Big data refers to large data sets that are difficult to manage or analyze with traditional data processing tools. In today’s world, the amount of data is growing exponentially, especially with technological advancements such as social media, AI, and IoT. These tools are continuously improving how data is harnessed and stored, making it more accessible than ever before. Big data has enormously impacted how businesses operate, especially when it comes to decision-making.
This is because, with this data, companies can know more about their consumers than ever before, giving them a world of information they can use to make better decisions about their business. In this article, I’ll look deeper at how this data explosion has changed how businesses operate and make decisions. Some subjects I’ll cover here include what exactly big data is, the evolution of data in business, case studies of successful data implementation, and the key areas where big data influences decision-making, amongst other subjects. Let’s get started!
Understanding Big Data
I’ll start this article by defining big data and its evolution in business. Keep reading to learn more.
What is Big Data?
As mentioned, big data is a term used to describe the complex and huge volumes of data that businesses generate and collect daily. While the term may have different interpretations depending on who you ask, it is generally defined in terms of volume, velocity, and variety. Let’s have a closer look at what these terms refer to:
● Volume: The amount of data that’s stored and collected from various devices, such as transactions and social media.
● Velocity: The speed at which big data is collected and generated. Data is often generated in real-time, which would require businesses to process it ASAP, as I’ll look at later on.
● Variety: The different types of data collected. This can range from structured data (like numbers and dates) to unstructured data (like social media posts or emails).
Apart from Volume, Velocity, and Variety, there are two other ‘V’s’ that can be used to describe big data.
● Veracity: Veracity refers to the quality of the data collected. The higher the veracity of the data, the more accurate it is.
● Value: The usefulness of the collected data; a reference to what businesses can do with the collected information.
Data can be categorized into three main types: structured, unstructured, and semi-structured. Let’s have a quick look at what these mean:
● Structured Data: This type of data can be organized in spreadsheets and databases, making it simple for traditional analytics tools to analyze it. This data type is often used for customer relations management and financial analysis, for example.
● Unstructured Data: Any data that does not have a predefined structure. Platforms such as Facebook and Instagram can generate hundreds of terabytes of data daily. Unstructured data can also include information from site surveys and emails. This is used by businesses to get more information about how their customers perceive their brand, for example.
● Semi-structured Data: As the name implies, this type of data is more organized than unstructured data, but it’s still not easy to analyze. It isn’t organized in spreadsheets, but it still might have some organizational elements. This data type is sometimes used for web analytics and content management systems.
The Evolution of Data in Business
It’s safe to say that we now have access to more data than ever before, but businesses have always collected data on their customers, as this is a key way to know what their customers want and need. Merchants even tracked which clients bought what and then used that data to help them make better business decisions. At first, this was merely done by observation and tracked by hand; however, once the digital world started to open up, all this information was kept electronically.
When data was recorded manually, this was quite time-consuming and costly. However, when the digital world opened up, companies began using more advanced tools to track specific consumer behaviors, leading to data-driven marketing. With the advent of technologies such as cloud computing and data analytics tools, the way data is organized has been revolutionised. Businesses can make decisions based on actual evidence rather than intuition.
Cloud computing, for example, has made it much easier for businesses to store large amounts of data online. Indeed, instead of spending money on equipment and infrastructure, cloud services handle all of that at a much lower price. Data analytics tools, such as Hadoop and Spark, have also been key in helping businesses collect and organize data from myriad sources, helping them understand their businesses better and make more informed decisions.
The Role of Big Data in Business Decision-Making
In this section, I’ll take a closer look at the role of big data when it comes to decision-making, all of which can help improve the efficiency of businesses.
Data-Driven Decision Making
Data-Driven Decision Making, or DDDM for short, refers to using big data from big data sources to make more informed and guided business decisions. When using big data, companies can increase the accuracy of their decisions, reduce risk, and exchange strategic planning, as outlined below:
● Increased Accuracy: DDDM allows businesses to make decisions based on actual data, rather than relying on assumptions. This leads to more accurate decisions.
● Reduced Risk: Analyzing past and real-time data makes it easier for businesses to anticipate potential problems that might arise during their operations. Again, this helps them make more informed decisions, which in turn can reduce costly mistakes.
● Enhanced Strategic Planning: Using predictive analytics can help businesses in many ways. For example, by anticipating trends, big data can help businesses take action before challenges arise and learn about the impact of different scenarios.
Case Studies of Successful Big Data Implementation
Now, let’s look at the popular coffeehouse Starbucks, which is using big data for its decision-making:
● Starbucks: Starbucks is a great example of a company using big data to enhance its customer experience. The launch of their mobile and rewards app allowed Starbucks to gather a huge amount of data about their clients, such as what their favourite type of coffee is, what time they buy it, and where they get it from. This allows Starbucks to give its customers a highly personalised experience.
