From Information to Insights: Analysing Big Data Trends

Data is arguably the most underutilised global resource with the potential to be the driving force behind any business in the current tech-driven global business landscape.

In fact, the unstructured and structured data being generated daily on a global scale is growing exponentially. This only lends to the notion that data will only continue to potentially impact the global business community in unimaginable ways,but, if tapped into with the right mediums.

Herein lies a challenge as many companies still face technical limitations storing, disseminating, and interpreting this transactional data to derive actionable insights. This systemic challenge, in its own way, gave birth to a relatively nascent domain called Big Data.

The Big Data application use cases are picking up steam as more firms are choosing to automate their business processes by leveraging real-time data-driven models to support fact-based decision-making.

In today’s blog, we aim to dig into some of the exciting and new Big Data trends that every enterprise (irrespective of the industry) should look out for in 2023. So, without further ado, let’s delve in!

What is Big Data?

Before we get started, let’s define Big Data.

In practice, Big Data is an all-encompassing term used to describe sophisticated and extensive unstructured and structured datasets.

These are collated from a myriad of disparate sources such as social media, customer databases, documents, emails, medical records, the internet, and mobile apps.

Big Data is underpinned by four main underlying characteristics and principles, namely:

• Velocity: In the context of Big Data applications, velocity alludes to the speed at which a software program can process data. For the most part, programmatically managing the velocity of data dictates efficient data ingestion, processing, and data analysis techniques to extract meaningful insights in a timely manner.

Fortunately, the proliferation of smart devices radically changed the way businesses consider velocity. This is because most of smart devices now have sufficient computing power to process data in real-time, in contrast to similar devices of the 2000s.

• Variety: Big Data encompasses various types of data from heterogeneous sources and enterprises seeking to utilise the data for different purposes. Suffice to say, variety is a critical component of Big Data.

For more context, the heterogeneity of Big Data cuts across structured data from databases, to unstructured data from Word documents and social media, and even semi-structured data like JSON or XML. In fact, dealing with this data heterogeneity sometimes dictates deploying disparate tools to handle the different data formats.

• Volume: Big Data inherently constitutes large voluminous datasets (as the name ironically suggests). Generally speaking, volume is an important characteristic of Big Data as it dictates how data is processed or which tools to capitalise to provide users with informative analytics regarding their data.
• Veracity: Fundamentally, veracity is a technical characteristic that revolves around the quality and reliability of Big Data. Intrinsically, Big data can sometimes be incomplete, or inconsistent, making it exceedingly challenging to ensure its accuracy and trustworthiness. As a result, leading to data errors, biases, or even data integration challenges.

So, addressing veracity dictates engaging in iterative cycles of data cleaning and validation whilst leveraging quality assurance mechanisms to ensure optimal data quality.

For the most part, the capacity to collect and aggregate large and complex datasets presents both challenges and unique opportunities for modern enterprises.

For example, the opportunity to leverage these datasets to extract valuable insights, patterns and trends can drive business strategies and improve operational efficiency. Or even innovate, and iteratively testing new concepts and hypotheses to improve existing products, and services.

On the flip-side, it also poses overarching challenges of how to utilise robust infrastructures and storage systems capable of performantly handling the sheer volume of the ever-growing datasets.

There is also the lingering question of data privacy and security, as well as the requirement for powerful computational resources and specialised technologies to optimally deal with these large datasets.

What is the significance of big data in the current digital age?

One can argue that Big Data is the bedrock of today’s digital age.

Why?

The answer hinges on its existing ability to revolutionise real-time decision support, with a high level of specificity and granularity.

For example, by analysing social media and browsing behaviour, enterprises now possess the ability to create a more comprehensive profile of their customers, and even derive narrower segments of their preferences.

As a result, enterprises now possess the unprecedented capacity to make more informed marketing and operational decisions to promote their products and service to those who more likely want them.

So, overall, Big Data presents a unique opportunity for more small and medium-scale enterprises (SMEs) to create consumer-responsive products that are based on precise data predictions than intuition, or lengthy processes of customer feedback.

Furthermore, with regard to inventory management, Big Data presents state-of-the-art data-driven mechanisms for predicting when sales will occur to help enterprises to order the precise stock batches required to meet demand. This is helping more SMEs avoid keeping capital tied up in inventory, or even incurring unnecessary costs.

