Big data analytics is a critical piece of any business workflow in this digital age. It has become much easier to collect and access data that allows you to optimize your overall business performance. With this information, you can uncover current market trends, identify customer preferences, make smarter business decisions, find additional ways to improve your efficiency, and that’s just the tip of the iceberg.
But there is a problem. The collection and processing of such massive volumes of data can chew up a large portion of your hands-on productive hours. To quicken the pace of this process, data analytics programs should be utilized. Through the use of artificial intelligence (AI) and sophisticated algorithms, these solutions can help you can transform your raw data into actionable insights in just a few mouse clicks.
There are plenty of factors involved, however, in finding the right analytics tool(s) to meet the unique needs of your particular organization. Not only do your specific needs need to be identified, but you then must evaluate those needs properly against the features of each potential solution. With so many choices and so many factors to be considered, the whole process can be overwhelming.
Let’s first explore a few general features and attributes that should be considered when assessing how well a big data analytics tool can meet your organization’s needs.
Integration Difficulty and Convenience Level
Big data analytics applications rely on structured and unstructured data received from a massive number of internal and external data sources. This underscores a need for the tool to be functional, but also to support data accessibility and systems integration. Here are a few features to think about:
- Big data accessibility:
Compare how the tool connects to big data architectures, as well as how it manages storage.
- Blending with your existing platform:
If there is an expectation that the big data analytics tool will merge with existing data management tools, practices and methodologies, you must consider how well the prospective analytics tool will operate in conjunction with them.
- Data Utilization:
Verify that the tool will be able to ingest and make sense of the emails, images, videos, social media streams and other unstructured data.
How Easy is it to Use?
Focus your evaluation on how easy the product is for your team to use for data analysis and verify the efficiency and accuracy of the models. Consider the following:
- Use Case Deployment:
Often, the same methods can be applied in many different business scenarios. If your organization is considering broader use cases, you may look at adopting tools with greater modeling flexibility.
Check to ensure the product offers visual techniques that enable effective development and analytics uses.
Ensure that the big data analytics tool and platform enables your analysts to work together to refine their applications while improving the re-usability of models to increase workflow consistency.
Other General Considerations
If a high level of execution performance is a business requirement, it’s critical to consider products that are engineered to provide the necessary performance configurations.
- Special Services:
Evaluate whether it would be necessary to get help with installation and training from the vendor or to provide specialty development services.
Prices of products influence a buying decision in almost every applicable case. Some big data analytics tools are quite costly while other tools cost very little or, in some cases, are free.
While you should keep these general features in mind, there are many others that, depending on your unique business needs, may need to be taken into consideration as you step through your evaluations of each potential analytics tool.
To help with your big data analytics journey, here is a list of some of the leading products available on the market: