Big Data Analytics: Its Benefits, Challenges and Uses

What are Big Data Analytics?

Big data is a familiar term, but what does it mean, and why is it essential for business? Big data describes large, difficult to manage (structured and unstructured) volumes of data that flood businesses daily. What is critical is what companies do with that data - big data is analyzable for insights that improve business decisions and offer confidence to make strategic decisions.

An IT services provider like V2Soft helps businesses maximize revenue and increase efficiency by tapping into the power of their data. V2Soft's big data analytics model gives companies a competitive advantage with information to make quicker decisions and proactively take advantage of opportunities.

Why do Businesses Need Big Data Analytics?

1. Reduced costs: Big data technologies like cloud-based analytics can significantly reduce costs when storing large amounts of data, for example, a data lake.* Plus, big data analytics helps organizations find more efficient business methods.
2. Making faster, better decisions: The speed of in-memory analytics combined with analyzing new data sources (such as streaming data from IoT) helps businesses analyze information immediately and make fast, informed decisions.
3. Developing and marketing new products and services: Being able to gauge customer needs and customer satisfaction through analytics empowers businesses to give customers what they want when they want it. With big data analytics, more companies have an opportunity to develop innovative new products to meet customers changing needs.

What do all these benefits mean? They mean improved operations, better customer service, and the ability to create personalized marketing campaigns.

Big Data: The Facts

A conventional definition of big data is a set of massive data that is extraordinarily complex and unorganized. Big data defies standard and straightforward data management methods previously used until the recent rise in data. Big data sets cannot be processed in traditional database management tools and systems; they do not fit into a standard database network.

Thanks to the evolution of the internet, businesses, economies, stock markets and governments operations and functions evolved. Also, it's changed the way people live. While all the change happened (and happens), there is a noticeable rise in collective information floating around. That amount continues to bloom, so storing data manually is overwhelming. Generated and stored data grows exponentially - in fact, data volume more than doubles every eighteen months¹.

As a field of study, big data involves data management and analytics that intend to uncover hidden patterns, trends and unknown relationships within large datasets. Big data incorporates increased computing power (capacity, speed), cloud storage, advanced software tools (like data visualization), open-source platforms like Apache Hadoop, NoSQL databases like Cassandra or MongoDB, data modeling, and business intelligence.

Big Data's History

Many trace big data back to 1663 during the bubonic plague when a man named John Graunt faced an overwhelming amount of information during his disease study. He was the first person to use statistical data analysis, and statistics as a field expanded to include data collection in research in the early 1800s².

For years, accessing and storing enormous amounts of information for analytics has been around, but big data gained traction in the early 2000s when Gartner analyst Doug Laney defined big data as the three Vs³.

The Three Vs

Big data is often defined as high volume, high velocity with a large variety (known as the three Vs) of information assets, mostly from new data sources.

Volume refers to vast amounts of data generated from cell phones, social media, transactions, smart devices, industrial equipment, videos, images, audio, etc. In the past, storing all that data would have been too costly - but nowadays, cheaper storage using data lakes*, Hadoop and the cloud ease the burden.

Velocity refers to the speed at which these incredible amounts of data are generated, collected and analyzed.

Variety refers to the different kinds of data: we no longer have only structured data (data that can be displayed correctly in a data table like name, phone number, ID, etc.), but current data is unstructured, primarily data like images, audio, social media updates etc.

Big Data Classifications

Business data has three classifications: structured (business) data, unstructured data and semi-structured data.

Structured data: is the most familiar data classification - it's what we see in daily life. Blog posts, phone numbers, addresses and birthdays are examples of structured data. Structured data delivers business value when organizations use existing systems and processes for analysis. This data is usually from internal sources like CRM applications.

Unstructured data: is information without a predefined data model or associated format. Examples include email messages, text documents, social media feeds, video/audio recordings and XML files. Businesses glean value from unstructured data when they efficiently use their existing systems and processes for analysis purposes. Typically, this data comes from external sources like social media platforms and internet-based data feeds.

In addition to the three data types mentioned above, we can add two elements to the list:

Variability: data flows change often and significantly. This inconsistency is a challenge, but enterprises need to know about social media changes and trends and how to best manage data loads.

Veracity: refers to the data's quality. Since data comes from a multitude of various sources, it can be challenging to link, match, clean and transform it across systems. Organizations must connect and associate data links, relationships and hierarchies. These connections and associations must be made, or the data can spin out of control.

Why Big Data is Important

Big data's importance lies in how it is used. By analyzing data from any source, one can learn how to streamline resource management, improve operational efficiency, improve product development, drive revenue and growth, and support intelligent decision-making.

