How do companies use big data
Internet services and devices collect and store an immense amount of information, encompassing every facet of our lives. That data is gathered by businesses and used to help them innovate and acquire a competitive advantage. It is a complex puzzle that can unlock the secrets of our past, present, and future.
In this article, I will start with a brief explanation of key terms related to Big Data. Then we’ll get to the main focus of this article: how do companies use Big Data? We will examine how Big Data analytics improves decision-making and boosts business process capabilities.
But let’s start with the fundamental question.
What is Big Data?
Big Data refers to the vast and diverse information generated in the digitally interconnected world. The distinguishing characteristic of Big Data is defined by 3Vs: volume, velocity, and variety. Some definitions add two Vs: veracity and value, making it 5V in total. Let’s take a closer look.
Big Data involves a massive volume of information that exceeds the capacity of traditional data management tools. It makes it hard or even impossible to process and analyze effectively using conventional means.
Moreover, it showcases significant variety, as it comes in different formats and from various sources, making it a complex and challenging entity to work with. It encompasses a wide array of data types, including structured and unstructured data, such as text, images, videos, sensor readings, social media interactions, and more.
The word velocity refers to the unprecedented speed at which Big Data is generated and updated, with information streaming in real-time from numerous sources.
Value refers to the significance and potential insights that can be extracted from Big Data from the business standpoint. Amidst the vast sea of information, the actual value lies in the ability to analyze and interpret the data to gain meaningful insights and identify previously hidden patterns.
Lastly, veracity is the aspect of Big Data that pertains to the reliability and trustworthiness of the data. With the immense volume of data from diverse sources, ensuring data quality and accuracy becomes a critical challenge. There may be errors, inconsistencies, or biases in the data, leading to misleading or erroneous conclusions if not addressed.
A glimpse of history
The early Internet brought unique data analysis opportunities. Web companies such as Yahoo, Amazon, and eBay started to gather data about customer behavior by looking at click rates, user locations specified by IP or visited subsites. Collected data was growing rapidly, and businesses needed new, innovative tools to harness it.
In 2013 Oxford English Dictionary included the term Big Data for the first time, but the expression is much older. Roger Mougalas coined the term in 2005. He referred to large data sets that were almost impossible to manage using the tools available at the time. In the same year, Yahoo released Hadoop, an open-source distributed computing framework. That revolutionary piece of software allowed for the processing of data that was too big to fit in a single machine. Hadoop consists of three main components: distributed file system (HDFS), resource manager (YARN), and processing engine (MapReduce). The Hadoop ecosystem has quickly expanded with various related tools: Apache Pig (released in 2006) - a high-level data-flow language, Apache Hive (2010) - an SQL-like query language for data warehousing, HBase (2009) - a NoSQL database running on HDFS and many more.
Hadoop is still used today. The popularity of its processing engine - MapReduce - has declined since the release of Spark in 2014. Spark is designed to overcome some of the limitations of MapReduce, particularly by introducing in-memory operations, which significantly speed up data processing tasks.
Today it’s easier than ever to start the Big Data journey with cloud providers offering a variety of services and solutions specifically designed to handle the processing, storage, and analytics of massive amounts of information. Those offerings empower organizations of all sizes to tackle Big Data challenges without requiring extensive hardware investments and complex infrastructure management.
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How do we store Big Data?
Collecting Big Data involves choosing the appropriate data storage architecture based on the specific needs and characteristics of the data.
Traditionally, data warehouses have been the leading approach for storing and managing data. They provide a centralized repository for structured data from various sources. The data is typically cleaned and transformed with the ETL (Extract, Transform, Load) process before being ingested into the warehouse. The warehouse stores data using a predefined star or snowflake pattern schema. They are optimized for analytical queries and provide a structured and consistent information view. The biggest downside of data warehouses is schema rigidity. Warehouses require upfront schema design, making them less flexible for accommodating fluctuations in the schema of input data. The ETL also induces some processing overhead and may be time-consuming and resource-intensive.
Data lake pattern addresses that issue. It’s a centralized repository that can store structured and unstructured data in its raw and native format. In a data lake, schema is not applied on the ingestion, but rather is deduced when data is read. This flexibility allows for easy storage and handling of diverse data types, making it suitable for data exploration and experimentation. Data lakes are well-suited for scenarios where the data structure is uncertain or when dealing with raw, uncurated data that requires data exploration and ad-hoc analysis. On the other hand, the lack of a predefined schema might make the querying challenging.
