ISACA Journal Author Blog

ISACA > Journal > Practically Speaking Blog > Posts > Big Data in Organizations

Big Data in Organizations

Adeniyi Akanni, Ph. D., CISA, CRISC, ITIL
| Published: 2/12/2018 3:08 PM | Category: Security | Permalink | Email this Post | Comments (0)

Big data is a huge volume of data that cannot be treated by traditional data-handling techniques because it is mostly unstructured and complex. Thus, proper collation, coordination and harnessing of such data is necessary for relevant users, such as chief information officers, IS auditors and chief executive officers, to make meaningful decisions. My recent Journal article describes a 6-stage cycle for implementing big data for organizations, especially commercial banks. This is illustrated by the acronym DIRAPT, which stands for definition, identification, recognition, analysis, ploughing-back and training. I consider DIRAPT to be a cycle because there is a need to repeat the stages over and over:

  • Definition of scope—Large volumes of data are generated per second from machines (such as automated teller machines and mobile devices). It will not make any sense to treat all data from all fronts all at once. So, banks must define the scope to be covered at a time and extract meaningful information.
  • Identification of skill set—Careful selection of manpower with the requisite skills is very important before a successful implementation. Experienced staff should be picked from operations, marketing, control and other departments to contribute their skills for successful implementation. A rich blend of skilled people will go a long way to determine the success of such implementation.
  • Recognition of data sources—Effective data tracking and measurement must stem from identified sources.
  • Analysis of output—Examination of data (both structured and unstructured) within the scope is appropriate information for management use. The review may require specialized analytics tools such as Hadoop, NoSQL, Jaspersoft, Pentaho, Karmasphere, Talend Studio and Skytree.
  • Ploughing back experience—Experience gathered over time can be built up and reused. No 2 projects will be exactly the same, but experience gathered from a previous project can always be used to help in subsequent ones.
  • Training and retraining—Training is a continuum. There should be training before, during and after each cycle of implementation. Lessons learned at every stage should be well coordinated and recorded for reference purposes. Training should be encouraged at every stage.

The DIRAPT cycle can prove beneficial to organizations, such as commercial banks, to enjoy the dividends of big data.

Read Adeniyi Akanni’s recent Journal article:
Implementation of Big Data in Commercial Banks,” ISACA Journal, volume 1, 2018.

Comments

There are no comments yet for this post.
Email