Data for Breakfast Around the World
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Big data emerged in the 1990s with the dawn of the internet and widespread adoption of digital-first business practices. Organizations gained access to an influx of data points about their business functions, customers and industries as a whole. Big data is made up of large, complicated data sets that have grown and expanded beyond the operating capabilities of traditional data management systems.
Big data often includes not only large sets of traditional structured data, but also semi-structured and unstructured data in a variety of formats.
Enterprises can now collect robust data on a variety of formats, including but not limited to audio files, web pages, internal processes, customer transactions and more. Given the complexity of big data, different tools and resources are required to properly collect, manage and analyze all the information.
The emergence and ever-expanding growth of big data in the last several decades has presented massive opportunities for organizations to uncover new insights and improve decision-making.
In this article, we’ll dig into the unique characteristics of big data and how — when harnessed effectively — it can help organizations improve efficiencies, innovate and grow.
Big data refers to large and complex data sets that may include structured data, such as inventory data, and unstructured data like audio files or social media content. Since these data sets are massive and continue to grow over time, they often cannot be contained in traditional data management systems.
In recent years, data storage costs have also decreased, meaning that organizations can store and retain more of their data. While this increases the potential for drawing insights, it also has added complexity. Organizations now require more analytical tools and expertise to pull insights from these large data sets to make thoughtful business decisions.
Big data is a collection of large amounts of diverse and complex data sets. It works by collecting large volumes of data from various sources, often in real time. These data sources include metrics on internal business processes, customer sentiment, engagement and more.
The speed at which data is collected means there is a ton of information that systems need to process. To manage it all, data engineers and analysts need to process and structure the data using specialized cloud-based computing systems that have more storage and computing power than traditional systems. To make sense of all the data, organizations use machine learning and specialized machine learning practices to effectively analyze the data. Organizations look for patterns and trends in the data to inform transformative business decision-making.
To make the most of big data and to have it most effectively impact business potential, organizations have adapted to their data practices and processes. Organizations now recognize that they need the most powerful and up-to-date data collection, processing, storage capabilities and analysis.
Big data has five key characteristics known as the "Five V's of Big Data,” which illustrate how big data is different from traditional data sets. The V’s are: volume, velocity, variety, veracity and value. Below we break down each characteristic:
Today, there’s simply more data for organizations to store, manage and analyze. With more information available, organizations need to adjust to best use and handle their data as it continues to grow.
Organizations now create data faster than ever. This reality pressures organizations to process and analyze data at a faster rate — often in real time — to make quick and impactful decisions. Customers also expect near-instant feedback on recommended products to buy. To keep us with customer demands, organizations need to adjust.
Big data includes different data formats, including unstructured data like free-form text, images, videos and more. It also includes structured data, such as spreadsheets, and semi-structured data like sensor data. Managing this variety requires flexible databases and tools to enable comprehensive data analysis.
Accuracy is an issue with big data. Because of the multiple sources and types, and the sheer quantity, the potential for error is high. Yet reliable data is essential to accurate analytics and well-informed decision-making. Organizations need to take it on themselves to ensure data quality through data cleaning, validation and verification efforts.
Accurate, high-quality data can provide considerable business value, increasing revenue, uncovering efficiency and sparking innovation. Recognizing where potential value may be found in big data can help organizations form a more effective strategy for exploiting it.
Big data has the power to dramatically improve business operations, leading to optimized business outcomes. Benefits of big data include:
Big data enables organizations to make more informed and strategic decisions. For managing their supply chain, organizations can effectively and methodically analyze complex data sets, developing reliable predictions to better manage inventory supply and ordering needs. Using automation and real-time insights can drive further overall business impact.
Organizations can analyze customer data to better understand customer needs and behavior. This enables organizations to create more tailored campaigns for each customer type, placing customers’ unique needs at the center. Organizations can develop customer profiles to deliver tailored personalization based on demographic information, marketing engagement and more.
Across all organizations, every department can leverage data to optimize operations. This can include streamlining processes and reducing waste by using big data analysis to forecast maintenance needs, predict trends, implement process improvements and make staffing shifts.
Big data opens the doors to predictive analytics and forecasting capabilities. With big data, organizations can review trends, customer behaviors, customer feedback and broader market trends to improve existing products or develop new ones.
Even though big data has significantly shifted how organizations gain meaningful insights about their businesses, it is not without its challenges. Below we break down some of the most prevalent difficulties organizations face in regard to big data.
Constantly evolving laws and regulations are a significant challenge. Organizations must comply with various privacy and security laws, such as GDPR and HIPAA, which can be difficult when data sets are large and consistently growing. Customers also have high expectations that businesses are protecting their personal data. That increases pressure on businesses to implement data security measures that protect customer data.
With more data comes more storage needs and processing resources. Managing these storage tools requires costly, specialized resources. Even with cloud services, storing and managing all that data is demanding and resource heavy. Organizations need to recruit specialized talent who can effectively and efficiently connect and collaborate with the existing workforce.
With an influx of large qualities of complex data, organizations need skilled employees, including data analysts and data engineers, to wrangle and make sense of the data. Having data is one thing, but having the right employees to interpret the data, identify patterns and make recommendations is where the true value lies. Organizations additionally need technically savvy business leaders who are receptive to making innovative, data-driven decisions that go beyond a familiar spreadsheet or gut instinct.
