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The small business owner's guide to big data

18th Jan 2017
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At this point in the evolution of business, trade, and consumerism, virtually all businesses are heavily influenced by technology and the internet. As a business owner, you’ve probably heard the term “big data” time and time again. If you’re a numbers person, you also probably know a thing or two about how companies are using customer data to serve them better and make more profits.

If you’re interested in finding better, more efficient ways to market your product or service, the mining and application of big data is a practice you will invariably need to familiarize yourself with. A 2015 survey by Gartner found that over 75% of companies are investing (or planning to invest) in big data.

While questions are already being raised whether big data has lost its sheen, VC Matt Turck of FirstMark believes that “in many ways, we’re still in the early innings of the Big Data phenomenon.”

That is evidenced by the fact that the number of companies providing ancillary products and services in the space keeps increasing:

big data landscape
Matt Turck, Jim Hao, FirstMark Capital

Big data enables companies to make more educated and informed business decisions to optimize their operations across the board. Basically, it provides action-driven information that shows what is working well, what needs to be improved, and what can safely be scrapped.

What exactly is Big Data?

Big data is a term that describes the collection and use of large volumes of information that influences business on a day-to-day basis in order to make the best decisions. However, it’s not just the amount of data that is important. What really matters, is how companies decipher and use it. These insights can be analyzed to influence strategic business moves in a faster, more intelligent way.

This information consists of vast amounts of records on people across the entire online landscape. The data comes from sources like sales charts, web traffic, social media, and customer support.

When discussing big data, the two main terms vital to understanding its purpose are structured and unstructured.

Structured data refers to information that has been organized into a specifically formatted database in a way that its elements can be used for precise processing and analysis. Some examples would be names, dates, and addresses.

Unstructured data is the generic tag for information not contained to a certain data type or any other system or structure. This type of information can be textual, non-textual or a combination of both. Examples would be email messages, documents, instant messages, slide decks, and audio files or video files.

Big data is extremely useful on many levels. It provides us with detailed insights on how people interact with their communities and the world around them. Perhaps the most useful role big data plays in business world is in the innovation of methods of customer profiling. It lets us know how and where people spend their time and money, as well as what trends are emerging or dying.

What makes data big?

Big data is frequently broken up into four characteristics known as the 4 V’s: The extensive volume of information, the variety of different types of data, the velocity in which the data needs to be processed and analyzed, and of course, the value that must be extracted from the data.

Volume – Big data delivers a tremendous amount of information. However, it is the granular data that is truly influential to decision making. Therefore, brands must have an intuitive system that processes the high amount of unfiltered/unstructured data that has unknown value. The main task at hand is sorting through all the information from diverse things like business transactions, social media insights, or machine-to-machine data to find relevant takeaways. This used to be an incredibility difficult task. Luckily, technologies like Hadoop have made it much more manageable.

Variety – Big data comes in many shapes and sizes. Unstructured data requires a lot of complex processing to for actionable perceptions to be gained. Once this wide range of information is properly deciphered, there are many common threads between the structured and unstructured data that lead to similar conclusions or reveal parallel paths to the goal.

Velocity – Streams of new data can come in at unprecedented speeds; you must deal with them in a timely manner if you don’t want to get buried or thrown back into endless delays. Typically, it’s better to process high velocity data as soon as it’s collected rather than storing it to be analyzed later. Some applications, as in in IoT for example, require real-time evaluation.

Value – Now, the trick in using all of this information is knowing how it can benefit your operations. Understanding things like customer sentiment, preferences, locations, patterns, and behaviors are crucial in making educated business decisions that keep you moving forward. Discovering value within big data comes down to asking the right questions, knowing what to look for, and making appropriate inferences.

Two more important Vs that have been put forward are validity and volatility.

big data attributes
IBM

How can I learn more?

On a global scale, the amount of big data being created and stored is nothing short of incredible. With the digitization of most services and transactions, there is unbelievable potential for businesses across all industries to gain valuable insights to make use of. That being said, there is only a very small amount of big data that actually gets analyzed. In fact, a study found that 99.5% of big data isn’t put to proper use.

Perhaps one of the biggest roadblocks is that a lot of companies simply do not have a firm grasp how it works and what it can do for their business. Luckily, there are many options available to help deepen your understanding.

For instance, Python is a great place to start when you’re exploring how to interpret, analyze, and manipulate data streams. It has a wide range uses as a general programming language that can utilized for qualitative and analytical computing. Python is commonly used in a broad range of fields including finance, oil, physics, and signal processing. Zeolearn offers a phenomenal course to get you familiarized with Python and how you can carry out predictive analysis based on your big data findings.

Python course

Knowing your way around big data and what it can do for your future is only half the battle. The next big challenge is implementing your knowledge into your day-to-day operations.

One of the major advantages of using big data in business is identifying where the issues and snags are, then finding the solutions. As a business owner or manager, earning a Lean Six Sigma certification is a great way to learn about the detailed approach to using data-driven results to collaborate and improve company practices. Knowledgehut offers a top-to-bottom course that will prepare you with everything you need to ace the certification exam.

Six Sigma course

Getting yourself up to speed on big data and how to properly implement it could very well be the best business decision you’ve ever made.

While there are challenges in arguably every area where big data is being used, brands everywhere are learning more and more about how effective it can be. As marketers, analysts, and statisticians continue to decipher big data, it will gradually become easier and more accessible to everyone.

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