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THE 5 KEYS OF BIG DATA

03.10.2023

THE 5 KEYS OF
BIG DATA

03.10.2023

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“In a data-driven world, information is not just power; it’s the compass guiding us towards a brighter tomorrow.”

KEY TAKEAWAYS

If you only have 2 minutes, here you go with the shortcut. But if wanna know how to leverage this knowledge and address challenges, I suggest reading the article. You will find case studies as well, how the big players do it.

Data Volume: Big data is characterized by the sheer enormity of information generated daily, underscoring the need for robust data handling strategies.

Data Velocity: The speed at which data flows demands real-time processing capabilities to harness its full potential effectively.

Data Variety: The diversity of data types, from structured to unstructured, necessitates adaptable data management approaches for comprehensive analysis.

Data Veracity: Ensuring data accuracy and reliability is paramount, as unreliable data can lead to misguided decisions and conclusions.

Data Value: Ultimately, the value extracted from data drives its importance, emphasizing the need to derive actionable insights to benefit organizations.

INTRODUCTION TO THE DATA TERMS

Understanding important data terminologies is critical for firms hoping to prosper in today’s data-driven environment. This article delves into five fundamental features of data, such as the five keys to big data, the five data elements, basic data terminology, and data analysis words. Along with this investigation, we will look at real-life case studies to demonstrate the practical implementations of these concepts.

The term “big data” has evolved into more than a buzzword; it is now a critical driving factor behind decision-making processes. To properly navigate this data-driven universe, it is necessary to delve into its intricate components, the five keys of big data. In the next chapter, you will get a deeper understanding of, what the 5 keys are for big data. If you went through this article, I highly suggest taking a look at our other posts as well that go into the details of business intelligence and data-driven decision-making processes. 

SO, LET’S BEGIN, SHALL WE?

Oh, and just to get a feeling about why big data and working with data is important. I leave these statistics here. In the next few years, the world market for big data analytics will grow at a rate of almost 30 percent per year. By 2025, it will be worth more than 68 billion U.S. dollars, up from about 15 billion U.S. dollars in 2019 (Statista, 2022). As you can see, the increase is enormous. 

(Big data analytics market revenue worldwide in 2019 and 2025: statista.com)

THE 5 KEYS IN THE WORLD OF BIG DATA

Let’s not waste too much time, go deep with the terms immediately. 

Volume

This first key component refers to the sheer magnitude of data generated every day. For instance, consider the astounding fact that in 2021 alone, it was estimated that a staggering 2.5 quintillion bytes of data were created daily across the globe. Businesses can leverage the concept of data volume in several ways to their advantage:

Data Storage and Infrastructure Planning: Understanding the large volume of data generated assists firms in planning their data storage and infrastructure requirements. They can invest in scalable storage systems, cloud services, and data centers to properly handle the growing volume of data.

Data Management and Governance: It is critical to manage and organize enormous amounts of data. Businesses can employ strong data management and governance policies to ensure data quality, security, and regulatory compliance.

Analytics and insights from data: A large amount of data opens up additional options for businesses to gain valuable insights. They can obtain a better understanding of client behavior, market trends, and operational effectiveness by evaluating this data. This can be accomplished using big data analytics technologies and approaches.

Customer Service: Big data can be utilized to improve customer service. Chatbots and AI-powered systems can be used by businesses to evaluate client inquiries and give faster more accurate responses.

Velocity

Every second, social media sites like Twitter and Facebook handle millions of tweets, posts, and interactions. This shows how quickly data moves around in the digital world. Businesses can use the concept of data velocity to their advantage in several ways, as it can provide valuable insights and opportunities for competitive advantage. Here’s how businesses can use data velocity, its value, and why they should focus on it: For businesses to properly focus on data velocity, they need to spend money on the following:

Data Infrastructure: Make sure you have the right data infrastructure, which includes storage systems and data processing systems that can handle large amounts of data in real time.

Analytics Skills: Either make or get the analytics tools and knowledge you need to quickly handle data and draw conclusions from it. If we take website performance and user behavior as an example, Hotjar is one of the leading tools out there.

