Datapine is a digital platform that provides users with a business intelligence opportunity to visualize, explore, and share their data with dashboards. According to datapine, the corporate platform is a contemporary one, where all pertinent business data is placed in one central area. As datapine illustrates, the data tool empowers users to self-generate data and share it on the platform, a trusted and secure source for fast, flexible, and efficient decision-making. According to Haynes et al., datapine provides users with an opportunity to locate spatial record sources through an automatic self-discovery of consolidated accessible web depository (593). Therefore, datapine can be a digital tool that makes the location of data sources in the web a more straightforward task despite the periodical updating of server locations.
There are a lot of advantages and disadvantages of datapine, from the description and its usage. According to Technology Advice, the benefits of the tool include: the interface has a drag-and-drop feature for users, the dashboards are pretty interactive, and the self-analytic approach is also comprehensive. According to Owusu, business intelligence systems yield significant positive growth and learning effect on users and the performance of both the user and a business entity’s internal operations (19). The benefits of datapine, as illustrated by datapine, include: efficient and faster decision making is enhanced by the visual data. The user interphase is friendly and easily navigable; hence, all-inclusive training is not necessitated. The system is mobile since it can be accessed and used in any network-enabled device from any part of the world. The use of a datapine system increases business intelligence and professional skills, enabling the organization to maintain its competitive edge.
On the other hand, as Technology Advice depicts, the disadvantages of the tool include: the system does not provide support features for analysis of unstructured statistics and automatic assimilation. Network failure could make it quite difficult to log in if the user is located in remote areas, thus delaying transactional and other data-dependent decision-making processes. It lacks the expected language for processing, and some features necessitate users to possess knowledge and skills of SQL.
Available charts or the available dashboard types that datapine uses to represent data and information include pie charts, line charts, bar, column, scatter, and area charts. All these charts are integrated and organized with trackable and organized Key Performance Indicators (KPIs) for more accessible data navigation. As datapine further describes, corporate data can be easily viewed from any angle through the use of user-friendly and interactive dashboard features, including chart-zooms, click to filter, drill-downs, and hierarchical filters, among others. The nature of data used in datapine encompasses textual data, multimodal, social media, health, and transactional data. The generated data can be shared through platforms such as manual exports, active dashboards, and automated e-mails, among other methods. An excellent example of the datapine application is its use on various companies’ customer experiences in different industries. The various fields in the datapine usually contain other datasets that are analyzed using a pine data tool.
Case studies performed using the datapine tools are quite a number. An example is Owusu’s Business intelligence systems and bank performance in Ghana: the balanced scorecard approach. In the study, evaluation is done to determine how organizations that adopt and implement the use of Business intelligence systems perform, and in this case, banks are the entities used. The balanced scorecard was also included in the study. The author found out that positive impacts on organizational performance and the growth of customers were attained, although; its implementation and the effects identified do not directly relate (Owusu 15). The other example is the one done by Bossen et al. titled Data work in healthcare: an introduction. The authors argue that considerable interaction between people, technology, and data production is essential for data to be beneficial, especially in healthcare institutions (Bossen et al. 468). In order to organize the large volumes of data generated, data organization tools and business intelligence systems should be adopted by health institutions for better professional and organizational performance.
SAS Business Intelligence
Statistical Analysis System (SAS) is business intelligence software designed for data analytics, criminal investigation, data management, and predictive analysis (SAS). The system enables data mining, retrieval, and statistical management of data. To manage all the enormous data generated by companies, Key Performance Indicators (KPIs) are involved. As illustrated by Azeroual and Theel, the business intelligence tool allows organizations to make informed decisions, thus reducing costs, optimizing company operations, minimizing risks, and adding organizational value (33). Comprehensive data analytics in big data by companies leads to the process of developing a business intelligence model that will lead to the actualization of corporate objectives.
