Big Data analysis tools Procurement guide

Big data analytics tools enable users to analyze a wide variety of information-including structured transactional data and social media posts, Web server log files, and other forms of unstructured and semi-structured data. Once the organization decides to purchase a big data analysis tool, the next step is to develop a process to evaluate the available products and then find a product that best suits your needs and requirements.

Below we will describe the necessary features and specific attributes that may be used to assess the extent to which the various big data analysis tools meet the needs of the enterprise. You then write a plan request (RFP) that explains how you can use these tools to address your organization's needs.

First, breadth and depth of modeling techniques

Vendors have applied different levels of modeling, and have developed different complexity analysis capabilities accordingly. The breadth of analytic modeling supported by a single tool reflects the different approaches provided. Some examples include regression techniques, time series models that predict change values based on past trends, classification and regression trees (also known as cart), and neural networks.

The depth of modeling techniques reflects two aspects of the approach used: the flexibility of algorithmic maturity and modeling techniques that support more accurate development models. In other words, what level of expertise is needed in data mining and predictive analytics to understand which categories of models are currently being developed and how to use a specific tool to complete modeling? Experienced data analysts are interested in vendor products that offer a large number of analytics capabilities, while more professional analysts and statisticians prefer tools that can further analyze specific analytical models.

Second, Integration and accessibility

Big data analytics applications often rely on an increasing number of internal and external data sources, including structured and unstructured data. This has led to functional requirements that support data accessibility and system integration.

Third, Unstructured Data Utilization

Verify that the product can use different types of unstructured data (documents, emails, images, videos, presentations, social media channel information, etc.) and be able to parse and utilize the information received.

Four, Big Data accessibility

Compare vendor tools to connect big data architectures, including distributed data stored in Hadoop, and files stored in various scale-out storage (such as nosql data such as MongoDB or Apache Cassandra).

V. interoperability with existing platform components

This is important if you want to mash up analysis methods in some traditional data management and BI technologies. For example, many analysis tools support the invocation of analytic models through traditional SQL queries. This form of interoperability allows the use of the structure of the predictive model to produce queries and reports that are typically available to traditional data analysts.

1. Connectivity It is important to evaluate connectivity, or the ability of the product to access other systems, and to provide the existing platform with the ability to generate reports and analysis as a data source.

2. Ease of use There are some big data analytics products that vendors have developed from scratch, while others are based on the open source R statistical language. In either case, this type of assessment focuses primarily on the usability of the product for analyzing data, developing models, and determining the effectiveness and accuracy of the model.

3. Business Analyst Availability

Can business analysts without a statistical background also be able to easily develop analytics and applications? Determine if the product provides a visual method for easy development and analysis.

4. Flexibility to deploy different business use cases

The same algorithmic approach can be applied to different business scenarios in many different industries. If your organization is prepared to do a limited number of such analyses and focus on more common use cases (such as Customer life cycle value analysis, deceptive behavior analysis, or retention prevention), then you might want to sacrifice some flexibility in your technology choices. However, if your organization wants a larger, more constrained approach to analysis, you should look for some more flexible modeling techniques.

Big Data analysis tools Procurement guide