It’s hard to put a figure on the volume, velocity, and variety of data businesses have to deal with. Take, for instance, YouTube, which has to cope with a petabyte of new data daily. The same for Facebook is over 500 terabytes. When flying, a jet engine throws up 10 terabytes of data within half an hour. These insane volumes of data have a name, “BIG” data!
It is existentially useful for businesses when mined properly. But what is it, anyway? Let’s find out!
Big Data Spelled Out
Big data is exactly what it means, large chunks of datasets emanating from typically new sources. The source can be mobile apps, social media, wearables, satellites, emails, or anything smart enough to observe its ambiance, and gather and relay data effectively.
It is hard to capture, manage, store and make sense of big data with low latency. Traditional processing software and tools fail the growing complexity and influx of data. That calls for Tableau, Apache Hadoop, Xplenty, MongoDB, and other specialized, new-age tools to deal with it.
Characteristics of Big Data
Big data is characterized by four Vs. These are Variety, Volume, Velocity and Variability.
- Volume: Varying from terabytes to petabytes, it is really big, and hence, the name. Think of volume as the first requirement of data to be branded as big. Rather, the data needs to be enormous to derive the maximum value out of it. To put that in perspective, one petabyte is at par with 1,000 terabytes, which is in turn, equal to 1,000 gigabytes. A petabyte can accommodate approximately 11,000 4k movies and 500 billion pages of text. You require 1,000 high-storage PCs to store one petabyte.
- Variety: Big data is complex, featuring structured, unstructured and semistructured types of data. Relational databases suffice for structured data but cannot accommodate unstructured and semi-structured forms of the same emanating from emails, videos, audios, social media profiles, and so on. With big data being bombarded from heterogeneous sources, accessing, storing and processing it is a challenge.
- Velocity: Thanks to the penetration of IoT, the rate at which data is generated has upped unprecedently. The data relayed by RFID tags, sensors and the like has to be processed and analyzed with a sense of urgency, preferably in real-time to make the most of it. Any delays render the data obsolete. Typically, the data with the greatest velocity isn’t written to the disk but streamed straight to the memory.
- Variability: Big data is all but consistent, coming in disparate formats and from disparate sources at disparate speeds. These inconsistencies are often hard to deal with but help determine how different the datasets are. The insights, thus drawn, help identify patterns and make key business decisions accordingly.
How Does Big Data Work?
Leveraging big data for business gains is an intricate process, involving multiple levels, datatypes, sources, touchpoints, systems, users, and whatnot. Let’s map its journey.
1) A Big Data Strategy in Place
It all begins with putting a comprehensive strategy in place. From gathering and sharing to storing and processing data, every aspect needs a proper strategy that aligns with your bottom line, budget and organizational objectives. Your preparedness to deal with the unprecedented data influx will decide how well you can leverage it.
2) Ascertain the Data Sources
Data has to emanate someway from some sources. It could stream from wearables, smart vehicles, or anything else powered by the Internet of Things (IoT). Social media is a key source with each customer-brand interaction generating videos, audios, pics, and texts.
Mobile apps, websites, and other touchpoints also throw in tons of data and so do data lakes, cloud, and open data sources. Identifying your sources early on helps tap into the big data for business leverage. A pro tip: the data sources should correspond to your goals.
3) Manage the Big Data
The implementation gets underway with having in place the computing power to capture and process enormous amounts of datasets bombarding at rapid speeds. Plus, measures to ensure smooth dataflow and identify and integrate “quality” data should be there.
When it comes to on-site storing, the conventional data warehouses kick in. However, cloud, data lakes and the like provide more flexible, economical and convenient storage alternatives.
4) Analyze Big Data for Big Business Gains
Making sense of something of this scale requires leading-edge technologies, including grid computing and in-memory analytics. However, it’s a standard practice to narrow down the data based on utility.
Only the relevant information, which could be historic or transactional, is analyzed while the rest is skipped outright to reduce processing time and cost. And, when it comes to analysis, predictive analytics steps in, helping preempt risks and opportunities.
Why is Big Data in High Demand?
