Big data is a concept that is used to drive results for the analytics practice of any industry. It’s a collection of traditional and digital data that makes up large data sets and can be analyzed to reveal patterns, trends, and associations. As a result, big data allows for better business decisions, increased revenue, and decreased operating costs. Big data and its associated practices can completely transform businesses and/or create new ones.

To help you understand big data on a deeper level, let’s take a look at the four characteristics that help define it.

  • Volume: This is the scale of how much data there is and can help define whether or not your data is actually considered “big.”
  • Variety: Big data can come in many forms, including structured data, unstructured data, video, text, etc.
  • Velocity: This refers to the speed of how your data is generated and used. Most big data comes in at real-time.
  • Veracity: This is the quality of your data that is captured. You want data that is valuable so that you accurately analyze the results.

In today’s world, there’s data being generated from social media, log files, videos, voice messages, texts, machines, and more. Collecting all of this structured and unstructured data, both inside and outside of companies, and then leveraging that data to drive better business decisions can have a significant impact on your organization.

Let’s take a closer look at the ways big data can impact your business.

1. Big Data Helps Businesses Become More Data-Driven and Leverage Insights to Make Better Decisions.


By utilizing the correct big data platforms and technologies, analyzing a store of data to gain insights becomes possible. Due to big data having such large volumes and variety, often times businesses are able to uncover insights that were previously impossible to see.

For example, when big data is used properly, it promotes the ecommerce revolution by allowing an organization to target their customers with the right product at the right time. With an alarming increase in the number of in-store shoppers turning into online shoppers, big data can help you visualize and understand the behaviors and needs of your customers. These behaviors and needs can be analyzed through different channels, like social media apps. From there, you can spot ongoing trends that customize the user experience and lead to better decision-making within your business.

2. Big Data Enables Customer Intelligence and Customer Analytics.


Before big data, marketing was traditional mass marketing. Personalization and customer experience were not central to the goals in marketing. Now, big data has enabled a new paradigm in customer intelligence and analytics by no longer making assumptions about who a customer is (i.e., interests, desires, needs, etc.) but instead being able to understand what the customer actually does. By leveraging the data generated by customers, the ability to generate insights around what actions they take on their journey, along with their intent, becomes a reality.

These quantitative insights on actual customer behavior allow a business to develop a deep understanding of who their customers are, what they want and need, and when they need to be served. Some examples of customer analytics and intelligence include:

  • Personalized recommendations for cross and up-sells from user generated preference data.
  • Product development based on customer journeys and pain points .
  • Personalized end-to-end user journeys using customer segmentations built from user-generated data.

3. Big Data Helps Improve Operational Efficiencies.


Big data truly promotes lean management when it comes to running operational structures within businesses. With big data, you can optimize your supply chains by using things like smart labels or sensors.

For example, John Deere placed sensors in every one of their tractors. The incoming data from those sensors was used to predict whether a tractor would fail or not. Collecting large amounts of streaming data can be used for analysis and to build predictive models that help to streamline operational processes and improve overall business efficiency.

Additionally, companies like UPS take advantage of big data generated from smart labels. From the analysis and tracking of these labels, UPS can deliver information such as when a package is estimated to arrive. This allows UPS to optimize the supply-chain flow of the company.

4. Big Data Improves the Product or Service Offering.


By observing and analyzing your company’s data trends from different channels, such as social media, customer reviews, and click stream data from a website or mobile application, you are able to deliver insights that improve what your business offers, whether that is a product, service, or even both.

This practice of big data analytics is a large driver of the ecommerce revolution. For example, if your goal is to increase sales to customers, you may want to understand what they like and don’t like about your current products. Let’s say your company sells clothing. By using data generated from users on social media, a sentiment analysis can be conducted to determine what they like and don’t like about specific pieces of clothing. By combining this data with demographic data, you can create segmentations of customers to better understand how certain groups of customers feel about products, thereby guiding product development decisions. This ultimately allows you to create better offerings to your customers.

5. Big Data Helps Increase Revenue and Reduce Cost.


With an improved product/service or customer experience, this leads to an increase in revenue for your business and helps reduce costs. Using the data gathered, along with advanced analytics techniques, machine learning, and data science, organizations are able to build solutions that improve acquisition, improve retention and growth, provide a better user experience, create new revenue streams, and increase LTV. Below are some examples of systems that can be developed from big data solutions.

  • Advanced product/customer analytics that identify a market gap or need of customers that is not being met, leading to new products or services and new revenue streams.
  • Look-alike models and lead scoring models to decrease acquisition cost, increase acquisition of high value customers, increase conversion rates, and increase overall acquisition efficiency.
  • Predictive churn models to assess risk of current customers churning. This allows intelligent intervention resulting in decreased churn and increased LTV.
  • Recommendation engines based on past user purchasing and browsing patterns can greatly increase average purchase value through cross selling, upsetting, and better product-customer match.
  • Predictive and prescriptive analytics can reduce physical device down time, allowing for planned maintenance instead of emergency maintenance, and increase per device output, resulting in increased revenue and reduced costs.

 

Summary

A rapid growth in electronic and internet connected devices has generated a massive increase in data generation. From this growth of data, a need developed to collect, manage, and process this data. These are big data technologies, and they can help companies gain a massive competitive edge. Insight generation from big data, as well as predictive modeling and data-powered services, drastically increase the likelihood of success for your business. Whether that success is achieved through reducing costs, increasing revenue, improving the customer experience, or a combination of all three, all depends on what goals you have set for your business. Big data and big data technologies give your business the advantage.

About the author

Jefferson Duggan

New eBook Release: A Guide to User Journey Analysis

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