In a world driven by data, it comes as no surprise that data analytics is one of the hottest fields in the world right now. If you have been on the internet at any point in the last few years, the terms data science or data analytics will have crossed your path. These terms are umbrella terms that encapsulate a multitude of factions, each of which is interconnected. The term data science rings the same as medicine; there is a lot more underneath the surface.
When we look around, the world is a lot different from what it used to be 10, or 20 years ago. We have seen rapid digitization of processes, automation has become commonplace, and there is always some new innovation coming around. Some are hits, others aren’t but regardless of all this, data is generated. A significant transformation in recent times is the immense, almost meteoric, rise in the amount of data being generated. In 2022, roughly 2.5 quintillion bytes of data were being generated daily.
This is a dizzying number, almost too large to comprehend but it is the amount of data that is created every day. Thanks to this mammoth amount of data being generated, the world of data science has been able to flourish. Leveraging the powers of artificial intelligence, machine learning, the internet of things, and the internet has all allowed for data-driven decision-making.
What is data-driven decision-making? In the simplest of words, this is the process of using real-world data and insights to make decisions based on customer and consumer behavior. As an organization, your primary goal is to generate a profit and scale operations. More profit means more scalability which will in turn translate to better business opportunities.
In the past, executives were able to make decisions based on the limited data that they had. As beneficial as this was, there was a lot of untapped potential. In 2022, we have a plethora of data to go around. An organization can now understand what its clients want because they have a factual awareness of trends and behaviors. Before we get into the nitty-gritty, it is important to understand what we mean by data analysis.
In the simplest of words, data analysis is the process of taking raw data and transforming it into something useful. When performing data analysis, a data analyst will take raw data, clean it, organize it, save it, and process it to generate insights. An insight is a finding generated from data analysis. For example, a data analyst studied declining sales for a certain category of products.
He observed that brand A was performing better than brand B. He ran a few tests on the data and was able to deduce that the latter was lacking because it had increased its price with no significant increase in value. As a result, customers opted for brand A since it cost them less and was the same thing. This is an insight and it is what the people in charge used to make decisions about. This is an incredibly simple example but it sums up the process of data analysis.
Moving forward, an organization’s success hinges on its competitiveness. It is imperative for an organization to ensure that it remains relevant and up-to-date with the latest business practices. In the modern age, consumers have become accustomed to providing feedback. With social media and websites, this has become increasingly simple and efficient as well. As an organization or business owner, you want to ensure you provide as much value as possible to your customers and stakeholders to ensure revenue and growth. The easiest way to get this done is by using big data.
Big data is a term used to condense all of the data types out there into a single term. Using this big data, an organization has the power to learn many different things regarding their business. It allows them to understand what a business needs to add or remove, how to optimize its practices, and also how to generate more data for better insights.
What makes data analysis important?
Regardless of how big your business is; you only have access to a finite amount of resources. For some, it’s a thousand dollars and for others, it’s a billion dollars. Both organizations want to make the most of this, ensuring that every available resource is used as efficiently as possible. Using data analysis, an organization can find out which demographic is interacting on social media, searching the relevant keywords, and how many conversions are coming through.
As an advertiser, big data will allow you to find the right people who need to see your ads. If someone has fly fishing as a hobby, it makes little sense to advertise makeup products to them. It should be known that this process involves trial and error and it can constantly change. Real-time data generation allows data analysts to use the latest information to generate insights to allow for more dynamic advertising.
The job doesn’t stop once you found the right people. After identifying and engaging your target demographic, the next step is to understand how current offerings are faring. Using data analysis, an organization learns more about how its customers behave. First, they found the right demographic. Next, they find out how they interact with the product or service. For example, they will see which age group makes the most purchases, which age group uses which product, which location sells what type of product, how good or bad a product is performing, and so on.
Using these insights, it becomes simpler for brands to adjust and implement. For example, if a vendor sells ice-creams, they want to ensure their stock is sold as soon as possible. They have 4 flavors on offer: vanilla, strawberry, caramel, and coffee. After 3 months, the data showed that the first three flavors were selling out but the last one was barely moving any stock. When they dove deeper, they learned that customers felt that the coffee flavor tasted too strong and would leave a bitter aftertaste.
They also learned that the light brown packaging looked dull and compared to the other flavors, was not as appealing. Finally, the coffee flavor cost more than the other which was a defining factor. These insights allowed the manufacturer to invest time and effort to rectify these issues by tweaking the flavor to tone down the bitterness, updating the packing to make it more eye-catching, and bringing its price to par with the rest of the flavors.
As a business, efficiency should be one of your top priorities. If you put in 10 units of resources, you want at least 8 units of output since 10 units of output is unrealistic. The solution? Big data. If your business seems to be putting in too much with little results, an audit is important. You need to see how resources are being utilized by different departments. This is important because an organization’s net profit is calculated after subtracting operating expenses from gross profit. In order for maximum profits, these operating expenses must be controlled, or else profitability takes a severe hit.
