In recent years, terms such as data science, big data, machine learning, and artificial intelligence,among others, have become popular in the world of Digital Marketing being used in many cases interchangeably and generating confusion.
For this reason, at Kiwop, a web development agency and Digital Marketing, we want to help you understand exactly what data science is and how it helps us in marketing.
As the term suggests, Data science is the science that studies data. But this general explanation leaves us with many doubts, what exactly does it mean?
Let’s start with a clarification between the concepts of Data Science and Big Data,which are the most confused.
What is Data Science
Data Science is a science focused on the study of data. Takes care of extracting information from large amounts of data to later interpret them.
The goal of Data Science is to make decisions using a set of tools that allow knowledge to be extracted from the data.
Large data processing is not achieved only by using traditional analysis methods. Data Science therefore involves programming, data mining, machine learning, statistical, mathematics, and data visualization skills, in addition to the business knowledge of the sector in which it is being applied.
What is Big Data
The concept of Big Data is used to describe large volumes of data. Big Data includes structured data, semi-structured data, and unstructured data:
- Unstructured data: digital images, audio or video files, mobile data, sensor data, web pages, social networks, emails, blogs, etc.
- Semistructured: XML files, system log files, text files, etc.
- Structured data: transaction data, databases, etc.
These massive data are characterized by the 3V:
To those that have been added more “V” in recent years such as: Value, variability, among others.
Big Data and Data Science
Big data and data science have transformed today’s digital and technological age. Both terms are closely related to each other, the main difference between them is that the concept of data science falls within the concept of big data.
Data science is conducted within the realm of big data to obtain useful information through predictive analytics, where results are used to make smart decisions.
Without big data there would be no concept of data science. And without Data Science, big data would have no value.
Main differences between Big Data and Data Science
- Large volumes of data are distinguished by 3V: variety, speed and volume. While data science provides the methods or techniques for analyzing them.
- Big Data focuses on technology (Hadoop, Java, Hive, etc.) and analytics tools and software. Data Science, on the other hand, focuses on decision-making strategies and the dissemination of data using mathematics and statistics.
- Big data extracts information from large volumes of data while Data Science uses machine learning algorithms and statistical methods so that computers can obtain as accurate predictions as possible of the data obtained.
How Data Science helps in marketing
Data is everywhere and grows incessantly. In order to assimilate and extract value from this large amount of data, the need arises in companies to have professionals capable of transforming the extracted data into corporate value, or what is the same, useful information that facilitates decision-making within companies.
Studying data science can help companies turn the large amount of data into value insights that facilitate the right decision-making.
The data is obtained through different channels:
- Mobile devices
- Social media
- Online stores
These are just a few of the fonts used. Our tastes, routines or movements generate valuable data for companies that want to know their customers in detail. However, the interpretation of unstructured data does not add any value to companies.
For data interpretation, Data Science includes:
- Data cleansing and restructuring
- Data analysis
- Definition of the right business questions to meet the objectives of the company and can be treated analytically
- Visualization of data with graphs to extract intelligence from them.
- Presentation of insights and business recommendations
- Creation of data-centric products for companies that use analytics to generate new technology solutions.
Data Science requires (in addition to analytical capacity) business knowledge and business vision to extract and transmit recommendations tailored to the needs of the company.
Data Science in Digital Marketing
In today’s digital marketing world we have large amounts of information that we can extract through numerous channels:
- Data obtained by installing applications
- Virtual stores and websites
- CRM systems
- Customer databases
- Advertising platforms
- Social media
- Analytical web traffic tools like Google Analytics
These are just a few of the channels from which we can extract information for our Digital Marketing and Inbound Marketing strategies. But data is received in large volumes and at ever faster speed, so if you don’t know how to interpret it effectively and at the right time, it loses all their value for the right decision-making and only generates chaos.
The today’s digital marketing and online advertising companies require professionals from more scientific sectors and with a business and analytical profile, who also have the necessary knowledge for the application of the data science tools to leverage data obtained and make effective business decisions. If you want your marketing strategy to have the best results, it’s imperative that your digital partner masters data science.
With a good implementation of data science,you can obtain crucial information and achieve levels of marketing segmentation and user interaction that until recently we could not have.
- It allows companies to anticipate the needs of their users and send them offers and adapted and personalized content with greater conversionpossibilities.
- Predict user behaviors to reduce business risk through the right decision-making
- Detect trends and behavior patterns that allow you to design new products.
- Develop marketing and communication strategies tailored to the tastes, geographic data and other relevant information of our potential customers.
- Generate sales opportunities: From segmentation we can see how customers change and locate sales opportunities by implementing strategies such as Up Selling and Cross Selling.
- Avoid loss of customers by observing the behavior of customers who leave the purchase.
- Detect fraud.
Depending on each company, the analysis needs are different and you can find very varied uses to the data.
In any case it is essential to clearly establish the objectives to define the data that is most interested in knowing.