Let’s take an example. When a long-term client visits a new Starbucks location, the store’s point of sale system can identify the customer and give them their preferred order. Other than that, based on previous orders they’ve made, the app can recommend new products that they’re more likely to enjoy. This way, this system can predict what their client might want next. Starbucks also uses geo-location data from the app to send tailored promotions when customers are near a specific store, increasing the likelihood of them visiting the shop, which, in turn, boosts sales.
Key Areas Where Big Data Influences Decision-Making
What other areas does big data influence when it comes to decision-making? Keep reading to learn more.
Customer Insights and Personalization
One of the main reasons big data is so crucial for businesses is that it allows them to understand customer behaviour, preferences, and trends. But how exactly do companies do this? Let’s look at some popular tools:
● Customer Segmentation: By segmenting their customers based on factors like location, behavior, and purchase patterns, businesses can create more targeted and personalized marketing campaigns. This way, customers will be exposed to ads that are more likely to resonate with them.
● Predictive Analytics: This branch of analytics makes predictions about future outcomes. This way, businesses can use this data to create personalised experiences for their clients, which are, in turn, likely to make them more satisfied, boosting their loyalty.
● Sentiment Analysis: Also referred to as opinion mining, sentiment analysis is a tool that allows businesses to analyze the emotions and attitudes expressed in large datasets, such as social media and reviews. This way, sentiment analysis can enable brands to gain insights into public opinion, the perception of their brand, and whether their customers are satisfied and then make decisions accordingly.
Operational Efficiency and Cost Reduction
Big data tracking can greatly help supply chains, offering real-time visibility, predictive analytics, and, ultimately, better decision-making. One company that uses big data for these reasons is GE Power, whose turbines and generators contribute to 30 percent of the whole world’s electricity. In the last couple of years, this multinational conglomerate has been using big data to improve predictive maintenance, detecting any problems that might need maintenance before they happen and preventing downtime, which would increase their costs.
Amazon also uses big data for similar reasons. To ensure maximum efficiency, Amazon utilizes supply chain optimization, which in turn allows Amazon to carry out orders quickly by connecting with manufacturers and using data to track their inventories. Big data is also used to find the closest warehouse to a customer to reduce shipping costs and improve delivery speed, which might encourage users to keep shopping with them.
Risk Management and Predictive Analysis
Risk management refers to creating strategies and rules that an organization employs to reduce risks. Similarly, predictive analysis involves using big data to predict future trends. Both are essential to making informed, proactive decisions. A good example of a company that utilizes risk management and predictive analysis is Amazon. Amazon uses big data to optimize its supply chains. With big data, Amazon can deliver its products fast, and this is done through its licensed anticipatory delivery model.
Amazon uses their customers’ data, which they retrieve from their purchases, to predict what their users will buy and when, and then they’ll also make a guess regarding when they will require them. This predictive approach minimizes the risk of stockouts or delays, ensuring that items are readily available for prompt delivery. In this way, Amazon improves customer satisfaction and mitigates supply chain disruptions, optimizing both cost and delivery speed.
Product Development and Innovation
Many big companies use big data as it drives innovation by providing insights into market trends and consumer needs. With big data, companies can ensure that the products they are developing are what their consumers are looking for. They can do this by retrieving information from social media, customer reviews, and purchase history. This, in turn, helps ensure that the launch of a product is a success when it is available in the market. One example of a brand utilizing big data for product development is Netflix.
With the power of data and predictive analytics, Netflix was able to learn more about what their consumers would be responsive to, ensuring that the new shows they launch on their site are successful. Starbucks is another great example to mention here. There are many cases of Starbucks shops being opened very close to each other, but none suffer. The reason for this is before it opened, Starbucks used big data to determine the success of these branches, taking data on location and customer behavior. They even do this when releasing new products, ensuring that these are products that their customers would want.
Challenges of Utilizing Big Data in Decision-Making
I spoke a lot about the incredible benefits big data can offer businesses; however, everything comes with pros and cons. Before utilizing big data in your operations, it’s important to look at its challenges when it comes to decision-making.
Data Quality and Accuracy
To make reliable predictions and decisions with big data, the data that organizations have hold of must be accurate and relevant. Unfortunately, although larger datasets can help make more reliable predictions, they are also more prone to containing inaccuracies or being incomplete. Inaccurate or poor-quality data can lead to incorrect insights and bad decision-making, leading to an organization losing lots of money.
This is where data governance becomes crucial. Essentially, data governance refers to what businesses do to ensure that their data is usable, accurate, and secure. More specifically, it means setting policies on the way this data is collected and stored. Effective data management strategies are key to ensuring that data is consistent and trustworthy, leading to more accurate insights and better decision-making.
Data Privacy and Ethical Considerations
As more data is taken from individuals to help upkeep the operations of organizations, data privacy, and ethical considerations naturally soon become an issue. Laws such as the General Data Protection Regulation (GDPR) in the EU and the California Consumer Privacy Act (CCPA) in the U.S. have set strict guidelines for how businesses collect, store, and use personal data.
