Blog
06.08.2022
Data Fundamentals

Big Data Analytics: Types, Challenges, Tools, and the Shift to Cloud

“Big data analytics” refers not just to massive sets of diverse data, but also to the tools and processes used to generate value from that data. Big data analytics can deliver insights that businesses are unable to uncover through traditional analysis of discrete datasets; the big data analytics process is invaluable, but companies can’t perform it effectively with their legacy processing and storage infrastructures.

Here’s an overview of the big data analytics process, the common challenges companies face in performing it, and the increasing move to the cloud and cloud-native tools to support the undertaking. But first, let’s take a closer look at exactly what big data is and how big data analytics differ from more traditional methods of analyzing data.

Types and characteristics of big data

Five characteristics—known as the “5 Vs”—are key in understanding big data analytics: volume, velocity, variety, veracity, and value.

Volume refers to the size and the number of datasets. By definition, a big data environment requires a large volume of data. The larger the volume, the more storage and computing power is required to acquire and process the data.

Velocity is the speed with which data is generated and how quickly that data moves. Companies create new data at higher velocities as they store more information, use more applications, and get information from new sources like sensors and other Internet of Things (IoT) devices. And they need that data to flow rapidly so that it’s readily available as needed for decision making. A big data environment generally consists of both high data velocity as well as high data volume.

Variety refers to the different types of data available. Most big data environments are highly varied and contain more than one of the following data formats:

Structured data. Typically contained in rows and columns, structured data is the easiest type of data to store, search, analyze, and organize. Until relatively recently, it was the only type of data businesses could readily use; this has changed drastically over the last few decades and now, just an estimated 20 percent of all data is structured data. Examples of structured data include addresses, customer star ratings, financial and accounting information, and location tracking from devices.
Unstructured data. Today, unstructured data makes up a far bigger percentage of the world’s data. It cannot be contained in the rows and columns of a database, which makes it more difficult to search, manage, and analyze. Unstructured data is usually stored in applications, data lakes, and data warehouses. The recent proliferation of AI and Machine Learning solutions has enabled businesses to process and work with unstructured data more easily. Examples of unstructured data include audio files, open-ended survey responses, photos, presentations, video files, text files, satellite imagery, and social media content.
Semi-structured data. Semi-structured data combines characteristics of both structured and unstructured data. It may have properties like metadata or semantic tags that make it easier to organize than unstructured data, but it also doesn’t completely conform to a rigid structure. For example, an e-mail message contains unstructured content, but it also includes structured elements—such as email addresses, time sent, and more.

Veracity refers to data quality and accuracy. In a collection of data, there may be missing fields, incorrect information, or a lack of valuable insight. Veracity indicates the level of trust or confidence in a collection of data and a high level of veracity is a requirement for effective big data analytics.

Value refers to what an organization can do with a given collection of data. The value of big data analytics depends largely on the insights that can be gleaned. Companies must have an idea of the types of results they are looking for and their value to the business. It’s a big waste of time and money to expand significant resources on collecting, storing, and analyzing massive amounts of data if the results will not ultimately help your business.

Big data analytics vs. traditional analytics

Companies perform big data analytics to uncover patterns and trends that can’t be discovered using traditional tools and methods. Big data analytics and traditional analytics differ in several ways:

Traditional analytics

These methods are performed on a discrete data set or a smaller collection of data sets. This type of analysis is traditionally done after the fact, referred to as historical analysis, and is known for its accuracy in allowing organizations to understand the impact of a course of action or a new strategy. The results of the analysis can be used as a basis for future decisions, but it is not meant or intended to be predictive in a real-time sense (for example, like fraud detection based on recent credit card transactions, or recommendation for a product based on recent customer behavior).

Big data analytics

Big data analytics involves massive amounts of data from far more sources than the information analyzed by traditional methods. The four key types of big data analytics include:

Descriptive analytics, which allow businesses to determine what happened and when.
Diagnostic analytics, which identify patterns to help explain why and how an event occurred.
Predictive analytics, which gauge what’s likely to occur in the future based on historical data.
Prescriptive analytics, which help businesses determine what can be done better and what steps to take next.

The big data analytics process in the cloud.

Until fairly recently, companies performed big data analytics in their on-premise computing environments, relying on clusters of servers for storage and compute power, managing the infrastructure with a layer of software on top, and running analytics with additional applications. This era was marked by the growth of Hadoop projects and the wide variety of technologies to manage and optimize these environments.

