Tackling the challenges of big data is a daunting task to undertake. Before breaking into the ocean of big data, companies had best be aware of the possible pitfalls that are waiting for them out there.

Big data has become a new buzzword recently. There is no question about that. 2017 marks a drastic shift in using big data in the business sector. More and more organizations are ready to pilot and adopt big data as a central component of their policy. It’s a new field that is booming but still faces many challenges.

Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway. Geoffrey Moore

In this blog post, we are aiming to elaborate on the role of big data in the modern world, potential roadblocks businesses tend to encounter and tools which may ease the pain of big data analysis and optimize workflow by tackling the challenges of big data.

We live in a data-driven world, and it gets noisier each day. I mean now we are all experiencing the effects of big data since it is slowly but steadily creeping into our daily lives. Its usage can be traced almost everywhere: in online shopping, advertising, music streaming, the Google Maps system, etc. Have you ever wondered how the Internet appears to know what you’ve been looking at online? Why do you always get online adverts that are very much relevant to you? How can Spotify deliver music that is geared towards your taste? Why is GPS so trustworthy and how can Google Maps give such real-time reports about traffic conditions? Well, that all comes down to big data. So, as you see, big data is used not only in the business sector but also in our day-to-day life. A few people, indeed, understand that big data is actually helping make our lives easier. If ‘Big Data’ still remains very much a black hole of understanding, continue reading, and today we will try to clear up this issue for you.

Despite all of the fuss around big data, the majority of companies still haven’t realized the potential that big data holds. In fact, it gives information that allows businesses to refine and streamline their services, so as to enable them to gain the edge over competitors and offer a better customer experience. Big data leads to better decisions and strategic business moves.

According to IDC, worldwide revenues for big data will grow from $130.1 bln. to $203 bln. in 2020. Click To Tweet Mind-blowing, isn’t it?

The availability of data, a new generation of technology, and a cultural shift toward data-driven decision making continue to drive demand for big data and analytics technology and service. Dan Vesset

Traditional Data vs. Big Data

Before we go any further let’s establish what the term ‘Big Data’ actually entails and how the results it yields differ from those generated from traditional analytics.

Many say they are already doing big data. What they indeed mean by this is that they have big – often huge – datasets stored in traditional structured databases (RDBMS – Relational Database Management System) using SQL (Structured Query Language). That does not imply, however, that they are ‘doing big data.’ Big data doesn’t just mean simply a lot of data. Click To TweetIt would be a huge misconception to think so. As Bernard Marr once stated big data is smart data that comes in various formats from various sources: social media, public records, traditional business systems and increasingly from the Internet of Things.

I prefer the term ‘smart data’, which emphasizes that thinking intelligently about what to do with your data, and how you can use it to achieve your aims, is far and away a more important element of the big data equation than the simple size. Bernard Marr

The term ‘Big Data’ gained momentum in the early 2000s when industry analyst Doug Laney explained the notion of big data as the three Vs:

  • Volume

Companies gather data from a variety of sources, including business transactions, social media, and information from sensor or machine-to-machine data. In the past, it would’ve been a problem to store such a huge amount of information, but today, new technologies have eased this burden.

  • Velocity

Velocity is the measure of how fast the data is coming in. The data now is no longer static. It is updating every second. So, a few seconds-old tweets or status updates on social media don’t interest today’s users anymore.

A key tenet of big data: Data changes, and those who can recognize the changes and react quickly will have the upper hand. Click To Tweet

  • Variety

Data can be stored in different formats nowadays. For example, database, excel, csv, or in a simple text file. Sometimes, the data may be in the form of video, SMS, pdf or even something we might not have thought about (emails, recorded sound, etc.).

To sum up this paragraph, it is worth mentioning the difference between traditional data and big data. Traditional database systems are based on the structured data that is stored in fixed formats. This is traditionally the way that computers have stored data. It can be arranged neatly into charts and tables consisting of rows, columns or multi-dimensioned matrices. Examples of the unstructured data include traditional ERP reports or Excel spreadsheets. Big data uses both semi-structured and unstructured data. This can include video data, emails, pictures, recorded sounds or text written in human languages. The traditional system database can store only a small amount of data ranging from gigabytes to terabytes. However, big data stores and processes a large amount of data which consists of hundreds of terabytes of data or petabytes of data and beyond. The core difference of big data from traditional data is that it specifically refers to a system that gives data-driven insights based on what is happening now (in real time). To get a better understanding of what big data is, watch a short interview with big data guru Viktor Mayer Schönberger:

Tackling the Challenges of Big Data

Security and Privacy

It is a big challenge to keep such a vast ocean of data secure. Companies need to invest money to ensure security because the bigger your data, the bigger the target it presents to criminals to steal and sell it. We need to rethink security for information sharing in big data use cases. Many online services today require us to share private information (think of Facebook or Twitter), but we do not understand what it means to share data, how the shared data can be linked, and how to give users fine-grained control over this sharing. Data theft is a growing area of crime. Indeed, five of the six most data serious thefts of all time (eBay, JP Morgan Chase, Adobe, Target, and Evernote) were committed over the last recent years. Closely related to the issue of security is privacy. Failing to follow applicable data protection laws can lead to expensive lawsuits and even prison time.

