Key Points

• Big data is generally unorganized and needs manipulation to be readable.
• Small data is collected internally or externally and is easier to interpret.
• Both are useful for any organization, depending on the intended use case.

Big data vs. small data, which one are you choosing for your business? Data is a big market right now, something that it has held the distinction of for the last decade or more. Data drives business decisions, calls forecasts for future trends, and so much more. As such, it helps to have a plan in place on how to choose to collate and interpret that data.

When looking at business data, we can separate them into big and small data categories. While at first glance these might seem like a no-brainer to suss out the details of, there is quite a bit of distinction to make when considering both. As such, we’re going to define and go over the benefits of both and determine which is the right call for your business going forward.

What Is Big Data?

Big data is a bit of a misnomer of sorts. We aren’t talking about something that might encompass a single server in your network room. Big data refers to massive data sets, spanning volumes of storage so vast that you would need whole server farms to effectively serve it. This is ostensibly delivered through collating data from much larger sources, like social media posts, e-commerce reviews, and so forth.

Manipulating and preparing big data for interpretation isn’t for the faint of heart in the slightest, as it is an unstructured set of information that often lacks any sort of similarity between data points. Typically, most firms and organizations are going to use external management services to leverage these data points.

Further, they’re also likely to employ trained data scientists, usually with a background in computer science or data science, to interpret the data. In its raw state, it’s unusable. However, when presented in a curated format, it can reveal quite about your business’s current standing, trends in the market, and so much more.

Dealing With Volume

When you’re dealing with petabytes upon petabytes of information, it takes time to sift through. When it comes time to discuss the processing needs of big data vs. small data, big data is always going to require more resources. This is thanks in part to the sheer size of it.

Processing in volume needs special consideration, you aren’t running this solely on a single workstation after all. That’s where frameworks like MapReduce or Spark come into play. These distributed frameworks do the processing off-site, which has its drawbacks as we’ll discuss.

Where big data excels is in highlighting patterns. This allows the end user to make informed decisions about where to head next with their organization, versus flying solely based on historical data from internal sources.

Pros and Cons of Big Data

There are a few things that come to mind when talking about the pros of big data. For starters, you have a vast lake of sorts to pull your information from. Since it is a steady and rapid flow of data, you have plenty of opportunities to comb through and determine patterns that might benefit your organization.

One major con to consider is the lack of veracity in your data sets. If you’re pulling massive amounts of data from a site like X, you’ll have to sift through misinformation and outright lies. Further, big data has quite an intense resource demand.

You’re offloading processing and storage to off-site locations, meaning your organization isn’t in full control of what it can handle. This might not be a big deal for larger enterprises, however, it is something to consider if your company prefers to do everything in-house. Ultimately, the desire to pursue this type of data is going to rely on your needs as an organization.

What Is Small Data?

Small data is a bit of a misnomer just like big data. Small data refers to data sets that can be largely collated and manipulated in-house. If you’re a small to medium-sized organization, this is what you’ll largely be dealing with when considering data analytics.

Internal data sources are far more trustworthy as a whole, thanks in part to not having external influences to sway the bias of your data’s trends. Processing needs are greatly reduced, as we’ll discuss in a moment.

It does bear mention that the sheer size of small data means you don’t need specialized employees to take a closer look at things. If you’ve got internal analysts with any foundational knowledge of statistics, that is more than enough to take a closer look at the likes of collected small data in your organization.

Internal Management

There is certainly the possibility of using small data with external service providers. However, one of the major things to keep in mind is that small data is readily handled in-house. If you’ve got servers and the storage to accommodate it, you can more than handle the needs of small data in-house.

The thing to keep in mind with small data is the notion of control. This is one of the big differences we’ll discuss later when comparing big data vs. small data. You have a far lower signal-to-noise ratio, meaning you can cut through the chaff and get down to what matters at the moment.

Collating the data points is relatively relaxed, and the pace of collection is slow, given the speed of any organization. Compared to the surge in information from big data, this comes off as more manageable as a whole. However, this isn’t a perfect solution either.