Data Governance: Use strong data governance techniques to ensure the quality, safety, and compliance of your data, especially when you’re dealing with private customer data.

Scalability: Make sure that as your business grows, your tools can handle more and more data. 

But trust me, the value is rewarding, let me show you why:

Real-time Decision Making: Businesses can make choices in real-time thanks to data velocity. They can react right away to changing market conditions, customer tastes, and operational problems because they collect and process data quickly. This flexibility can help you get the most out of your processes, give better customer service, and take advantage of new chances as they come up.

Competitive Advantage: Being able to handle and process large amounts of data quickly can make a big difference in a market where competition is high. A business is more likely to stay ahead of the competition if it can quickly change to meet new customer wants and follow new market trends.

Innovation: The speed of data can also help drive innovation. Businesses can find new patterns, opportunities, and customer insights that can drive product development and growth by quickly collecting and analyzing data all the time.

Variety

Data is a multifaceted entity, arriving in various forms, including structured, unstructured, or semi-structured data. An illustrative example can be found in the world of retail, where businesses collect and analyze diverse data types, such as sales data, customer reviews, and social media sentiment analysis. Dealing with a variety of data is a significant challenge for businesses for several reasons:

Complexity: The amount of complexity varies for each type of data. It’s pretty easy to work with structured data, like sales numbers in a spreadsheet. However, it can be harder to work with unstructured data, like customer reviews or social media opinions, and may need more advanced techniques to analyze.

Integration: It can be hard to combine and connect different data sources. It’s hard for businesses to get a clear picture of their processes and customers when data is spread out among different systems and departments.

Quality of the Data: The quality of different kinds of data can be very different. It can be hard to make sure that unstructured data is correct and reliable because it might have mistakes, be inconsistent, or be missing information. To address the challenge of data variety, businesses can take several steps:

Cross-functional Collaboration: To get the most out of the ideas from different types of data, encourage teams and departments to work together. Cross-functional teams can help the whole company find chances and solve problems.

Always learning: Know about the newest tools and trends in data analytics and management. Businesses need to change with the times if they want to stay competitive in the area of data management.

Cleaning and Preparing the Data: Make sure the data is consistent and correct by using methods for cleaning and preparing the data. This could mean cleaning, transforming, and standardizing unorganized data before it is analyzed.

Advanced Analytics: To get useful information from unstructured and semi-structured data, like customer reviews or social media opinions, use advanced analytics methods like natural language processing (NLP) and machine learning.

Veracity

In spite of this, the world of data is not perfect and often appears in a less-than-ideal state. Data can come in many different formats, some of which are messy, some of which are uneven, and some of which are full of mistakes. Even with these problems, making sure the accuracy of data is still the most important thing in today’s data-driven world.

It’s clear that people are worried about this most when it comes to financial companies. These businesses check and double-check all of their customer data very carefully because they know how important it is to their daily operations. In the financial sector, bad or wrong data can have very bad results, such as losing money and not following the rules, as well as hurting the company’s reputation and making customers not believe it.

This means that checking the accuracy of data is not just a good idea; it’s a must. Financial institutions put a lot of money into data quality control. To ensure the security of their data assets, they use advanced algorithms, data cleansing techniques, and strict validation processes. This commitment to data accuracy shows in every part of their business, from figuring out risks and finding scams to helping customers and following the constantly changing rules set by regulators.

In a world where data drives every choice, the accuracy of data is very important for the success and safety of financial institutions and all data-driven industries in general. So, the search for accurate data continues to spur new ideas and strength in a data environment that is getting more complicated.

Value

Lastly, the ultimate objective of big data is to extract actionable insights and tangible value from the wealth of information collected. A poignant illustration can be found in healthcare, where providers leverage patient data to enhance treatment outcomes, thereby realizing the inherent value of data-driven decision-making.

REAL-LIFE CASE STUDIES

Hotjar - Website Optimization and User Experience

Hotjar - User Experience

(Hotjar logo: seeklogo.com)

Hotjar, a popular web analytics and user feedback tool, demonstrates how big data can be employed to enhance website optimization and user experience.

Volume: Hotjar processes a substantial amount of data daily, collecting information from millions of website visitors across various industries, and providing an extensive dataset for analysis.

