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Businesses and processing Big Data

Posted by: Tom Morris

Big data refers to significantly large sets of data with a specific interest in human activity and interaction, which could be key to achieving successful business outcomes. Identifying patterns in big data enables businesses to recognise any trends which could be used to predict future insights or needs of the business. People Analytics and HR teams can decipher this information and provide solutions that businesses need within critical areas of growth. These are the teams that could be considered as one of the most important resources when improving your business performance. Big data can be considered as an analytical tool that offers great potential to solving more sophisticated business outcomes, however it is not without its challenges.

We create more than a billion gigabytes of new data everyday and this data can be taken from anywhere, used and then analysed by businesses to find solutions to factors such as time reductions, cost reductions, new product development and smart decision making. These factors combined with high-powered analytics can accomplish business related tasks that determine root causes of failures, issues/defects in near real time, calculate buying habits, recalculate entire risk portfolios within minutes and to detect fraudulent behavior before it has any affect. Although a relatively new term, the concept of ‘big data’ actually gained momentum in the early 2000’s and has now been developed into four straight forward ‘V’ definitions by Doug Laney, an industry analyst.

Velocity – The data received comes in at an unprecedented speed and it’s constant. This means that is must be dealt with in a timely manner. Radio-frequency identification (RIFD) tags and sensors are the driving forces that deal with torrents of data in near real-time.
Volume – This is where data is collected from a variety of sources such as social media, business transactions, sensors or machine-to-machine data. In the past, storing this amount of data would have been a problem, but due to new technologies that are constantly developing, the burden of this has eased.
Variety – This is when data comes in all types of formats including numeric data, structured to unstructured, traditional databases to unstructured texts documents, email, video, audio, stock ticker and financial transactions.
Variability – The flow and varieties of data fluctuate and can be inconsistent. For example, when there is a trend in social media or any daily/seasonal events which trigger periodic peaks. This can be difficult to manage with unstructured data.

Currently around 50% of organisations have data analytics in place but it is estimated that 40% are yet to consider an actual solution to their big data.

Data scientists and analysts take masses of data and use mathematical statistics along with programmes such as Minitab, statistical software and Microsoft Excel all of which help to identify relationships through regression analysis. Unstructured data contains value which can be from both external sources such as Twitter and Facebook posts as well as internal sources, for example customer emails. Big data can be analysed with software tools that are commonly used as part of advanced analytics disciplines such as data mining, predictive analytics, text analytics and statistical analysis and mainstream business information software. Data visualisation tools can also play a role in the analysis process. The biggest challenge however is that big data sets are so large and complex that traditional data processing just isn’t up to the job and companies are having to invest huge amounts of time and money into IT teams to manage, maintain and mine it for information.

We produce a huge amount of data on a daily basis and businesses and large corporations often find themselves struggling to digest and organise their data. In cases where there is no effective way of dealing with the constant influx, valuable information can clog the network, and this is where important information regarding business performance can go unnoticed or undiscovered. Balancing the approach between analytical and intuitive as well as being open to new ideas about how to gain value from data, enables businesses to make more informed decisions and the analytical findings can lead to more effective ways of marketing and new revenue opportunities.


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