What is big data analytics? Why is it big? What are the key features of big data analytics? These were my questions when coming across the term big data for the first time. Luckily, it’s a pretty simple answer. Big data analytics tools are exactly what they sound like — they help users collect and analyze large and varied data sets to explore patterns and draw insights.
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Big data can be anything from customer preferences to market trends, and it helps business owners make more informed data-driven decisions. But how do you know if you need big data analytics tools? What’s the difference between BI and big data? What features should you be looking for in an analytics tool? We’ll cover that and more.
This Article Covers
Big Data Characteristics
Big data is characterized by 5 V’s:
- Volume: Organizations collect data from different sources, including IoT (Internet of Things) devices, transactions, videos, images, audio, social media and more. In the past, storing big data was an expensive affair, but it has become much more affordable due to the emergence of technologies like data lakes and Hadoop.
- Velocity: With the IoT’s continued growth, businesses generate huge volumes of data at a rapid pace that needs prompt handling. RFID tags, sensors and smart meters drive the need to deal with data torrents in real time.
- Variety: Variety refers to structured, semi-structured and unstructured data. Data in the form of emails, photos, videos, audio, PDFs, devices and more poses a serious challenge for storage, mining and analysis.
- Variability: In addition to dynamic data velocities and varieties, data flows vary constantly and are unpredictable. It is vital for businesses to gauge and manage daily, seasonal and event-triggered data loads.
- Veracity: This idea refers to the quality of data. Since the data originates from different sources, it’s difficult to cleanse, transform, match and link data across systems. Connect and correlate relationships, hierarchies and information linkages to ensure robust data quality.
How Does Big Data Analytics Work?
Big data analytics involves collecting, cleaning, processing and analyzing massive data sets to gain useful insights.
Set a Strategy
Big data strategies help oversee and improve acquiring, storing, managing, sharing and analyzing the data within and outside the organization. Developing a robust business strategy that considers present and future business objectives is vital. It calls for treating big data as a valuable business asset instead of a byproduct of applications.
Identify Big Data Sources
Identify big data sources such as IoT, social media, web, databases and more. IoT devices like wearables, smart cars, medical devices and industrial equipment generate immense amounts of data.
Social media data stems from interactions on Facebook, Youtube, Instagram, Snapchat and more. It includes vast amounts of data in the form of images, videos, sound, text and voice. This information’s semi-structured or unstructured nature poses a unique challenge for storage, consumption and analysis.
Public data comes from open data sources like the European Union Open Data Portal, data lakes, cloud sources, customers and suppliers.
Access, Manage and Store Big Data
Modern systems provide the speed, power and flexibility to access huge amounts and types of big data. Along with secured access, companies need methods to integrate data, build pipelines, ensure quality, offer data governance and prepare it for analysis.
Big data is stored on-premise in a traditional data warehouse or through low-cost options such as data lakes, pipelines and Hadoop.
Analyze
With advanced technologies like grid computing or in-memory analytics, organizations can use big data for analysis. You can also leverage machine learning and artificial intelligence capabilities to analyze data and gain insights.
Make Intelligent Decisions
Trusted data facilitates better data-driven decisions that are profitable in the long run. In order to stay competitive, leverage big data to draw useful analyses.
Key Requirements
Let’s look at the key features of a big data analytics solution.
1. Data Processing
One of the most important features of big data analytics solutions is data processing. Data processing involves raw data collection and organization to derive inferences. Data modeling takes complex data sets and displays them in a visual diagram or chart. This process makes them digestible and easy to interpret for users trying to utilize that data to make decisions.
A data model in Board.
Data mining tools extract and analyze data from different perspectives and summarize it. They are especially useful on large, unstructured data sets collected over time.
Big data analytics tools should import data from sources such as Microsoft Access, Microsoft Excel, text files and other flat files. Merging data from multiple sources in multiple formats reduces labor by preventing the need for data conversion and speeding up the overall process by importing directly into the system.
The same goes for export capabilities — being able to take the visualized data sets and export them as PDFs, Excel files, Word files or .dat files is crucial to the usefulness and transferability of the data collected in earlier processes.
- Data Modeling
- Data Mining
- Data File Sources
- File Exporting
2. Predictive Applications
Identity management (or identity and access management) is the organizational process for controlling who has access to your data. It manages data for everything that has access to a system, including individual users, computer hardware and software applications.
Identity management also deals with issues including how users gain an identity with access, protection of those identities and support for other system protections such as network protocols and passwords. It determines whether a user has access to a system and the level of access that user has permission to utilize.
