Harnessing Big Data for Business Growth: A Better Way to Grow

Almost every large company or business gathers their user’s data and uses it to improve their products and services. Google collects user’s data to match queries with useful results. Spotify uses it to make personalized playlists. Netflix does this to analyze viewing patterns and preferences.

It’s not just the large companies using big data. Even local companies like Xfinity, spanning over 41 states, use it to personalize customers experience by targeting them with ads. find out about the geographic locations of potential customers in order to target them with ads.

But, raw data is pretty much useless on its own. However, with a proper strategy, you can get a lot of useful information from it. In this article, we’ll take a look at how you can harness the power of big data to skyrocket your business.

Understanding Big Data

Big Data is data that’s too complex or large to be handled by traditional processing methods. It’s known for the three Vs. These are Velocity, Variety, and Volume.

Velocity is the requirement to process efficiently and quickly, Variety refers to a range of formats, and Volume is the size of the data. 

Data is only valuable if it can be understood and protected. Otherwise, what’s the point, right?

The goal of big data is to provide real-time insights that you can use to improve your services. Real-time processing is one of the biggest goals for companies that attempt to deliver value to customers seamlessly and consistently. This will allow them to cut costs, discover newer ways to improve profits, and reach out to new customers.

The Benefits of Big Data for Businesses

Now that we have a grasp on the concept of big data, let’s take a look at how it can benefit businesses.

• Data-Driven Decision Making (DDDM)

Basically, data-driven decision-making uses data to find patterns and insights to use in making business decisions. This can help you increase sales and profits, optimize operations, and improve your team’s performance. 

To get the data, you can do several things. These include creating surveys, getting responses from them, and using user testing tools that allow you to observe how your customers use a service or product. This will help you make changes and improve your product, even before the user experiences a problem.

You can also use these insights to improve the quality of a product you’re going to launch in the market. Lastly, DDDM can help you analyze any shifts in the data to determine threats or opportunities.

• Operational Efficiency

Streamlining your business goals is something that every business or company strives to achieve. This can help them increase productivity while reducing costs.

Using analytics from big data can help you identify any inefficiencies in your business processes to optimize your supply chain and increase operational efficiency. When you implement data-driven solutions, you can get a lot of cost savings, all while maintaining or even improving the quality of the service or product.

• Market Trends and Competitive Analysis

Market analysis and competitor analysis involve analyzing the trends, dynamics, and needs of the market to make an informed business decision.

By analyzing the market, you can use comprehensive insights to make an informed decision. Using big data can revolutionize the process by providing customer insights about their brand perception and their sentiment.

You can also use social media to get these statistics, along with customer surveys and feedback forms. By doing this, businesses and companies can get insights into customer behavior and their preferences to offer a better product or service.

Big Data Tools and Technologies

Now that we know about its benefits, let’s take a look at some of the big data tools and technologies that can be used for business intelligence.

Data Analytics Platforms

Big data analytics platforms help you wrangle the huge amount of information you have. It allows you to store it in a manner where it’s organized in an understandable way to provide useful insights. Some of the platforms you can use are:

• Hadoop: This scalable and cost-effective big data processing platform is an open-source framework that allows you to store and process big data. The software is designed to manage and store massive amounts of data. It uses MapReduce, which is a programming model used to process and assess large parallel datasets.
• Spark: This fast and efficient processing engine provides exceptional computational performance. Its programming model uses Resilient Distributed Datasets (RDDs), which support parallel processing as well as programming languages like Java, Python, and Scala, which makes it easier for developers to create big data applications.
• Tableau: This tool uses a drag-and-drop interface to allow users to create fully interactive dashboards to get insights about data. It can also be integrated with multiple sources of data to provide you with a view of critical KPIs. Navigating the software can be a little hard for people who don’t have an analytical background.

Data Warehousing

Data warehouses act as a repository for storing and assessing information, which receive data from several sources regularly including transactional systems, databases as well and other sources.

These data warehouses are centralized storage systems that allow you to store, analyze, and interpret the data for better decision-making. The data in these storages are non-volatile, integrated, and subject-oriented.

This means that the data provides topic-wise information, rather than the overall processes of a business. The data is integrated from multiple sources into a consistent format, and once the data is entered, it becomes read-only.

Data Visualization Tools

Data visualization tools can help you get various insights into the data you collect. Some of the tools that allow you to do this are:

• Google Charts: It’s one of the most user-friendly tools you can use for visualizing large data sets. Google Charts holds a variety of chart galleries, ranging from simple line graphs to complex hierarchical charts. It can render the charts in SVG/HTML5 format and supports most modern browsers.
• Data-Driven Document (D3): It’s a Javascript library that can be used for visualizing big data in any way you want. It’s not a tool like others, so you need to have a good grasp of Javascript to shape the collected data. The collected data can be rendered using SVG, HTML, and CSS.
• Canvas: Canvas.js is another Javascript library that has a simple API design and several themes you can use to customize the interface. It’s more convenient and faster to use compared to other Flash or SVG charts and can also be used on smartphones.

Implementing a Big Data Strategy

Big data is worthless without a strategy. Since it is a valuable asset, it’s important to use it the right way. While your strategy should vary based on your business goals, there are some things you can do to use your collected data the right way.

• Identify an Opportunity

A good strategy is to import the data and integrate the source of your data with external communities. Make sure to develop metadata and semantics to cluster, classify, and identify the trends in your data.

Conceptualize the insights of your data to extract valuable data such as simulation, associations, correlation, regression, segmentation, predictive, and trending.

• Define a Big Data Architecture

Big data architecture keeps evolving, and every day, the cost to develop a reliable and scalable platform keeps becoming lower.

The architecture of big data can vary by industry, the type of data, and the business problem. Focus on how to acquire data, its format, and structure, how frequently you can get it, its size and nature.

Consider the infrastructure, such as where you’re going to store it. The cloud can help you with real-time scalability whereas local data can be a bit hard to manage. Security of the data is one of the biggest concerns in big data, since compliance, privacy concerns, and regulations play a huge part.

• Develop Big Data Analytics

Since big data applications can vary based on the industry, here are some of the key trends for building big data analytics:

• Identifying new revenue streams
• Improving customer experience
• Organizing agile marketing and campaign
• Implementing fraud prevention and detection
• Regulating law enforcement and security intelligence
• Managing research, plans, and development

Bottom Line: The Future of Big Data in Business

Big data is already being used by big companies to understand the needs of their customers better and provide even better products and services.

In 2024 and beyond, we can expect to see some major changes in big data. With Machine Learning and Artificial Intelligence being in the limelight now, it’s sure to change the landscape. Now, it will improve the capabilities of big data beyond just self-driving cars, fraud detection, and customer experience.

With a focus on neural engines in hardware, we can expect to see improvements in system performance as well as decreasing storage costs and latency.