There’s hardly any resource more fundamental to the strategy and success of a business than data. With simple deductions drawn from this data, such as what kind of products a segment of consumers buy, companies can tune their strategy for better business decisions. But there’s data, and then there’s big data.
Advances in computing have resulted in an increase in the data available to companies. There’s quite simply been an explosion in the amount of data that we generate every day.
While spreadsheets and nicely coded files allowed us to keep a reasonable amount of data, the computing ability to compile, store and organize data on a large scale meant information could be available at the click of a button.
Now, every two days, we create as much data as we did since the dawn of time until the year 2000. Mind blowing isn’t it? And we’re not close to stopping. Currently, there’s about 5 zettabytes of data playing around (if you didn’t know, that’s about 5 trillion Gigagbytes). But by 2020 (yes, in two years’ time), information available will have grown to 50 zettabytes and by 2025, there’ll be 163 zettabytes.
That data is “Big” by any sense of the word. But how much of a role does it play in the strategy and progress of a company? What exactly does it mean and how is it changing our world? Here’s what you need to know about big data and its importance.
What is big data?
It’s almost a funny paradox how big data is considered “Big”. It all started with the explosion in the amount of data available. This big bang happened sometime around the early 2000s and has been rapidly expanding since.
Every time we use a digital device, we generate data. When we go online, the data footprint gets higher. Making a Google query, using some GPS, playing music; all of these actions generate huge amounts of data that we don’t even know is there.
On top of this, there’s a whole lot of computer generated data in the mix too. Data is created and shared every time your smart fridge tells your smartphone there’s no more milk in the fridge. In factories, at businesses all over the world, equipment is increasingly equipped with sensors that gather and transmit data.
All of this data (in its gargantuan monstrosity) can be referred to as big data. The definition is somewhat vague but, big data is large, complex sets of data that arrives from many sources so fast that we’re hard pressed to use it.
The concept isn’t new. As far back as 2000, big data was described in terms of its huge volume, the incredibly high velocity with which it is created and the variety of sources that generate and share this data.
It may be structured, semi-structured or unstructured. Structured simply means that the data is neatly tagged and categorized. For instance, it could be huge amounts of data relating to how many Google users search for the term “AI” in a day.
Unstructured data on the other hand is data that is recorded without much intent or purpose. It’s mostly recorded without being in active use. So, it’s neither neatly tagged nor categorized. This type usually comes in the most variety (videos, text, images), the largest volume, and greatest velocity.
But the thing is no one was really able to either make sense or make use of this data. Traditional means of storing and processing information were simply inadequate to cope with the data. It was pretty much useless. Until now.
Businesses and organizations are now able to leverage all this information to gain powerful insights that foster intelligent decisions.
How does it work?
The point behind big data, and any kind of data, is: the more you know, the more you can do. Quite simply, information is power and in the world we live in today, power is actively measured by how much you know and can act on.
By accessing increasing amounts of data and comparing more data points, it is possible to begin to see relationships that were previously hidden and harness powerful insights from them.
Big data projects use cutting edge analytics that leverage machine learning and AI to harness the potential insights. The process of harnessing the value in this data has three steps.
First, the data is sourced from several places and applications. Generally, traditional means of data integration are incapable of handling this amount of data which are normally in the hundreds of terabytes and even petabytes. In order to help this, the Hadoop and Spark frameworks were created to house it. This process also involves cleaning up the data and presenting in a form that can be harnessed.
The next step is to create a customized solution for managing and working with the data. In this step, companies can decide whether to store the data on premises or in the cloud. The cloud is a popular choice for many companies since they can easily adjust resources to use only what they need at any moment.
The last step is to analyze the data. You know what they say. Your seed remains only a seed until you plant it. That’s when you unlock its value. It’s at the step of analysis that companies can unlock the clarity and insights they need to take their business further.
Why is it important?
Imagine if you knew ten years ago, everything you now know. Think about what you could have done, the steps you could have taken, what you would have become. That’s exactly what big data does for companies. Make no mistake, the insights that they gain from the data they’ve been collecting gives them access to insights that were almost invisible before.
Companies like Netflix and Procter & Gamble are now able to leverage this data to drive product development. They use the data to build predictive models for new products by curating the attributes of past products and the relationship between those attributes and their commercial success. This helps them quickly learn what to sell, when to sell, and how to sell.
There are also several areas like predictive maintenance, customer experience, fraud and compliance, operational efficiency where big data and machine learning are being used to fuel corporate strategy.
RapidMiner + Big Data
The problem with big data is its size. It’s so big that very few companies have the capacity to harness, much less analyze and benefit from the data. Right now, data scientists spend up to 80% of their time collecting and preparing data before they can begin their analysis.
And data is only getting bigger. As it stands, data volumes are doubling in size every two years. If companies are to enjoy the benefits of unlocking insights from big data, they need solutions that allow them to get these insights, quickly and simply. Organizations shouldn’t have to worry about the technology orchestration, but rather focus on the data driven insights .
That’s where RapidMiner Radoop comes in. With Radoop, it’s possible for companies to integrate their big data process flows in RapidMiner Studio while using Apache Spark as the execution framework. Learn how to eliminate the complexity of data science on Hadoop and Spark by leveraging our industry leading code-free user interface, while we seamlessly connect to your Hadoop cluster.