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Big Data Explained

“You can have data without information, but you cannot have information without data.”

Daniel Keys Moran (DKM)

Big Data is a term that describes large, hard-to-manage volumes of data – both structured and unstructured – that inundate businesses on a day-to-day basis. But it’s not just the type or amount of data that’s important, it’s what organizations do with the data that matters. Big Data can be analyzed for insights that improve decisions and give confidence for making strategic business moves.

What is Big Data?

“Big Data” is one of the commonly used buzz words of our current era, but what does it really mean?

Here’s a quick, simple definition of big data. Big data is data that is too large and complex to be handled by traditional data processing and storage methods. While that’s a quick definition you can use as a heuristic, it would be helpful to have a deeper, more complete understanding of big data. Let’s take a look at some of the concepts that underlie big data, like storage, structure, and processing.

How Big Is Big Data

It isn’t as simple as saying “any data over the size ‘X ‘is big data”, the environment that the data is being handled in is an extremely important factor in determining what qualifies as big data. The size that data needs to be, in order to be considered Big Data, is dependant upon the context, or the task the data is being used in. Two datasets of vastly different sizes can be considered “Big Data” in different contexts.

To be more concrete, if you try to send a 200-megabyte file as an email attachment, you would not be able to do so. In this context, the 200-megabyte file could be considered Big Data. In contrast, copying a 200-megabyte file to another device within the same LAN may not take any time at all, and in that context, it wouldn’t be regarded as Big Data.

However, let’s assume that 15 terabytes worth of video need to be pre-processed for use in training computer vision applications. In this case, the video files take up so much space that even a powerful computer would take a long time to process them all, and so the processing would normally be distributed across multiple computers linked together in order to decrease processing time. These 15 terabytes of video data would definitely qualify as Big Data.

Types Of Big Data Structures

Big data comes in three different categories of structure: un-structured data, semi-structured, and structured data.

UnstructuredData is data that possesses no definable structure, meaning the data is essentially just in one large pool. Examples of unstructured data would be a database full of unlabeled images.

Semi-structured Data is data that doesn’t have a formal structure, but does exist within a loose structure. For example, email data might count as semi-structured data, because you could refer to the data contained in individual emails, but formal data patterns have not been established.

Structured Data is data that has a formal structure, with data points categorized by different features. One example of structured data is an excel spreadsheet containing contact information like names, emails, phone numbers, and websites.

Metrics For Assessing Big Data

Big Data can be analyzed in terms of three different metrics: volume, velocity, and variety.

Volume refers to the size of the data. The average size of datasets is often increasing. For example, the largest hard drive in 2006 was a 750 GB hard drive. In contrast, Facebook is thought to generate over 500 terabytes of data in a day and the largest consumer hard drive available today is a 16 terabyte hard drive. What quantifies as big data in one era may not be big data in another. More data is generated today because more and more of the objects surrounding us are equipped with sensors, cameras, microphones, and other data collection devices.

Velocity refers to how fast data is moving, or to put that another way, how much data is generated within a given period of time. Social media streams generate hundreds of thousands of posts and comments every minute, while your own email inbox will probably have much less activity. Big data streams are streams that often handle hundreds of thousands or millions of events in more or less real-time. Examples of these data streams are online gaming platforms and high-frequency stock trading algorithms.

Variety refers to the different types of data contained within the dataset. Data can be made up of many different formats, like audio, video, text, photos, or serial numbers. In general, traditional databases are formatted to handle one, or just a couple, types of data. To put that another way, traditional databases are structured to hold data that is fairly homogeneous and of a consistent, predictable structure. As applications become more diverse, full of different features, and used by more people, databases have had to evolve to store more types of data. Unstructured databases are ideal for holding big data, as they can hold multiple data types that aren’t related to each other.

How Big Data Works

Before businesses can put big data to work for them, they should consider how it flows among a multitude of locations, sources, systems, owners and users. There are five key steps to taking charge of this “big data fabric” that includes traditional, structured data along with unstructured and semistructured data:

• Set a big data strategy.

• Identify big data sources.

• Access, manage and store the data.

• Analyze the data.

• Make intelligent, data-driven decisions.

1. Set a Big Data strategy

At a high level, a big data strategy is a plan designed to help you oversee and improve the way you acquire, store, manage, share and use data within and outside of your organization. A big data strategy sets the stage for business success amid an abundance of data. When developing a strategy, it’s important to consider existing – and future – business and technology goals and initiatives. This calls for treating big data like any other valuable business asset rather than just a byproduct of applications.

2. Identify Big Data sources

• Streaming data comes from the Internet of Things (IoT) and other connected devices that flow into IT systems from wearables, smart cars, medical devices, industrial equipment and more. You can analyze this big data as it arrives, deciding which data to keep or not keep, and which needs further analysis. 

