What Is Big Data?

Big data is a collection of structured, unstructured, and semi-structured data sets that grow exponentially over time. It refers to a large volume of information, the speed at which it is created and collected, and the extent of data points covered (otherwise known as the three “Vs” of big data).

• Volume. This is the amount of data processed. As its name suggests, big data describes high volumes of low-density, unstructured data. An example is the typical feed from social media sites such as X and Instagram. In either case, we are talking about terabytes to petabytes of data.
• Velocity. This is the rate at which data is received. Ideally, big data should be processed, evaluated, and acted upon in real-time or near real-time.
• Variety. This refers to the types of data available. Traditional data types were structured (organized and formatted in specific ways). However, most big data today are semi-structured to unstructured and require additional processing to derive meaning.

Essentially, big data was the natural result of the ever-increasing demand for data. As more complex data sets were created and needed to be processed, business leaders struggled to manage them with traditional data processing software. Big data allows massive volumes of information to be analyzed to address various business problems.

Big data is often considered the raw material used in data mining.

Two new factors in big data

Over the years, IT experts have added two new aspects of big data to consider: value and veracity. In a digital-first workplace, your data must be both valuable and truthful. After all, it does not matter how much data you have, whether it’s processed quickly or of various types, if the data itself cannot be relied upon.

Arguably, data is the most valuable currency in the world today. Some of the biggest companies found success partly because of their ability to efficiently and accurately process data. This competence, in turn, enables business leaders to make more accurate and precise decisions.

As such, there is a growing agreement that the best big data is one that meets all five criteria.

How big data works

Big data is categorized as either structured, unstructured, or semi-structured.

• Structured data, which is normally quantitative data, is organized and easily decipherable by various machine learning algorithms. This makes structured data much easier to input, search for, and manipulate.
• Unstructured data, on the other hand, is usually qualitative, making it harder for traditional data tools to process. However, with the rise of social media and similar platforms, most businesses need to analyze this type of data.
• Semi-structured data falls in between the two categories. Some examples include emails, electronic data interchange (EDI), and JSON (JavaScript Object Notation).

We can summarize these categories as follows:

	Pros	Cons	Use Cases
Structured Data	Easy to understand and process: Structured data does not require extensive knowledge of data processing. Can be accessed by more tools: More tools are available in the market that can analyze structured data.	Limited use cases: Structured data can only be used for its intended purpose. This limits its use cases. Rigid storage rules: Generally, changes in data requirements for structured data require updating every data point, which can cost time and money.	Online booking: Hotel booking, for example, requires data to “fit” into specified data sets. Accounting: Highly data-specific industries, such as accounting, benefit from using structured data to process transactions.
Unstructured Data	Adaptable: Unstructured data, by its very nature, is adaptable. This widens your potential data pool and can strengthen your decision-making processes. More variety and volume: Unstructured data is easier to collect.	Intended for more experienced IT professionals: Unstructured data is better handled by experienced data analysts. Specialized tools: You would need more specialized tools to analyze unstructured data.	Data mining: Businesses can use unstructured data to analyze consumer trends. Predictive data analysis: IT pros can also use unstructured data to predict possible market shifts. Chatbots: Unstructured data can be used to perform text analysis and appropriately respond to customer queries.

Regardless of its category, big data is usually stored in a data warehouse, where it is analyzed using specific software. The completed analysis can be used for various reasons, including delivering targeted campaigns, writing hyper-focused content (which may be useful for niche markets), or informing your endpoint management strategy. Either way, big data is meant to help you make more informed decisions about your organization.

Advantages and disadvantages of big data

The pros and cons of big data can be condensed into a single philosophical question: How much information is too much? 

In general, having more data enables MSPs, IT enterprises, and other companies to better tailor their marketing strategies and products to their target audience. By understanding what their customers want, businesses reduce the risk of creating or developing unnecessary services.

However, big data can also become distracting – consequently reducing its usefulness. The constantly growing and evolving data can, sometimes, become noisy. This can lead to “analysis paralysis”, where business leaders no longer know what to do because they overanalyze every data asset.

Additionally, as we’ve discussed with the types of big data, some data may require special handling and processing before it can be acted upon. This can become challenging for startups with a limited IT budget.

Big data best practices

While every company is different, here are a few big data best practices to keep in mind, especially when considering operational efficiency.

• Have specific goals. To reduce the risk of becoming overwhelmed with the amount of big data your company receives and needs to process, it’s best to have a specific business goal tied to the big data. For example, will you be using big data for a new project? Is it for marketing? Determining the specific use case of your big data ensures that your team is aligned with what and how they process the data.

• Upskill your team members. One of the biggest challenges in big data is a skills shortage. Consider training certain team members to be more adept at handling and managing big data as an investment in your long-term success.

• Have a centralized database. Create a space where team members can share knowledge and manage communications about big data. This way, everyone is aligned with your company’s general knowledge of big data.

• Use all categories of big data. You gain better insight into your company and target audience when you can successfully connect and integrate structured, unstructured, and semi-structured data.

• Have a well-planned cloud provisioning strategy. You must have a methodical resource management strategy to handle, analyze, and process big data in the cloud. This ensures better (and more secure) integration, database summarization, and analytical modeling.

Leveraging big data for proactive endpoint management

With a proven track record of enhancing IT efficiency from day one, NinjaOne empowers organizations to leverage the power of data to optimize endpoint performance, strengthen security, and streamline operations. By harnessing the insights derived from vast datasets, businesses can make data-driven decisions that drive tangible results and a competitive advantage.

NinjaOne’s IT management software has no forced commitments and no hidden fees. If you’re ready, request a free quote, sign up for a 14-day free trial, or watch a demo.