Data has always been relied upon to make business decisions. It's good data that allows businesses to understand how well things are going, and where improvements need to be made. With competitive pressures forcing businesses to improve their decision-making, the ways in which businesses collect and analyse data are always evolving. Businesses operating on gut feel and handwritten notes are often left behind by businesses using the powerful tools of today.
The Evolution of Data Analytics
To emphasize the importance of modern data analytics, let's first look at history. The earliest preserved writing records were found in Sumer (today's Iraq) and incredibly, they date back over 4,000 years ago. You might be asking yourself what this has to do with data analysis, and the answer to that is that these clay tablets contained lists of ploughmen who were employed by the state at the time.
More than that, the inscriptions also calculated their wages, and later clay tablets from the time found records of livestock, grain, and other commodities. These records were used to track inventory and sales, as well as identify any areas of improvement. It's amazing to think that even so long ago, the importance of analysing data was well-known.
If we fast forward to the modern world, data analytics tools have come a long way. It wasn't that long ago that businesses relied on traditional methods of data collection and analysis. They used spreadsheets to manually gather information, and this was often both a time-consuming process and prone to error.
Over time, paper spreadsheets were replaced by software that automated much of the process. This then led to a focus on not just automated systems, but real-time analysis. This has become especially important today where everything moves so quickly and businesses often don't have time to sit around and weigh things up.
It's hard to say exactly how they will evolve in the future, but more widespread integration is probably a good guess. Data analysis tools of the future will have the ability to integrate many different types of software and read many forms of data to get a complete understanding of a business. They will also be able to create very reliable forecasts of customer behaviour, allowing them to run effective simulations for different strategies. Many people believe big tech companies already understand customer behaviour better than the customers themselves. How far this will go is an unsettling thought.
The Role of Predictive and Prescriptive Analytics
When we talk about data analysis, predictive and prescriptive analytics are two different but equally important tools in the decision-making toolbox. To understand how they work, we must first understand how they differ from descriptive analytics.
Descriptive analytics is about looking at historical data to identify patterns and trends that have already happened. This could be collating the results of a survey or looking at how a previous marketing campaign performed. Predictive analytics uses those patterns and trends to try and predict what may happen in the future, and prescriptive analytics takes things a step further and aims to identify the best actions a business can take based on these forecasts.
These types of analysis have really come into their own with the invention of new technologies like artificial intelligence (AI). Whether AI will completely transform society is unknown, but one thing it does exceptionally well right now is analyse large amounts of data. Not only does it do this significantly faster than a human can, but it's also able to identify trends and correlations that a human might have otherwise missed, and then make predictive assessments and prescriptive suggestions.
Although this can be extremely powerful, businesses have to make sure they are careful. If the data you're feeding a tool is of low quality, or worse than that, inaccurate, the predictions will be unreliable and the recommended decisions to make will be wrong. Businesses need to make sure that their data collection methods and processes are good and that the data they're collecting is accurate.
Data Analysis in Different Business Sectors
Another consideration for businesses is that the role of data analysis varies in different industries. Let's take the finance sector, for instance. Let's say that an investor wanted to use an instrument like the ES futures to speculate on the S&P 500. They could use tools to analyse historical market trends to predict which way the markets might move. The sophistication of these tools varies massively, with simple tools being preferred for something like futures trading. Where it gets interesting is with AI that can take into account different types of economic data along with market trends. There are massive implications for AIs making investment decisions, especially if humans struggle to understand the data analysis.
Contrast that to the healthcare space, where data analysis can instead be used to guide decisions around a patient's treatment plan. By analysing the health history of the patient, as well as their current situation and medication needs, practitioners can predict what problems are most likely to arise in the future. Pre-empting problems like this could be used as a means of saving costs by the hospital administration, too.
If we look at another industry like retail, customer insights are everything. The behaviour patterns of buyers especially can be analysed to drive all sorts of different decisions. Marketing campaigns, product development, stock and supply chain management, staffing needs and many more can all be affected by these insights. You could even go as far as saying it's impossible to run a retail business without strong data analysis tools.
The manufacturing industry is different again. Instead of trying to predict what products a buyer wants, their focus is more on the efficiency of operations. One way this plays out is in predictive maintenance. Machines are fitted with sensors which relay their operational status in real time, and this data allows manufacturing businesses to plan in advance for maintenance. Doing this instead of waiting for a machine to break down saves both money and time.
The last industry to highlight today is entertainment. You may have noticed that when you're scrolling Netflix the shows and movies that get recommended to you are different from when one of your friends is scrolling on their account. This is because Netflix and other streaming companies are constantly analysing your viewing habits, and combining that with your demographic information to predict what else you might like. Doing this keeps people on their platforms for longer and is more likely to create a long-term customer.
Although only a few different ones were highlighted today, that list of industries could have been hundreds long. The role of data analytics in modern business is undeniable, and it will only become even more important in a future where technological advancements make this kind of analysis both cheap and accessible for everyone.
