- Data Storage and Analytics
- Article
Small is better? Using big data is being questioned!
Highlights
- While Big Data has been vigorously used in the past years to convert zettabytes of structured, semi-structured, and unstructured data into a visually immersive format, its popularity is declining. Companies are steadily shifting towards small and wide data analytics for understanding data and making informed decisions.
- Even Gartner predicts that by 2025, about 70% of organizations will shift their focus from big data to small and wide data. It's believed that the pandemic indirectly contributed to this change.
In this article
Back to the Basics: What is Big Data?
Any data that's too extensive (in terms of volume, variety, and frequency) to be dealt with using traditional tools or methods is called Big data.
Big data is not new. It has been there for several decades. But it was not used extensively back then because there wasn't enough data!
However, internet usage has burgeoned over the years. In 2021, the overall data generated across the globe was estimated at around 79 zettabytes, according to Statista. And it is all set to double in 2025.
A massive chunk of the above data is generated, gathered, and stored by large social media (Facebook), eCommerce (Amazon) and search engine (Google) enterprises, in addition to several digital finance exchanges (stock market data).
Real World Example: Amazon uses Big Data pretty effectively. It captures all data that helps them determine customer preferences, products that customers are more likely to buy, etc. And all this is used to improve customer experience and services and target social media campaigns.
Amazon's "frequently bought together" feature is big data analytics in play.
What is Small Data and Wide Data?
Small data is the data that is small enough for easy human comprehension without needing any extra infrastructure or tools.
Unlike Big Data, Small Data is all about collecting limited data from individual sources within an individual organization or solving particular problems. While big data focuses on future trends or the bigger picture, small data helps make data-driven decisions, solve present-day problems, improve sales, marketing strategies, etc.
While small data focuses on a single data source, wide data (as the name suggests) brings together different (wider) types of data from unrelated sources and converts them into useful information.
Fact: Wide data is way more extensive than small data but is smaller than big data.
Real World Example: Let's say there's a small B2Bcompany that sells accounting software. And they're not able to generate quality leads. Now, using small data, the company can find what they're doing wrong, tweak it and thus produce better results.
While small data focuses on a single data source, wide data brings together different types of data from unrelated sources and converts them into useful information.
Why are Organizations Leaning Towards Small Data?
Small data is important to your business
1. Small Data Enhances Decision-Making Processes
Small businesses usually look forward to dealing with their present-day problems, such as improving lead generation, delivering better customer experience, or improving their marketing strategy. And the data that's required to cater to the above needs is missed by big data analytics.
On the other hand, small data is more personalized, human, and company-specific and has all the insights needed to enhance the decision-making process and thus solve present-day problems.
2. Small Data Makes More Sense for Small- and Mid-Sized Organizations
Small- or mid-sized organizations usually don't have enough data to be called "big" yet. Even if they do, procuring, storing, and using it for analytical purposes is extremely costly as it requires new infrastructure to string data and special tools for analyzing it. And unfortunately, most companies lack that kind of money.
On the flip side, small data can be accommodated in an excel sheet. It comes with fewer rows and variables, which makes sourcing, managing, and using small data convenient and affordable.
3. Small Helps Overcome Big Data Constraints
Big data comes with different constraints that make it quite irrelevant in some cases.
For instance, data analytics revolving around AI development needs more recent data, and that too in smaller chunks. This is possible with small data and not big data.
Moreover, how humans behave has changed drastically over the years. For instance, who would've predicted that apps like UBER, Amazon, or fast-food delivery apps would be so popular? Even more interesting is the advent of COVID-19. No one anticipated that, and all predictions made using big data were futile.
How can Businesses Benefit from Small and Wide Data?
Why are businesses aggresively embracing small and wide data?
1. Small and Wide Data Helps with Personalization
While big data is about the long term and shows the general trends, small data focuses on specific aspects such as what drives prospects, customers, or leads to make purchases or take any action.
Therefore, using small data, businesses can understand their clients more precisely by offering them better personalization. It helps
Identify the target audience - their needs, intent, and interests.
Communicate personalized offers.
Customize marketing plans individually for the prospects.
Determine which channel is best for communication.
2. Small and Wide Data Help with Real-Time Insights
Sometimes businesses need immediate insights, which big data cannot provide. Big data requires both resources and time to process and quickly becomes obsolete.
However, small data can be processed immediately, giving quick or even real-time insights and enabling faster decision-making.
With real-time insights, businesses can:
Tweak and improve marketing strategies quickly.
Enhance lead generation.
Understand the triggers that motivate customers to buy and use them frequently.
Change marketing tactics.
3. Small and Wide Data is Easy to Manage
Big data requires extensive resources to source, store, and use, making it hard to manage both logistically and financially.
In contrast, small data is easy to source, store, and effectively use, allowing businesses to:
Respond to customers almost instantly with a solution to their queries.
Work on huge data sets without necessarily needing a team.
Disruptions such as the COVID-19 pandemic are causing historical data that reflects past conditions to quickly become obsolete, which is breaking many production AI and machine learning (ML) models. In addition, decision making by humans and AI has become more complex and demanding, and overly reliant on data-hungry deep learning approaches.
Wrapping Up
Small- or mid-sized organizations that find big data too complex must look at small and wide data more closely.
Big data has offered companies, researchers, and industries access and understanding of tons of data, impacting decision-making processes for several years to come. However, big data is quickly becoming redundant for organizations using data analytics to improve operations, products, etc.
Small and wide data are quickly replacing big data in organizations with significant success in decision-making and problem-solving. Small and wide data are more specific, affordable, usable, and manageable, making them more relevant than ever.
Small- or mid-sized organizations that find big data too complex must look at small and wide data more closely.
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