Did you know that 6.3 million searches happen on Google every single minute?
Moore’s law tells us that computer processing power doubles roughly every two years. This allows for greater amounts of data to be accessed, collected and analysed.
As data grows and evolves, businesses are trying to keep up. This has led to organisations pursuing big data analytics—the processing and analysis of large and complex data sets.
How Big Data Analytics Fits Into the Decision-Making Process
According to IBM, big data analytics “allows for the uncovering of trends, patterns and correlations in large amounts of raw data to help analysts make data-informed decisions”. Having this capability is crucial because the success of a business depends on the quality of decisions made by its leaders.
In order to make good decisions, managers require information—defined by Mallach (2020) as “data that has been organised and processed to be meaningful to a person who will use it”.
Herbert Simon’s research tells us that decision-making, part of the larger problem-solving process, happens in 3 phases: intelligence (gathering information), design (formulation of alternative choices), and choice (selecting an alternative).
Big data analytics is primarily used in the intelligence phase for gathering information, often in real-time, and empowering managers with key insights to make informed decisions.
Characteristics of Big Data
Big data is often characterised by the “five V’s”: Volume (the massive amount of data that’s generated), Velocity (the speed at which it is generated), Variety (the diversity in data types), Veracity (the quality and reliability of the data), and Value (the value it provides).
This data falls into one of three categories:
- Structured: Data adhering to a rigid schema (e.g., entries in a SQL database).
- Semi-structured: Data that includes metadata that describes its structure (e.g., HTML code).
- Unstructured: Data generated by people (e.g., text, audio, video, and images).
Due to its unique characteristics, analysing big data is not a straightforward endeavour; the process of collecting and analysing big data generally involves five steps:
- Acquisition: Acquiring data from various sources (e.g., social media, sensors, blogs).
- Storage: Storing large volumes of data securely in data centres or warehouses.
- Preprocessing: Extracting high-quality data from large datasets, and removing noise and missing or inconsistent values.
- Analysis and modelling: Extracting useful information from the data.
- Visualisation: Showing the information on charts or graphs, making it easier to spot trends.
Applications in business
Businesses can achieve success with big data by focusing on smaller datasets with more targeted goals, while leaving room to scale once those goals have been accomplished. This approach avoids situations in which big data analytics reveals relationships and patterns that provide little value to the organisation.
Forecasting
Big data can reveal relationships between different variables, or insightful trends in historical data. Using predictive analytics, organisations can then leverage this information to forecast what might happen in the future if certain actions are taken. The fact that big data stems from multiple sources can make predictive analytics even more powerful.
Market research
The information gleaned from big data is helpful in spotting patterns that are often indicative of larger industry shifts. For example, the fashion industry analyses consumer purchase information to predict future trends, and tourism companies analyse data from social media, mobile apps, and booking platforms to provide personalised recommendations.
Risk management
Researchers have found that AI algorithms trained on large volumes of data have the potential to perform credit risk assessments, and provide in-depth information to financial institutions about whether to lend to a borrower. Big data has also been used to create models that measure systemic risk within the financial industry.
Technologies
Due to the unique characteristics of big data, and the inherent challenges in analysing semi-structured and unstructured datasets, several innovative tools and techniques have been developed.
Artificial Intelligence & Machine Learning
Popular machine learning and statistical techniques used in predictive analytics include support vector machines, neural networks, decision trees, and linear regression.
Neural networks are particularly effective at analysing big data. Not only can they model complex patterns, but unlike traditional machine learning techniques, they can learn from different data types (e.g., text, images, sound) without manual help.
Apache Hadoop
Running complex algorithms on such large datasets is computationally expensive. Hadoop lightens the workload by distributing tasks across multiple computer clusters.
Hadoop’s distributed architecture consists of a master node that assigns tasks to worker nodes. Once the worker nodes complete their tasks, they return the results to the master node, which then combines them to generate the final output.
Security and privacy challenges
As big data repositories grow, so do security and privacy concerns. Organisations can implement various measures to help mitigate these risks. This includes:
- Access control: Controlling access to certain types of information helps prevent it from falling into the wrong hands. Instituting a data classification system can help in knowing who should have access to what and prevent leaks.
- Encryption: The Encryption process involves converting human-readable plaintext to unintelligible “ciphertext”. This requires the use of a cryptographic key, which should always be stored separately from the data itself.
Big Data Analytics Has a Big Future
Organisations can leverage big data analytics to uncover valuable trends, insights, and patterns that augment managerial decision making.
As exponentially more data is created over the coming years and new techniques are developed to analyse these large datasets, companies will gain even deeper insights, enabling them to anticipate market shifts, optimize operations, and enhance customer experiences.
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