Collecting and storing data

The generation of Big Data involves the processing and storage of large quantities of information (typically multi-terabytes) that change fast and take multiple forms.

Unfortunately, traditional technologies can not scalably manage and process this multi-terabyte data.

Bottom line, Big Data needs a home—a centralised repository where it is programmatically accessible in real-time by different business entities for them to manipulate or aggregate.

This is where Cloud computing takes the helm to aid in the management of the vast amounts of information that underpin Big Data applications.

In practice, cloud computing companies provide enterprises with a means to store, process, and analyse their massive datasets. They do this without the need for significant upfront infrastructure investment from the SME.

Furthermore, cloud computing resources can be scalably increased or decreased, based on demand to ensure efficient resource utilisation and cost optimisation.

Additionally, cloud computing providers typically offer various specialised tools to make it easier for enterprises to manage and extract the best value from their large datasets.

For example, they provide data warehouses and data lakes for enterprises that maintain higher data management requirements.

For context, data warehouses are centralised data management systems for specialised business intelligence purposes that are deployed to programmatically consolidate historical data from various sources, and support high-performance queries to deliver structured and organised data reporting.

Data lakes, on the other hand, are centralised scalable data repositories designed to hold vast amounts of raw data in its native format, till it’s required for analytics applications—without the need for upfront schema or data transformation.

They primarily provide advanced flexibility and agility for enterprises that seek to capture data at scale and then refine and process it as needed for their particular use cases.

Preparing and processing data

To ensure data quality and consistency when preparing and processing data, specific techniques are typically exploited when handling Big Data. For example:

• Data cleaning is an exercise that revolves around the identification and correction of data errors, inconsistencies, and inaccuracies. It typically includes tasks like standardising formats, removing duplicate records, and handling missing data values.
• Data integration is fundamentally the process of programmatically reconciling data from multiple sources into a consistent and unified format. Data integration exercises typically involve programmatically mapping and aligning data elements from disparate database systems to derive a single, cohesive and comprehensive view of the data.
• Data transformation is an exercise that revolves around the conversion of data from one format/structure to another to enhance its usefulness. Fundamentally, data transformation exercises seek to ensure that the data is in an appropriate format for analysis to support specific business requirements.

Analysing Big Data trends

Analysing big data trends allows organisations to uncover meaningful insights in order to make data-driven decisions.

By understanding and identifying trends, patterns  within Big Data, enterprises can gain valuable insights into their customers’ behaviour and operational efficiency. Thereby, staying ahead of the competition with the ability to make proactive decisions rather than relying on guesswork or intuition.

Different advanced techniques exist to execute data analysis, like statistical analysis, data mining, and machine learning. In practice, statistical analysis revolves around the application of mathematical models to understand patterns, relationships, and trends in Big Data.

Data mining principally focuses on uncovering patterns and relationships within datasets to derive useful information. Machine learning is an artificial intelligence-driven approach that leverages unique algorithms and computational models to make predictions or decisions from data.

Collectively, these data analytics techniques are helpful in uncovering actionable patterns and making data-driven projections across various domains like business, finance, healthcare, and more.

However, without visualisation tools to help present the generated analytics, these data analysis techniques might not be effective.

Visualisation tools are helpful in interpreting and presenting Big Data trends in a meaningful and compelling way. Therefore, making it easier for users to understand and discover patterns, correlations, and trends that might otherwise go unnoticed in raw data.

Overall, visualisation tools offer users a means to explore data from different perspectives, with the ability to filter the data into specific subsets in order to gain a more holistic comprehension of the data’s story.

What tools analyse Big Data?

As we have alluded to, visualisation tools facilitate the communication of information derived from the execution of data analysis techniques to a wider audience in a visually compelling manner. Currently, the five most common tools used to visualise and analyse Big Data in the industry are:

Tableau

Tableau is an interactive Big Data analytics tool that is helpful to users desiring to run multiple data tools simultaneously. This unique tool offers users real-time data processing capabilities and the advanced ability to manage Big Data, regardless of its size or source, in a user-friendly manner.

Cassandra

Cassandra is an open-source Big Data tool built by Apache that oversees data storage for big data programs. It is expressly designed to be highly scalable and performant whilst handling large amounts of data across multiple commodity servers, with no single point of failure.

R

R is a high-performance programming language for statistical computing. It is helpful for individuals seeking to process large amounts of data from disparate sources. The R software packages are open-source and free for public use, making them popular for those searching for an affordable Big Data visualisation and analysis tool.