• Social media data stems from interactions on Facebook, YouTube, Instagram, etc. This includes vast amounts of big data in the form of images, videos, voice, text and sound – useful for marketing, sales and support functions. This data is often in unstructured or semistructured forms, so it poses a unique challenge for consumption and analysis. 

• Publicly available data comes from massive amounts of open data sources like the US government’s data.gov, the CIA World Factbook or the European Union Open Data Portal. 

• Other big data may come from data lakes, cloud data sources, suppliers and customers.

3. Access, manage and store Big Data

Modern computing systems provide the speed, power and flexibility needed to quickly access massive amounts and types of big data. Along with reliable access, companies also need methods for integrating the data, building data pipelines, ensuring data quality, providing data governance and storage, and preparing the data for analysis. Some big data may be stored on-site in a traditional data warehouse – but there are also flexible, low-cost options for storing and handling big data via cloud solutions, data lakes, data pipelines and Hadoop.

4. Analyze the data

With high-performance technologies like grid computing or in-memory analytics, organizations can choose to use all their big data for analyses. Another approach is to determine upfront which data is relevant before analyzing it. Either way, big data analytics is how companies gain value and insights from data. Increasingly, big data feeds today’s advanced analytics endeavors such as Artificial Intelligence (AI) and Machine Learning (ML).

5. Make intelligent, data-drivendecisions

Well-managed, trusted data leads to trusted analytics and trusted decisions. To stay competitive, businesses need to seize the full value of big data and operate in a data-driven way – making decisions based on the evidence presented by big data rather than gut instinct. The benefits of being data driven are clear. Data-driven organizations perform better, are operationally more predictable and are more profitable.

Advantages Of Big Data

Ability to process Big Data in DBMS brings in multiple benefits, such as-

• Businesses can utilize outside intelligence while taking decisions. Access to social data from search engines and sites like facebook, twitter are enabling organizations to fine tune their business strategies.

• Improved customer service. Traditional customer feedback systems are getting replaced by new systems designed with Big Data technologies. In these new systems, Big Data and natural language processing technologies are being used to read and evaluate consumer responses.

• Early identification of risk to the product/services, if any

• Better operational efficiency. Big Data technologies can be used for creating a staging area or landing zone for new data before identifying what data should be moved to the data warehouse. In addition, such integration of Big Data technologies and data warehouse helps an organization to offload infrequently accessed data.

• Predictive analysis will keep you ahead of your competitors. Big data can facilitate this by, as an example, scanning and analyzing social media feeds and newspaper reports. Big data also helps you do health-tests on your customers, suppliers, and other stakeholders to help you reduce risks such as default.

• Big data is helpful in keeping data safe. Big data tools help you map the data landscape of your company, which helps in the analysis of internal threats. As an example, you will know if your sensitive information has protection or not. A more specific example is that you will be able to flag the emailing or storage of 16 digit numbers (which could, potentially, be credit card numbers).

• Big data allows you to diversify your revenue streams. Analyzing big data can give you trend-data that could help you come up with a completely new revenue stream.

• Big data is important in the healthcare industry, which is one of the last few industries still stuck with a generalized, conventional approach. As an example, if you have cancer, you will go through one therapy, and if it does not work, your doctor will recommend another therapy. Big data allows a cancer patient to get medication that is developed based on his/her genes.

• If you are running a factory, big data is important because you will not have to replace pieces of technology based on the number of months or years they have been in use. This is costly and impractical since different parts wear at different rates. Big data allows you to spot failing devices and will predict when you should replace them.

Challenges Of Big Data

  • One of the issues with Big data is the exponential growth of raw data. The data centres and databases store huge amounts of data, which is still rapidly growing. With the exponential growth of data, organizations often find it difficult to rightly store this data.
  • The next challenge is choosing the right Big Data tool. There are various Big Data tools, however choosing the wrong one can result in wasted effort, time and money too.
  • Next challenge of Big Data is securing it. Often organizations are too busy understanding and analyzing the data, that they leave the data security for a later stage, and unprotect data ultimately becomes the breeding ground for the hackers.

Methods Of Handling Big Data

There are a number of different platforms and tools designed to facilitate the analysis of big data. Big data pools need to be analyzed to extract meaningful patterns from the data, a task that can prove quite challenging with traditional data analysis tools. In response to the need for tools to analyze large volumes of data, a variety of companies have created big data analysis tools. Big data analysis tools include systems like ZOHO Analytics, Cloudera, and Microsoft BI.

Final Thoughts

The amount of Big Data is already massive, but it is expected to grow exponentially as new technologies such as the more pervasive IoT devices, drones and wearables will jump into the fray. 90 percent of the big data in the world today has been generated in the last 2 years, and the recent advancements in Deep Learning are playing a key role in helping businesses decrypt this precious goldmine of information. Big Data and Business Analytics solutions are now a mainstream technology, and together with AI and automation, they represent the foundation upon which the digital transformation process is built.

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