The 3 Components of Developing Big Data Capabilities

Before we dive into the depths of Big Data, let’s first define Big Data services. Any activity within an organization that requests the collection, normalization, analysis, and presentation of data is a Big Data service. The responsibility of such a service may include the required hardware and software that is necessary to execute said activities, particularly if dedicated to Big Data capabilities. Big Data services may provide ad hoc data analysis and/or continual scheduled data analysis. The scope of the service may be distinguished by customer, application, product, or business service: each with a different binding service level agreement.

Developing Big Data capabilities has three major parts:

  1. Developing the Vision
  2. Developing the Data Analytics Capability
  3. Developing the Infrastructure

Developing the Vision

In order to develop the vision for Big Data, it all begins with understanding the needs of the business and potential “problem areas” (areas that need improvement or opportunities that need investigating) currently and over time. The Big Data vision should answer the question, “How can Big Data be used effectively to support the organization’s achievement of its strategic goals and objectives?” Developing the Vision defines the goals and objectives of Big Data and how it will provide value to the organization.  Often, the adoption of Big Data in most cases will vary.

Some organizations may initialize its efforts with a seemingly insignificant business process, that being a small pilot program. The intention here is to test its capabilities. Others may have had existing data management programs for quite some time and are simply looking to expand their capabilities. No matter the situation, developing the vision encourages the organization to define its expectations clearly, as well as plan its implementation of Big Data carefully.

Developing the Data Analytics Capability

To develop the data analytics capability, focus should be set on the handling, analysis and presentation of data within an organization. This section will cover the majority of the overall development process. This process aims to develop an organization’s capabilities.  The following process is recommended for an organization’s development of its capabilities with regard to data analytics. The process should be implemented for each Big Data service or project for service improvement.

The steps are as follows:

  1. Develop the individual data analytics process(es).
  2. Determine the sources of data.
  3. Automate extraction and validation of data.
  4. Build appropriate business rules (reducing anomalies and false positives).
  5. Prioritize areas of concern.
  6. Refine and document solution.
  7. Increase capacity based on demand.

Developing the Infrastructure

The final part involves the development of the infrastructure, which will provide guidance in order to be able to meet the processing, storage, and transnational needs of the Big Data service. The emergence of the said service’s technologies has created an intense need to understand the requirements necessary in infrastructure, as well as the need to continuously seek out greater efficiencies with regard to infrastructure design. Some of the significant issues that are results from Big Data are as follows:

  • Capacity
  • Security
  • Access/Privacy
  • Latency
  • Flexibility
  • Cost

The technologies utilized in Big Data will often address two or more of the issues mentioned earlier. Usually, the collaboration between intersecting technologies will provide greater benefits as compared to when implementing a single technology on its own.

The dedicated team at The Art of Service has designed a step-by-step toolkit to aid any IT professional in the implementation of Big Data capabilities in any organization. The toolkit aims to introduce Big Data concepts, and provide you with the tools to successfully create a workable Big Data culture in your organization.  Download the Big Data Toolkit at Toolkit Cafe today!

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