The data lakehouse pattern is a more recent approach that combines the benefits of data warehouses and data lakes. It uses a unified storage architecture to store both structured and unstructured data. The ability to flexibly store diverse pieces of information makes it similar to data lakes. At the same time, it enables structured querying and data management capabilities, similar to data warehouses. Data Lakehouse leverages technologies like Delta Lake to achieve it.
All of the previously mentioned approaches rely on centralized data storage solutions, which might lead to bottlenecks and difficulties in scaling as the organization's data volume and complexity increase. It also places a heavy burden on a centralized data team. Data Mesh is a relatively new architectural paradigm for managing an organization’s data. It proposes a decentralized and domain-oriented approach to data handling. The core idea is to treat data as a product and to distribute its ownership and responsibilities across different business domains. Each business unit becomes responsible for managing its data, including quality, governance, and access. With data mesh, domain experts have greater control over their data. On the downside, splitting the data into smaller parts might increase the overall complexity of the data model from the standpoint of the whole organization.
Big Data storage is often applied on on-premise servers with various SQL or NoSQL databases. This is common for institutions that prefer to keep sensitive data in-house. In the cloud, we can choose from multiple engines designed for large-scale data warehousing and analytics workloads: Snowflake, AWS Redshift, Google BigQuery, and many more.
How do companies use Big Data?
As we already discussed, companies gather incomprehensible amounts of data. A single airplane produces 20 terabytes per hour from just engine sensors. But without business context, that data is just a series of ones and zeros taking up disk storage space. It becomes invaluable only if we can properly analyze that data to get practical insights.
The Big Data inquiry is the basis for decision-making in many industries. It helps improve therapies and patients' lives in healthcare, making sensible marketing decisions or detecting frauds. Lastly, data is the fuel of the contemporary AI revolution. Machine Learning models use massive amounts of information for training - the quality and integrity of the data provided impact the efficiency and correctness of the AI.
I presented classic use cases for four industry sectors that benefited from Big Data analytics in the following paragraphs.
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Let’s start with the retail industry. It’s a great example of the practical application of data science. The whole sector generates enormous volumes of data from various sources, including online and offline transactions, customer interactions, inventory levels, supply chain activities, and more. Its use of big data showcases the power of data-driven decision-making, customer-centric strategies, and operational optimization. It highlights how big data can transform a traditional sector. I present a few most prevalent use cases of Big Data analytics below:
Retail companies leverage data science and analytics of the customers’ behavior to improve their offerings dynamically. They gather this information each time a user logs into their account and buys something or just browses the store's selection of goods. When customers come back, they are presented with products catered to their style and taste based on their prior purchases and browsing history. Even if they are not looking to buy more things, the tailored offers lure them into making extra purchases.
A great example of behavioral analytics applied achieved through Big Data is Target’s case. Target’s data engineers found certain products, including unscented lotion and vitamin supplements, which indicated that the customer might be pregnant when bought together. Using that information, Target’s engineers established a pregnancy prediction score. It allowed them to do targeted (no pun intended) advertisements for baby-related products to women that scored high in prediction function.
Retailers employ Big Data analytics to optimize inventory management. By precisely forecasting demand and examining historical and current data on sales, they can avoid overstock and stockouts. As an illustration, retailers might employ Big Data analytics to estimate seasonal product demand.
Another example of Big Data analytics in retail is the process of adjusting prices by examining competitors' pricing, historical sales data, customer demand, and market trends. All of these factors are crucial for developing optimized and dynamic pricing plans.
Management of the supply chain
Retailers analyze logs on logistics, transportation, and inventory levels to optimize and streamline their supply chain operations. The result is a reduction in wait times and stockouts.
Market trend analysis
Big Data also helps retailers in analyzing market trends, customer preferences, and competitor data. They investigate data generated in social media platforms, customer reviews, and online forums to comprehend client sentiment and preferences. This can be used to spot new trends and improve its product offerings.
Big Data analytics has been a game-changer for the healthcare sector, revolutionizing how medical care is delivered. We wrote a whole article about it some time ago: “Big Data in Healthcare”. Medical companies collect massive amounts of patient data, like electronic health records (EHRs), genomic information, and real-time monitoring data. That data can be used to spur medical innovation and improve treatment outcomes.
An outstanding illustration of Big Data analytics is real-time data monitoring of COVID-19 cases enabling public health professionals to identify hotspots or track disease transmission.
