Big data, Industry 4.0, Data Science: Those are just a few buzzwords that are used in the business world when talking about information and reporting on it. The agriculture industry is no exception since the Internet of Things allows farmers to collect data at an incredible speed.
Nevertheless, let us look at some detailed explanations of a few of the related phrases:
Definition – Business Intelligence is all about being able to collect and react to relevant information from the business environment, whether internal or external. The challenges lie in identifying which information could lead to a strategic advantage if it is reacted to in a timely fashion and in being able to interpret information to support taking the correct action.
Business intelligence tools include reports, graphs and charts often presented as dashboards. These tools can only transpire into strategic advantages if care and diligence were taken when selecting the information to be presented and not “reporting for the sake of reporting”.
Software that is used for business intelligence could, for example, be:
InsightSquared Sales Analytics, Klipfolio, ThoughtSpot, Cyfe, TIBCO Spotfire, Alteryx Platform, Domo, Looker, Sisense, Microsoft Power BI, Tableau Desktop, TIBCO Jaspersoft, Tableau Online, Microsoft BI, Google Charts, and Oracle Analytics Cloud (Source)
Definition – Big data usually refers to data sets with sizes beyond the ability of commonly used software tools to capture, store, manage, and process data within a reasonable time and value.
The following characteristics can describe big data:
- Volume: The quantity of generated and stored data. The size of the data determines the value and potential insight, and whether it can be considered big data or not.
- Variety: The type and nature of the data. This helps people who analyze it to use the resulting insight effectively. Big data draws from text, images, audio, video and completes missing pieces through data fusion.
- Velocity: The speed at which the data is generated and processed to meet the demands and challenges that lie in the path of growth and development. Big data is often available in real-time. Big data are produced more continuously. Two kinds of velocity related to big data are the frequency of generation and the frequency of handling, recording, and publishing.
- Veracity: It is the extended definition for big data, which refers to the data quality and the data value. The data quality of captured data can vary greatly, affecting accurate analysis.
- Exhaustive: Whether the entire system is captured or recorded or not.Fine-grained and uniquely lexical
- Respectively: the proportion of specific data of each element per element collected and if the element and its characteristics are properly indexed or identified.
- Relational: If the data collected contains commons fields that would enable a conjoining, or meta-analysis, of different data sets.
- Extensional: If new fields in each element of the data collected can be added or changed easily.
- Scalability: If the size of the data can expand rapidly.
- Value: The utility that can be extracted from the data.
- Variability: It refers to data whose value or other characteristics are shifting to the context they are being generated. (Source: Wikipedia)
Definition – Data science is an interdisciplinary field focused on extracting knowledge from data sets, which are typically extensive (Big data). The field encompasses analysis, preparing data for analysis, and presenting findings to inform high-level decisions in an organization. As such, it incorporates skills from computer science, mathematics, statistics, information visualization, graphic design, and business.
As such data science is a career option that could be very rewarding for individuals interested in mathematics, data and making sense of the information around us.