Implementing data analytics can often feel like jumping into a deep dark hole — which is why we outline four broad analytics techniques and how they can be applied.
Descriptive analytics looks back at historical data and aims to answer the ‘what’ questions. It does this by combining existing raw data from multiple data sources and creates valuable insights. An example of descriptive analytics might be the revelation that last month saw a particularly high return rate of ‘Machine A’. While this is valuable data that contributes to business insights, it would simply tell us what happened, without really telling the user what caused these issues. That is because descriptive analytics is backward-looking, and to truly understand the core issues requires human expertise.
Data-driven companies usually aim to push beyond using only descriptive analytics, combining other types of data analytics to create better business insights.
Through Diagnostic Analytics, historical data can be measured against other data to answer ‘why’ something happened. Diagnostic analytics allows businesses to drill down into the details, to identify patterns and trends, and to potentially identify flawed assumptions and dependencies. It can give in-depth insights into any number of problems. Taking the return rates of ‘Machine A’ as an example, diagnostic analytics may reveal that the month with the highest return rate coincided with high return rates of other products that came in the same shipment. Further analysis and investigation may reveal that the container in which goods were delivered in was damaged en-route to the store, causing damage to not just one line of products, but all products in that shipment.
Moving towards more modern analytics methods, we have predictive analytics. This type of analytics tells us what is likely to happen and when it might occur by combining the results of descriptive and diagnostic analytics to detect patterns and to predict future trends.
Referencing the same ‘Machine A’ example again, predictive analytics may proactively take into account factors that were not obvious from the outset. For example, predictive analytics may reveal that return rates increased with seasonality, and that the recent spike was not a one-off, but rather a yearly trend.
Predictive analytics can be an invaluable tool for forecasting. To harness the power of predictive analytics requires conscientious implementation and continuous optimisation of data and algorithms. As data is intrinsically historical, the results of your predictive analytics will be highly dependent on the quality of data and stability of the situation.
Looking towards a not-too-distant-future is the rise of prescriptive analytics. As the name suggests, prescriptive analytics recommends the best future actions to take. Through machine learning and artificial intelligence, prescriptive analytics can help to circumvent any potential likely problems and capitalise on emerging trends. Prescriptive analytics uses algorithms to combine historical data with current data to make relevant prescriptions for the situation at hand.
Some common prescriptive analytics can be found in online bookstores including bookdepository, Better World Books and Barnes & Noble, which makes book title recommendations to buyers based on their browsing and purchase history.
There you have it, the 4 common techniques you can use to break down big data into meaningful results that can improve the efficiency and revenue for your business. Do remember that there isn’t a ‘gold’ standard or right way to do it. You should match the right data and analysis to help uncover better insights for your company.
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