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The power of Big Data: Unpacking the 5 Vs for Effective Data Management



Big data is the collection of data from different sources and is often described by five characteristics: volume, velocity, variety, veracity and value.

A truly digital company generates most of its revenues digitally and makes data-driven decisions. The big challenge is to leap from a subjective or speculative decision-making process to a data-rich one. Big Data analysis is the foundation for this leap to be possible by storing, processing and interpreting the generated data, and turning it into insights and knowledge. At a business level, the savvy use of data can make a huge difference, for instance, improve customer service, detect inefficiencies, predict failures early or develop competitive products and revenue streams. 

To deal with data effectively and start seeing its advantages, take note of the 5 characteristics that make it big data. They highlight the challenges and opportunities of working with extensive data. 


The Five Vs of Big Data

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1. Volume

Today, each person produces approximately 2 MB of data every second, predicted to double yearly. Every minute alone, 188 million emails are being sent and around 4.5 million searches are done on Google. 
The volume of data is so large that more than traditional data processing tools are required to handle it. The ever-increasing number of connected devices and machines, social networks, e-commerce and deployed sensors are some contributions to our gargantuan data generation world.
This is why Cloud, Cloud Native and advanced analytics (or AI) are among the top-growing disciplines in tech.


2. Velocity

The speed at which data is generated, processed and stored so that it can be interpreted in real-time or near-real-time. For instance, a sensor in a manufacturing plant may generate data every second. Analyzing this data in real-time may be critical to detecting any events before escalating into costly problems. A similar example would be the early collision sensors many of today's cars are equipped with. That data must be collected and processed early enough to avoid an accident. 

Our world is filled with many digitally connected interdependencies for which data collection and processing speed become highly valuable. This is where connectivity plays a crucial role and the reason for the development of fog and edge computing disciplines.


3. Variety of types

The data collected comes from different sources such as websites, sensors, mobile devices, social networks, and in different formats such as video, text, numbers, image, audio or metadata. These can come from within organizations or externally and can be structured, semi-structured and unstructured.
The variety of data requires using different tools and techniques to process and analyze it, given that for instance, text processing differs from images.


4. Veracity

Refers to the quality and accuracy of the data. Data can be incomplete, inconsistent, or contain errors. With the increase in volume and variety, it becomes challenging to ensure the accuracy and reliability of the data.  Veracity is critical to ensure that the insights and decisions made from big data analysis are reliable and accurate. Processes and procedures must be in place to ensure the data is trustworthy.

When processing data visualization plays a critical role. This is where graphing and dashboarding tools capable of handling the right volume of data and compiling it are essential.


5. Value

The value is the extraction of meaningful insights and knowledge gained from big data analysis. This analysis aims to help organizations make better decisions and gain competitiveness. The data itself has no value and will always need to be processed and analyzed to extract valuable information. The value of big data can be measured by the insights gained and the impact on business outcomes.

A big part of our work at Seidor Opentrends is finding ways for our clients to make sense of their data to drive business value and instill a data-driven growth mindset. Once we have a better understanding of customers' needs, there are techniques to retain them, and we specialize in that through growth hacking strategies.


Next Steps

Analyzing data can positively impact organizations by improving their services and products, reducing costs and increasing efficiency. You can make more precise data-backed-up decisions thanks to identifying behavior patterns, trends or anticipating threats. 
Every business needs a data strategy that treats data as a source of innovation, new revenue and strategic advantage. If you are in the early stages of defining and implementing big data start with this 6-step guide or contact us to learn more.


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FAQs about big data

How can big data be used to improve business decision-making?

Big data can be used to improve business decision-making by providing insights into customer behavior, market trends, and operational efficiency. For example, to track customer purchase patterns to identify trends or needs, which can then be used to develop new products or services. Big data can also be used to monitor operational efficiency to identify areas where costs can be saved.

What are the challenges of big data management?

The challenges of big data management include:

  • Data volume: The sheer volume of data that is generated and collected can make it difficult to store and manage.
  • Data velocity: The speed at which data is generated and collected can make it difficult to process and analyze.
  • Data variety: The different types of data generated and collected can make it difficult to integrate and analyze.
  • Data veracity: The accuracy and reliability of data can be difficult to ensure.
  • Data value: The insights that can be gained from data can be difficult to identify and extract.

What are the benefits of big data for businesses?

The benefits of big data for businesses include:

  • Improved customer insights: Big data can be used to gain insights into customer behavior, preferences, and needs. This information can be used to improve customer service, develop new products and services, and target marketing campaigns.
  • Increased operational efficiency: Big data can be used to identify areas where costs can be saved and productivity can be increased. For example, big data can be used to optimize supply chains, track inventory levels, and monitor energy usage.
  • Enhanced decision-making: Big data can be used to make better product development, marketing, and operations decisions. For example, big data can be used to predict demand, identify risks, and assess the impact of changes to the business environment.
  • New business opportunities: Big data can be used to identify new business opportunities and develop new products and services with the potential to monetize.