Data storage

Companies are now considering such options as data lakes, which can allow them to collect and store massive quantities of unstructured data in its native format. The problem lies in the fact that data lakes have to be constructed wisely otherwise they quickly become a useless wasteland. Other prefer data warehouses. Warehouses, on the contrary, are less agile and are quite expensive for storing large data volumes. They can only store data that has been structured, while a data lake is universal in this regard. Data warehouses have been around for several years, whereas data lakes are relatively new. Thus, the ability to secure data in a data warehouse is much higher than the ability to secure data in a data lake.

On the flip side, nowadays NoSQL databases seem to be an ultimate trend among mainstream enterprises. NoSQL means Not Only SQL, implying that they still can support SQL-like query languages, but basically, NoSQL databases concentrate on processing unstructured data. In this way, they differ significantly from relational databases that support only structured data. Generally, we may single out four types of NoSQL databases:

  • Key-value data stores are the simplest type of NoSQL databases that are used to store data as a collection of key/value pairs.
  • Document stores are designed to store semi-structured data as documents, typically in JSON or XML format.
  • Wide-column stores keep data in tables with rows and columns similar to RDBMS, but names and formats of columns can vary from row to row across the table.
  • Graph stores use graph structures to store, map, and query relationships. They provide index-free adjacency, so that adjacent elements are linked together without using an index.

Data representation

Big data analytics amounts to nothing unless you report the results properly to the right people in the right way. Representing the data in a well-structured format which is readable and understandable to the people is highly important. Visualization is one of the best ways to represent the analyzed data. Handling the unstructured data and then representing it in a visual format can be a challenging task to do. Here we enumerated for you several powerful big data visualization tools which may come in handy:

  • Google Chart is a free, simple-to-use visualization tool that has a rich gallery of interactive charts and data tools that are quite efficient in handling real-time data.
  • Tableau is a great tool that can connect to almost any database. Tableau is available in several distinct versions The pricing differs in each a lot. Here are Tableau’s new monthly prices: Tableau Desktop Personal – $35; Tableau Desktop Professional – $70 ; Tableau Server –  $35; and Tableau Online –  $42.
  • D3 or Data Driven Document is a Javascript library for visualizing big data. It’s called a “document” because D3 is based on the standard model for web pages: the Document Object Model (DOM). D3 helps you bring data to life using HTML, SVG, and CSS.
  • Hightcharts makes it easy for developers to set up interactive charts on their web pages. It is a free tool for non-commercial purposes.
  • Canvas is also free for non-commercial usage. It has a simple API and 10x better performance. Its charts are responsive and can run across devices including iPhone, Android, Desktops, etc. This is a great option if you are planning to build your experience in Big Data before using a tool commercially.

Businesses should spend far less effort in collecting and aggregating their data and more time gaining insights from it. According to IDC, 90% of the unstructured data is never analyzed. In other words, it is lost. It is very difficult to digest such huge volumes of information that is coming from various sources and is constantly updating. One may get lost, therefore, it’s better to refer to appropriate tools for maintaining and processing large collections of data. We will dwell upon them in the next chapter.

Big data analytics technologies are maturing. Selection of appropriate tools becomes vital when it comes to tackling the challenges of big data. Regardless of the approach we take to collect and store the data, if we don’t have an appropriate tool for analysis, it is of no use to have this data well stored.

Below there is a list of top five open source big data analysis platforms and tools:

  • Apache Hadoop is a programming framework based on Java that offers a distributed file system and helps organizations process big data sets. It supports the running of applications on large clusters of hardware. Hadoop enables the management and analysis of any kind of data from log files to video and can facilitate the analysis of decentralized data across a number of storage systems.
  • Hadoop MapReduce is the original framework for writing applications that process large amounts of both structured and unstructured data. The MapReduce framework has two types of key functions. First, the map function which separates out data to be processed and second, the reduce function which performs data analysis. As MapReduce involves two-stage processing, it’s believed that a large number of varied data analysis questions can also be answered with it.
  • Apache Storm is a free real-time big data-processing system. It is quite simple and can be used with any programming language. Storm has many applications: real-time analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is a good choice for big data analytics as it integrates with existing technologies, which makes processing of big data much easier.
  • GridGrain is a Java-based and open-source middleware solution that enables real-time big data analysis on a distributed computing architecture. GridGain is an alternative to Hadoop’s MapReduce. The open source version can be freely downloaded, or you can choose to purchase a commercially supported version. GridGrain requires Windows, Linux or the Mac OS X operating system.
  • HPCC Systems (High Performance Computing Cluster) is a free open source, massive parallel-processing computing platform for big data processing and analytics developed by LexisNexis Risk Solutions. Developers, data scientists and technology leaders adopt HPCC Systems because it is cost-effective, comprehensive, fast, powerful and scalable.

In this article, we have thoroughly analyzed what big data means in the contemporary rapidly-changing world and have predicted for you several problems you might encounter. But what about those unforeseen challenges that may still lie ahead? It’s better to be always on the alert and plan for any issues that may arise. Thus, we have also provided you with some of the best tools you might find useful while tackling the challenges of big data.