Data Analytics is a broad subject and has come to the forefront of industry during the digital age as many companies look to utilise the value of data by analysing its contents to drive decisions.
But what is Data Analytics and why bother with it?
Firstly, looking at the definition of both words:
And
Every modern business looks to collect information, from its sales and customers to suppliers and competitors. This Data can then be used to explain trends/patterns, make decisions or influence approaches. Analytics specifies the use of a systematic computation and more explicitly, a computer program has been used to perform a few operations. These collective operations make an algorithm which produces relevant statistics, measures, or Key Performance Indicators (KPIs). The topic of data analytics in business refers to the interpretation of information gathered to help managers and decision-makers in their choices.
So, these statistics, measures and KPIs inform us about trends/patterns, help make decisions and influence our approaches. An example of these could be gathering information on what time people are likely to buy fish and chips and simply opening a fish and chip shop during the times that customers seem most likely to buy.
A more advanced analysis could be collecting mobile usage data to place adverts for fishing rods to Instagram users that follow various accounts that use the hashtag ‘fish’.
The simplest loop to implement for an SME is to capture, analyse, decide, refine:
In our fish and chip shop example, this would be to capture the information about when people want to buy their food. Next, we can visualise the data in a simple, effective way that can help make a quick decision. A line chart showing the number of google searches for fish and chips nearby.
The following plot is from Google Trends, a tool provided by Google that is worth a visit if you're about to open a chip shop. The following results are for the whole of the UK over the previous 24 hours.
Naturally, we expect there to be a peak after office working hours from 5 pm or just before as people begin to consider their dinner but perhaps more unexpectedly there are spikes as night-shift workers finish. Their decisions making is far faster after a long nights work. The total interest could be assumed to be the area under the line curve and although there are two spikes late in the evening at 2 am and 4 am, the competition could be tougher in built-up areas. Perhaps more time researching using trends would help narrow down the opening of our fish and chip shop.
This blog will look to explore the broader topic of mathematics, statistics, data science and computer science, particularly in their role of optimising businesses processes and creating advanced insights that generate value for companies. We hope to provide you with a constant stream of ideas that will lead to innovations in your business!
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