It is challenging to effectively combine multiple types of data sources. For example, retailers might seek to combine in-store sales and website click data, or use purchase and shipping data to better support a customer inquiry; a healthcare system might need to bring together electronic health records, lab results and insurance to form a complete treatment plan for a patient. Such integrations require new tools and technologies to manage this data influx, specialized data analysts and other IT resources.
Various business functions across industries can use big data to achieve great things. Here are a few examples of how big data supports different industries:
Healthcare can thoughtfully leverage big data to support its mission while meeting regulatory requirements. Healthcare organizations can improve the patient and healthcare provider experience by combining various patient data sets for a holistic view of a patient’s health. Big data can pull together electronic health records, family history, data from wearable devices, insurance information and more to influence the course of care for a patient. Data around scheduling needs and medical supplies can help optimize staffing and supply chain operations. And end-to-end data governance can help insurers and healthcare providers satisfy strict privacy requirements.
In finance, organizations can use big data to analyze a customer’s spending habits to detect possible identity theft in real time. By taking it a step further, they can implement additional security features around authentication. Having a comprehensive view of transactions and other customer information can help organizations stay aligned with continuously evolving security and compliance requirements. Finance organizations can better serve their customers by using data to analyze customers’ spending habits. They can use that intel to recommend specialized offerings to help customers reach their financial goals.
Insights from big data can help enable effective, targeted marketing efforts. By tracking customer journeys and spending patterns, retailers can better understand their customers’ needs and wants. They can use this insight to develop personalized marketing campaigns with customer-specific product recommendations. They can also better manage their supply chain operations, sales projections and other factors, and improve their product development based on customer feedback.
Manufacturers can draw insights from big data to improve fabrication, assembly lines, supply chain management and more. For example, organizations can use sensor data to predict when routine maintenance is needed and predict equipment failures to prevent downtime and reduce overall spend on repairs. By identifying the patterns that predict when a malfunction will happen before it occurs, manufacturers can better plan and more effectively allocate resources.
Government and public service organizations can use big data to better understand the needs of their communities. Organizations can get ahead of safety concerns, for example, by pulling together traffic data and driver trends to optimize roadways and improve road maintenance. This can help government organizations make improvements faster, bolstering trust among residents that government organizations are acting in their best interest.
To help organizations stay focused and not get lost in too much information, data analysis should support clear business goals. Aligning analytics efforts to priorities can minimize false starts and dead ends, leading business leaders to high-value insights more quickly.
Low-quality or irrelevant data can lead to flawed decisions. For example, a retailer might make poor sales projections if the data set includes duplicate records, sales of products other than the one under analysis, or data that's too old to still be useful. Organizations must employ strong data governance frameworks and reliable data quality tools and techniques so that data is timely, accurate and relevant.
With expanding data, organizations need to grow their data storage capacity, staffing resources and IT processes to support data management and analysis at the petabyte scale and beyond. Modern scalable solutions include distributed storage systems, cloud-based data lakes and advanced processing frameworks that can automatically scale resources as needed, with maximum efficiency.
Protecting sensitive data and complying with evolving privacy and compliance regulations require organizations to implement effective guidelines for processing data. Prominent regulations like GDPR and HIPAA require strict security measures to prevent breaches of confidential customer data. Customers want to trust that their data is safe, so protecting that data is a high priority for any business.
Data scientists, IT teams and business leaders must work together to use data to achieve business goals. Techniques to build a broad, collaborative data culture include creating cross-functional teams, internal innovation projects or competitions. Other ideas include encouraging pilots of new tools or processes, making external learning resources available, and sharing tips, techniques and findings through lunch-and-learn sessions.
Big data is complex, including diverse and varying data sets. While this is an asset for organizations because it can generate a continuous influx of potential insights, it can also be challenging for organizations to store and effectively analyze data to generate valuable outcomes.
Looking ahead, big data capabilities, like the data itself, will only continue to grow. The continued evolution of big data analytics tools and technologies will both drive innovation and raise ethical considerations. Businesses will have to grapple with how they store, manage and analyze data in an ethical way.
AI and ML innovations, including the emergence of natural language processing and generative AI in data analytics, will become increasingly mainstream. This will democratize data by allowing less technical users to “ask questions of the data” directly, without requiring data scientists to convert a business question into code. The result can be faster, better decisions. The Internet of Things, in which multiple devices in an organization's network provide sensor data, and edge computing, in which data processing is done at the periphery, will generate more data and increase the need to automate actions.
In short, the future of big data is more. More data, more tools, more hunger for insights, and more value for the organizations that learn to master it.
1. Data storage and management: Designed to store and manage massive data sets, which may be structured, unstructured or semi-structured. Technologies include distributed file systems, NoSQL databases, data warehouses and data lakes.
2. Data processing and computation: Built for transferring data between relational databases. They efficiently collect, aggregate and move data from different sources to a centralized data store.
3. Data warehousing and analytics: Facilitate the reading, writing and managing of large data sets via highly scalable, serverless and cost-effective cloud data warehouses.
4. Data visualization and reporting: Used by business intelligence teams, these interactive dashboards for data visualization, reporting and robust analytics.
5. Machine learning and AI: Providing the advanced computational power, these types of algorithm-driven software find patterns and insights in big data.
6. Orchestration and management: Programmatically author, schedule and monitor workflows via open source systems for automating deployment, scaling and application management.
Big data has large, diverse data sets with data from a variety of formats, including text, audio and video. It is unstructured so does not fit easily or cleanly in a traditional database, meaning it needs more robust processing to provide value.
Traditional data is made up of structured data with clear parameters and can be easily stored and analyzed within standard, traditional databases.
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