It is no secret that today’s organizations are looking for new opportunities to improve the overall customer experience, while increasing revenue and reducing costs.
This data can be the key to succeed in the process. This is why all industries are trying to exploit the use of data and AI as strategic resources to achieve better business outcomes in these times of economic uncertainty.
In fact, data has become a crucial asset for companies of all sizes. They all have access to a very large amount of data that can offer huge opportunities to collect insights about customers while, at the same time, support the business’ decision.
Data-driven insights allow the creation of products that meet customers’ needs and allow the growth of any business.
In this article, we will explore different types and characteristics of Data and how we can leverage them as its best for the benefits of any business through Google Cloud Platform (GCP).
About Big Data: why is their role so crucial?
Let’s start giving a clear definition of Big Data.
“Big data refers to extremely large and complex datasets that can’t be easily managed, processed, or anlyzed using traditional data processing methods or tools. It typically involves the collection, storage and analysis of massive volumes of structured, semistructured and unstructured data from various sources, such as social media Company’s ERP/CRM, Social Media, Sensors, Devices and other transactional systems”
Gartner
So, Big Data are extremely large data sets collected by organizations that can be mined for information and used in machine learning projects, predictive modeling and other advanced analytics applications
Obviously, they are not all the same. There are actually three primary types of Big Data: structured, semi-structured and unstructured.
But let’s dive deeper into the matter:
- Structured data: Structured data is highly organized and can be easily processed and analyzed. This type of data is typically stored in relational databases and includes data such as financial transactions, customer data and inventory records. Moreover, structured data is organized into predefined fields and is easily searchable.
- Semi-structured data: Semi-structured data is less organized than structured data but still characterized by some form of organization. This type of data is typically stored in formats such as XML or JSON and includes data such as emails, social media posts and web logs. Semi-structured data does not have a predefined schema, but just some level of organization to make it easier to analyze them in comparison with unstructured data.
- Unstructured data: Unstructured data is the most complex and challenging type of Big Data. This type of data is typically not organized and does not have a predefined schema. Examples of unstructured data include audio and video files, images and text documents. Unstructured data is difficult to analyze using traditional methods and requires advanced tools and techniques such as natural language processing and machine learning.
If we want to be picky, in addition to these primary types of Big Data there is also a fourth type called dark data. Dark data refers to data that is collected but not used for any specific purpose, including things like server logs, sensor data and call recordings. Dark data can be useful if properly analyzed since they can provide valuable insights into areas such as customer behavior or operational efficiency.
The 5Vs of Big Data
The main characteristics of Big Data are commonly referred to as the “3 Vs”, that refer to Volume, Velocity and Variety. However, in the last few years two additional characteristics have been added: Veracity and Value.
Let’s take a closer look at each of these characteristics:
- Volume: Big Data refers to datasets that are too large to be processed and analyzed by traditional data processing tools. This means that the volume of data is one of the defining characteristics of Big Data. With the advent of IoT devices, social media platforms, and other data sources, the volume of data being generated is growing at an unprecedented rate.
- Velocity: Big Data is generated at a high velocity, meaning it is produced rapidly and must be analyzed in real-time or near-real-time. Social media platforms, financial markets, and other applications generate data at a high velocity, making it challenging to process and analyze the data in a timely manner.
- Variety: Big Data is generated in many different formats, including structured data (such as transactional data) and unstructured data (such as social media posts, videos, and images). The variety of data formats adds complexity to Big Data analysis, as different techniques are required to process and analyze each data type.
- Veracity: Veracity refers to the reliability and accuracy of the data. Big Data can be noisy, meaning it may contain errors or inaccuracies that can lead to incorrect conclusions if not properly addressed. Veracity is a critical characteristic of Big Data, as it can impact the accuracy and usefulness of the insights generated from the data.
- Value: The ultimate goal of Big Data analysis is to extract value from the data. Value can be in the form of insights that help businesses make informed decisions or identify new opportunities for growth. The value of Big Data is directly proportional to the quality and accuracy of the insights generated.
