Big Data for Business – What It Is and Why It Matters

A woman and a man discussing big data for business

Big data technological innovation will completely reshape how processes are run in many industries. 

Businesses are just starting to catch on to the potential of big data. But some companies are already running with it while others lag behind.

Big data helps organizations by providing them with information that they can use to make decisions. 

Big data can tell companies how their customers are feeling, what they like, and what they want. It’s also used in analyzing trends, which can help businesses decide where to open new stores, or how to spend their marketing budgets.

That’s why more and more companies are trying to get their hands on big data to drive big decisions. 

A survey of IT and business executives from 85 large companies conducted by NewVantage revealed that 91.9% were accelerating the pace of their investments in big data and related artificial intelligence initiatives, while 96% reported that they got successful results. 

However, even big organizations are struggling to take full advantage of big data environments. According to an annual survey published in 2021, Only 39.3% of survey respondents said their companies managed data as a business asset. 

While only 24% said they had established a data-driven organization.

Therefore, here’s the crux. We are going to help the companies — small and large — to up their big data game. 

We will define big data, show you why it matters, talk about why it’s important for businesses, give some examples & use cases, and more. Make sure you stick through the end. 

What Is Big Data – in Simple Terms 

Big data refers to a collection of data sets so large and complex that it becomes difficult to process using traditional data processing applications.

The definition of big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three V’s which is expanded today into 5V’s:

1. Volume – Big data has no minimum size level, but it usually involves a large amount of data — terabytes or more.

2. Variety – big data encompasses a wide range of data types that can be processed and stored in the same system.

3. Velocity – Big data sets frequently include real-time data and other information that is generated and updated at a rapid pace.

4. Veracity – This refers to how accurate and trustworthy various data sets are, which must be determined upfront.

5. Value – Organizations must also understand the business value that big data sets can provide in order to use them effectively.

Why Big Data Matters So Much to the Businesses 

Big data is all about making sense of vast amounts of information generated by people, organizations, and devices. To do so, businesses need a way to store and analyze large amounts of data.

Big data is an extremely valuable asset for a business if the business knows what to do with it. The key aspect of big data is its volume, velocity, and variety. 

Meaning, it comes in large quantities at a high speed from different sources, such as social media, smartphones, or transactional systems. Big data can provide business analytics tools with an abundance of information to make better decisions. 

Using Big Data for business to make better decisions using analytical reports

Big data can help improve customer service as well as operational efficiency and profits for a business. 

Many companies use big data to enhance their marketing efforts by creating customized ads based on customer interests gleaned from analysis of their online behavior. 

Other uses include predictive maintenance for manufacturing plants and financial services firms that can use big data to offer risk management services to their customers. 

Big data could also help detect fraud in real-time. And also provide a better understanding of how customers interact with a brand’s website or product.

Real-world Examples of Big Data and Use Cases 

Here are some recent big data examples that will amaze you:

  1. The Obama 2012 presidential campaign was able to achieve a 95% accuracy rate with their fundraising efforts by using predictive analytics. This allowed them to target voters who were likely to give money.
  1. Walgreens has begun using patient prescription records for research into cancer treatments –  Walgreens’ massive database gives researchers access to the medical histories of more than 8 million people.
  1. Google Flu Trends, which analyzes search engine queries for flu-related terms, has been able to predict the spread of flu outbreaks with 91% accuracy.
  1. A piece of software called Timeliner has been able to reconstruct the past 200 years of Earth’s history by analyzing hundreds of thousands of images

Now, these were some big examples but the story is the same. That is — collecting a large amount of data to reach your goals. Your goals can be:

  • Using near-real-time data to make decisions
  • Detecting outliers that may obscure the real story
  • Analyzing seasonal trends to scale up and down staffing
  • Increasing efficiency by utilizing monitoring data to identify process bottleneck

Common Big Data Technologies and Tools 

The big data era began in 2006, with the release of the Hadoop distributed processing framework. Hadoop is an open-source framework for storing and processing large amounts of data on a distributed cluster of machines.

Naturally, a large number of technologies were built around Hadoop including the Spark data processing engine. 

While Hadoop’s built-in MapReduce processing engine has been partly overtaken by Spark and other newer technologies, other Hadoop components are still used by many organizations. 

The new common tech and tools for big data environments can be divided into the following categories:

  • Non-relational database management systems, such as Hadoop Distributed File System (HDFS). These systems are often called “NoSQL” databases because they don’t use the relational model.
  • Non-relational data warehouse management systems, such as Apache Hadoop Hive and Apache HBase.
  • New open-source analytic platforms, such as Cloudera Impala, Apache Drill, and Apache Spark. Although not tied to a specific underlying technology or big data structure, these platforms offer an alternative to proprietary analytic engines.
  • SQL query engines. Examples include Drill, Hive, Presto, and Trino.
  • Data lake and data warehouse platforms. Examples include Amazon Redshift, Delta Lake, Google BigQuery, Kylin, and Snowflake.

Best Practices for Big Data and Analytics

Big data can only be as good as the tools you need to analyze and manage it. Here are some best practices to get the most out of your big data initiatives:

  1. Have a plan before you collect the data.
  2. Have a governance strategy in place.
  3. Establish who is responsible for maintaining/updating the data, and how often it will be updated.
  4. Create an effective user interface that allows users to access all the information they need, including analytical reports and dashboards that can be customized by business unit.
  5. Make sure you have a way of measuring and tracking changes in the data. Especially critical when you need to determine whether or not there has been a security breach.
  6. Make sure you have a backup and disaster recovery plan in place so that if something does go wrong, your company is protected against the loss of critical business data.

1. A Big Change in Traditional Databases

As the world is evolving, we are all producing unstructured data by using IoT, social media, sensors, and other systems that generate huge data. 

This is where NoSQL databases like MongoDB and Cassandra come into action. They will be adopted by more vendors and graph databases like Neo4j will see more attraction.

2. We Will Still Be Seeing Hadoop but With New Features

A popular big data tool, Hadoop, will have advanced security features to take on the enterprise-level lead. When Hadoop’s security projects, such as Sentry and Rhino, are stable, Hadoop will be flexible enough to work in more sectors. 

And businesses will be able to leverage their capabilities without fear of security breaches.

3. Demand for More Cloud Solutions 

AI and IoT enable faster data generation, which benefits businesses that use them wisely. IoT applications will require scalable cloud-based solutions to manage an even greater volume of data. 

Many organizations have already adopted Hadoop on Cloud, and the rest should follow suit in order to stay competitive in the market. 

4. Real-time Speed Will Be Crucial 

In the near future, companies will have the data sources and the ability to store and process big data. 

The deciding factor on their performance will be the speed at which they can deliver analytics solutions. 

The processing capabilities of big data technologies like Spark, Storm, Kafka are being fine-tuned with speed in mind, and companies will soon advance using this real-time feature. Read more about such trends in detail: The Future Trends for Big Data

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