Hadoop

Hadoop is an open-source big data framework/distributed file system that facilitates the distributed processing of large data sets across different clusters of computers using simplified programming models. Hadoop has a reputation for its intense processing power that allows users to expeditiously analyse large datasets within a relatively short timeframe.

Integrate.io

Integrate.io is a no-code data pipeline platform for integrating and processing Big Data before transferring it to the cloud. The unique program can programmatically collate data from multiple disparate sources into a single platform in a user-friendly interface that minimally uses code. This makes it easily accessible to technology-averse individuals.

Insights from big data

Big Data has the undeniable potential to revolutionise how companies approach invention and innovation.

For example, by analysing online reviews and customer feedback, large enterprises like Walmart have an unmatched ability to innovate via customer-driven design insights to improve sales.

Nonetheless, the exploitation of Big Data varies from business to business.

For example, some well-established businesses are exploiting big data to streamline processes, and create efficiencies. On the other hand, some newer businesses are identifying different ways to use sensors and capture the data they generate in order to add value to existing products and services.

Nonetheless, one thing is for sure, data is a new source of growth, and most future innovations will be data-driven, powered by an Internet of Things (IoT) ecosystem.

And it will be sure to touch all major sectors like health, agriculture, education, transportation, manufacturing, smart grids, and even domestic home applications!

Applications of big data analytics

Big data analytics has several real-world application use cases across different industries, for example:

Big data application in healthcare

The advent of telemedicine and wearable devices in the healthcare industry has created a unique opportunity for healthcare providers to tap into Big Data analytics.

For example, health gadgets like the FitBit activity tracker and Apple watch can be used to provide real-time feed to their doctor to empower patients to take control of their health.

Relatedly, freely accessible public health data and Google Maps have been uniquely deployed by the University of Florida to generate visual heatmaps that allow for faster analysis of healthcare information, especially when tracking the spread of chronic disease across the community.

Big data across government agencies

Governments across the globe engage with substantial amounts of data almost daily collected from their citizenry. In essence, they have to keep track of citizens’ health statuses, birth records, energy utilisation, and even geographical surveys.

For instance, the U.S Food and Drug Administration (FDA) uses the analysis of Big Data to discover actionable patterns to identify and examine unexpected or expected occurrences of food-based infections. The FDA also employs data analytics to improve the integrity of FDA-regulated products throughout their product lifecycles by helping fill knowledge gaps and inform regulatory decision-making.

Big data in the securities industry

The U.S. Securities Exchange Commission (SEC) exploits advanced Big Data techniques to monitor financial market activity via network analytics and natural language processors that are designed to catch illegal trading activity in the financial markets.

The SEC’s AI-driven approach programmatically examines “blue sheet” data to detect illicit patterns and determine potentially suspicious activity. After establishing suspicion, the SEC then determines the correlative relationships between traders to identify which potential sources of material non-public information they could have in common.

On the other hand, big banks, retail traders, and hedge funds use Big Data for trade analytics purposes, for example, for high-frequency trading, sentiment measurement, pre-trade decision-support analytics, and predictive analytics purposes etc.

Big data in media and entertainment

The Spotify on-demand music service leverages the Google Cloud Platform to collect data from its users worldwide and then subsequently leverages the analysed data to provide informed music recommendations to different users.

The platform even incorporates proprietary algorithms that utilise Big Data to programmatically ‘comprehend’ the music taste of each user in order to steer them towards fresh songs, and artists. Thereby, elevating customer experiences.

Big data application in weather patterns

IBM Deep Thunder is a groundbreaking research project by IBM that provides near-real-time but short-term weather forecasting through the high-performance computing of Big Data. The computing initiative is primarily designed to provide local, high-resolution weather predictions that are customised to weather-sensitive business operations.

In practice, the computer system is also being engineered to utilise machine learning to analyse past weather events for businesses. It helps to better predict how future variations in temperature and wind might affect consumer buying patterns or supply chains.

10 trends in Big Data analytics in 2023 to follow

Big Data is impacting businesses of all sizes across various sectors. By the same token, technological advancements in Big Data techniques and technologies are progressing by leaps and bonds. Here are some of the standout Big Data trends we believe SMEs can tap into:

Data as a service

Data as a Service (DaaS) is a relatively new cloud-based service model that provides users data on-demand through a subscription or pay-per-use basis. It allows enterprises to utilise data from external sources in a scalable and convenient manner without needing to manage the underlying infrastructure themselves.