Today, organizations are increasingly harnessing the power of the cloud for big data analytics. Moving their data into cloud data warehouses and data lakes for analytics allows them to scale resources up or down as needed, gives them easy access to the latest cloud-native tools, and lessens the burden of managing the environment.

However, the core elements of the big data analytics process remain the same: ingestion, storage, processing, cleansing, and analysis.

Data ingestion

Ingestion involves identifying data sources and collecting data of all types—structured, unstructured, and semi-structured in a cloud data platform. Sources can include applications, databases, enterprise systems, flat files, IoT sensors, and more. In many big data analytics environments, collection typically occurs in real time or near-real time for rapid processing.

Companies build extraction, transformation, and loading (ETL) or extraction, loading, and transformation (ELT) pipelines to move data from sources to centralized repositories for storage and/or processing. In ETL, data transformation occurs within the ETL tool before the data flows to the cloud platform repository; ELT allows organizations to transform data after loading it into the cloud platform, taking better advantage of cloud storage, processing, and security protocols.

Data storage and data processing

For big data analytics, today’s organizations are increasingly storing their data in cloud data warehouses, data lakes, or lakehouses. Data warehouses contain data that an organization has already filtered and processed; data lakes are pools of raw, unprocessed data; lakehouses are a convergence of both technologies.

To convert unprocessed data into its most digestible and usable form, companies can perform batch processing or real-time processing. In batch processing, data accumulates over a certain period of time for scheduled treatment as a “batch.” Real-time processing, on the other hand, occurs in a matter of seconds or milliseconds. Though more complex to implement, real-time processing enables organizations to make rapid decisions based on information that is always up to date.

Data cleansing

Cleansing helps ensure that analytics is done on accurate, high quality data, and delivers data integrity. Using various tools and platforms, companies perform cleansing processes to eliminate duplications, errors, and inconsistencies from their data sets.

Data analysis

Practices, techniques, and technologies for turning big data into actionable insight include:

Natural language processing (NLP), a technology used to tap into patterns, connections, and trends across disparate data sources.
Text mining, an advanced analytical approach applied to big data in its textual forms (blog posts, emails, tweets, etc.).
Sensor data analysis, a process for assessing data that is continuously generated by sensors installed on physical objects.
Anomaly detection, a technique used to identify data points standing apart from the rest of a data set.
Data visualization, a variety of tools and techniques for giving a visual context to information, making it easier to identify patterns within large data sets.

Challenges in big data analytics in the cloud

Finding the right data professionals

Many businesses report that a lack of qualified data science professionals is one of the biggest barriers in advancing their big data analytics initiatives. Companies are rushing to capitalize on the promise of big data and the modernization of their data analytics environment in the cloud, but demand for specialized skills is currently outpacing supply.

Making data fully accessible

Data is of little value if business users can’t easily access it. Organizations need to ensure that decision makers can gain the insight they need, whenever and wherever they need it. Investing in big data analytics technologies that can make the data easily accessible to the tools used by the business is essential.

Ensuring data quality

Analytic output can only be as accurate as the underlying data that is relied upon to generate it. Analytics performed on data that contains errors and inconsistencies can cause serious problems if the results are driving important business decisions. Technologies that help you validate and cleanse your data and get it analytics-ready as quickly as possible can be invaluable tools in your big data analytics arsenal.

Working in an integrated ecosystem

If a company makes changes to information in one system but doesn’t update that data in another system, inaccurate data may be used in analytics. With proper data integration and orchestration in a centralized cloud data platform, updates can automatically carry over across the entire data platform.

Addressing security concerns

Big data analytics offer exciting new possibilities for companies, but the sheer size of the endeavor can raise concerns about security and privacy. For most businesses, working with a platform and trusted provider that allow for control and ownership over the data involved are non-negotiable. This is another reason why companies are increasingly moving to cloud platforms for their big data analytics and leveraging the security protocols of these environments.

Best big data analytics tools in the cloud

Instead of deploying a single comprehensive big data analytics solution to perform, companies typically need to rely on a combination of platforms and technologies.

Cloud Data Platforms

Snowflake

Description

An innovative SaaS data platform natively designed for the cloud using a modern SQL query engine that enables you to build data-intensive applications without operational burden.