You’ve probably heard it before from a smaller enterprise—you may have even said it yourself;  “our company doesn’t have ‘Big Data,’ and therefore does not need data analytics, Business Intelligence (BI) or data science.” 

There is widespread misinformation that because a company is small in terms of revenue or employees, they don’t have Big Data. When they hear the term, they think of giants like Amazon and Google, and quickly turn down the idea that Big Data solutions are a necessity for them. 

Let’s set the record straight.

While it may be true that small-sized companies don't have the data analysis needs of Google or Amazon or Microsoft, those smaller companies still have incredible amounts of data to sift through. 

If, for example, the company is a sales-based organization, they certainly generate invoices and marketing lists. They probably also have some type of Customer Relationship Management (CRM) solution and thus have suspect, prospect and customer/client information stored within a database. They have financial information such as receivables, payables, and general ledger. 

What about all the emails, instant messages, Skype chats, and recorded video downloads (YouTube, webinars, etc.)? 

What about all of the spreadsheets, Microsoft Word documents, PDFs, PowerPoint slide decks, eCommerce transactions, CAD files, JPEG, GIF and TIFF files, text documents, archive files (zip), MP3,iTunes, and WAV files? 

That’s just the tip of the proverbial iceberg. They also have a plethora of data that they don't even consider on a day-to-day basis: machine data. Data such as web server log files, emails and email logs, firewall traffic logs, server logs, PC and network device traffic logs. 

What about phone logs? Database transaction logs? Mobile, anyone?

All of this data “sits” on their corporate network (whether on-premise or cloud). In fact, person for person (per capita, if you will), a 25 employee company probably has as much data as Microsoft or IBM, or Google or Amazon.

So how is that data analyzed and consumed by the organization? Sadly, most of that data is likely ignored! Many of the clients I speak with don’t go much beyond descriptive analytics (e.g. generating historical reports as a comparison to the present.) 

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Some may even try to diagnose specific issues by asking, “Why did that happen?” But very, very few use the data to try and predict what will happen next. They instead use their “intuition” or worse, they just guess. Nor do they actually plan the appropriate actions to take if, in fact, those predictions do come to fruition. They don’t even think about prescribing actions until after an event occurs. 

As an example, even though cyber security breaches are in the news daily, few organizations bother to monitor or manage security events. Yet clearly, they could benefit from even a rudimentary analysis of their information security data.

Most companies will argue that budgets are tight, as is bandwidth. And that may be true, but ignoring the data, especially as it grows exponentially, is not the answer.

Many, if not most, organizations in the small-medium-business (SMB) space don’t need Hadoop or Apache Spark clusters. Nor do they need a robust team of data scientists running around ready to apply solutions to any problems that may arise.

But there are many aspects of Big Data analytics that can be applied to small businesses so that they may reap the benefits of proactive, data-driven decision-making.

Whether that means finding a third party solution or providing an in-house team with the necessary tools to manage that data is going to vary based on a company’s needs. But foundational courses specific to the platforms and frameworks a business uses are the obvious solution to preparing a team to deliver data-driven results. 

Smaller companies can and should take advantage of the new-age big data tools and techniques that the larger organizations are using because Big Data isn’t just for big enterprises.