What is Big Data?
Big data refers to the voluminous and constantly growing amounts of data that an organization has that cannot be analyzed using traditional methods. Big data, which includes both structured and unstructured data types, is often the raw material for organizations to run analytics on and extract insights that can help them craft better business strategies. It is more than a byproduct of technological processes and applications. Big data is one of the most important assets today.
Big data can be made up of traditional structured data, unstructured, or semi-structured data. An example of unstructured—and constantly growing—big data is the user-generated data on social media. Processing such data requires a different approach than to structured data coupled with specialized tools and techniques.
Big data is the byproduct of the information explosion of today. All areas of business and everyday life contribute to the burgeoning pile of big data: retail, real estate, travel and tourism, finance, social media to technology, every aspect of our lives from how many steps we take to our financial histories is data.
Back in 2017, around 3.8 billion people, around 47% of the world’s population, was estimated to be using the internet. The number and variety of smart electronic devices has skyrocketed over the past few years and continues to grow. Our daily output data is estimated to be 2.5 quintillion bytes and growing.
With the number of people who use the internet rising exponentially, data never sleeps.
The figures below will help shed some perspective on the size of the Big Data behemoth. This is what is happening in the cyber realm every minute. You do the math.
- Weather channels receive 18,055,555 forecast requests
- People make 176,220 calls using Skype
- Instagram users post 49,380 photos
- Netflix users stream 97,222 hours of video
Characteristics of Big Data
The five V’s of Big Data are universally accepted:
If we think of big data as a pyramid, volume will form its broad base. The volume of data that companies across the globe manage began to skyrocket around 2012 when organizations began collecting more than three million pieces of data every day. Since then, this volume is estimated to double about every 40 months, according to an MBA Professor at Antonio de Nebrija University.
The term 'velocity' refers to the speed that data is generated.
It is not just the volume of big data that can be an asset: how fast it flows, i.e. its velocity, is important too. The closer it is to real-time, the better in terms of competitive advantage for companies looking to extract actionable and valuable insights from it.
An example of this is whether a food delivery company decides to buy a Google Ads campaign on the basis of its sales data 45 minutes into the start of a major sporting event. The same data will have lost its relevance a few hours later.
Technologies driving this need for rapid data include RFID tags, smart metering, and various kinds of sensors.
Variety refers to the spectrum of sources from which a company can acquire big data and the plethora of formats it can appear in. This includes places like smartphones, in-house devices, social media chatter, stock ticker data, and data from financial transactions. The source has to be particularly relevant to the nature of the business for which the data is being collected. For example, a retail company must be tuned in to what users are saying on social media about its recently launched clothing line. A manufacturing company would less embedded value in following social media.
A variety of data can also extend to help organizations with understanding customer profiles and personas. For instance, a company would find it helpful to know not just how many people open their newsletter, but also why they opened it and distinguishing characteristics of the audience.
Veracity calls into question the quality and accuracy of data. Clean data is the most trustworthy. Organizations must connect, cleanse, and transform their data across systems in order to trust it. They need hierarchies and multiple data linkages to keep control of their data.
At the apex of the pyramid sits value, the ability to extract viable business insights from within the avalanche of data.
Value is being able to predict how many new members will join the website, how many customers will renew insurance policies, how many orders to expect, and such. Value is knowing who one’s best customers are and who will fall off the map in a few weeks or months, never to return.
Companies gain value through their ability to monetize the insights provided by big data. They get to know their customers better and continue to make more relevant offerings.
Major Types and Sources of Big Data
This is the data that comes from the Internet of Things and connected devices. It is data that flows into systems in chronological order. It can stream into IT systems from a multitude of connected gadgets such as smartphones, wearables, smart cars, industrial equipment, and medical devices. Streaming data can be analyzed on a first-in or continuous basis, scanning it to see if it is worth storing for further analysis, or whether it can be safely discarded.
Social Media Data:
The millions of daily interactions on social media platforms such as Facebook, Instagram, YouTube in the form of pictures, images, GIFS, videos, voice, comments (text) and sound files make up the repertoire of social media data. This is especially valuable for sales, support, and marketing campaigns. The challenge lies in the fact that it is mostly in unstructured or semi structured form, so additional processing is needed before it can be analyzed.
Publicly Available Data
This refers to the enormous number of open data sources including data.gov portals by major world governments.
The remainder of big data comes in from the cloud, data lakes, vendors, suppliers or customers.
How Big Data Gets Processed
Processing big data begins with setting up a strategy to harness it. The next step is to identify and catalog its sources, locations, systems, users, and owners and how it flows in. Then create an infrastructure to store and manage the data to be readily accessible for analysis, the final step to facilitate data-driven decision making. This protocol is useful to manage traditional structured datasets as well as unstructured and semi structured data.