Big Data's Benefits

Combine big data with high-functioning analytics and one can:

• Find the root cause of issues and defects in almost real-time
• Detect irregularities accurately and quickly - faster than a human is able
• Recalculate risk portfolios in minutes
• Sharpen deep learning models capability to categorize and respond to variables
• Detect fraudulent behavior before it damages an organization

Uses for Big Data

• Improve customer acquisition and retention - big data empowers companies to dial in on what customers want, which creates a solid customer base. Big data also follows consumer patterns, and it uses that data to trigger brand loyalty. For example, Amazon mastered this technique by offering one of the most personalized experiences available on the internet.
• Target and focus marketing campaigns - businesses use big data for tailored product offerings for their target market. No more ad spending on fruitless campaigns; big data makes a sophisticated analysis of customer trends, including online purchases and monitoring point-of-sale transactions.
• Develop risk management solutions - today's business climate is high-risk. Big data is instrumental in developing risk management solutions because it improves its management models and creates more intelligent strategies.
• Innovate products - big data helps companies update current products and innovate new ones. With large amounts of data, businesses can distinguish the best match for their customer base.

How Big Data Analytics Works

Big data analytics refers to collecting, processing, cleaning and analyzing large datasets to help organizations operationalize their big data.

Data Collection

Data collection is different for every organization. With today's technology, organizations gather structured and unstructured data from various sources - from cloud storage to mobile applications to in-store IoT sensors. Some data will be stored in data warehouses where business intelligence tools and solutions can access it easily. Raw or unstructured data that is too diverse or complex for a warehouse may be assigned metadata and stored in a data lake.

Data Processing

Once data is collected and stored, it must be appropriately organized to get correct results on analytical queries, especially when large and unstructured. Available information is growing exponentially, making data processing a challenge for organizations.

One processing choice is batch processing, which looks at large data blocks over time. Batch processing is applicable when there is a longer turnaround time between collecting and analyzing data. Another choice is stream processing, which looks at small batches of data at once; this shortens the delay between collection and analysis, allowing for quicker decision-making. However, stream processing is more complex and often more expensive.

Clean the Data

Data, big or small, requires scrubbing to improve data quality and get more robust results. All data must be formatted correctly, and any redundant or irrelevant data must be eliminated or accounted for. Dirty data can confuse and mislead, creating flawed insights.

Analyze the Data

Getting big data into a usable form takes time. Once it's ready, advanced analytics processes can turn big data into significant insights.

Five Types of Big Data Analytics

Prescriptive analytics is a form of data analytics that allows business decision-makers to put raw data into prescriptive analytics software to gain intelligence about financial management, customer service, sales, marketing and other tasks. Prescriptive analytics software uses techniques and tools like simulation, heuristics, graph analysis, neural networks and machine learning to improve analytics and generate increasingly exact metrics. The insights gained from prescriptive analytics are then shared with teams to improve workflows and business structures.

Diagnostic analytics is used to find why a specific event happened in the past. This advanced analytics looks at historical data with techniques like data discovery, data collection and identification of trends and patterns within the data. This type of analytics also uses data mining to find anomalies and patterns within large data sets and "drill-down," revealing extra detail levels from data sets.

Descriptive analytics is a way to analyze data and/or content. It is used to identify meanings and patterns in historical data, such as annual financial reports. Descriptive analytics explain what happened at a precise time, such as the factors that contributed to sales growth over a prior year.

Predictive analytics use algorithms, statistics, machine learning and data to predict future results based on historical data. Four elements define predictive analytics: a focus on prediction, fast analysis measured in hours or days, the accuracy of the projections, and how understandable is the breakdown (the research). To put it simply, predictive analytics has to easily and quickly predict conceivable future outcomes based solely on past data.

Cyber analytics apply algorithms to detect, analyze and prevent security breaches. This process helps organizations protect their networks and is a critical part of any business. When using the most recent data-driven methods, cybersecurity experts ensure their systems are protected against threats from hackers, viruses and other malicious attacks.

Challenges of Big Data

Let's address some challenges associated with big data.

Rapid Data Growth

The fast growth of data makes it a challenge to garner insights. More data generates every second from when the data is relevant and valuable; therefore, it must be picked up for more analysis.

Storage

Organizations require the appropriate tools and technologies. Otherwise, large amounts of data are challenging to manage and store.

Syncing

Synchronizing across data sources indicates that data from various sources might not be equally up to date.

Security

Vast amounts of data may become a target for threats organizations must face security challenges with proper authentication, encryption, etc., to keep data secure.

Unreliability

Big data is not 100% exact – it may have redundant, incomplete or contradictory information.

Big Data's Future

What is trending in big data? We will likely see more reliance on cloud storage, AI/ML-powered automation, ethical customer data collection, data fabric technology growth, and vector similarity search evolution.

