What is Big Data and the Effects On Small to Medium Sized Business?

How Will Big Data Affect Your Small or Medium Sized Business?

What is Big Data and the Affects On Small to Medium Sized Business?Every day businesses, large and small, are already contributing to and benefiting from big data. The information element has become so ingrained in daily transactions, many don't even notice its presence. However, every time information is entered onto a website, an email is sent, or an online financial transaction takes place, it is very likely becoming part of a larger complex of archived information. That resource is becoming increasingly available for smaller business to take advantage of for planning, strategy, marketing, finding new opportunities, and evaluating past practices. The trick is in understanding what big data is, and how it can be used.

What is Big Data?

In short, big data involves enormous volumes of data that are very complex for query and filter to get useful data. It also grows and changes at a pace too quickly for many data tools to easily handle. A simple query could take weeks to execute, as the analytical tool gets lost in a never-ending ocean of data. Big data today is not just the information itself; it incorporates the tools, management of data and subject matter in terms of collection, storage, and utilization. 

The History of Big Data

Big data emerged decades ago. The most familiar form of early, large database usage, were search query engines like Yahoo, AOL and Google. Since the digital age arrived, the prevalence of big data and sources that produce it has become astronomical since those early “million hit search” days. Today, entire buildings are dedicated to the storage of big data; many corporate users are relying on even bigger companies like Microsoft, Google and Amazon, just to handle the storage and computing.

What Are the Business Uses for Big Data?

The primary uses of big data are in the statistical information that can be gleaned from it. Through finding emerging patterns, using clustering to identify common traits and forecasting future estimates, companies gain new insights and products that never existed before. An entire new statistic and reporting industry has emerged and is used worldwide. Today, this area of use is generally known as “data science”.

The second benefit is married to the first - taking the value of existing statistical and categorical data, then producing predictions as to where future trends may go. There is no crystal ball involved here. This area, called “predictive analytics”, takes historical data sets and applies formulas to estimate how future trends will emerge. As more information is included in the sets, the estimates become more and more accurate.

These benefits, individually and combined, help produce the third big benefit – better decision-making. Whether it’s through traditional paper reports or big screen dashboard reporting tools, managers and leaders are now tapping into vast pools of information that is quickly summarized; this helps with decision-making about the business’ future direction. The use of big data and data science can produce more accurate assessments of how to react to market changes and move forward. 

Technical Requirements for Using Big Data

There is no question that computers and Internet capability are absolutely essential for any business to tap into big data. Accounts with cloud storage are also a key required element if the business doesn’t have its own significant storage capability, as well. However, the tools to analyze big data don’t require rocket science. Instead, one needs to have a basic understanding of how statistics work, a strong knowledge of what the data means, and a good understanding of data available to query. These skills create actionable, infinite information possibilities. There are useful, low cost or even free classes to help staff get started in spreadsheet training, database management, and SQL programming. Better yet, there are big data consultants or firms to accelerate solutions to produce quicker returns on the investment.

These three subject areas provide the foundation for anyone to operate big data analytical tools on their own.

Effective Big Data Collection Processes

After implementing the basic approach and collecting the necessary equipment or environments to be functional, the next big step is figuring out what data to collect, and how to store it. Data can come in all sorts of sizes and shapes. It can be something as simple as a collection of email information. Google, for example, has made an entire business model of culling through all the millions of emails Google account users provide by using Google Mail, inherently giving the company permission to scan those messages, and find trends. Other sources can be in the form of geospatial data – the marrying of map information with table data, and creating new visual geographic products. Just about every day now, we are presented with a new map, slicing and dicing data on a map of the country. Most companies have sources of data at their fingertips. It’s just a matter of identifying the source, and a method of capturing it, to then store the collected data.

Effective Big Data Storage Systems

Traditionally, businesses have used relational databases (like Microsoft Access, SQL Server and Oracle) to store information. Relational databases are simply a set of data tables related by some type of identifier, commonly called a key. Relational databases do not scale well for modern Big Data efforts. Sometimes data is spread out between several databases across a company, and traditional methods for getting data are slow and not very useful. When you merge these databases into a singular large database, you get is a mixture of data from multiple databases that are tied together by the same key or even common traits. That relationship can then produce reports that is not possible for individual databases to create on its own. However, without knowing what one is building a relationship upon, it’s like blindly constructing a house. This is where knowledge of SQL programming comes in, where you learn the “why” to the database, “how.”

NoSQL is a mature alternative to the traditional relational database. NoSQL generally means “Not only SQL (Structured Query Language). This means that information can be stored in a variety of ways that are designed to support the three “V”s of Big Data. These V’s are Velocity, or able to handle real-time changes quickly; Volume, or able to “scale wide” as the data collected grows; and Variety, able to adapt to changes in structure and complexity of the data. Information can be stored in a “Document DB”, which can be thought of as storing data in a category list. If the data changes, add another bullet to the list or an entirely new list. Another option is Key-Value, where the data has a key, just like a relational database, but related items are stored in the value. The value can contain a single word, a sentence, a paragraph, a data table…almost anything. The advanced version of a Key-Value is a Column, but data is stored in a column that holds lots of other columns of data. The concept of keys is the same, but the value would be like an entire data table. Those columns are then clustered into an overarching topic.

The final version is Graph storage, which can be thought of like a Venn Diagram on steroids. Each topic has a relationship with other topics that “intersect” and can be followed to find other related topics that may not have been obvious at first glance.

Effective Ability to Share and Transmit Big Data Securely

Cloud access capability has opened up the door, not just for storage, but for transfer and co-working with data and data products. Instead of being constrained to physical limitations of the hardware carried from one party to another, it can simply be stored in the cloud then accessed remotely by everyone with permission to do so. Topical search results are a great example of this flexibility. Search results can return millions of data “hits” as well as pictures, news, shopping or other similar things. It’s generally not the kind of thing to manually carry from one computer to another. However, with cloud sharing these results can be bookmarked, accessed, shared and reviewed without physical equipment limitations. The cloud approach to searching data has turned Google into one of the largest and most profitable companies in the world. 

How do Big Data and Machine Learning Come Together?

Why should a business take a look at machine learning? Because of the efficiency and automation possibilities, machine learning can be the advantage a small business needs when playing with big data, but not employing enough staff to handle it manually. Why again? Machine learning involves a system learning how to interpret and handle big data independently. The more the system gets access to and works with the data, the more accurate the system functions will become. This is not magic; it’s an application of algorithms in action, and the system learning from its mistakes to curate and manage data better. The benefits of machine learning are plentiful: automated data management including collection, correction, storage, and reporting. Another benefit is when multiple systems work together in the cloud, the considerably faster speed in which machine learning can identify new data trends. This speed gives the ability to produce insights faster than dozens of data analysts can produce manually.

Closing Thoughts

A jump into big data doesn't happen overnight; it's not like buying a new software tool, installing it, and using it within an hour or two. A good amount of design and thought goes into the implementation, but the rewards can be extremely powerful. Small businesses today have a huge advantage with the capabilities of the Internet, cloud-based tools, and scalable online apps versus sunk infrastructure. It's a world that didn't exist a few years ago.

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