Guide to Data Analytics

Data Analytics has become a buzzword in the last few years. Data Analytics is the process of extracting information from large amounts of data. It covers many different aspects such as marketing effectiveness, business intelligence, predictive analysis, and more. 

Data analytics are used to extract useful insights from big data sets and can be extremely useful in the world of marketing. You should use it to find out more about your target audience and learn from their experiences.

Data analytics has become an important part of many organizations today due to the benefits of its accurate application. It plays a vital role in improving operational efficiency, enhancing productivity, and getting the results you expect from your marketing campaigns. There are so many benefits to analytics and taking time to learn from your data. Let’s learn more about it.

What’s the difference between Data Collection and Analytics? 

This is important to understand, especially if you are hiring staff to help you make better decisions for your business. 

In short, Data Collection is only the act of finding data points and organizing them so that they can be neatly viewed on an excel sheet or dashboard view. Analytics is the practice of analyzing data with the purpose of understanding what is causing the changes and building action plans for how to get desired results.  

If you hire a Data Collection Assistant, don’t expect them to tell you what the data means. If you hire a Data Analyst, be prepared to get a higher return on your investment. The rate for good data analysts is usually higher than Data Collection Assistants, but the investment is worth it. 

Data Analysts can do a lot, so you need to know what kind of data analytics you want them to perform, and how you want the findings presented to you. 

Here’s more on types of data analytics you can expect from an analyst. 

Types of Analytics by Purpose: 

Diagnostic analytics uses statistics to diagnose problems in systems. Diagnostic analysis helps find out why something went wrong by looking at all available evidence. 

A common use case for diagnostic analytics is finding errors in software applications. When diagnosing issues, we look at logs, metrics, and system states. We try to understand the problem by analyzing the symptoms rather than just focusing on one piece of the puzzle.

Descriptive Analytics

Descriptive analytics provides descriptive reports summarizing data. Descriptive analytics helps us answer questions like “What happened?” or “How did things change?”. 

Descriptive analytics often requires aggregating multiple pieces of data together. For instance, when reporting on website usage, we need to combine page views, time spent on each page, number of visitors per day, and more.

These are the different uses of analytics, but there are different types of data analytics too. Let’s dive into those now.

Types of Data Analytics by Method:

The main difference between the types of analytics is the amount of structure. There’s unstructured data analytics, semi-structured data analytics, and structured data analytics.

Structured Data Analytics

This is the process of analyzing a set of well-defined facts or events that have been collected in an organized manner. Examples include customer relationship management systems, enterprise resource planning, supply chain management. 

These types of applications collect information from various sources such as sales transactions, financial statements, inventory levels, etc. The main goal of this analysis is typically to make better business decisions by using these facts to predict future trends.

Semi-Structured Data Analytics

This refers specifically to data that has some sort of organization, but not enough to fit into one of the other categories above. For example, email messages may contain headers indicating who sent them, when they were received, what was discussed, and more. 

Semi-structured data can also refer to data that does not follow a strict schema. In many cases, this data type will still need to undergo preprocessing before being used for analysis.

Unstructured Data Analytics

This type refers to any kind of data where there is no formal structure for organizing it. This includes text documents, images, audio files, video clips, web pages, social media posts, sensor readings, etc. 

Unstructured data can be analyzed with techniques like natural language processing, machine learning, sentiment analysis, topic modelling, clustering, anomaly detection, among others. Now that we’ve gone over the types of analytics now to get you started you’ll need to know the tools to use.

Business Analytics Tools

Now that we’ve gone over the types of analytics now to get you started you’ll need to know the tools to use. Below you will find the most well-known tools for analytics and reporting.

• Excel: A spreadsheet program designed primarily for creating reports but can perform some basic statistical calculations. It is hard to avoid Excel in the world of business.

It has a simple user interface, and it’s easy to learn, so you don’t need any programming skills to use this software plus you can create charts with up to 100 columns and 200 rows of data in each column, add images, and create charts.

• Tableau Software, Inc: is a leading provider of interactive visual analysis software and services that help people see and understand data. It provides an intuitive interface to analyze all types of data in one place. The biggest benefit of Tableau is that the reports and charts you can make are fantastic.

• SAS Enterprise Miner: An integrated suite of analytic functions available through a graphical user interface. Includes modules for descriptive statistics, regression modelling, time series analysis, clustering, classification, text mining, visualization, and much more.

Great for analytically looking through research and surveys.

• Apache Spark: It is a fast and general-purpose cluster computing framework. It provides an abstraction of the underlying data storage, which can be either distributed file systems or databases such as HBase, Cassandra, MongoDB, etc.

Their DataDirect program develops a new programming model that would allow users to write applications using familiar SQL syntax on top of large datasets stored across clusters of commodity hardware.

Marketing Analytics Tools

Marketers need a whole different set of data and analytics tools. If you need to get analytics on how well your marketing is performing, you’ll need to implement these tools: 

• Google Analytics: Will provide statistics directly from Google
• Google Search Console: Helps with keywords and if there are any issues to troubleshoot.
• Social Media Management Tools: Allow you to watch over several platforms and metrics from one location.
• Social Listening Tools: Can help with monitoring engagement and what platform users are saying.
• CRM [Customer Relationship Management] Tools: Allow you to store customer information and record problems as they arise.
• Email Marketing Tools: Allow you to send, create, and optimize email campaigns.

Wrap Up

Data Analytics has become increasingly important as businesses have begun using it more often to learn more about their audiences and building reports. As a result, many companies now need skilled professionals who understand how to use this technology effectively. You can find out how we can help your company grow, giving you expert knowledge and action.

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