Velocity: The tool enables real-time tracking of user interactions, including heatmaps, session recordings, and feedback surveys. This rapid data collection and analysis allow website owners to respond promptly to user behavior.

Variety: Hotjar deals with structured data from user clickstream data, unstructured data from user comments and feedback, and semi-structured data from surveys and polls. Combining these data types helps website owners gain a holistic view of user experience.

Veracity: Data quality is crucial to understanding user behavior accurately. Hotjar employs data validation techniques to ensure the accuracy and reliability of the data collected from websites.

Value: By analyzing user interactions and feedback, Hotjar helps website owners identify pain points, optimize website design, and improve user experience. This leads to increased engagement, conversions, and ultimately, the value of the website.

Tesla - Automotive and Safety

Tesla logo
Tesla logo

(Tesla logo: logo.wine)

Tesla uses “big data” to improve the safety and performance of its cars. The company is also considered one of the biggest tech/data companies, not even an automotive company.

Volume: Tesla’s fleet of cars creates a huge amount of data because sensors and cams are always gathering data about road conditions, how well the cars are running, and how the drivers are acting.

Velocity: Data from Tesla cars is sent to the company’s computers in real-time, so they can be analyzed and responded to right away. Over-the-air changes can make vehicles safer and better at what they do because of this real-time processing.

Variety: Tesla gathers structured data about the diagnostics of the vehicles, unstructured data from camera feeds, and semi-structured data from driving logs. This variety of info helps Tesla make their cars better in both hardware and software ways.

Veracity: The safety of data is dependent on its purity. Tesla uses strong data-checking methods to make sure that the data they get from their cars is correct and reliable.

Value: Tesla has been able to keep improving its Autopilot and Full Self-Driving features by using this data. This has made the cars safer and better at driving themselves. This not only makes their goods more valuable, but it also helps the progress of technology that makes cars drive themselves.

(You can also find the source of the video on The Hated One’s YT channel)

NASA - Space Exploration and Scientific Discovery

Nasa logo
Nasa logo

(Nasa logo: worldvectorlogo.com)

Not a surprise, but interesting to see how NASA utilizes data to conquer space.

Volume: NASA collects massive amounts of data from numerous space projects, such as satellite observations, Mars rover data, and telemetry from spacecraft investigating other planets.

Velocity: Data from space missions is returned to Earth in real-time, allowing scientists to make quick decisions about mission operations and data processing.

Variety: NASA works with structured data derived from spacecraft telemetry, unstructured data derived from satellite photos, and semi-structured data derived from rover logs. This diversified data allows scientists to conduct in-depth studies of the universe.

Veracity: Data precision is critical in space exploration. NASA implements stringent validation procedures to assure the accuracy and dependability of data collected from remote spacecraft.

Value: NASA has made significant discoveries regarding our solar system, galaxies, and the origins of the universe through examining space data. This scientific research not only adds to our understanding of the universe but also helps to progress technology on Earth.

CONCLUSION

In conclusion, in the current era of data-driven environments, it is crucial for enterprises to possess a comprehensive understanding of essential data terminology in order to thrive in the ever-changing digital realm. In the present essay, we have extensively explored five fundamental components of data: the five fundamental principles of big data, the five indispensable data elements, rudimentary data terminology, and the crucial terms associated with data analysis.

Nevertheless, the mere acquisition of information is insufficient; it must be complemented by practical insights in order to fully actualize its capabilities. In pursuit of this objective, we have undertaken an exploration of empirical case studies, elucidating the manifestation of these principles across a wide range of industries and situations. These instances serve as persuasive illustrations of the profound impact of data, enabling enterprises to make well-informed choices, promote creativity, and ultimately gain a competitive advantage in the contemporary data-driven environment. In light of the ongoing development of the digital era, it is evident that the persistent endeavor to acquire proficiency in handling data will undeniably serve as the crucial factor in accessing novel prospects and capitalizing on the vast array of opportunities within the increasing realm of data analytics. 

If you are interested in topics like this, feel free to check our other articles as well that provide more details about business intelligence and data-driven decision-making process.

Until the next article, peace!

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