless for your business.
Monte Carlo simulations
Monte Carlo simulations were conceptualized after WWII to improve decision-making during chaotic times, and it was named after a casino district in Monaco because the modeling approach deals with chance. When faced with uncertain values, data analysts use multiple values, rather than a simple average.
Time series analysis
This technique helps data analysts make accurate forecasts for the future by measuring the same variable at different periods. For example, think about the amount of school supplies sold at certain times. A time series analysis would likely reveal a steep drop-off in June and July thanks to summer vacation.
Types of data analysis
Data analytics can be broken into four categories: descriptive, predictive, prescriptive, and diagnostic.
Descriptive analytics examines the past and describes what happened by looking at data sets for patterns and trends. This is the core of most businesses’ analytics. Until recently, analytics has primarily been a rearview mirror look at a business.
Predictive analytics, as the name implies, is used to make predictions and educated forecasts using historical data. Predictive analytics extrapolates trends into the future to understand possible outcomes. This is more complex than descriptive analytics because it uses probabilities for predictions instead of simply interpreting existing facts.
Use predictive analytics by first identifying what you want to predict and then by bringing existing data together to project possibilities to a specific date. Statistical modeling or machine learning are commonly used with predictive analytics. Through this, a business can protect itself from risk and set realistic goals for the future.
Prescriptive analytics builds on predictive analysis by helping you understand why future outcomes might happen. It uses data from sources like statistics, machine learning, and data mining to identify future outcomes and reveal the best choice. It’s the most advanced of the three because it provides actionable insights instead of raw data. It’s a complex process that uses many tools like algorithms, machine learning, and big data.
The last type of analytics we’ll talk about is diagnostic. This method uses data to investigate the past and explain why things happened the way they did. It’s a more complex version of descriptive analytics, that goes beyond looking at what happened to determine why it happened.
Diagnostic analytics helps businesses duplicate good outcomes and avoid negative ones. It applies data to figure out why something happened so you can develop better strategies without so much trial and error.
Data analytics tools
Gone are the days of managing data solely through spreadsheets and PowerPoint graphs. Thanks to new tools and software, analyzing and presenting data has greatly improved in recent years. Now data analysts use programming languages to manipulate databases, data visualization to distribute results through dashboards, and data mining for processing large sets of data. Data analytics tools now power data to be more than the sum of its parts.
So, what tools do data analysts use? Typically, they’re well-versed in software like Microsoft Excel and programming languages such as SQL, R, SAS, and Python. They use these tools to carry out data mining, statistical analysis, database management, and sharing their findings with stakeholders. Storytelling and data visualization are useful methods for communicating with stakeholders.
Get started with data analytics
Data analytics can explain how your business has performed — and why — to help make predictions about what to do in the future. When you’re ready to get started, look around your business and determine the areas you’d like to examine more closely. What are some persistent and troubling problems? Then, begin collecting data related to those areas.
Adobe can help
Adobe Analytics lets you mix, match, and analyze data from any point in the customer journey. With in-depth analysis, comprehensive reporting, and predictive intelligence, you get the insightful foundation you need to build better customer experiences. Backed by Adobe Sensei, Analytics uses AI to deliver predictive insights based on the full scope of your data.