Medical Data Analysis
Healthcare organizations are using Big Data analytics to sift through vast amounts of data to discover patterns in population health, the incidence of diseases, and the effectiveness of treatments. Healthcare facilities can use this information to develop new treatment protocols, allocate resources more wisely, and support public health initiatives like disease surveillance and outbreak management.
Individualized Medical Care
Big Data makes personalized medicine possible. It allows adapting a patient's medical care to their genetic profile, lifestyle, and other characteristics. It enables medical professionals to create tailor-made treatments for patients with challenging medical ailments, including cancer, cardiovascular diseases, and uncommon genetic abnormalities. For instance, medical facilities can use genomic data to pinpoint alternative targeted cancer treatments depending on the genetic abnormalities of the patients. Please read our case study to learn how software companies help fight cancer.
Pharmaceutical companies gather biological, chemical, and clinical data to boost the development of new drugs. The pharma industry uses machine learning algorithms to forecast drug efficacy and toxicity, hence cutting the expense of clinical trials.
Initial Predictive Analytics
Healthcare companies use collected data to forecast disease outcomes and identify individuals at a high risk of contracting specific illnesses. For instance, Machine Learning models can use data gathered from wearable devices to predict health problems like heart attacks.
Data analysis related to patient scheduling, resource allocation, and supply chain can help healthcare organizations improve their operational efficiency. This includes streamlining processes, cutting costs, enhancing patient flow, and lowering employee burnout.
Media companies can better understand their audiences with Big Data analytics. The paramount goal is better user engagement and retention rates.
A suggestion for content
Media platforms analyze user behavior using Big Data analytics to suggest content that may interest them. All major music streaming platforms generate playlist recommendations based on user listening patterns. The video platforms use data analytics to produce content suggestions from users’ viewing habits.
Optimization of Advertising
Businesses can use Big Data to understand consumer behavior and preferences. This gives the ability to deliver more precise and efficient advertising, resulting in improved ROI.
Media firms can forecast the type of material that will be popular in the future by analyzing data on user behavior and consumption habits. They can lower the risk of content flops and make smarter investment decisions.
Monitoring of Performance
Media companies use Big Data analytics to track the performance of the content on numerous platforms, including social media, streaming services, and websites. This can assist businesses in identifying trends and improving their content strategy.
Big data analytics is essential for the financial services sector. FinTech companies need it to improve customer experience and safety, manage risks, and boost operational effectiveness.
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Banking organizations utilize Big Data analytics technologies to identify chances for cross-selling, upselling, and customizing offers and promotions. When analyzing client feedback, they may also use sentiment analysis to determine customer preferences and attitudes toward the institution.
- Risk Control
The aid of Big Data analytics enables financial organizations to manage credit, market, and operational risks more effectively. Financial institutions may analyze previous market data to spot trends and patterns that will help them decide how much risk to take.
- Regulatory and compliance reporting
Analytics performed on real-time data on transaction logs aid banking organizations in meeting regulatory obligations. Financial institutions can automate the process of collecting and analyzing regulatory data to ensure compliance with rules like Know Your Customer (KYC) and Anti-Money Laundering (AML)
- Analytics for trading and investment
FinTech uses Big Data analysis to examine market patterns, financial information, and investing tactics, allowing organizations to make better trading and investment choices. Financial institutions can examine market information, such as stock prices or trade volumes to spot new investment opportunities and enhance trading tactics.
- Management of loans
Another use case of Big Data among financial companies is to forecast loan default rates, evaluate borrower eligibility, and analyze credit risk. The result is streamlining loan management procedures and lowering the likelihood of default.
- Identifying fraud
Fraud can take various forms, like identity theft, unauthorized credit card transactions, or loyalty program scams. FinTech companies can discover potential problems by investigating transaction patterns, consumer behavior, and previous fraud data. They examine real-time data to spot suspicious trends like transactions from several places in a short period or money transfers of unusual size. Machine Learning models can identify unusual trends in customer behavior, such as sudden changes in buying habits.
Big Data has transformed how companies operate and make decisions across various industries. Data analytics is crucial for staying ahead in today's competitive landscape. With the rising importance of AI, it will get even more significant.
Thinking of blending Big Data with Machine Learning to unlock your data's untapped potential? In a data exploration process, we can validate your idea and prepare a PoC which lets you know how to move forward.
Reviewed by Łukasz Lenart