Untitled Flashcards Set

Use of Big Data by Organisations

Data analytics is the collection and analysis of data to find patterns and draw conclusions. The more data is available, the better the resulting analysis and findings.

Data analytics can be significantly enhanced using artificial intelligence approaches such as machine learning. Machine learning is an approach whereby developers build a programme that creates a model based on a set of initial data, and the model can then be used to, for example, make predictions such as customer demand for Product X.

The programme ‘learns’ from the additional data by comparing its output to the actual results and adjusting its model accordingly.

So, as data about real-world sales of Product X are collected and fed into the programme, the model is improved and produces more reliable predictions. So, the more data it is given, the better a machine learning programme gets at making forecasts.

One of the most significant benefits of big data is its impact on decision-making. Big data, combined with advanced analytics and other technology, provides better business insights and enables better decision-making.

6.3.1 Organisational Strategy

Data analytics can be used to simulate many different scenarios rapidly and relatively cheaply (compared to the time and effort involved in human-developed scenarios). This method can determine the likely best markets and approaches to meet growth and profitability ambitions for profit-focused businesses. It could predict the impact of climate-related events and enable governments and charities to provide relief where and when needed. It can also be used more individually, for example, to provide medical diagnoses.

6.3.2 Customer Satisfaction And Forecasting Demand

Big data analysis allows organisations to understand their stakeholders’ preferences and needs. For example, customers can be sent shopping offers tailored to their habits and needs. Big data from sources such as visitor website numbers, customer surveys and social media posts can be combined with data relating to the economic climate and competition to spot trends, predict customer demand, and prevent issues from arising.

An example of a solution relating to addressing issues early is Gnip. This is a service that collects data from social media. As you can imagine, social media creates vast volumes of data every hour. Analysis of social media posts often highlights what people care about, which is valuable for business decision-making. This is sometimes called ‘social listening’ – finding out what people think about an organisation – its customer service, product or service, or its attitude towards the natural environment and this information can be broken down into demographics to find out what group or type of person is expressing the opinions.

Suppose there is a small but growing stream of dissatisfied messages relating to a specific product. In that case, the business can proactively address the problem before it becomes a widespread and widely communicated complaint that could adversely affect the company’s image.