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 inform better business decisions. The complexity of analyzing big data requires various methods, including predictive analytics, machine learning, streaming analytics, and techniques like in-database and in-cluster analysis.
Big data analytics is when data inputs become so vast and voluminous that greater computing capabilities are required to process all of the data coming in from multiple sources. Big data is generally characterized 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 the arrangement of data architectures, data management, integration, and analysis. But organizations that bring together unstructured data sources like social media content, video, or operations logs with existing structured data like transactions are able to add context and generate new, and often, richer insights for better business results.
Further, another component of big data is the increased speed with which incoming data is generated 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 the new standard for operational excellence in their prospective 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, as analysis of unstructured data sources can lead to new insights. But it’s important that data stewards and decision makers both know the quality, accuracy, and trustworthiness of the data used for insight generation and decision-making.
The evolution of big data analytics
The rise of structured and unstructured data known as big data has radically transformed the function of business intelligence (BI) by converting data into action and adding value to the business. While big data analytics has increased opportunities to uncover valuable insights across the business, it has also presented new challenges in capturing, storing, and accessing information. In the era of big data analytics, BI challenges have grown due to an exponential growth in the volume of data, the variety of data, and the velocity of data accumulation and change. This shift has placed significant new demands on data storage and analytics software, posing new challenges for businesses. But it also creates great opportunities for implementing big data analytics for competitive advantage. To realize this value, organizations must invest in big data analytics to increase their capacity to gather and store big data but also to turn that data into insights for the business.
Why is big data analytics valuable?
Using big data analytics, organizations can find interesting new opportunities to build business today and tomorrow. It can take data collection and data discovery to an entirely new level. Big data analytics combines data at rest (traditional structured data) with data in motion (unstructured data in the moment) to identify opportunities and take advantage of them in real time.
Big data is already a fact of life for many enterprises, but the sheer volume and massive complexity of big data can feel overwhelming. Companies suddenly must struggle with making sense of and creating opportunities from both data at rest and data in motion, from structured, unstructured, and multi-structured data. Only big data analytics can help companies deal with this inundation of big data and capitalize on the value hidden in these massive, complex data sources.
Harnessing big data analytics also represents an expanding set of potentially lucrative opportunities. And while it is unknown exactly how it will be used one, three, or five years down the road, the imperative of turning big data into competitive advantage means no one can afford the luxury of waiting to see how things materialize. The window of opportunity for action is getting shorter—the sense of urgency stronger. To support big data analytics, companies should implement a unified data architecture along with analytical and data visualization software to realize the potential of the big data era and minimize its risks.
Getting value from a big data investment requires being able to manage 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.
What are the benefits of big data analytics?
Implementing big data analytics can yield rich opportunities for your business. Below are just a few examples of the value that can be found through big data analytics.
- Holistic of the Business: Big data analytics can help organizations gain a data-rich, consistent, and comprehensive view of the business. User-friendly analytic dashboards and business applications increase data-driven decision making and enable non-technical users to operate based on accurate, timely information instead of gut instincts.
- Faster Time to Action: Organizations require big data analytics to enable everyone across the organization to anticipate situations and opportunities, ask relevant and timely questions, and get the answers they need to take decisive action. These actions may even be automated to ensure a quick response.
- Visibility into the Unknown: To discover unseen or hidden trends and patterns in large, complex data sets, businesses should use big data analytics. This will allow for faster identification of strategic opportunities or risks to the organization.
- Self-service Data Discovery: Big data analytics can allow for users to explore data and get answers without the need for specialized, in-depth data modeling. This reduces dependence on IT and accelerates the decision making process.
Steps for big data analytics
To ensure a successful big data analytics program, use the following steps to ensure it is on track. These steps are typical for any analytics program, but especially important for big data analytics.
- Capture: It all begins with capturing and collecting data.
- Understand: Big data is only useful as the raw material that can be transformed into insight and understanding.
- Model: While many insights may be intuitively obvious, others will require more robust analytic capabilities to uncover.
- Anticipate: These new insights must then be operationalized so that users can act upon them in real time. This can then help companies anticipate opportunities and make efforts to improve the business.
- Decide: The power of real-time big data analytics is its ability to enable real-time decision making. It should be used to take advantage of insights and react to situations as they arise.
- Act: Once the appropriate action has been determined, organizations must act fast. Oftentimes automation can be implemented in these cases to ensure the business is taking advantage of these opportunities.
- Monitor: Finally, this process should be regularly monitored and refined to ensure that big data analytics at your company is being used effectively.
Essential big data analytics capabilities
Because big data analytics deals with large, complex data sources, organizations must adopt solutions that support the following capabilities.
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.
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.
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 accelerates the decision-making process.
Common data sources for big data analytics
Big data analytics involves bringing together data from multiple disparate sources, structured and unstructured. Here are just a few of those complex data sources:
- Big data platforms
- Transactional customer data
- IoT/sensor data
- Event streams
- Operations logs
- 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
Big data analytics continues to grow in popularity due to the breadth of applications. Big data analytics can be used across multiple industries. The following are some examples of different use cases for big data analytics.
- 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
- Internet of Things (IoT)
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