Using big data in conjunction with the IoT, an organization can actively monitor different metrics of a business in real-time. For example, the owner of a coffee shop has noticed that his utility bills are a lot higher than usual, particularly electricity. He observed that he was using inefficient incandescent lightbulbs which were using more power when they didn’t need to.
Moreover, he noticed that his water heater was running a lot longer than it needed to. He now had two choices, reduce usage or replace these items with something more efficient. Seeing as the fall/winter season was upon him, he knew that people would flock to coffee shops so business would be good. He needed to mobilize his store in a way where he was making as much profit as possible.
The coffee shop owner replaced his incandescent lightbulbs with smart lightbulbs and replaced his traditional water heater with a smart water heater. These two upgrades gave him more control since smart home tech lets you monitor usage statistics as well as schedule when it should run. For example, the water heater can be programmed to run during off-peak hours and the lightbulbs can be programmed to use cooler colors and automatically turn off after a set time.
This effort can be applied across the board to boost efficiency, bring down costs, and automate a variety of repetitive tasks. As a result, you save time, effort, and money.
One of the key outcomes of data analytics is predictive analysis. As the name suggests, predictive analysis uses past trends and insights, present market conditions, and future trends to forecast growth. By using data to make decisions, an organization uses real-world metrics to alter its processes. They aren’t going out on a whim, they are using information from the real world and giving their consumers what they want and need. When a business uses data analysis, it equips itself with the right tools needed to promote and sustain growth as well as avoid anything that might end up costing them more than it should.
Understandably, big data isn’t a magic wand that will solve everything and prevent something bad from ever happening. However, it will allow a business to operate at the peak of its ability using data from the real world.
Data analysis allows a business or entity to learn more about itself, the world around it, the past, and the future. It gives us the ability to understand why certain things transpired the way they did and what needs to be done to ensure things work out better in the future. Using insights generated from data analysis mobilizes the user with all the tools and knowledge it will need to remain competitive.
Furthermore, as you collect more data to analyze, you learn what you need and what you don’t. By studying different metrics, you gain an understanding of what is needed to make better decisions and what can be discarded or used elsewhere. Once again, data analysis allows for better decision-making across a variety of areas.
So far, we have made a compelling case for data analysis and why it is important. This part will talk about how data analysis is used in the real world.
The transportation sector is one of the largest in the world. There are millions of motorbikes, cars, trucks, trains, and aircraft around the world. These vehicles are tasked with getting people and goods from point A to point B. These vehicles cover varying distances, consume different amounts and types of fuel, are used by different professions, and have several other metrics involved.
Data analysis allows us to make more informed decisions about how certain fuels impact the environment and how it needs to be managed. For example, if a certain grade of petroleum is more harmful compared to other sources, data analysis will show the impact it has on the environment while also showing how much sells in which area. This will allow for a more efficient deployment of resources.
Using data analysis, transport companies are able to schedule their services better. Train operators have different trains going to different stations at varying frequencies. If station A sees 20,000 visitors daily, it will have more trains going to it. On the other hand, station B sees 100 daily visitors, so it will have fewer trips scheduled.
Using data analysis and predictive analysis, law enforcement agencies are able to control crime. For example, data analysis will allow them to use historical data to see which areas are more prone to criminal activity. It will allow them to understand what causes a surge in this area and also what demographic is responsible. Using data analysis, law enforcement will make more informed decisions when it comes to crime control and patrolling.
The application of data analysis in security can directly result in reduced crime as well as more efficient use of police resources. This will result in reduced crime which is ultimately the goal of every police force.
Risk management is often one of the most common real-world uses of data analysis. Insurance companies use data analysis when dealing with customers to ensure everyone gets what they want. By studying customer data along with other key metrics, it becomes a lot easier to provide better services.
Risk detection is one of the most heralded achievements of data analytics; particularly in the finance sector. Using AI, financial institutions can keep an eye on millions of transactions and immediately identify fraudulent or illegal transactions. It can run 24/7 and instantly flag a suspicious transaction. Once again, it makes processes more effective and efficient by saving time, effort, and money.
The logistics industry operates on the notion of being as efficient as possible. One of the key aspects of their business is delivering parcels, when promised, as promised. With stiff competition, logistics companies need to ensure they are planning and implementing the best possible practices across the board. Using data analysis, they can plan the most ideal shipping routes, reduce delivery times, use more energy-efficient delivery methods, and also bring down delivery costs in every way possible.
Data analysis is one of the most important fields for the future. As Moore’s law is defied on a daily basis, we will see greater data generation and an increased need for it to be analyzed. As the world becomes more intelligent and competitive, data-driven decision-making will become the new norm and in order to ensure this, it is important that data analysis is understood.
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