It is the new capital, helping make sense of the world around us. It can translate into tangible and actionable insights across multiple use cases. Here’s your checklist.
Big Data in Business
Big data-driven organizations are likely to thrive, regardless of the industry or competition.
- Customer Acquisition
Big data beef up your knowledge of potentials and customers, their changing preferences and behaviors. Businesses can, thus, implement focused customer acquisition strategies. Tailoring customer experiences is a possibility as well, which helps entice new customers and retain the existing ones. Simply put, it’s indispensable for creating brand allegiance.
- Restricting Overheads
By incorporating big data, businesses can save big on operational, marketing, logistics, and other expenses. How? Well, with patterns and insights, decision-making is more precise and effective. Businesses are well-positioned to bring down costs and losses. With cloud computing, datalakes and Hadoop, storage and analysis of huge amounts of information become economical and flexible.
- Better Customer Relations
When the customer feedback across all touchpoints is integrated and analyzed, businesses get a clearer picture of customer satisfaction levels once the sale is done. As such, devising post-sales strategies to improve customer relations is a possibility. Plus, they can stay on top of the customer journeys and provide buying assistance if needed at any stage.
- Well-Targeted Marketing
The treasure trove of information on potentials, customers and markets allows businesses to create and implement well-targeted marketing strategies to raise brand/product/service awareness, generate leads and convert potentials into buyers and buyers into loyalists.
- Optimized Supply Chain
When you have complete visibility of logistics and supply chain, identifying areas of concern is easy and quick. With big data providing insights into weather, road conditions and traffic in real-time, determining the shortest and safest supply routes is also possible.
- E-commerce Analytics
With rising competition and changing buying behavior, online vendors are hard-pressed to switch to digital hybrid operations and keep track of buyers, inventory, et cetera. Again, big data comes to the rescue, keeping vendors progressive and profitable.
Others Uses of Big Data
The use case of big data extends beyond the domain of business into other fields as well. Some of the top areas that leverage it are:
- Education: It is driving the creation of learning-oriented, interactive and personalized courses and programs while also transforming grading systems.
- Insurance: It is allowing insurance companies to map threats, evaluate risks and factor in the clients’ vulnerabilities to offer personalized coverages accordingly.
- Healthcare: The possibilities are endless with big data transforming patient care, clinical research, and disease identification, treatment, prevention and tracking.
- Government: It is allowing governments worldwide to address issues at scale, alongside creating and implementing policies and ensuring last-mile delivery.
- Security: With the threat of online fraud looming, big data can be the answer. It allows the identification of frauds and fast-tracks regulatory reporting.
- Meteorology: It’s instrumental in ushering accuracy in weather forecasts, studying calamity patterns and developing more effective warning systems.
- Transportation: The sector relies on big data for safety, savings and sustainability.
Trade-Offs
Even something as effective as big data isn’t immune to trade-offs. Some of the most important ones are:
- With information growing at inconvenient rates, managing it is a challenge even with leading-edge technologies.
- Curating relevant, high-quality datasets is a tedious task, accounting for up to 80 percent of data scientists’ time.
- As big data technologies change rapidly, businesses find it hard to keep pace.
- It is beneficial only in the long term. The inferences are misleading in the short term.
- It is also linked to privacy violations and increased social stratification.
Key Takeaways
- Big data represents monumental amounts of diversified information bombarded at striking rates. It can be structured, semi-structured or unstructured, subject to factors.
- Anything smart and digitally connected can serve as a source. Smart cars, apps, websites, appliances, jets, social media, personal electronics, and so forth.
- Big data-driven businesses have a better understanding of their market, customers, avenues, vulnerabilities and local preferences. It translates to a competitive edge.
The Bottom Line
Big data is big in every sense – size, diversity, complexity, scope and applications. Full-fledged to small, businesses leverage it to restrict overheads, boost operational efficiency, create well-targeted marketing campaigns, build a strong talent pool, improve pricing, and more.
How well a business uses big data determines whether it will prosper or fade into oblivion. The possibilities are simply endless across other fields as well, helping address issues that remain untackled hitherto. It’s more than a game-changer. Rather, it’s a game in itself.
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