What Is Big Data?
Have you ever wondered why so many great apps, programs, software, and services are available for free? This is because when you get something for free in our modern, connected world, the chances are that you are the product that is being monetized.
You are providing valuable information (data) to businesses, and they are using this information to engage, market, and sell you things. With billions of people connected over the internet, interacting and sharing information, plenty of data is generated every day.
All this data, and collected data from other online and offline sources, can all be categorized under the umbrella term of “big data.”
Let’s discuss big data, how it works, how organizations and businesses use it, and its pros and cons to give you a deeper understanding of the term.
What Is Big Data?
Big Data refers to the vast and diverse sets of data, or information, that grow at escalating rates. It is best explained as encompassing the “3 v’s” of big data:
- Volume – The quantity of data or information
- Velocity – The speed, “velocity,” or rate at which data is generated and collected
- Variety – The scope or “variety” of data points that are being explored
Most big data is sourced from data mining, a division of Business Intelligence (BI) responsible for “mining” raw data. However, this data arrives in various formats and requires different processes to become more structured for practical use in data analytics, another division of BI.
However, big data encompasses not just unstructured data but structured data as well. Businesses around the world can gain immense value from big data analysis.
Big data can be gathered from multiple sources, including internal company data points, public databases, publically shared online information (websites and social media platforms), collected data from apps and devices, markets, and many more.
Once collected, these large, unstructured, and complex data sets can be stored, structured, and used for data analytics, machine learning, and other valuable things that help businesses learn, grow, and succeed in competitive markets.
As mentioned, big data can be either structured or unstructured, and these are the two main categories of big data:
- Structured Data
Structured data is all the clean data and information, meaning an organization already manages it in its existing spreadsheets, databases, or data warehouses. This data is typically numeric, formatted to a standard, and stored in that standard format.
- Unstructured Data
Unstructured data is all the data and information that is unclean, which means it is unorganized and unformatted to a standard. This type of data does not fall into a preset category, model, or format.
Unstructured data includes videos, emails, and documents, collected from various sources like social media platforms, websites, and other online and offline sources. These sources and their data help organizations gather information on the business, market, and customer demands and trends.
How Does Big Data Work?
Our modern, connected world generates more data than ever before in history, and there are rapidly increasing sources to collect this data from. Big data can be sourced from publicly shared online information, voluntarily provided information (from apps and personal devices), and even through traditional sources like questionnaires and sales information.
Additionally, with more Internet of Things (IoT) devices and modern sensors than ever before, big data can be collected across a broad spectrum of conditions, locations, and situations.
Regardless of its type, source, or format, big data is stored in databases. It can be formatted to a standard, managed, organized as per the business’s needs, and analyzed for incredible insights on operations, efficiency, customers, markets, and more.
However, data management and data analytics are not easy, especially when large, complex data sets of big data are concerned.
This is where Business Intelligence solutions and tools are used. Things like data warehousing, data analytics software, machine learning programs, and cloud-based solutions are the leading BI solutions and tools for handling big data for organizations.
Where Can Big Data Be Used?
Big data is most useful for data analytics, where correlations are determined by finding relationships between different data types and using this information to produce valuable insights. For example, a business may use its sales and marketing data to determine if its marketing efforts are effective and may need tweaking.
Or a company may use their customer data to determine which ones are the best targets for their next marketing campaign. This type of data analysis can be done in-house or through third-party service providers who specialize in processing and analyzing bag data.
Many businesses use big data analytics through such experts to turn raw data into valuable, actionable information. Companies also frequently use big data for targeted marketing, rapid reporting, market trend analysis, and valuable business insights for decision-making.
Nearly every department of a business or organization can effectively utilize the findings from data analytics. This includes sales, marketing, human resources, research and development, management, and administration departments.
The primary purpose of big data is to increase productivity, revenue, the efficiency of an organization, and the speed at which products and services get to market. It also helps reduce time, effort, money, and other valuable resources needed to target audiences, gain market adoption, and ensure customer satisfaction.
Pros and Cons of Big Data
The ever-increasing, massive quantities of available data “offers” both solutions and problems for businesses. Generally, having more data on consumers, prospects, markets, and operations allows companies to utilize it for their sales and marketing efforts.
They can operate more efficiently, make better decisions, increase revenue and savings while acquiring a high level of customer satisfaction and repeat business. This is because the more collected data you have, the deeper and more effective your data analytics can be.
However, with more data comes more challenges as well. Businesses need to ensure that their collected data is accurate, complete, and worth analyzing before using it. Big data can often create overload and noise, reducing its usefulness.
This is why it is essential to determine proper data signals from the noise of the large volumes of big data. In such cases, determining what makes the data relevant is critical.
Other challenges include the resources needed to store, manage, and secure this data correctly. While third-party service providers and cloud-based solutions can help store, manage, and secure data, it may still not be enough.
Data security is of great importance, and businesses must take steps to protect data from malicious actors.
Moreover, there is also an ethical issue about how sensitive or personal data is being used, either for profit or exploitation in some ways. You only need to look at the recent Facebook-Cambridge Analytica Data scandal to realize the negative implications of big data and data analytics.
Conclusion
Big data has incredible uses and can be very beneficial for business; however, it must be collected, stored, secured, and used responsibly. Business Intelligence solutions and tools that use big data are created to help businesses provide better products, services, and experiences to customers.
If you want to learn more about big data or wish to utilize big data for your through effective BI solutions and tools, please visit our website today.