In 2023, we hope to see more companies exploiting this service mode as a part of their business processes with the aid of data exchange marketplaces. Data exchanges are essentially intermediary platforms that facilitate the transaction data between data providers and consumers seeking specific datasets or data services.

Vision A.I.

In 2023, we expect more enterprises to generate actionable insights from video data at scale whilst leveraging vision A.I.

Advances in computer vision, and Edge technology are currently making it easier to build vision A.I. models that solve problems across different industries—for example, waste management.

DataOps

DataOps is a data methodology and discipline that emphasises agile and iterative techniques for dealing with the lifecycle of data as it flows through an enterprise. For example, from generation to archiving, storage, transportation, processing, and management.

This holistic approach merges data engineering and data science teams in order to support a company’s data needs in a manner akin to how DevOps helps scale software engineering.

In 2023, we optimistically expect to see more companies adopting DataOps to improve the speed, quality, and business value of their data-related activities.

Hybrid cloud solutions

The increased adoption of hybrid cloud services was one of the top data trends for 2022. In 2023, we expect this unique trend to continue as more SMEs look to adopt hybrid clouds that combine the best aspects of private and public clouds.

Use of augmented analytics

Augmented analytics(A.A.) is a relatively nascent concept that revolves around the deployment of enabling technologies like machine learning (ML), natural language processing (NLP) and artificial intelligence (A.I.) to automate insight generation, data preparation, data processing in order to augment how people explore and analyse Big Data.

With augmented analytics, activities typically handled by a data scientist are now being automated in delivering insights in real-time. In 2023, we expect to see more SMEs going down the route of A.A. to explore data and generate more in-depth reports and predictions.

Edge computing

Edge computing is a compelling solution in the realm of data processing, offering the ability to process Big Data quickly while conserving bandwidth and ensuring improved security and data privacy. It involves networks and devices located near or at the user’s end, enabling data processing in close proximity to its source. By bringing data processing closer to its origin, edge computing enables faster and more extensive processing, resulting in real-time actions and expedited delivery of actionable insights. These advantages make edge computing an attractive choice for businesses seeking efficient data processing.

 

In 2023, we anticipate a growing interest from data-driven enterprises in adopting edge computing. The primary motivation behind this is the desire for faster analysis, which allows businesses to maintain a competitive edge in their industries. 

Composable analytics

In 2023, we will see the deployment of more composable data analytics models to enable SMEs to better innovate, differentiate, and grow digitally.

In practice, composable data analytics provide a flexible, user-friendly, and seamless experience by leveraging a variety of low-code or no-code tools.

Data lakes

Data Lakes have advanced appreciably to enable company decision-makers to see updates in real-time to make more timely decisions.

We expect to see more startups investing more in data lakehouses to better access their enterprise data, and enable real-time reporting for quicker, data-driven decision-making.

No-code solutions

No-code tools enable stakeholders to leverage their Big Data, without having to continually engage the data team. This frees up data scientists to work on more intensive activities whilst encouraging data-driven decisions in the company since engaging with data is now something that every employee is capable of.

No code tools have the potential to democratise Big Data in 2023. As such, we expect to see more SMEs widely deploying no-code tools in their daily operations.

Restricted data governance

Lastly, in 2023, we expect to see more restrictive data governance. The logic is that the growing demand for data-driven decision-making will dictate more transparency around data.

So, expect regulatory authorities globally to tighten their grip on data security compliance requisites.

Conclusion

The core goal of Big Data analytics is to help SMEs make more informed business decisions by enabling analytics professionals to analyse large volumes of transactional data. Thereby, achieving cost reductions, faster decision-making, and even providing new offerings for customers.

According to Fortune Business Insights, the global Big Data analytics market was valued at $271.83B in 2022 and is expected to grow from $307.52B in 2023 to $745.15B by 2030. This project clearly echoes the theme of this blog, being that Big Data will continue to grow and affect how organisations look at business information.

As such, it is imperative that more SMEs intentionally consider the Big Data trends we have analysed in this article, and integrate them into their business operations. Unless they risk being left behind!

Reach out to us for more information on how to get started with your big data analytics journey in Singapore.