Reliance on Cloud Storage

Big data comes into companies from many directions. With the expansion of streaming and observational data and tech growth, storage is challenging because traditional on-site data storage cannot handle incoming terabytes and petabytes of data. Therefore, cloud and hybrid clouds offer simplified, scalable storage solutions.

Growth of Data Fabric Technology

Data fabric architecture allows businesses to store and retrieve needed data sets distributed across on-site, cloud and hybrid network infrastructure. This is an important part.

Closing the gap between available data and information and how much of that is to be turned into knowledge and insights to produce personalized, compelling products and services and improve overall business efficiency.

What prevents businesses from closing the gap? Technology + scale + people = complexity, which is not a bad thing. The challenges arise when the vast scale of data makes it difficult to get anything done.

Consider:

• 60 -73% of enterprise data goes unused⁴
• 30% of IT development time is spent trying to make interfaces work together⁵

How Does Data Fabric Help?

Universal data access: Data fabric allows access to all data sources, types and domains, especially in a multi-hybrid cloud strategy.

Creates efficiencies: Data fabric thinks holistically. Therefore, it creates efficiencies throughout data collection, organizing and analysis to process and leverage data expediently.

Enforceable policies across the business: If one can access more data and it is collected much faster than before, the right people must access the correct data at the right time.

A data fabric strategy offers many benefits to a business, but the cost and time savings should be a reason to decisively consider a data fabric strategy.

Finally, a data fabric uses people and technology to close the available data and knowledge gap. An organization has to produce dynamic products and services and improve overall business efficiency.

Ethical Customer Data Collection: Big Data's growth is in consumer data or data constantly connected to consumers like streaming devices, IoT devices and social media. Data regulations such as the General Data Protection Regulation (GDPR) are among the strictest consumer data privacy laws. In 2021, Luxembourg's data protection commission (CNPD) proposed an $886 million fine against Amazon for the company's collection and usage of personal data⁶.

We need to mention that many large organizations that once collected and sold personal data are now changing by making consumer data more expensive and harder to purchase. It's common to see smaller organizations opting into first-party data sourcing or collecting their own data to comply with data laws, maintain data quality, and save on costs.

AI/ML-powered automation: Big data analytics-powered AI/ML automation is a remarkable trend for customer-facing requirements and internal operations. Big data offers a breadth and depth for automated tools to replace human actions at an enterprise level. With AI/ML solutions using big data input, expect more predictive and real-time analytics in everything from workflow automation to customer service chat bots.

V2Soft: Tools and Technologies to Manage Big Data

V2Soft helps organizations work smart and make sound decisions with our big data analytics model. Our model delivers a competitive advantage by empowering businesses to make quick decisions and take advantage of opportunities. Our technology includes mark logic implementations, Hadoop solutions, Tableau, business objects, Cognos, IBM SmartCloud, Informatica, Qlik and New Relic Solutions.

We offer end-to-end big data services, including storage, applications and analytics, along with a complete infrastructure to help analyze data using:

• Data mining
• Predictive analysis
• Search engine optimization (SEO)
• Social media optimization (SMO)
• Data warehousing
• Business intelligence

Reach out to a V2Soft expert today to determine if your business is a good candidate for big data solutions.

Definitions:

Data Lake: According to Amazon Web Service, a data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data, and run different types of analytics from dashboards and visualizations to big data processing, real-time analytics, and machine learning to guide better decisions.

Sources

1. NIST Big Data Public Working Group Definitions and Taxonomies Subgroup. (2015, September 16). NIST Big Data Interoperability Framework: Volume 1. NIST Big Data Program. Retrieved March 31, 2022, from http://dx.doi.org/10.6028/NIST.SP.1500-1. pp 12-15

2. J. H. Marsden and V. A. Wilkinson, "Big Data Analytics and Corporate Social Responsibility: Making Sustainability Science Part of the Bottom Line," 2018 IEEE International Professional Communication Conference (ProComm), 2018, pp. 51-60, doi: 10.1109/ProComm.2018.00019.

3. Rivera, E. (2021, June 17). The V's of big data. Marbella International University Centre. Retrieved March 31, 2022, from https://miuc.org/vs-big-data/

4. Barrett, J. (2018, April 12). Up to 73 percent of company data goes unused for analytics. here's how to put it to work. Inc.com. Retrieved April 4, 2022, from https://www.inc.com/jeff-barrett/misusing-data-could-be-costing-your-business-heres-how.html

5. G. Mori, F. Paterno and C. Santoro, "Design and development of multidevice user interfaces through multiple logical descriptions," in IEEE Transactions on Software Engineering, vol. 30, no. 8, pp. 507-520, Aug. 2004, doi: 10.1109/TSE.2004.40.

6. Leggett, T. (2021, July 30). Amazon hit with $886M fine for alleged data law breach. BBC News. Retrieved April 4, 2022, from https://www.bbc.com/news/business-58024116