The advantages of SAS include the following; decision making is aided through the visual data that is summarized, easily accessible, accurate, and contemporary (Owusu 18). The system allows organizational professionals to solve all unstructured, semi-structured, and structured data effectively. The SAS system is equally easy to learn, can accommodate extensive data just like datapine, and requires specific programming skills in particular segments. The security of data in the system is guaranteed, and customer support assists users whenever they need help. The graphical user interphase is quite interactive, easy to use and navigate. According to Azeroual and Theel, SAS’s disadvantage is that due to its complexity as software, it requires special permission, is created in a closed environment, thus not open for public use, and is costly (31). The procedural language of the system is tough, and a SAS enterprise is required to mine data.
The nature of data used in the SAS business intelligence tool is measured in terms of value, volume, veracity, velocity, and variety. The data nature is social media data, textual data, multimodal and health-related data (Ajah and Nweke 21). According to Azeroual and Theel, there are (201 available charts in SAS dashboard 8), including bar charts, line charts, scatter plots, column and area charts (33). An excellent example of the SAS business intelligence tool results is the bar chart represented in Azeroual and Theels’ case study comparing business start-up areas and the percentage with which data analytics is done (35). Several case studies have been done by researchers based on the use of SAS business intelligence digital tool for data analysis. An example is a case done by Attaran et al. titled; Opportunities and challenges for big data analytics in U.S. higher education: a conceptual model for implementation. In the article, the authors focus on a conceptual model which they propose for adoption and use in institutions of higher learning for data analytics.
The system’s potential benefits, the challenges expected, the primary qualities, and the high points of success are also addressed in the article. The other example is the case done by Azeroual and Theel titled The effects of using business intelligence systems on excellence management and decision-making process by start-up companies: a case study. The article delves deeper into the likeliness of business intelligence systems to business start-ups. Convenient suppliers and providers of intelligence systems for start-ups, existing implementable opportunities, challenges, successes, and the purpose of the designs are also evaluated in the article.
Recommendations and Conclusion
The two identified digital business intelligence tools are used for data analysis to help organizations actualize their data objectives through informed decision-making. Although they perform almost similar tasks, datapine is open to public access and does not require a specific license to operate. At the same time, SAS is enclosed and requires permission to work. For organizations to effectively implement and use data for decision making, the following recommendations are essential;
- Datapine tool should be used when using spatial data sources to achieve certain areas of interest, such as understanding general information about a particular organization and its operations. On the other hand, SAS should be used, especially when analyzing big data that requires breakdown, sorting, and categorizing to create intelligence models for decision-making.
- Users should choose the type of tool depending on the affordability level. SAS is quite costly, requires a license to operate is not available for public access. It also has inbuilt software that requires special knowledge to decode. On the other hand, datapine is available for public use with open access and has a natural, easy communication language.
- Datapine is recommended for use by small, medium, and start-up companies, while SAS can be limited to medium and established firms.
Works Cited
Ajah, Ifeyinwa Angela, and Henry Friday Nweke. “Big Data and Business Analytics: Trends, Platforms, Success Factors, and Applications.” Big Data and Cognitive Computing, vol. 3, no. 2, 2019, p. 32.
Azeroual, Otmane, and Horst Theel. “The Effects of Using Business Intelligence Systems on an Excellence Management and Decision-Making Process by Start-up Companies: A Case Study.” International Journal of Management Science and Business Administration, vol. 4, no. 3, 2018, pp. 30–40.
Bossen, Claus, et al. “Data Work in Healthcare: An Introduction.” Health Informatics Journal, vol. 25, no. 3, 2019, pp. 465–74.
Datapine. Modern Business Intelligence & Dashboard Platform | Datapine. 2021. Web.
Haynes, Myles, et al. “Pine: A System for Crowdsourced Spatial Data Source Discovery While Map Browsing.” Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, 2018, pp. 592–95.
Owusu, Acheampong. “Business Intelligence Systems and Bank Performance in Ghana: The Balanced Scorecard Approach.” Cogent Business & Management, edited by Shaofeng Liu, vol. 4, no. 1, 2017, pp. 1-22.
SAS. Business Intelligence & Analytics Software. 2021. Web.
Technology Advice. “Datapine Reviews.” TechnologyAdvice, 2021. Web.
