Data rich environments Marketing Analytics

Introduction

In the analytics and marketing sector, the prominent factor for its expansion is the data. This data refers to the online footprint of a consumer, which in its raw format is recorded, contextualized, and presented in a factual format. The strings of processes involved in drafting the final output are done through various marketing tools. One of the widely used tools to analyze this data in Artificial Intelligence and Machine Learning algorithms. The data generated and gathered from the consumers cannot be manually analyzed as it will require a hundred men to work continuously with keen eyes to spot a pattern.

Rather than employing a hundred people, Big Data companies install complex algorithms, Artificial Intelligence, for example, and analyze this data accurately. With a few downsides of it being biased, if not programmed properly, a coder or a developer is hired and is a part of the core team to keep an eye on the algorithm and reprogram it if need be.

Various Types of Data

Data plays a fundamental role in shaping a business's future. If a company can gather the right set of data, interpret it and act accordingly on the perceived data, the company's course will be on the road to success. The data comes in various formats, and the amount of data accessible to the companies is far greater than it used to be a couple of years ago. The types of data can be categorized into two categories: Structured and Unstructured Data.

• Structured Data

The data in a fixed field, within a record or a file, is defined as structured data. Storing of Structural Databases is mostly done in the relational database (RDBMS). The content in this type of data could be text or numbers, and the sourcing can be both manual or automatic. For it to be sourced automatically or manually, it must be within the RDBMS structure. SQL (Structured Query Language) is the programming language used for Structured Data. IBM developed this language in the 1970s. Examples of structured data are phone numbers, addresses, names, credit card numbers, etc.

• Unstructured Data

Unstructured data is all of the data, but it is not structured. This type of data has no data model and is mostly stored in its native format. Unstructured data can be textual or non-textual and is stored in a non-relational database. Example of a non-relational database in NoSQL. Unstructured data is of two types, human-generated and machine-generated. Examples of human-generated data are text files such as emails, presentations, etc. Data from social media, communications including phone recordings, chats, and media like audio files and video files. Examples of machine-generated data are satellite imagery, atmospheric data, space exploration, surveillance of photos and videos, traffic and weather data.

• Semistructured Data

The third type of data, semistructured data is which falls between the other two types of data. Semistructured data is also a type of structured data but does not fall into the formal structure of the relational database. Even though it doesn't entirely fall into the relational database, it still employs the same features as that of the structured database, such as searching for elements, tagging system, or markers. Semistructured data is also termed the self-describing structure.

Apart from types of data, it's essential to learn how the data is stored, processed, and delivered to the marketing analytics businesses. Usually, the data is processed in warehouses or lakes. Unlike the conventional warehouses or lakes, the definitions of data warehouses and lakes differ exponentially. The data is processed in centres where a huge computing unit is installed, which performs the heavy-duty work of analyzing and processing algorithms like Artificial Intelligence.

Data Warehouses And Data Lakes

Data Warehouse: A system that aggregates, stores, analyzes, and processes information from various different sources and delivers it to business intelligence systems is called a Data Warehouse. Data Warehouse's purpose is to give valuable insights to the businesses by evaluating the data that has been sourced and analyzed to derive an accurate pattern. Data warehouses and Business Intelligence platforms are at their prime since Business Intelligence gives insights to companies that serve as a business advantage, which is the secondary reason for its current business popularity.

There are three different data warehouses, Data Mart, Enterprise Data Warehouse, and Operational Data Store. A Data Mart is nothing but a repository that holds data that is in the interest of multiple businesses at the same time. Enterprise Data Warehouse is also a repository that holds data in a standardized format obtained from multiple sources. The data is factualized and segregated before ingesting it in to the warehouse and making it available for other businesses. An Operational Data Store has data stored obtained from various transactional systems. The Operational Data Store (ODS) stores the data in the Enterprise Data Warehouse for long-term analytics of the data.

Data Lakes: A Data Lake is also a repository that stores a huge amount of data, but it is unrefined. The data is stored without being passed through a transformation layer or, better yet, an integration layer. The data that has been imported could be unstructured, like media files, images, PDFs, etc., or it could be structured like relational databases or else semistructured where those could be JSON files or even CSV files. The differences between Data Warehouses and Data Lakes are distinguished based on their storage solutions. Data warehouses use the ETL processing layer where E stands for Extract, T for Transform, and L for Load. In this method, the data is thoroughly analyzed and passed on to the target for immediate consumption. No processing is required for this type of data. This method is often known as the schema-on-write method.

In Data Lakes, the ELT processing layer is applied. Data is ingested and passed on to the target without applying any processing. The data is processed on-demand with specialist tools, like Big Data analytics, and this type of method is known as the schema-on-red method.

Various Methods Of Analyzing Data

Data in its various forms can have content beyond imagination. To analyze all of the data, it would take years for a human to do so. Apart from it being a time-consuming job, it's also a tedious job. A tired person will be incapable of noticing any patterns or important fragments of data. To ease the stress, various techniques are used to analyze the same. The most common algorithms used to analyze the data are Machine Learning, Artificial Intelligence, Natural Language Processing, etc.

Artificial Intelligence: Artificial Intelligence is a term given to a set of instructions that conform to an algorithm that acts and processes data like a human being, given that the machine, computer, or robot has no consciousness and is emotionless. A few examples of AI are the facial recognition system in a smartphone which allows a person to unlock the mobile using his/her face, and voice assistants, which over the years have become so advanced that they speak the natural language.

Machine Learning: Artificial Intelligence is a superset of machine learning. Although Machine Learning is similar to AI, it can learn from the data ingested and reprogram itself. It cannot perform any action unless specified.

Natural Language Processing: It is a stem of AI that primarily aids a computer in understanding, processing, and analyzing human language. NLP's aim is to understand what is being typed, spoken, and to make sense of it to facilitate functions based on the same.