The digital era has created a huge amount of data available online. It’s estimated that the global data stream will grow to 175 zettabytes by 2025 and that the big data analytics market will hit the 103 billion USD mark by 2023. This is where things get exciting for business owners. They now have high volumes and a variety of data at their disposal to inform their strategic and operational decisions. That’s why today, we will take a look at how big data is used in business.
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The large volume of data we generate every day is meaningless unless we crunch it with big data analytics and data science. In fact, big data will only keep getting bigger and more complex as the years go by. So you can choose to get up to speed with the statistics or run the risk of living under the rocks.
To help you understand the impact that big data has on business and how big data is used in business, we have rounded up some of the ways in which leading companies are using this technology to outperform the competition. But first, it’s essential for you to understand what big data is.
What is big data?
Big data in business is a term used to refer to the massive and complex sets of data that are generated and transmitted from a wide range of sources. These datasets are so voluminous that the classical data processing software can’t manage them. It’s often characterized by the three Vs:
This describes the size of the data. It plays an imperative role when determining the value of data. It’s also used to ascertain whether data can be considered big data or not. In today’s technology, data sets are hitting the larger bytes such as petabytes and terabytes.
This refers to the heterogeneous nature and sources of data. It can either be categorized as structured or unstructured. Structured data has been organized into a well-defined structure that makes it easy to be deciphered by machine learning algorithms. They include credit card numbers and addresses. On the other hand, unstructured data is information that doesn’t have a predefined model, and therefore cannot be processed and analyzed by conventional data tools and techniques. They include social media posts and mobile activity.
This refers to the speed at which data is generated, collected, and distributed across different platforms. Let’s say you’re running a marketing campaign, and you want to get insights into how people perceive your brand. How do you do it?
You can license some Twitter data to gain access to a constant stream of tweets and then perform a sentiment analysis on the data. This kind of Twitter data is called ‘the firehose’ because of the huge amounts of data produced in tweets. The higher the velocity rate, the faster you acquire the data, and the more valuable your data will be. Several factors affect the velocity of data. These include the number of people using the internet and the number of sensors present in an IoT-enabled device. High-velocity data should be processed with advanced tools such as algorithms and analytics to reveal in-depth information.
The big data concept has been around for years, as well as data science and data analytics. But before its first mention, businesses used more manual methods to uncover trends and gain insights from data. This involved capturing and examining numbers manually on a spreadsheet. Modern big data analytic systems are speedy and more efficient. They impact business operations in several important ways, regardless of your field of specialty or the size of your firm.
How is big data used in business?
DIALOGUE WITH CUSTOMERS
Brands nowadays can collect a massive amount of data across multiple touchpoints. This includes customer purchase history, product searches, and social media entries and comments. Through analysis of that data, they can create richer customer experiences, which translates to increased sales.
However, it’s easier said than done. Even the most prominent businesses around are yet to tap into the full potential of big data to enhance customer experiences. Suppose you’re collecting a tremendous amount of data only to use it for coupons or occasional discounts. In that case, you’re throwing away the opportunity of engaging in a real-time, one on one dialogue with your customers.
Here’s how you can use big data to take your customer service experiences a notch higher:
- Understand customer sentiments: These days, customers understand their priorities. They shop around to compare different options before making a purchase. Some even reach out to businesses and demand special treatment. And others want to be thanked for buying goods from a company. You can use big data to connect with your customers on an emotional level and get insights into how they feel about your brand. Analysis of both quantitative and qualitative feedback should help you offer more personalized services and run more charged marketing campaigns.
- Identify metrics that need improvement in the contact center: Metrics can give you insights into the experiences your customers are having while interacting with your brand. Some of the call center metrics and KPIs that you should track include call abandonment rate, call volume trends, average handle time (AHT), first call resolution (FCR), and average speed to answer (ASA). They help you identify the pain points in contact center practices and resolve them immediately.
A real example of a company that uses big data analytics to improve customer service experience is Facebook. You might have noticed how this social media platform creates short videos containing your old photos in an attempt to remind you of anniversaries, birthdays, or friendships. This is done with the help of big data. Facebook also assesses every piece of data to give you better services every time you log in.
CREATE NEW REVENUE STREAMS
Big data is intimately linked to big-money opportunities. Data analytics gives businesses the edge by helping them save money, gain a larger market share, and increase profit margins.
The idea of creating new revenue streams using big data is not tied down to one single vertical. But rather, it involves the evaluation of three key areas:
- Business performance according to key performance indicators (KPIs): KPIs such as product performance, average purchase value, and sales growth can help businesses grow their revenue. However, before you start tracking any KPI, you need to set clear business goals to determine the best way to track progress. Many business owners fail to achieve results because they focus on the wrong KPIs or on one type over others. For the greatest potential of revenue growth, you should track KPIs that are aligned to the customers’ needs and can be deployed into the most relevant channels.
- Analytic skills development: New reports highlight that 70% of employers in the United States prefer job applicants with data analytics and data science skills. They specifically look for candidates who can look at complex problems from different angles and develop the most effective and efficient solutions. Why? Data analysis and problem-solving skills bundled together are imperative to both an organization’s business and data side. It’s important to evaluate your business’s analytics, and insights need to help you unmask gaps and shed light on the kind of data you need to track.
- Decision-making: Tracking the right KPIs and enhancing the analytic capabilities of a company sums up to nothing without data-driven decision making(DDDM). DDDM helps brands gain insights from data and helps establish guidelines on pressing issues such as the kinds of projects to be funded. This makes businesses less vulnerable to risky decisions going wrong.
