Your Guide to Software Selection

Features of Big Data Analytics & Requirements

What is Big Data analytics? Why is it big? These were my questions when coming across the term Big Data for the first time. Luckily for both of us, 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. This data can be anything from customer preferences to market trends, and is used to help business owners make more informed, data-driven decisions.

But how do you know if you needBig Data analytics tools? What’s the difference between BI and Big Data? What features of Big Data should you be looking for in an analytics tool? To answer these questions, we’ve compiled a Big Data requirements checklist to help you get on the right track.

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Data Processing

Data processing features involve the collection and organization of raw data to produce meaning. Data modeling takes complex data sets and displays them in a visual diagram or chart. This makes it digestible and easy to interpret for users trying to utilize that data to make decisions.

Data mining allows users to extract and analyze data from different perspectives and summarize it into actionable insights. It is especially useful on large unstructured data sets collected over a period of time.

A visualized data model from Board BI

Big Data analytics tools should enable data import from sources such as Microsoft Access, Microsoft Excel, text files and other flat files. Being able to merge data from multiple sources and in multiple formats will reduce labor by preventing the need for data conversion and speed up the overall process by importing directly to 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.

Modeling
Data Mining
Data File Sources
File Exporting

Predictive Applications

Identity management (or identity and access management) is the organizational process for controlling who has access to your data. Identity management functionality manages identifying 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 at financial transactions. Analytics can be an early warning tool to quickly and efficiently identify potentially fraudulent activity before it has a chance to impact your business at large.

Identity Management
Fraud Analytics

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Analytics

Big Data analytics tools offer a variety of analytics packages and modules to give users options. RIsk analytics, for example, is the study of the uncertainty surrounding any given action. It can be used in combination with forecasting to minimize the negative impacts 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 text that was written about or by customers. Analytics software helps you find patterns in that text and offers potential actions to be taken based on what you learn. This kind of analytics is particularly useful for drawing insight about 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, pictures, etc. Social media analytics is one form of content analysis that focuses on how your user base is interacting with your brand on social media.

Statistical analytics collects and analyzes data sets composed of numbers. The goal is to draw a sample from the total data that is representative of a total population. Statistical analysis takes place in five steps: describing the nature of the data, exploring the relation of the data to the population that provided it, creating a model to summarize the connections, proving or disproving its validity, and employing predictive analytics to guide decision-making.

Predictive analytics is a natural next step to statistical analytics. This feature takes the data collected and analyzed, offers what-if scenarios, and predicts potential future problems.

Risk Analytics
Decision Management
Text Analytics
Content Analytics
Statistical Analysis
Predictive Analytics
Social Media Analytics

Reporting Features

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 allows users to make snap decisions in heavily time-constrained situations and be both more prepared and more competitive in a society that moves at the speed of light.

A reporting dashboard from Dundas BI

Dashboards are data visualization tools that 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 — these are data sets gathered from or filtered by location that can garner useful information about demographics.

Real-Time Reporting
Dashboards
Location-Based Insights

Security Features

Keeping your system safe is crucial to a successful business. Big Data analytics tools should offer security features to ensure security and safety. One such feature is single sign-on. Also called SSO, it is an authentication service that assigns users a single set of login credentials to access multiple applications. It authenticates end user permissions and eliminates the need to login multiple times during the same session. It can also log and monitor user activities and accounts to keep track of who is doing what in the system.

Another security feature offered by Big Data analytics platforms is data encryption. Data encryption involves changing electronic information into unreadable formats by using algorithms or codes. While web browsers offer automatic encryption, you want something a bit more robust for your sensitive proprietary data. Make sure the system offers comprehensive encryption capabilities when looking for a data analytics application.

Single Sign-On
Data Encryption

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Technologies Support

Your analytics software should support a variety of technology and tasks that may be useful to you. A/B testing is one example. Also called split or bucket testing, A/B testing compares two versions of a webpage or application to determine which performs better. It catalogues how users interact with both versions of the webpage and performs statistical analysis on those results to determine which version performs best for given conversion goals.

Another big data analytics feature 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: allows data to be stored 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 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 both within an organization and between organizations.

A/B Testing
Integration with Hadoop

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 to focus on. Make sure to check out our comprehensive comparison matrix to find out how the best Big Data analytics systems stack up for these requirements.

Did we miss any important requirements? Was this list helpful? Let us know your thoughts in the comments.

Bergen AdairFeatures of Big Data Analytics & Requirements

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