Top five areas that use Data Analytics
Data Analytics is one of the fields of Data Science, and it can change your business for the better. Understand how it works and check out the areas most impacted by data analysis!
No matter what your business or industry, Data Analytics can help you make decisions.
Correctly analyzed data, aligned with business objectives, can transform the reality of a corporation.
That’s why the demand for Data Science and Data Analytics solutions has only increased over time. That’s because understanding the patterns that influence initiatives can be the difference between the success and failure of successful business strategies, especially in times of crisis.
Want to know why Data Analytics has become a rising possibility for businesses? Follow along and find out why more and more companies are becoming data-driven!
Step 1: Start with culture!
In the last decade, data has become the most valuable asset in understanding user behavior. It ensures the prominence of disciplines such as Data Analytics and Data Science even in the most varied business models.
However, this game-changing concept didn’t happen overnight. To create a dynamic capable of consistently turning numbers into insights, you need to have direction, invest in infrastructure, and trained professionals.
But the most indispensable requirement is culture.
Culture is the basis of everything. Even in terms of becoming proficient in data-driven decision making. We are talking, of course, about data culture.
It’s virtually impossible to have teams think analytically about data before you set goals for them to organize themselves around.
So, let’s start at the beginning: the first step in the journey towards data culture is to spread an analytics culture within your company.
What is Data Analytics?
Data Analytics is the process of cleaning, transforming, and modeling unstructured data (Big Data) in order to uncover useful information.
Part of this objective is achieved through ETL processes (extract – transform – load), highly efficient strategies that standardize data from different sources according to specific rules.
The idea is to transform data into valuable information and, with that information, generate business knowledge.
If your company isn’t data-driven yet, you may be wondering why this process is so important. It’s simple: because it guarantees:
- Data integration agility;
- Scalability;
- Operational security;
- Performance;
- Data quality.
Want to dive into the world of data and start building a sustainable data culture? Check out our E-book “Data-first: Complete Guide to Making Your Business Data-Driven”.
But first, let’s get to know the three types of analytics most used in Data Analytics.
What is Data Analytics Analytics?
Data is not uniform. Unlike code, it varies constantly. This is due to its origin, which is totally susceptible to change: the real world.
Therefore, your analysis also needs to fit and vary depending on a few factors. We will highlight three important factors for a Data Analytics strategy to be successful:
- Data quality;
- Qualified analysts;
- Organizational commitment to data-driven decision-making.
To better understand the benefits that companies can obtain when implementing a Data Analytics strategy, check out our list of the three most common types of analytics.
While we are only presenting three in this article, the list can be much longer. If you’re looking for more information, feel free to contact one of our experts to learn more about the many possibilities surrounding Data Analytics.
1. Predictive analytics: increase business predictability
When a company invests in structuring its data, it can work with what we call predictive analytics.
Basically, with the help of data scientists, it is possible to use technologies such as Machine Learning and Artificial Intelligence to turn data into insights that provide more accurate predictions of the future.
This increases business predictability and can help you make decisions today that will positively impact the future of your business.
2. Prescriptive analysis: using past performance to generate recommendations for the future
The objective here is to determine the probabilities for a given decision.
For example, a manager might be wondering how much return they would have if they increased their investment in sales training by 10%. Prescriptive analysis can provide.
3. Descriptive Analysis: Tracking Key Performance Indicators to Understand the Current State of Business
Descriptive analysis, as the name implies, is concerned with describing exactly what is happening right now. The object of this study is the present.
It might not seem as useful within a healthy setting, but companies lose a great amount of money and resources to invisible challenges, which can cause bottlenecks that affect processes for years.
Descriptive analysis focuses on questioning what is happening to answer why it is happening. Identifying the root of the problem makes challenges much easier to solve.
Top five areas that use Data Analytics
1. Education
One of the biggest pains in the education sector is students dropping out. Within the context of the pandemic, these numbers have soared. While at the same time we’ve witnessed an exponential increase in course offerings.
While an increase in course offerings is very positive, it also increases the number of dropouts. A large number of students enroll and end up stopping courses midway to start another one. This causes immense damage to institutions that have invested in digital infrastructure.
Among other possibilities, Data Analytics can help by using data to predict which and how many students are most likely to drop out of courses. Schools can use this information to generate strategies aimed at retaining these students, ensuring the survival of the business.
2. Banking and Financial Sectors
These two sectors are based on saving money and reducing risks. Data Analytics can help with the two biggest challenges of these industries: predicting fraud and tracking and managing defaults.
In simple terms, the strategy consists of analyzing the consumption profile of customers seeking loans or taking out insurance.
In just a few seconds, Data Analytics can identify and answer whether this person will be able to pay what they owe and the best interest rate for their profile.
3. Retail + Logistics
You may have noticed that many online stores already know what before you even express any interest. How is this possible? Data Analytics and its huge databases.
The retail sector was one of the first to invest in data analysis strategies. When you understand the consumer’s profile, it’s easy to anticipate their needs.
Extending this strategy to the Logistics sector, retailers are able to reduce the price of their products due to reductions in the time and cost of their deliveries. Through Data Analytics, companies can make products available in stock according to the preference of consumers of a particular region. Before they even make the purchase.
4. Security
Data Analytics can be used to predict crimes before they happen. It might seem like a scene from Minority Report, but all this is doing is determining areas where an increase in on-duty police officers can help lower crime rates.
This is already a reality in some cities in the United States.
5. Health
The costs of medical treatments are extremely high. While the cost of prevention is vastly lower. We’re talking about customers, health plans, hospitals, and clinics.
So it’s pretty obvious that the healthcare industry needs Data Analytics to help prevent disease. In the United States, this is already a reality: data analysis is used as a way to predict the possibility of a person developing a certain disease.
Data Analytics profiles are based on huge amounts of data, taking into account previous procedures and family history. With this, the health system can act preventatively, reducing the cost of health plans.
Data Analytics can also gather data from certain regions to calculate the probability of developing disease outbreaks. Something that will become increasingly necessary in the future.
Bring Data Analytics to Your Business!
If you’ve made it this far, you probably already have some ideas about how Data Analytics can help your business.
We know that the data analytics journey is no cakewalk, it takes time and effort. To shorten the path, ask for help! Consultancies can identify opportunities and help you implement a Data Analytics strategy. Why not contact us and chat with our data scientists about your goals and challenges?
