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Big Data

Definition of big data, success stories and the challenges

Big data is the buzzword that’s got people talking. Privacy has been a hot topic within the big data bubble and equally, marketing and finance are also very much in the thick of the action. With big data, brings a wealth of opportunities. Translating enormous volumes of data into valuable insights for businesses has created endless possibilities. But what exactly is big data? More so, how can big data work for your company? We provide you with the low-down of what the fuss is all about.

Big Data defined

It’s not easy to provide a comprehensive definition of big data but to the nearest meaning, due to the sheer volume of data available, traditional methods of data capture and interpretation are obsolete requiring a new approach for analysing data. Advancements in both hardware and software are major contributors to the big data revolution.

Examples of the use of Big Data

The success of predictive analysis is being put to the test through big data with the example of US department store Target. The US firm, based on an enormous amount of data about historical and current buying patterns claim to be able to predict whether a customer is pregnant or not; sometimes before the expectant mother is aware based on analysis data patterns in purchasing habits.

Another fine example is that of Mastercard, which receives vast amounts of information with each transaction. Amounts, time, place, store type or product… they're all tracked. As a result, Mastercard accurately segments clients accordingly which benefits retailers in various locations globally.

From big data to smart data

Latest developments with Big Data space

Big data is sometimes difficult to comprehend with the volumes floating about. To provide you with a glimpse of just how much data we’re talking about, these statistics should give you provide food for thought:

  • 1 hour of video uploaded to YouTube every second
  • 140 million tweets per day
  • 100 billion searches on Google per month

Furthermore, with the ever-evolving smart devices in our daily use including refrigerators, ovens, cars etc. the data volume is on a steep and upward trajectory with no end in sight. It’s claimed that an average of 400GB of day is available per person. Behaviour and characteristics are being tracked over a consumer’s lifetime.

Some of Big Data’s uses

According to research undertaken by Gartner, companies which make best use of big data in their decision making process are 20% financially better off. This should provide great incentives to companies to take big data seriously and embed it into their operations.

Big data can help companies to:

  • Help reduce financial risk
  • Contribute to new product development through data patterns
  • Increase personalisation through behavioural tracking
  • Accurate forecasting through use of intelligent data

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Challenges faced by Big Data implementation

With the sheer volume on the rise, big data can be challenging to implement and here are some the problems faced:

Meeting the speed of data

In today’s hyper-competitive business environment, companies not only have to find and analyse the relevant data they need, they must find it quickly. Visualisation helps organisations perform analyses and make decisions much more rapidly, but the challenge is going through the sheer volumes of data and accessing the level of detail needed, all at a high speed. The challenge only grows as the degree of granularity increases. One possible solution is hardware. Some vendors are using increased memory and powerful parallel processing to crunch large volumes of data extremely quickly. Another method is putting data in-memory but using a grid computing approach, where many machines are used to solve a problem. Both approaches allow organisations to explore huge data volumes and gain business insights in near-real time.

Understanding the data

It takes a lot of understanding to get data in the right shape so that you can use visualisation as part of data analysis. For example, if the data comes from social media content, you need to know who the user is in a general sense – such as a customer using a particular set of products – and understand what it is you’re trying to visualise out of the data. Without some sort of context, visualisation tools are likely to be of less value to the user. One solution to this challenge is to have the proper domain expertise in place. Make sure the people analysing the data have a deep understanding of where the data comes from, what audience will be consuming the data and how that audience will interpret the information.

Data quality

Even if you can find and analyse data quickly and put it in the proper context for the audience that will be consuming the information, the value of data for decision-making purposes will be jeopardised if the data is not accurate or timely. This is a challenge with any data analysis, but when considering the volumes of information involved in big data projects, it becomes even more pronounced. Again, data visualisation will only prove to be a valuable tool if the data quality is assured. To address this issue, companies need to have a data governance or information management process in place to ensure the data is clean. It’s always best to have a proactive method to address data quality issues so problems won’t arise later.

Start to analyse

Big Data and privacy

Another challenge faced revolves around privacy. Certain services users do not always know what information they reveal all. In addition, data may fall into the wrong hands or used in a later stage of things that initially was not thought of. Be open and honest about what data you use and for what purpose.

A cultural mindset

Besides the technological challenges, companies require the necessary in-house to establish and to extract valuable insights all together. In addition, it often requires a significant cultural change for a company to create a data -driven organisation.

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