The fuel powering many of today’s businesses of all sizes is data, which is the key behind data-driven transformations and artificial intelligence (AI) strategies. It is absolutely necessary in today’s business environment, and it is the focus of many top-level conversations.
Because data is so fundamental and integrated into business processes, it has branched off and now encompasses many different types, which can make it seem intimidating to some. While many people have heard of “big data,” they might not know exactly what it entails or that there are other types of data, such as “small data.”
Let’s start by first defining the two:
- Small Data: Small data includes small datasets that often impact decisions in the present, meaning it is usually small enough for humans to understand in terms of volume and format. Small data doesn’t have the same level of impact as big data when it comes to the overall business. Instead, it has a greater impact on short-term and current decisions.
- Big Data: The term “big data” has become highly popular over the past few years. It is large collections of structured and unstructured data that are too complex for humans to process. Almost 2.5 quintillion bytes of data are created each day, which has led to the rise of big data. It refers to the massive volumes of data produced digitally, including web data generated by emails, websites, social networking sites, streaming platforms, and more. Big data also refers to the large data sets that are too complex to be processed by conventional data processing methods, meaning new algorithmic techniques must be used.
The Three V’s of Big Data
Big data is often defined by experts by using the “three V’s,” which are volume, variety, and velocity. These three v’s are one of the major differences between big data and small data.
- Volume: Data volume is the amount of data available for processing. Big data requires a large volume of information, while small data does not to the same extent.
- Variety: Data variety is the number of data types. While data was once collected from one place and delivered in one format, such as excel or csv, it is now available in many non-traditional forms like video, text, pdf, social media graphics, wearable devices, and more. This level of variety requires more work and analytical power to make it manageable.
- Velocity: Data velocity is the speed at which information is acquired and processed. Because big data consists of massive chunks of information, it is usually analyzed periodically. On the other hand, small data is capable of being processed far quicker, which is why it often involves real-time information.
Benefits of Small and Big Data
There are many benefits to using small data instead of big data. To start with, it is everywhere you look. For example, social media is filled with small data about users, and smartphones and computers create small data each time they log into applications.
Here are some of the other main benefits of small data:
- Easier and more actionable: Small data is easier for humans to comprehend and to process. It is more actionable in the short term, meaning it can translate to business intelligence right away.
- Visualization and inspection: Small data is far easier for visualization and inspection since it is impossible to do so manually with big data.
- Closer to the end user: One of the best ways to understand a business is to focus on the end users, and since small data is closer to the end user and often focused on individuals’ experience, it can help achieve this.
- Simpler: Small data is simpler than big data, which makes it easier for everyone to understand, from stakeholders to the decision-makers. Nearly anyone can understand small data, which is helpful for organizations looking to equip all of their employees with data-driven power.
With all of that, it’s still important to recognize that big data is an incredible tool in business, and it holds many of its own advantages over small data.
Here are some of the main benefits of big data:
- Better customer insight: Big data sources shed light on customers and help a modern business understand them.
- Increased market intelligence: The use of big data can also lead to a deeper and broader understanding of market dynamic. Besides competitive analysis, it can also assist in product development by prioritizing different customer preferences.
- Supply chain management: Big data systems integrate data on customer trends to enable predictive analytics, which helps keep the global network of demand, production, and distribution working well.
- Data-driven innovation: Big data tools and technologies can lead to the development of new products and services. Even the data itself can become a product after being cleaned and prepared.
- Improved business operations: Big data can improve all sorts of business activity by helping optimize business processes to generate cost savings, boost productivity, and increase customer satisfaction. It can also improve physical operations by combining big data and data science to inform predictive maintenance schedules, for example.
Big Data is Not Always Better Data
There is a lot of hype around big data, but it is not always preferable. While big data has been the more popular of the two, small data is increasingly becoming recognized once again as an important player in this new business environment. One of the major reasons big data might not be preferred over small data has to do with security and storage.
Security is highly crucial when dealing with large amounts of data, but big data can make this extremely challenging for some organizations. As big data grows, it also gets difficult to store and manage. The traditional databases used for small data are not designed for big data. Because of this, big data databases favor performance and flexibility over security.
Future of Small and Big Data
While big data will continue to be popular among businesses of all types, small data will likely keep increasing in importance and popularity. One of the main reasons behind this is that small data is enabling smaller enterprises to get involved in this data-driven world.
Some of the same techniques used for big data will continue to be applied to small data, such as artificial intelligence and machine learning, which can lead to smarter but less data-hungry AI solutions.
Although it is possible to analyze small data without computers, machine learning and statistical methods help better understand the data and identify patterns that would otherwise be impossible if done manually. These patterns can then provide a deeper understanding of a business and its customers, and when derived from small data, they can often be more informative than big data analytics, which are sometimes more difficult to translate into actions.
Whether a company decides to leverage the power of small data or big data, it is certain that the importance of data will only continue to increase. We will see many new types of data in the future, and together, all of these types make up our data-driven world.