To harness the potential of big data, companies and organizations use advanced technologies and analytics techniques to store, manage, process and analyze data. The goal is to uncover valuable insights, patterns, trends and correlations within the data to inform business decisions, improve operations and drive innovation
So, now that you know more about big data, here’s the most important thing: how can we get those exploiting them?
What Are The Benefits Of Leveraging Big Data with Google Cloud?
There are actually different benefits a company will get from leveraging its data at best.
But first, it is crucial to understand the challenges companies are facing today to see what are the benefits of leveraging big data through Google Cloud:
- EVOLVING CONSUMER BEHAVIOR: Customer journeys are increasingly multi-device, complex and unique.
- SILOED AND COMPLEX DATA SOURCES: Data from CRM systems, email tools, ad platforms, and offline data sources.
- RISING USER EXPECTATION: Consumers are more privacy-conscious.
- INDUSTRY CHANGES: The industry is shifting to meet higher user expectations. Limited use cases for measuring and reaching users with pixels.
- IDENTIFYING THE RIGHT TECHNOLOGIES: Making the right decisions on technology stacks.
So, developing a first party data strategy with GCP means building the foundation for machine learning and companies that link first-party data sources see:
- 2X incremental revenue generated from a single ad placement, communication, or outreach.
- 1.6X improvement in cost savings over companies with limited data integration.
- +35% improvement in campaign performance using advanced technology with active human supervision: machine learning powers modeling & automation solutions that drive campaign performance and help quantify the impact of your advertising investment.
- Marketing insights: Bring together data from siloed systems to gain comprehensive insights
- Audience segmentation: Leverage Google Cloud machine learning capabilities to build differentiated audiences
- Customer experience: Deliver enhanced customer experience to your users by understanding the sentiments
So, it is clear how Big Data has become a crucial asset for any business.
Let’s take a step forward: activating advanced analysis by leveraging Google Cloud Smart Analytics
Today’s world is a data-driven one. So many companies are constantly looking for innovative ways to gain and collect valuable insights from the massive amount of information at their disposal.
Where traditional analytical approaches can fail because of the complexity and scale of modern data sets, AI promises to unlock unprecedented capabilities. Analytical models are mathematical representations of real-world systems, designed to capture patterns, relationships and trends within data.
Well, these models can help you gaining a deeper understanding of your operations, customers’ behavior or market dynamics. How? By harnessing the power of machine learning, deep learning, natural language processing and other techniques, you have the possibility of automating repetitive analytical tasks or discovering complex patterns and insights.
What about Smart Analytics by Google Cloud? Smart Analytics is a part of Google’s platform of next generation of analytics solutions.
These solutions make it easier for customers to manage, access, use, and visualize data to accelerate transformation and empower innovation through better and more timely business insights.
In particular, who need Smart Analytics are the ones who value the following voices:
- Decrease cost
- Improve their user experience
- Increase productivity and employee efficiency
- Drive business innovation or transformation
What is GCP Smart Analytics approach?
- Data ingestion at any scale
- Reliable streaming data pipeline
- Data lake and data warehousing
- Advanced analytics
Through these steps you will actually leverage analytical models and AI, activating advanced analysis, gaining valuable insights and improving decision-making in various domains.
Want to optimize the benefits of big data? Here’s how Hoverture can help you
We support our customers by defining an innovative approach to the world of Data and Smart Analytics, that allows them to take full advantage of data. We unlock our clients’ potential and help them become a data-driven organization, turning data into actionable insights to drive the business and improve performance.
We offer our clients a data-driven approach based on data that integrates both the current data present in the company (Marketing, Shopper Marketing, Trade marketing, OOH, …) with external data relevant to the business.
Furthermore, thanks to the partnership with Google Cloud, we provide the company with a set of solutions based on Google Cloud, in order to minimize the impact on the systems and make the business autonomous.The technology offered by Google Cloud Platform allows to enhance all the company’s data independently of the system or tool they come from. This to support the identification of new opportunities that can make the business skyrocket.
We provide services across four areas to accelerate benefit realization leveraging data and analytics:
- Data Engineering
- Data Enrichment
- Analytics Modeling and AI
- Data Visualization and Insights Generation
