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Businesses of all kinds have found great advantages in using big data technology to unlock an abundance of insights.
Before its introduction, most business intelligence and analytics applications were limited to structured transactions and financial records housed within relational databases and warehouses - leaving much potentially relevant data unutilized despite having great potential use cases. No longer. Today, there is ample scope for unexploited resources, such as text data that don't fit this mould, to be utilized and utilized properly.
Big data environments enable organizations to process, manage, and analyze a wide array of forms of information ranging from email and customer databases to clickstream records on the internet, log files, and photos shared to social networks such as Instagram or Flickr as posts by their followers on these networks, sensor data analysis medical records.
And many more.
Businesses are trying to use data more and more effectively for strategic decision-making and strategy implementation.
NewVantage Partners conducted a poll among IT and business executives from 94 large companies; of those polled, 91.7 % stated their organizations are increasing investments in big data projects or other data and AI initiatives; 92.1 % claimed tangible business results as outcomes of such efforts.
On the downside, many blue-chip organizations are finding it challenging to maximize the business potential of their analytics, AI, and big data environments.
Only 26.5% of survey participants indicated their company has become data-driven; 39.7% acknowledged they view information assets as business assets.
This comprehensive reference to big data for businesses covers what big data is, its business benefits, issues it presents, and effective use practices to enable organizations of all sizes to extract more value from data entering their systems.
Furthermore, a technology overview and examples of use cases are presented throughout this book, along with links to articles for greater detail, as well as professional guidance regarding managing big data programs.
Why Is Big Data Important For Businesses?
Before the advent of big data platforms and technologies, most businesses were only able to utilize a fraction of their data assets for operational and analytics applications, leaving much of it as "dark data," meaning processed yet never revisited or used again.
Businesses can maximize the use of their assets when employing effective big data management procedures.
Businesses using big data analytics have expanded the range and value of data analyses available to them and gained from doing them and can take advantage of big data in many different forms: machine learning, predictive analytics, data mining, streaming analytics, and text mining all benefit from new opportunities provided by big data; big data applications also utilize this discipline for supply chain management, fraud detection operational problem identification consumer understanding, etc.
Effective marketing and advertising campaigns, enhanced business procedures, higher revenues, lower expenses, and robust strategic plans are among the many outcomes of well-executed business operations that could provide an edge in the marketplace and superior financial outcomes for an organization.
Furthermore, big data facilitates advances in science, law enforcement, smart city projects, medical diagnosis/treatment, and various government activities, providing advantages over competitors while producing superior financial returns for an enterprise.
What Are The Different Types Of Big Data?
Big data encompasses multiple forms of structured information. Big data systems store transactions, customer records, financial and stock market information, as well as other structured forms for analytics, uses that extend beyond standard business intelligence (BI) reporting applications available within traditional warehouses - although typically, they do not perform transaction processing themselves.
Big data environments stand out by their capacity to manage unstructured and semistructured data that doesn't readily fit relational databases, including text found in emails, surveys, contact center transcripts, documents, social media posts, audio/video files as examples of unstructured data; activity logs from servers, networks, websites mobile applications IoT devices industrial equipment are an example of semi-structured data sources.
What Are The Business Benefits Of Big Data?
Donald Farmer of TreeHive Strategy described big data as the lifeblood of modern business in his piece about its advantages for companies.
He listed eight possible advantages associated with big data platforms for businesses, including:
- better insight into customer preferences, buying behavior, and sentiment;
- increased intelligence on market trends, products, and competitors;
- agile supply chain operations that can react quickly to problems and new business needs;
- recommendation engines that are better tuned to the interests of customers;
- data-driven innovation in product development and other business functions;
- the ability to support diverse analytics use cases with the same data sets;
- operational improvements, such as lower costs and proactive equipment maintenance, and
- ensuring that data and analytics platforms can meet future business needs.
Big data offers businesses significant benefits in several key ways. First and foremost, its insights enable data-driven decision-making strategies.
Furthermore, big data helps companies recognize developing market trends, cost-cutting measures, and new business prospects, while real-time analytics tools powered by big data provide up-to-date information and trouble alerts directly to operations managers, call center agents, and sales reps on frontlines as well as operations managers themselves.
Read More: 3 Things You Need to Know About Big Data: What's the Cost, Gain, and Impact?
What Are Common Big Data Challenges?
Big data presents unique challenges when it comes to handling, processing, and using it efficiently. Settings often become complex as multiple tools and systems must work in concert in order for big data settings to function as one cohesive whole - especially where large, diverse datasets or streaming data are involved.
Technology writer George Lawton identified and provided advice in an essay on how to prevent and resolve 10 big data deployment issues.
These issues fell into five different categories.
- Technical challenges: Building Big Data systems that can scale when necessary and choosing suitable tools and technology;
- Data management challenges, From mass data processing and storage to cleaning, integration, preparation, and governance - everything related to data can be addressed here;
- Analytics challenges: Assuring analytics data align with an organization's business strategy and meet demands;
- Program management challenges include managing expenses and recruiting personnel with big data expertise.
