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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[1] 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.
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.
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.
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:
A real example of a company that uses big data analytics to improve customer service experience is Facebook[2]. 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.
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:
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.
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[4], 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:
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[5]. 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?
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:
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.
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.
Big data in business refers to vast and intricate sets of data generated from diverse sources, surpassing the capacity of traditional data processing software. It’s characterized by three Vs: Volume (size of data), Variety (heterogeneous sources), and Velocity (speed of data generation and distribution).
Businesses leverage big data to collect and analyze customer data from various touchpoints, including purchase history, social media interactions, and product searches. This analysis allows them to understand customer sentiments, personalize services, and improve customer support processes.
Big data analytics aids in tracking key performance indicators (KPIs), developing analytic skills, and facilitating data-driven decision-making. By aligning KPIs with customer needs, businesses can identify opportunities for revenue growth and optimize their operations effectively.
Big data revolutionizes the product development process by enabling companies to collect and utilize customer feedback to minimize risks associated with launching new products. Predictive analytics helps in forecasting product performance and optimizing marketing strategies.
Big data provides advanced analytics for supply chain operations, improving inventory management, predicting customer behaviors, optimizing machine maintenance, and enhancing order fulfillment processes. It enables businesses to streamline operations, reduce costs, and enhance customer satisfaction.
6. How can big data be utilized for risk analysis and management in businesses?
Big data facilitates the detection and prevention of fraudulent activities, assessment of operational risks, and management of credit risks. By leveraging predictive algorithms and data science technologies, businesses can obtain real-time insights into potential risks and develop effective risk management strategies.
Companies like Facebook, Vodafone, Amazon, and UOB Bank utilize big data analytics to enhance customer experiences, optimize operations, and mitigate risks, thereby maintaining a competitive edge in their respective industries.
Big data analytics is accessible to businesses of all sizes, enabling them to make data-driven decisions, enhance customer experiences, optimize operations, and identify new revenue streams. With the right tools and strategies, businesses can harness the power of big data to achieve their goals and stay competitive in today’s market.
This article is an updated version of the publication from Oct 4, 2021.
References
[1] Networkworld.com IDC: Expect 175 zettabytes of data worldwide by 2025. URL: https://www.networkworld.com/article/3325397/idc-expect-175-zettabytes-of-data-worldwide-by-2025.html. Accessed Sep 30, 2021.
[2] Analyticssteps.com. How Facebook uses Big Data to enhance customer experience. URL: https://www.analyticssteps.com/blogs/how-facebook-uses-big-data-enhance-customer-experience. Accessed Sep 30, 2021.
[3] Amstat.org. New Report Highlights Growing Demand for Data Science, Analytics Talent. URL: https://www.amstat.org/asa/News/New-Report-Highlights-Growing-Demand-for-Data-Science-Analytics-Talent.aspx. Accessed Sep 30, 2021.
[4] Splunk.com. Big Data Generates a Big Return on Investment for Splunk Customers. URL: https://www.splunk.com/en_us/newsroom/press-releases/2013/big-data-generates-a-big-return-on-investment-for-splunk-customers.html. Accessed Sep 30, 2021.
[5] MordorIntelligence.com. SUPPLY CHAIN BIG DATA ANALYTICS MARKET – GROWTH, TRENDS, COVID-19 IMPACT, AND FORECASTS (2021 – 2026). URL: https://www.mordorintelligence.com/industry-reports/global-supply-chain-big-data-analytics-market-industry. Accessed Sep 30, 2021.
[6] WSJ.com. How manufacturers can achieve top quartile performance. URL: https://partners.wsj.com/emerson/unlocking-performance/how-manufacturers-can-achieve-top-quartile-performance/. Accessed Sep 30, 2021.
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