What is Big Data Analytics?
Big data analytics is the process of analyzing large, complex data sources to uncover trends, patterns, customer behaviors, and market preferences to help make better business decisions. The complexity of analyzing big data requires new analytic tools, such as predictive analytics, machine learning, streaming analytics, and techniques like in-database and in-cluster analysis.
Big data is generally defined by the four Vs:
- Volume: Large amounts of data
- Variety: Many different forms of data, unstructured and structured
- Velocity: Frequency of incoming data
- Veracity: Trustworthiness of data
Beyond the sheer volume of data, the complexity of the data being gathered presents challenges in data management, integration, and analysis. But companies that bring together unstructured data sources like social media content with existing structured data like transactions are able to add context and generate new, and often, richer insights.
Further, big data describes the increased speed of incoming data originating from proliferating sources such as sensors, mobile devices, web clickstreams, and transactions, leading to the need for real-time analytics. Organizations that are able to capitalize on what’s happening right now to prevent equipment failure, recommend an item for purchase, identify credit card fraud, and more, are quickly becoming leaders in their industries.
Finally, big data refers to the degree of data accuracy, precision, and trustworthiness. This is not to say that all data must be highly curated and clean, because messier data sources like social media can lead to new insights with certain analyses. But it’s important that organizations know the quality, accuracy, and trustworthiness of the data used for insight generation and decision-making.
The Impact of Big Data Analytics
At the core of analytics is the transformation of data into insightful action that adds value for an organization. But the rise of structured and unstructured data, also known as big data, has radically transformed the function of analytics.
While big data has increased the opportunities available to businesses, it has also created more challenges for capturing, storing, and accessing information.
This revolutionary shift places significant new demands on data storage and poses new challenges for analytical software. It also creates powerful opportunities for discovering and implementing new strategies to develop a competitive advantage.
Realizing these opportunities requires two things: the technological capacity to gather and store big data, as well as new tools for turning data into insights and, ultimately, value.
Getting Value from Big Data
Big data analytics can combine data at rest (traditional structured data) with data in motion (real-time data) to identify opportunities and take advantage of them in the moment.
Big data is already a fact of life for most businesses, but the sheer volume and massive complexity of big data can feel overwhelming. Companies must work to make sense of and create opportunities from both data at rest and data in motion.
Big data represents an expanding set of powerful new opportunities. While it is unknown exactly how big data will be used one, three, or five years from now, the imperative to turn it into a competitive advantage means no company can afford to wait to take action.
Getting value from a big data investment requires being able to exploit the data effectively. Searching for the pieces of information that add ROI for an organization can be like searching for a needle in a haystack, which is why many companies report low ROI on big data investments.
To realize the potential of the big data era and minimize its risks, businesses need a unified data architecture and analytical and data visualization software.
The Benefits of a Big Data Analytics Solution
Visibility into the unknown
A robust big data analytics platform allows users to discover unseen trends and patterns in large, complex datasets that foster faster identification of strategic opportunities and threats.
Single view of the business
With a unified data architecture, companies are provided with a data-rich, consistent, and comprehensive window, increasing decision-making effectiveness and enabling users to operate with the most accurate and timely information.
Fastest time to action
Big data analytics increases decision-making productivity by enabling anyone in the company to anticipate situations and opportunities, to ask relevant and timely questions, and to get answers that lead to decisive actions.
Essential Big Data Analytics Capabilities
Advanced Statistical and Machine Learning Calculations
Data science discovery tools and statistical computing take large amounts of historical data and use it to draw out new knowledge and find patterns. Machine learning helps create and train powerful algorithms, which can improve business processes and add business value.
You can automate action in real time by applying analytics and predictive models to live data. Using a visual development environment to quickly build and deploy streaming applications, you can enable operating systems to score data, send alerts, and take action at high speed for timely decisions attuned to the context.
To visualize big data, you need simple statistics and native out-of-the-box data connectors that facilitate fast importing of data into intuitive dashboards. This will allow you to bring to your business users the ability to analyze big data sources, make truly data-driven decisions, and continually leverage dashboards that speak to the needs of the business.
Data virtualization provides a modern data layer that enables users to access, combine, transform, and deliver datasets with breakthrough speed and cost-effectiveness. With data virtualization, users get data that is not just up-to-date, but up-to-the-minute, easy to find, easy to use, and easy to understand.
Data Asset Management
Data management enables consistent accessibility, delivery, governance, and security of data to meet an organization’s requirements using tools including master data management, data virtualization, data catalog, and self-service data preparation and wrangling.
Self-service Data Discovery
A big data analytics solution allows users across the organization to explore data and get answers without the need for specialized, in-depth data modeling. This reduces dependence on IT and dedicated business intelligence (BI) resources and greatly accelerate the decision-making process.
Common Data Sources for Big Data Analytics
- Big data platforms
- Transactional customer data
- IoT/sensor data
- Social media
- Web/online data
- Mobile device data
- Wearable devices
- Historical and real-time data
- Point-of-Sale (PoS)
- Text-based data
What are some top use cases?
- 360-degree view of customers
- Fraud prevention
- Security intelligence
- Price optimization
- Operational efficiency
- Supply chain efficiency
- Recommendation engines
- Social media analysis and response
- Preventive/predictive maintenance
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