first mate
Business Intelligence (BI) Tools: Business Intelligence tools are essential for decision
support. They encompass a range of software applications and solutions that enable
organizations to collect, analyze, and present data to facilitate decision-making. BI tools often
include features like data querying, reporting, and dashboards, making it easier for users to
access insights from their data. They play a pivotal role in transforming raw data into
actionable information, aiding executives and managers in making informed decisions.
Natural Language Processing (NLP): Natural Language Processing is another critical
technology for decision support, particularly in the context of unstructured text data. NLP
algorithms can extract valuable insights from sources such as social media, customer reviews,
and news articles. This technology not only helps in sentiment analysis but also in
categorizing and summarizing large volumes of text data, providing decision-makers with a
better understanding of public sentiment, market trends, or customer feedback.
Blockchain Technology: Blockchain has gained prominence in decision support, primarily
in industries where data security and transparency are paramount, such as finance and supply
chain management. It offers a decentralized and immutable ledger that ensures the integrity
and traceability of data. Decision-makers can rely on blockchain to verify the authenticity of
transactions, contracts, or records, reducing the risk of fraud and errors. This technology
enhances trust in decision-making processes, especially in complex, multi-party scenarios.
A variety of technologies contribute to decision support by providing tools for data analysis,
predictive insights, and enhanced data security. Business Intelligence tools simplify data
visualization and reporting, while Natural Language Processing aids in understanding
unstructured data sources. Blockchain technology ensures data integrity and trustworthiness,
particularly in industries where these aspects are critical for sound decision-making. These
technologies collectively empower organizations to make informed choices in an increasingly
data-driven world.
Reference
Liang, Y., Liu, X., Zhang, J., & Wang, Z. (2020). A decision support system for sustainable
supplier selection based on a novel hybrid MCDM method combining QFD and VIKOR
under fuzzy environment. Sustainability, 12(2), 589. [6]
The second
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The increasing availability of data. The amount of data that is being generated and collected
is growing exponentially. This data can be used to gain insights into customer behavior,
market trends, and other factors that can help organizations make better decisions.
The decreasing cost of data storage and processing. The cost of storing and processing data
has been decreasing, making it more feasible for organizations to collect and analyze large
datasets.
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The advancement of analytical tools and techniques. There are a wide range of analytical
tools and techniques available that can be used to make sense of data. These tools are
becoming more powerful and easier to use, making it possible for more people to participate
in the decision-making process.
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The need for real-time decision making. In many cases, organizations need to make decisions
quickly in order to stay ahead of the competition. Analytics can help organizations to make
better, faster decisions by providing them with insights into current events and trends.
The need for collaboration. In today’s globalized economy, organizations need to be able to
collaborate with partners and suppliers in order to make decisions. Analytics can help to
facilitate collaboration by providing a common platform for sharing data and insights.
Here are three technologies that have been used for decision support:
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Data warehouses: Data warehouses are centralized repositories of data that can be used for
analysis. They allow organizations to collect and store data from a variety of sources, such as
operational systems, customer relationship management (CRM) systems, and marketing
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automation systems.
Business intelligence (BI) tools: BI tools are used to analyze data and generate reports. They
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can be used to identify trends, patterns, and anomalies in data.
Predictive analytics: Predictive analytics is a type of analytics that uses data to predict future
events. It can be used to forecast demand, identify risks, and make other predictions that can
help organizations make better decisions.
These are just a few of the factors that influence the evolving needs for analytics and decision
support. As the world becomes increasingly data-driven, the demand for these technologies
will continue to grow.
Ref: Kranth, S., & Kumar, P. (2018). Plant disease prediction using machine learning algorithms.
International Journal of Computer Applications, 182(25), 1-5. 2