Big data means nothing unless an organization lays the groundwork to use it effectively to stay competitive. The first step of data monetization is finding its value within your company (by tracking important KPIs). From there, it’s relatively easy to use it to benefit your clients. If used correctly, big data can give you a personalized roadmap to uncovering new revenue streams.
NEW PRODUCT DEVELOPMENT
How do businesses come up with ideas, turn them into products/services, and choose which ones to introduce to the market? Many product managers say that designing products and services with the customers in mind is a good place to start. However, for the project to attain a positive outcome, they need to rely on data.
The era of big data has revolutionized the process of product development. Firms can now collect and use customer feedback to minimize the risk associated with launching a new product. The data can also be used to gain insights into how customers perceive a brand and re-develop the existing products to match their preferences.
According to Splunk, 91% of companies that put strategic emphasis on data can maintain their competitive edge in the market over the next few years. This success can be attributed to the development of products that fulfill customer desires. Through predictive analytics, businesses can foresee the performance of their products/services performance in the market and optimize their marketing strategies to attract customers.
Product development and service design is not a one-time project. It’s a continuous process that has multiple stages and follows a strict cycle as discussed below:
- Conception: This involves brainstorming and using customer feedback to develop ideas used to plan the development process.
- Development: This is the most critical stage of bringing a product/service into life. At this point, the ideas are polished and packaged into a viable project with the help of test groups and subject matter experts.
- Launch: As you introduce the new product/service into the market, you need to monitor the reaction of users closely. This will give you insights into how your product/service is likely to perform.
- Sustain: Once the product/service has been launched, you don’t leave it at that. You need to sustain its viability by keeping tabs on customers’ needs and preferences.
- Discontinue: After some time, you need to discontinue the product/service to introduce a new one to the audience. This marks the beginning of the whole process once again.
SUPPLY CHAIN MANAGEMENT
While the supply chain aims to streamline activities such as product delivery, cost savings, and quick service delivery, the presence of many manufacturers, vendors, and distribution channels adds to the complexity of the process, making data collection and analysis quite challenging.
However, big data provides big analytics for the supply chain to work with. This means greater insights, accuracy, and clarity for businesses. According to research, the supply chain big data analytics market will hit $9.28 billion by 2026. This is because more companies are beginning to realize the benefits of big data in identifying key insights to apply to their supply chain operations.
This information begs the question: Which processes of supply chain management benefits the most from big data?
- Inventory management: Big data has helped top online stores and big retailers to overcome several challenges. For example, managers can now forecast product demand and optimize their inventory to avoid wasting money on inventory and warehouse space that they do not need.
- Predict customer behaviors and usage patterns: Top telecom companies are using big data analytics and data science to analyze their clients’ habits and usage patterns. This enables them to come up with creative ways in which they can retain their subscribers. Vodafone, for example, is using big data analytics to forecast network growth and make plans for network expansions.
- Machine maintenance: Unexpected machine failures can cause losses to companies in terms of time and money. According to industry week, manufacturing industries lose about $50 billion each year to machine downtime. When combined with IoT devices, big data systems can detect and raise alarms on any irregularities in manufacturing.
- Order fulfillment and tracking: Efficient order delivery and traceability are essential for customer satisfaction and business productivity. Amazon, for example, can offer incredibly short delivery time and minute-by-minute tracking of orders. You can offer similar experiences by using up-to-date shipping information to optimize route deployment, item location, and delivery schedule. By doing so, you will also be able to cut the cost of delivery. So it’s a win-win situation.
Success isn’t all about how you run your business. Many social and economic factors play a crucial role in determining your accomplishments. Big data can help you identify and forecast external factors that pose a risk to your business. By incorporating predictive algorithms with data science technology, you can obtain real-time insight into the risks and develop a viable risk management strategy.
But how do you develop and implement a strategy that accommodates the wide scope of data you’re dealing with? It’s pretty simple. You need to collect internal data first to use it to gain insights into what will benefit your company. A proper analytics system should capture potential risk areas of weaknesses. To demonstrate this point, let’s look at some of how big data can be applied to manage risks in an organization:
- Prevention of fraud: Predictive analysis comes in handy when it comes to the detection of fraudulent activities such as money laundering. The large volumes of data collected from various sources can be used to monitor activities in various platforms and detect plans to engage in fraud before it happens. Governments and money lending institutions mainly use this approach.
- Operational risk in manufacturing: Big data can be used to assess important factors such as the dependability of suppliers. It’s also used to detect costly production defects by use of data analytics and sensor technology.
- Credit management: Credit can paralyze the operations of a business. You can mitigate this risk by analyzing data on historical spending patterns, airtime purchases, and other factors that may indicate misuse of money.
A good example of a brand that uses big data in business for risk management is Singapore’s UOB bank. Being a financial institution, it’s susceptible to incurring substantial financial losses. The bank recently tested a risk management system to calculate the time value at risk much faster. Initially, it would take 18 hours to calculate this kind of risk. But with the new system in place, the process takes a few minutes.
Final thoughts on big data in business
Big Data analytics has made a significant contribution to the success of many leading companies. By tracking the right KPIs, businesses can now do away with the guesswork. They make decisions based on tangible data. This guarantees accuracy. You don’t have to own a big business to use big data analytics. This technology is here for all businesses, and there’s no better time than now to utilize it for overall business success! And that’s our role. At Addepto, we help companies make the most of the data they process. See our big data consulting services to find out more.
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