Data privacy means collecting individuals’ personal data safely and securely. Organizations must ask for consent when asking for their customer’s data and explain what it’s used for. It’s also of utmost importance that these organizations don’t use this data in a discriminatory manner.
Integration and Management of Data Sources
As you can imagine, the range of data that’s available for companies to use is spread out on a variety of systems, and as mentioned above, this data can be stored in different ways. For data to be used effectively, it must be consolidated from the multiple sources that it’s collected from, providing a complete and accurate dataset for data analysis to take place.
If data isn’t properly integrated, it’s difficult for these businesses to make accurate predictions and use the data meaningfully. With this in mind, a strong data infrastructure must be used to ensure more reliable insights. This includes using data warehouses, cloud systems, and analytics tools that can seamlessly combine and analyze large volumes of data from various sources.
Skill Gaps and Cultural Resistance
Data skills are currently in very high demand, and we can expect the need for it to continue to grow. Despite this fact, there is still a huge gap in the data skills market and the proficiency level of these businesses employees when it comes to this field. This gap is caused not only by education factors but by many others, such as employee resistance, training resources, and budget issues.
Many organizations struggle to find workers with the specialized data skills required to use big data’s potential fully. Other than that, many businesses say there is a lack of budget for improving their workforce’s data skills. Cultural resistance is also important to mention here; naturally, when you’re not sure how something works, there can be fear around change, preferring traditional methods or fearing changes to their roles.
Future Trends in Big Data and Decision-Making
We’ve reached the end of this article, and before I wrap things up, I’d like to look at some future trends in big data and decision-making that we can expect shortly.
Advancements in Artificial Intelligence and Machine Learning
It’s clear to see that we are living in an age of vast amounts of data. Indeed, the range of data that businesses can get a hold of is so extensive that it’s become too complex for humans to analyze alone. With technologies such as Artificial intelligence and Machine Learning, it’s possible to explore a way to more significant amount of data than ever before, improving the accuracy of data-decision-making processes.
Indeed, by using advanced algorithms, businesses can notice patterns that traditional methods might miss. Recently, AI and big data have had a pretty interdependent relationship, with one relying on the other to operate efficiently. Using AI to transform raw data into strategic decisions quickly gives businesses a huge advantage over their competitors.
Real-Time Data Processing and Analytics
As the name implies, real-time data allows companies to analyze data as soon as it is generated. This way, data can be processed and collected instantly, allowing users to make quick decisions based on the newest data available. With real-time data processes and analytics, businesses can respond dynamically to the changing needs of their clients and market conditions, always being one step ahead of the game.
Financial institutions, for example, employ real-time analytics and machine learning algorithms to detect fraudulent transactions as soon as they are carried out. When a potentially fraudulent transaction occurs, real-time data processing flags and blocks the activity. This quick action helps protect the consumer and the organization from financial loss and potential damage to their reputation. This also ensures that the bank operates in the strictest security conditions.
The Growing Role of the Internet of Things (IoT)
IoT, or the Internet of Things, refers to the various devices that are used for data collection, such as smart home appliances, smart watches, security systems, and even automated vacuum cleaners, amongst several other things. These devices continuously collect and transmit data about their operations, surroundings, and user interactions.
This large amount of data generated by these devices can greatly enhance decision-making across industries. Whether that’s tracking consumer behaviour or monitoring the performance of equipment, IoT devices can help businesses make more informed decisions in real time, making businesses more efficient.
Conclusion
Big data has completely transformed the landscape of the business industry, allowing these companies to make better decisions that are more cost-effective. When using big data, companies can greatly enhance customer satisfaction, boosting customer loyalty and improving their sales. As mentioned in the latter part of the article, there are some issues with data that need to be addressed, especially regarding privacy and ethical concerns.
Indeed, it’s of utmost importance that businesses overcome these challenges and ensure they are operating within the strict guidelines set out by GDPR and CCPA. With that being said, the future of big data is promising, and in order for businesses to remain competitive in the ever-growing capitalist market, it can greatly help them make better decisions, reduce costly risks, and stay ahead of the game.
References and Further Reading
Interested in learning more about big data? Then I highly recommend checking out the sources below:
Articles & Studies:
● https://www.business.com/articles/reinventing-business-intelligence-ways-big-data-is-changing-business/
● https://journalofbigdata.springeropen.com/articles/10.1186/s40537-022-00659-3
● https://www.tableau.com/analytics/what-is-big-data-analytics
● https://www.nytimes.com/2012/02/12/sunday-review/big-datas-impact-in-the-world.html
Books:
● Big Data for Dummies – Judith S. Hurwitz (Author), Alan Nugent (Author), Fern Halper (Author), Marcia Kaufman (Author)
● Data Science and Big Data Analytics – EMC Education Services
● Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results – Bernard Marr
