Of course, you have often heard the word Data Science in this all-digital era, right?
This knowledge is a form of analysis that provides insight and business value that is more optimal and accurate. In particular, its practices, methodologies, tools, and training can provide invaluable information from large amounts of data.
Given that every day, there is an increasing amount of data, this also requires data practitioners to assist in processing this data.
Businesses nationally and internationally are now using data for the development and decisions of their company based on the data owned by the company.
This article will briefly discuss the many benefits of Data Science that you can apply to your business. Want to know what are the advantages? Immediately, we refer to the following article!
Advantages of Data Science for Business
There are at least 5 main advantages of data science in business:
1. Reduce risk and fraud.
Data scientists are trained to identify data that stands out in several ways. T
hey build statistical, network, pathway, and extensive data methodologies to model predictive fraud tendencies and use them to create alerts that help ensure timely responses when encountering anomalous data.
2. Provide relevant products.
One of the advantages of data science is that companies can discover when and where their products are selling best. This can help offer the right product at the right time and develop new products to meet customer needs.
3. Personalized customer experience.
One of the most valuable benefits of data science is the ability of sales and marketing teams to understand their audience at a very granular level. With this knowledge, companies can create the best possible customer experience.
4. Increase security
You can also use data science to improve business security and protect sensitive information. For example, banks use complex machine learning algorithms to detect fraud based on deviations from customers’ typical financial activities.
These algorithms can uncover fraud faster and with greater accuracy than humans due to the large volume of data generated daily.
Even if you don’t work in a bank, algorithms can be used to protect sensitive information through an encryption process.
Studying data privacy can ensure your company doesn’t misuse or share sensitive customer information, including credit card details, medical information, social security numbers, and contact information.
The combination of algorithms and human judgment can bring businesses closer to higher levels of security and ethical (as ethical) data use.
5. Predicting Future Market Trends.
Collecting and analyzing data on a larger scale can allow you to identify emerging trends in your market. Tracking purchasing data, celebrities, and influencers, as well as search engine queries, can reveal what products people are interested in.
By staying abreast of the behavior of the market you are targeting, you can make business decisions that will allow you to be ahead of the business competition.
Data Management activities
Data sources are very diverse, and the implications of the data collected also vary. In digital data standards, at least the typical data is divided into three types:
1. Structured data, namely data that has been managed, processed and manipulated in an RDBMS (Relational Database Management System). For example, table data is input from a registration form in a web service.
2. Unstructured data , namely in the form of raw data that has just been obtained from various activities and has not been adjusted into a database format. For example, video files obtained from the camera.
3. Semi-structured data, namely in the form of data that has a structure, for example, in the form of tags, still needs to be fully structured in the database system. For example, data with the same title but different fields are based on the characteristics of the filler.
The Data Engineer’s job is to align the three data types, including managing the data schema. Why is tidying up the data such an important task? When it comes to data with huge sizes, efficiency needs to be done in the data architecture.
The goal is to provide convenience and speed in managing and accessing data. For data scientists, the validity of data is also determined by how relevant the data source is, either as a complement or a comparison.
Meanwhile, data analysis activities are carried out in Python or R to manipulate data and use SQL to perform queries (including creating relationships) on data sources.
Coding is done when the data source has become a file extension ready to be processed.
Universally four general formats can be accepted by almost all data analysis systems, namely Comma-separated Values (CSV), Scripts (.py, .ipynb, .r, etc.), table application files (.xlsx, .qgs, etc.), and web programming files (.html, *.svg, etc.).
Data Scientist Roles
Most data scientists in the industry are proficient and trained in statistics, mathematics, and computer science. Their experience is broad-based, which also extends to data visualization, data mining, and information management. It is widespread for them to have previous experience in infrastructure design, cloud computing, and data warehousing.
Here are 6 ways how data scientists can add value to your business using data science:
1. Empower Management to Make the Best Decisions
An experienced data scientist is likely to become a trusted advisor and strategic partner to a company’s top management by ensuring that staff maximizes their analytical capabilities.
2. A data scientist communicates and demonstrates the value of divisional data
Its purpose is to facilitate better decision-making processes across the enterprise through measuring, tracking, and recording performance metrics and other information.
3. Directing Action Based on Trends
A data scientist examines and explores company data, after which they will recommend and define specific actions that will help improve divisional performance, better engage customers, and ultimately improve profitability. This will ultimately help determine your business goals.
4. Challenge Staff To Adopt Best Practices
One of a data scientist’s responsibilities is ensuring that staff understands and understands the company’s analytics product. They prepare a team for success by demonstrating the effective use of systems to extract insights and suggest actions. Once staff understands product capabilities, their focus can shift to issues that matter only to address key business challenges.
5. Identify Opportunities
During their interaction with the enterprise’s current analytical systems, data scientists will question existing processes and assumptions to develop additional methods and analytical algorithms. Their jobs require them to constantly increase the value derived from company data.
6. Decision-Making with Measurable Evidence
With the presence of data scientists, collecting and analyzing data from multiple channels have eliminated the need to take high risks. Data scientists create models using existing data that simulate various potential actions — this way, companies can learn which path will lead to the best business results.