Hiring and retaining skilled employees is often challenging due to an ever-increasing demand for key talent, including data scientists, data architects, and big data engineers.
Key Elements Of Big Data Environments
Big data management and analytics initiatives involve multiple components and functions.
Here are some core aspects that need to be included in project plans from the outset.
Big data architecture. Structured data can be stored in big data infrastructures using conventional data warehouses, while more frequently seen are data lakes.
Repositories of various data sets stored in original format, often based on technologies like Hadoop, Spark, NoSQL databases, or cloud object storage services like those provided by technology writer Mary K. Pratt, explain how other architectural layers facilitate data management and analytics operations in her essay on building big data architectures; strong architectures also give data engineers the foundations they need for creating pipelines to move information between apps or repositories.
Big data analytics. Analytics applications ranging from basic business intelligence and reporting to sophisticated analyses conducted by data science teams are the main applications for big data platforms.
Big data has proven especially advantageous to machine learning research efforts that now incorporate corporate strategies for discovering patterns and anomalies within massive datasets. Cognilytics managing partner Kathleen Walch offers additional details in her article about the ways big data and machine learning algorithms interact to enhance analytics performance.
Big data collection. Before the gathering and processing of large data sets can commence, they must first be collected - often from both internal systems and external data sources - to be processed and analyzed.
Due to its size, variety, and potential involvement from multiple sources, this can be an arduous project that necessitates many steps before analysis can even begin. With regards to privacy laws such as CCPA/GDPR etc, adding further complications, this challenge should read an article written by Pratt to understand more about gathering large datasets as well as what methods exist for efficiently handling this process.
Read an article written by Pratt in order to gain more insights.
Big data integration and preparation. Integrating large data sets is also key in large data environments.
It presents additional requirements and difficulties than found with conventional integration procedures. Traditional extract, transform, and load processes may not be adequate to address the volume, diversity, and velocity aspects of big data; data management teams, therefore, often must develop alternative big data integration strategies.
Data must first be cleaned, modeled, validated, and discovered prior to being utilized for analysis. This occurs once integrated and available for usage; data scientists or engineers often make this preparation in data lakes that store unprocessed information for this purpose.
Big data governance. Effective data governance is vital to ensure that big data collected meets both corporate data standards and privacy laws, but its administration presents unique challenges.
With an increase in monitoring various kinds of information sources coming in from various directions, controlling big data presents new problems for data administrators tasked with its oversight. Quality control management plays a central role in big data deployments as it often occurs as part of data governance programs.
Hence, new procedures must be developed when combined, for instance, when big data and quality problems collide.
Best Practices For Big Data Management And Analytics
An enterprise big data strategy provides firms with a solid framework from which they can begin developing an action plan for big data that defines objectives, principles, and vision.
Walch suggested four steps when setting out to craft such a plan:
- Define your company's business objectives to ensure that the strategy is aligned with them.
- Identify available data sources and assess the current state of data usage in business processes.
- Identify, prioritize, and document big data use cases that meet your business objectives.
- Formulate a project roadmap that includes a gap analysis of your data architecture and existing technologies, and then reprioritize the planned use cases if necessary.
Farmer provided six recommended big data practices in another article, including prioritizing business requirements over technological capabilities, gathering and archiving data for potential later use, managing large data sets iteratively for various analytics applications, and taking cloud computing into consideration to facilitate deployments and reduce costs.
Big Data Technologies And Tools
Hadoop distributed processing framework was first made public as an open-source platform that could process large volumes of data in 2006, marking an official starting point of big data analytics.
Since then, an ecosystem of technologies surrounding Hadoop, such as the Spark data processing engine, has evolved, while NoSQL databases provided alternative platforms for managing and archiving that weren't possible when dealing with relational SQL-based databases.
Many organizations still rely heavily on Hadoop's native MapReduce processing engine despite Spark and other more recent technologies having somewhat overshadowed it.
Overall, the following are now popular choices for big data environments:
- Processing engines. Spark, Hadoop MapReduce, and platforms for stream processing like Flink, Kafka, Samza, Storm, and Spark's Structured Streaming module are a few examples.
- Storage repositories. The Hadoop Distributed File System and cloud object storage services like Google Cloud Storage and Amazon Simple Storage Service are two examples.
- NoSQL databases. Cloud object storage systems like Google Cloud Storage and Amazon Simple Storage Service are two examples, as is the Hadoop Distributed File System.
- SQL query engines. Examples include Drill, Hive, Presto, and Trino.
- Data lake and data warehouse platforms. Examples include Amazon Redshift, Delta Lake, Google BigQuery, Kylin, and Snowflake.
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Commercial platforms and managed services.
Examples include Amazon EMR, Azure HDInsight, Cloudera Data Platform, and Google Cloud Dataproc.
Conclusion
Numerous businesses are turning to vendor-managed platforms offering big data as a service to streamline deployments and ongoing administration of big data systems.
By opting for cloud storage services like those provided by Cognilytica's Schmelzer, Schmelzer noted in his article about top big data trends. Businesses may "manage virtually unlimited new data by paying only when needed for storage capacity or computing capability, without maintaining large and costly data centers."