Pros and Cons of Small Data

The major pros to talk about with small data are that the information is accurate, structured, and uniform in appearance. You don’t need specialized staff members to come and format the data in an easily digestible format. Small data could readily be taken from a database straight to any form of visualization with minimal hassle.

When talking about the cons, data is slow to receive, as previously mentioned. While this might not mean much in a vacuum, if you’re looking to make informed decisions promptly, it is something worth considering. You might have deadlines to reach and this data is a key factor in completing.

Small data is of little consideration to larger enterprises. When you’re making informed decisions with a larger organization, big data is going to be more relevant as a whole compared to this data collection method. As such, applications with larger businesses are going to be limited.

Big Data vs. Small Data: What’s the Difference?

Now that we’ve taken the time to outline the pros and cons of each, let’s dive into which data type you’ll want to consider. There isn’t necessarily a better option when considering big data vs. small data. As we’ve highlighted, there is a massive difference in the collection, preparation, and presentation of both data types.

As such, determining which is better for your organization is going to come down to a few criteria in regard to your needs. In all honesty, the better workflow is to hybridize these data types. Taking historical internal data to guide things like production standards, while making informed decisions with the big data you’re able to collect.

Rather than put the horse before the cart, let’s take a moment to explore use cases. Depending on the size of your organization, this can be paramount to determine early in the decision-making process so you know exactly which approach to take.

Defining the Scope

Before deciding on which type of data to use, you need to consider the purpose of the information. If you’re looking to analyze flaws, faults, and defects in the production line, big data might be overkill. However, if you’re looking to gather customer opinions on a recently released product, then small data might not be enough.

It becomes important to define the scope of your efforts before investing in the infrastructure needed for either data type. Collating user opinions based on social media posts is going to be a costly endeavor when factoring in new employees, managed collection providers, and a processing framework to handle the data itself.

Most organizations have the pieces in place when it comes to collecting small data, but that only carries so much water when gauging larger decisions that can impact your organization as a whole. So, take the time to define the scope, consider the impact, and make a decision from there.

Suiting Your Needs

Not every decision you’ll make is tied to the likes of big or small data. It becomes important to consider the size of your needs. The collection of data can be seen more as a component in process improvement, or in product design if necessary. If you’re looking to see where work efficiency can be improved, huge data sets aren’t going to be useful.

If you’re looking to gauge future market conditions before the launch of a new product, employee performance metrics are going to be of little use. As such, it pays to take the time to consider exactly what you need before going through with your manipulation of whichever data set.

They have vastly different use cases, much like any other tool in a toolbox. While they might look similar to the average layperson, it’s like the difference between an impact gun and your average socket wrench set from a local department store.

The Right Infrastructure

Data is essentially useless without the mechanisms in place to handle it. Small data can be handled by just about anyone who can build a bar graph in Excel or has a foundational knowledge of a basic query language like SQL. Big data has different needs, with far heftier requirements. The average employee handling big data needs to be adept with the likes of Python, R, or Java among other languages.

Then there’s the matter of scaling, which is something I’ve seen far too many organizations fail to consider. Think of your data for a moment like a fish in a tank. A small tank is going to lead to an unhappy fish, especially when you start adding more and more into the tank. As such, the ability to have infrastructure that scales with your needs is something to keep in mind.

Big data needs to scale horizontally, which most service providers can more than accommodate. Small data is going to require an investment in infrastructure internally, as you’ll likely need more servers, more storage, and more analysts given the size of internal data sets.

Other Useful Tools and Concepts

Data is only one part of the equation when it comes to running a successful business. You might want to take a look at some other concepts to get your organization up to speed. Learning how to leverage FMEA in product design can help alleviate issues that arise later in production. This useful approach is one of the best ways to reduce defects in your production output.

Additionally, learning how to use a Pareto chart is a great way of pinning down the potential cause of issues in your processes. These are simple charts to learn, provided you’re gathering accurate information. Our guide covers how to use them to get the best results.

Conclusion

Big data vs. small data isn’t a competition, but rather tools that are suited for different jobs. At the end of the day, depending on the size of your organization, you might be looking at using both in your day-to-day operation. Just make sure you pick the right one for the job.