Identity management applications aim to ensure only authenticated users can access your system and, by extension, your data. It is a crucial element of any organization’s security plan and will include real-time security and fraud analytics capabilities.
Fraud analytics involve a variety of fraud detection functionalities. Too many businesses are reactive when it comes to fraudulent activities — they deal with the impact rather than proactively preventing it.
Data analytics tools can play a role in fraud detection by offering repeatable tests that can run on your data at any time, ensuring you’ll know if anything is amiss. You also have wider coverage of your data as a whole rather than relying on spot-checking for financial transactions. Analytics serves as a warning tool to identify potential fraudulent activity before it has a chance to impact your business.
- Identity Management
- Fraud Analytics
3. Analytics
Big data analytics tools offer a variety of analytics packages and modules. Risk analytics, for example, is the study of the uncertainty surrounding a given action. It can combine with forecasting to minimize the negative impact of future events. Risk analytics allow users to mitigate these risks by clearly defining and understanding their organization’s tolerance for and exposure to risk.
Decision management involves the decision-making processes of running a business. Decision management modules treat decisions as usable assets. It incorporates technology at key points to automate parts of that decision-making process.
Text analytics is the process of examining textual data. Analytics software helps you find patterns in the text and suggests potential actions. It is particularly useful for drawing insight into your customers’ wants and needs directly from their interactions with your organization.
Content analysis is very similar to text analysis but includes the analysis of all formats of documentation, including audio, video and pictures. Social media analytics is one form of content analysis that focuses on social media interactions.
Statistical analytics collects and analyzes numerical data sets. Draw a sample from the entire data set that is representative of a total population. Statistical analysis happens in five steps:
- Describing the nature of data.
- Exploring the data in relation to the population.
- Creating a model to summarize connections.
- Proving or disproving its validity.
- Employing predictive analytics to guide decision-making.
Predictive analytics is an evident next step to statistical analytics. This feature takes the data analysis results and offers what-if scenarios while predicting potential problems.
- Risk Analytics
- Decision Management
- Text Analytics
- Content Analytics
- Statistical Analysis
- Predictive Analytics
- Social Media Analytics
4. Reporting
Another vital feature of big data is reporting. Reporting functions keep users on top of their business. Real-time reporting gathers minute-by-minute data and relays it to you, typically in an intuitive dashboard format. This process allows users to make snap decisions in heavily time-constrained situations and be prepared and competitive in a society that moves at the speed of light.
A reporting dashboard from Dundas BI
Dashboards in data visualization tools present metrics and KPIs. They are often customizable to report on a specific metric or targeted data set. One example of a targeted metric is location-based insights gathered from or filtered by location that can garner useful information about demographics.
- Real-Time Reporting
- Dashboards
- Location-Based Insights
5. Security
Safeguarding your system is crucial to a successful business. Big data analytics tools should offer security features like single sign-on (SSO) to enhance safety. This authentication service assigns users a single set of login credentials to access multiple applications. It authenticates end-user permissions and eliminates multiple logins during a session. It can also log and monitor user activities.
Another security feature offered by big data analytics platforms is data encryption. It involves changing electronic information into unreadable formats through algorithms or codes. While web browsers offer automatic encryption, businesses need something more robust for sensitive, proprietary data.
Make sure the system offers comprehensive encryption capabilities when looking for a data analytics application.
- Single Sign-On
- Data Encryption
6. Technology Support
Your analytics software should support a variety of useful tasks. A/B testing is one example, also called split or bucket testing. It catalogs how users interact with both versions of the web page and performs statistical analysis on those results to determine which version performs best for given conversion goals.
Another feature of a big data analytics solution you should look for is integration with Hadoop. Hadoop is a set of open-source programs that can function as the backbone for data analytics activities. It’s made up of four modules:
- Distributed File System: Stores data in an accessible format across a system of linked storage devices.
- MapReduce: Reads data from this file system and formats it into visualizations users can interpret.
- Hadoop Common: The collection of Java tools needed for the user’s computers to read this data type stored under the file system.
- YARN: Manages the resources of the systems storing data and running analysis.
Integration with these modules allows users to send results gathered from Hadoop to other systems. It promotes interoperability and flexibility as well as communication within and between organizations.
- A/B Testing
- Hadoop Integration
Conclusion
Hopefully, now you have an understanding of what comes in most big data analytics tools and which of these big data features your business needs. Make sure to check out our comprehensive comparison matrix to find out how the best systems stack up for these data analytics requirements.
Did we miss any important big data features and requirements? Was this list of big data analytics capabilities helpful? Let us know your thoughts in the comments.
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