Over the last decade, we have witnessed the exponential growth of Big Data around the globe. Big data is now projected to grow to a massive 175 zettabytes by 2025 – up from 50 zettabytes today. The Internet has driven this growth – mainly due to the growing prevalence of rich media files, social networking platforms, and more recently, smarter, and connected IoT devices.
As businesses interact more with Big Data analytics and other insights, Big Data is simply going to lose its significance that it enjoys now – and is likely to be referred to as “data.” Even as data will continue to remain an important part of any business, the shift will gradually shift towards smaller data sets – or what is referred to as “Small Data.”
Is this the right time for businesses to shift their focus from Big Data to Small Data? Let us explore.
How does Small Data Differ from Big Data?
Primarily, Small Data is a subset of Big Data – with data volumes and formats that are small enough for better human comprehension and are easier to access and to derive actionable insights.
To understand Small Data, let us understand the three Vs that define Big Data:
- Volume – or the massive data volumes in Big Data comprising of both structured and unstructured data. Companies invest in large IT systems or platforms to derive valuable insights from these data volumes – mainly through Big Data analytics.
On the other hand, Small Data deals with smaller data volumes that impact business decision-making in a shorter time frame. Using data analytics tools, organizations can extract smaller datasets from larger chunks of Big data.
- Variety – or the variety of data types that businesses use when arriving at accurate insights. For example, for analyzing website traffic, companies look at various data types including the number of visitors, devices used, user demographics, and the popular channels that drive the traffic.
Small Data could simply look at one (or a few) data types (for example, traffic generated through social media marketing).
- Velocity – or the rapid speed at which Big Data is collected and analyzed – to keep it relevant for real-time decision making. With its huge chunks, Big data needs to be periodically analyzed at regular intervals.
Small Data focuses on smaller real-time chunks of data that can be processed quickly – without overwhelming the entire system.
Next, let us talk about the market factors that are driving Small Data adoption and how companies can benefit from it.
Small Data – Driving Forces and Business Benefits
As rightly put by Rufus Pollock of the Open Knowledge Foundation, “the hype around Big Data is misplaced – real business value lies in small and linked data.”
Here are a few favorable factors that are driving the interest in Small Data:
- Big Data is hard to leverage for specific insights (for example, in providing a personalized user experience through CX marketing campaigns).
- Small Data is already available through various social media channels and IoT devices.
- Small Data supports the “user first” approach that is the mantra for customer-servicing companies.
Why should businesses care about Small Data? Is it easier to manage than Big data? The answer is yes, provided you have the right data tools. Here are some business benefits of adopting Small Data in your organization:
- Easier to understand: No matter which business leader or decision-maker, Small Data is easier to comprehend and does not require any technical expertise. This can lead to better decision-making due to focused and accurate data insights.
- End-user focused: As mentioned earlier, companies can leverage Small Data to understand customer needs and provide improved services. In short, Small Data can help you achieve business success through the “customer-first” approach.
- Improved customer experience (CX): Guess what, customers also love and value Small Data. Whether it is for getting a good airfare or a personalized online campaign, customers enjoy the perks of businesses deploying Small Data into their CX initiatives.
- Accurate and actionable: Through its immediate and actionable insights, Small Data can be the building block for implementing other technology solutions including Customer Relationship Management (CRM), Business Intelligence (BI), and Customer Analytics.
How to Process Small Data?
Companies can only profit from Small Data if they have the right tools for processing the data. How can you process smaller datasets to discover valuable insights from them? While Apache Hadoop has been successful in Big Data analytics, can it also work for Small Datasets? The answer is a “Yes.”
Here are a few tips on how to deal with smaller datasets:
- Avoid complex models with many parameters
- Remove any outliers when dealing with a Small Dataset
- Select the most relevant and explicit features when the data is limited
- Look for ways to extend your dataset either using synthetic samples or data pooling from multiple sources
IoT expert, Ahmed Banafa believes that the “next decade belongs to distributed models and not centralized ones – and Small Data, not Big data.” This does not mean that Big Data is dead and no longer relevant. As a leading player in data analytics, Inteliment helps you leverage the power of both Big and Small Data to derive maximum RoI from your data initiatives by using platforms for big data engineering & data science like Rubiscape.
Which data model works best for your business? Get in touch with us for the best possible solution.