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3 Easy Steps to Finding Patterns in Your Big Data

March 08, 2019

Data has become a priority for all types of businesses. Thanks to Artificial Intelligence (AI), companies are now able to capture and analyze their customers on a daily basis. Customer data is a focus area and customer’s behavior can also be captured and analyze by companies.

Analyzing large amounts of data is now an important part of your company’s growth. It’s fundamental to understand how structured or unstructured your data can be. Data with many cases offer greater statistical power, while data with higher complexity may lead to a higher false discovery rate. We all know that businesses gather more data on their consumers and their business operation than a couple of years ago.

Big data describes the large volume of data both structured and unstructured. But our goal here is to determine how to analyze those data and use them efficiently. Big data can be analyzed for insights that lead to better decisions and strategic business moves. As data sets grow bigger and more complex, it is important to extract valuable insight from your data.

Due to the use of unstructured big data in businesses, big data analytics tools and methods are in high demand. The use of data analytics by firms is improving every year.

Big-data-wave

These analytics includes the use of advanced procedures on the data obtained from different sources which can have different sizes. This data set come from multiple websites, video devices, online networks, and many other sources.

Historically, companies have been able to track a limited amount of information about their customers such as name, phone number and transactions made. Today, because of phones, e-commerce and the rise of social media, companies are now able to track major information and turn them into big data.

Here are 3 Easy Steps to Finding Patterns in Your Big Data

Step 1: Organize and evaluate your data:

By organizing and creating steps for your project, you will be able to distinguish relevant data. By prioritizing your data you will also be able to separate the noise from the signals and focus only on significant signals; the ones that are important for your business. When your data are hidden it can be difficult to spot their signals so it’s better to found out which data will be useful for your company.

The ability to evaluate and analyze your large-scale data is definitely challenging but it’s an exceptional opportunity and competitive advantage for your business.

Step 2: Using high-performance analytics:

As data sets grow larger, it can be difficult to choose the appropriate approach. When using machine learning methods, it is significant to distinguish between supervised and unsupervised method. Note that supervised methods usually build models that predict pre-specified annotation such as the response to a treatment for example. On the other hand, unsupervised methods don’t use external information but they rather try to find patterns in data sets. Distinguish both of them is very important, you need to know which one to use and when to use it. Hidden patterns and unexpected trends in data can be found by using a combination of techniques from database technologies to machine learning.

Another method will be Apache Hadoop. Apache Hadoop is an open source software framework which consists of two main components: a distributed processing framework named MapReduce and a distributed file system known as the Hadoop distributed file system, or HDFS[2]. One of the most important reason for using this framework in this project is to process a large amount of data and do its analysis which is not possible with other system. The right tool can significantly increase your analytics skills and help you save a lot of time.

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Step 3: Make it sustainable:

Before diving into understanding your data, it’s important to ask yourself the right questions. When using unstructured data or structured data to answer questions, it’s significant to make sure your answer can evolve over time. In this case, it will important to create a way to refresh your data so you can respond to the same questions and also answer new questions. It’s a good way to handle changes when it happens.

Data turns to big data when its volume, velocity, or variety go beyond the abilities of the IT operational systems to gather, store, analyze, and process it. Most of the organizations are capable of handling vast amount of unstructured data using varied tools and equipments but with the rapidly growing volume and fast flood of data, they do not have the capability of mining it and derive necessary insights in a well-timed way.

For many firms, consumer data are a good way to understand and meet their customers’ demands. By analyzing their customer behavior, they will be able to modify or adopt new strategies to better suit the current market. In addition, they want to redefine their marketing strategy. This method will help companies understand how consumers engage with their campaigns. Finally, according to data scientists, some companies use their consumer data to secure sensitive information. This is the case for a lot of  banks and jobs related to government. They will use different methods such as voice recognition data to authorize the user to access their information and protect them from fraudulent attempts. Customer analytics helps turn big data sets into big value.

Business has now access to a quantity of new information about their customers including their reactions that can be found based on diverse stimuli. This is an opportunity to better understand the decision-making processes of their customers and make sure to satisfy their future needs. It’s important to narrow your search by focusing on which data are important for your business and how you can use them. It’s also significant to detect those signals and noises through analytics tools such as Artificial Intelligence.

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