Big data solutions—hardware and software to help store, manage, and analyze information on a massive scale—are in demand as companies increasingly recognize big data as a valuable resource they cannot afford to ignore. In response to the growing interest, vendors have slapped the “big data” label on a dizzying array of products and services, and the proliferation of choices can make it challenging for organizations to find the right tools to meet their needs.
There is no single right big data solution. Organizations are most likely to succeed with big data if they identify their own specific goals and requirements and choose the mix of hardware and software that will best support them. This article is a helpful guide to the types of big data applications on the market and how to find the right ones to meet your own particular needs.
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Types of Big Data Applications
There are many different types of applications that, individually or in concert with one another, meet different aspects of enterprise needs around big data. The table below provides an overview of the most common types and how they can be useful in the enterprise.
|Type of Tool
|Business Intelligence (BI) Platforms
|Data Mining Tools
|Predictive Analytics Tools
|Prescriptive Analytics Tools
|Streaming Analytics Tools
To some extent, the use case will determine the type or types of application you need. For example, a data warehouse and business intelligence (BI) solution can help expand existing financial reporting capabilities, while a data lake or data mining solution can help sales and marketing teams uncover new opportunities for increasing revenue and margins.
If you want to develop a data-driven culture in which everyone in your organization makes decisions informed by data, you’ll want a data lake, predictive analytics, an in-memory database, and possibly streaming analytics.
But matching solutions to needs is not always straightforward—the lines between different types of tools can be fuzzy. Some BI products have data mining and predictive analytics capabilities, for example, while some predictive analytics tools include streaming capabilities. The best approach is to define your goals clearly and look for products to reach them.
Key Decisions When Selecting a Big Data Application
No matter which type of big data application you select, you’ll need to make decisions to narrow down your options. Here are a few of the most important.
On-Premise vs Cloud-Based Big Data Applications
Cloud-based big data applications are popular for several reasons, including scalability and ease of management. Major cloud vendors also lead the way with artificial intelligence (AI) and machine learning research, allowing them to add advanced features to their solutions.
However, the cloud isn’t always the best option. Organizations with high compliance or security requirements sometimes must keep sensitive data on-premises. Rising data laws, like Europe’s General Data Protection Regulation (GDPR), make compliance an even bigger issue. If a cloud partner doesn’t meet these standards, the penalty for noncompliance can still fall on your organization.
In addition, some organizations have already invested in existing on-premises data solutions. In those cases, it may be more cost-effective to continue running their big data applications locally. However, on-premise solutions may quickly become expensive amid skyrocketing data volumes. A hybrid system may be best for those wanting to grow but retain some of their existing infrastructure.
Proprietary vs Open Source Big Data Applications
One of the appeals of open source software is the low total cost of ownership. While proprietary solutions have hefty license fees and may require expensive specialized hardware, open source solutions have no such fees and can run on industry-standard equipment.
However, enterprises sometimes find it challenging to get the open source solutions up and running and configured for their needs. They may need to purchase support or consulting services, and organizations must consider those expenses when calculating the total cost of ownership.
Some organizations have moved away from the open source market as cybersecurity has become a bigger concern. While proprietary software isn’t inherently safer than open source alternatives, it is more private.
Batch vs Streaming Big Data Applications
The earliest big data solutions, like Hadoop, processed batch data only, but enterprises increasingly want to analyze data in real time. That has generated more interest in streaming solutions such as Apache Spark, SQLstream, Amazon Kinesis, and others.
Even if organizations don’t think they need to process streaming data today, streaming capabilities are steadily becoming an industry standard and many organizations are moving toward Lambda architecture, which can handle real-time and batch data.
Characteristics to Look for in a Big Data Application
Evaluating the big data applications you are considering comes down to comparing how well they meet your requirements. Here are the most important factors to examine.
- Integration with legacy technology. Replacing existing investments in data management and analytics technology can be expensive and disruptive. Look for solutions that work alongside or augment current tools. Cloud solutions are often an ideal way to transition from legacy systems without abandoning them entirely.
- Scalability and flexibility. Big data stores get larger every day, and organizations need applications that will continue to work as volumes grow. This need for scalability is a key reason cloud-based applications have become an unofficial standard.
- Usability. Consider the learning curve for any applications—tools with easy deployment, simple configuration, intuitive interfaces, or similarity or integration with existing tools can provide tremendous value.
- Visualization. Inadequate analytical know-how is the leading barrier to big data analytics, so presenting information in an easy-to-understand format is important. Charts and graphs make it easier for human brains to spot trends and outliers, speeding up the process of identifying actionable insights.
- Security. Organizations must ensure their big data has adequate protection to prevent the sorts of large breaches that dominate headlines. That means looking for tools with security features like encryption and strong authentication built in or those that integrate with existing security solutions.
- Support. Even experienced IT professionals sometimes find deploying, maintaining, and using complex big data applications challenging. Don’t forget to consider the quality and cost of the support available from various vendors.
- Ecosystem. Most organizations need many applications to meet all their big data needs. That means looking for a platform that integrates with many other popular tools and a vendor with strong partnerships with other providers.
- Self-service capabilities. There’s a shortage of big data and analytics skills, and many organizations lack analytics professionals—instead, they seek tools that other business professionals can use with little to no extra training.
- Total cost of ownership. The upfront costs of a big data application are only a small part of the picture. Consider related hardware costs, ongoing license or subscription fees, employee time, support costs, and any expenses related to the physical space for on-premises applications.
- Estimated time to value. Another important consideration is how quickly you’ll be able to get up and running with a particular solution. Most companies would prefer to see benefits from their big data projects within days or weeks rather than months or years.
- Artificial intelligence and machine learning (AI/ML). AI and machine learning research are quickly becoming a mainstream part of analytics. Choosing a vendor that isn’t on the cutting edge of this research could make you fall behind the competition.
5 Tips for Selecting a Big Data Application
Clearly, choosing the right big data application is a complicated process involving myriad factors. Experts and organizations that have successfully deployed big data software offer the following advice:
Understand your Goals
Knowing what you want to accomplish is paramount when choosing a big data application. Your project is unlikely to succeed if you aren’t sure why you are investing in a particular technology.
Demonstrating success with a small-scale big data analytics project will generate interest in using the tool throughout the company. It will also help disperse investments for a better ROI.
Take a Holistic Approach
A small-scale project can help you gain experience and expertise with your technology, but choosing an application you can use throughout the business is important. You’ll generate better returns and enable closer collaboration if all teams can benefit from the same platform.
Once a companywide big data application is in place, take advantage of that collaborative potential. Many organizations are attempting to build a data-driven culture, which requires a great deal of cooperation among business and IT leaders. Doing so will minimize errors, improve efficiency, and deliver better results.
Find and Grow Data Talent
Fostering analytical talent from within through upskilling and reskilling will ensure your organization makes the most of these powerful tools, even in a tight labor market.
Big data—a variety of structured and unstructured data on a massive scale that’s too complex to be managed with traditional methods—is essential to the modern enterprise, and succeeding with it means finding and implementing the tools capable of keeping pace. Big data can provide the kind of information organizations need to make challenging decisions across all departments and in all industries.
Investing in big data solutions demands a thoughtful approach to identifying goals, establishing business requirements, and mapping out the tools and applications that will best meet them. Organizations that take the time to understand how big data can serve their long-term plans and build the infrastructure to support them are more likely to make it a successful part of their operational strategy.
Read our Complete Guide to Data Analytics to learn more about how enterprises work with data to inform all aspects of their decision making.