When developing a big data management strategy, it is imperative to factor in current and future business goals from a business growth as well as technology standpoint, and treating big data just like any other business asset of value.
Data can be stored either onsite in a traditional data warehouse, but cloud storage solutions have gained popularity in recent years. These are more economical and provide a certain degree of flexibility. Where processing is concerned, computing systems available today are equal to the speed, power, and agility necessary to meet the demands of accessing such massive data volumes. Integrating data, ensuring quality control, providing data governance and readying it for analytical tools to do their job are also necessary parameters.
Tools to Extract the Most from Big Data
Big data is what fuels the advanced analytics endeavors of our era, such as artificial intelligence. The more efficiently a company uses its collected data, the more potential it can extract out of it. Investing in software that can manage and analyze huge volumes of data, particularly in real time, is a vital step to big data management.
MapReduce, BigTable, and Hadoop: When large amounts of data are to be stored, and better or more efficient ways of conducting business activities are to be identified, tools like Hadoop and cloud-based analytics are tapped. These help in optimizing processes to deliver cost advantages.
Furthermore, the high speed of tools such as Hadoop coupled with in-memory analytics helps identify untapped resources, i.e. newer sources of data for analysis. The speed of capturing and analyzing data is a great asset for companies to make quick decisions.
Complex challenges need clever solutions. Platforms need to empower organizations with intuitive, simple interfaces that ensure even the least IT-savvy can use them. The platform should also be able to leverage the full spectrum of big data, resulting in accurate, real-time analytics. Being able to handle multi-terabytes of data clusters from diverse sources and successfully turn this into dashboards that provide useful insights and workflow analytics make a system successful.
Big Data Analytics: Insights
- Big data analytics yields a deeper understanding of current market conditions, customers’ buying behavior, product popularity, and so on, to optimize manufacturing or procurement planning.
- Similarly, big data helps a business zoom in on what their customers like, what demographic their paying customers fall into, and then come up with ways to reward and foster their loyalty to retain their customers over the long term.
- Keeping customers happy is crucial to the longevity of the business. The insights provided by big data go a long way in managing expectations and designing memorable and effective marketing campaigns for various customer personas.
- Big data analytics can also be a sentiment meter, measuring how consumers feel about your brand, service, or product. This can be a great help in managing the brand image. Big data insights can help improve online visibility and popularity and keep up the high ratings.
- Insights provided by big data analytics help companies to constantly innovate and redevelop their products to stay ahead of the competition. They help identify the root cause of failures, issues, and defects.
- Big data helps identify patterns, calculate risk portfolios, and intercept fraudulent behavior before serious damage is done.
Long-Term Benefits Derived from Processing Big Data
Once organizations have invested time and resources in the infrastructure needed to process big data, they can look forward to reaping the following benefits:
- Optimizing resources and inventory planning
- Better asset management
- More intuitive understanding of customer profiles
- Improved customer, vendor, and supplier relationships
- Shorter order-to-delivery times
- Better integration across their entire supply chain
- More effective strategic planning
- Shorter reaction time to address supply chain issues
- Improved customer service and faster turnaround
How Big Data is Impacting Various Sectors: Examples
Big Data in the Education Sector
Big data has driven major changes in the education sector, particularly in:
- Creating more customized, dynamic, and interactive learning and development programs
- Redefining the scope of course materials
- Modifying grading systems for more accuracy
- Career prediction and counseling
Big Data in the Insurance Sector
The insurance sector is relevant not only to individuals seeking life coverage, but also enterprises of various types and sizes. The common factor being that both people and organizations are vulnerable to times of adversity, calamities, and other uncertainties. As a result, data in the insurance sector can come in a variety of formats, from disparate sources, and is subject to change.
For instance, if a customer is interested in buying car insurance if traveling in a certain country, the insurance company can collect and run the data for driving conditions and road safety in that country, and adjust the premium accordingly. They can also gather the person’s driving safety record and factor that in before presenting him/her with a policy for purchase.
In addition to such risk assessment, insurance companies can also use big data for threat mapping. This means they can take into account the different possibilities in which things could go wrong with a particular customer or company which might lead them to filing a claim.
Big Data in Government
Big data has proved to be particularly impactful for governments across the world. It is instrumental in addressing complex issues, ensuring governance and influencing major events at not just a local, but also a national and global scale.
Big data has ushered in a huge new opportunity for garnering and collating accumulated data, and extracting useful insights from it, imbuing it with viability and context for various organizational processes.
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