Knowledge discovery in databases, commonly referred to as KDD, is an encompassing process of discovering useful information from large sets of data. It involves various processes, including data selection, cleaning, incorporation, transformation, data mining, pattern assessment and knowledge depiction. Essentially, KDD seeks to extract and uncover important data patterns, trends and correlations from massive data sets through various algorithms and methodologies.

• Large-scale Datasets: KDD is primarily utilized for vast data sets, where thorough data analysis would otherwise be complex and time-consuming.

• Streamlined Process: KDD follows a distinct process involving data preparation, data mining, and knowledge interpretation, among others. This allows professionals to establish a systematic approach to data analysis.

• Use of Algorithms: KDD employs an array of algorithms to analyze data and determine interesting relationships that lie hidden within the data.

• Multidisciplinary Approach: KDD involves expertise from various domains, combining database and data warehouse technology, machine learning, statistics, pattern recognition, and visualization to analyze complex data sets.

A well-planned implementation of KDD starts with a thorough understanding of the organizational needs. The next step should be data preparation, which includes data cleaning, integration, and transformation, followed by data mining and interpretation of the mined knowledge.

The execution of KDD requires careful planning, effective handling of datasets, meticulous application of suitable algorithms and interpretation of the findings. Evaluating the results and making suitable business decisions based on the findings is of paramount importance for successfully leveraging the power of KDD. Hence, a dedicated team with appropriate skill sets are necessary to guarantee a successful implementation of KDD within an organization. With technological advancements, KDD's importance is likely to grow even more, driving businesses to strategize long-term development with a strong emphasis on data analysis and interpretation.

The structured approach of KDD carries several advantageous aspects:

• Hidden Knowledge Uncovered: KDD processes can discover hidden patterns, anomalies, trends, and relationships that would remain unnoticed with manual data analysis.

• Enhanced Decision Making: As KDD uncovers deeper insight about data, it provides the opportunity for improved decision-making in a wide array of industries, like healthcare, finance, retail and more.

• Time-saving: By automating data analysis, organizations can save vast amounts of time that would otherwise be spent manually sorting and analyzing data.

• Cost-effective: Since KDD optimizes and automates large-scale data analysis, it reduces costs related to manual data analysis.

While KDD delivers a range of benefits, there are some potential disadvantages to consider:

• Data Quality: The efficiency of KDD is significantly influenced by the quality of data. Therefore, if the dataset is incomplete or inaccurate, the outcomes of KDD processes might be incorrect or misleading.

• Complexity: The KDD process, given its multi-step, intricate nature, can be complex and difficult to implement without proper expertise and training.

• Privacy Concerns: KDD processes operate on large volumes of data that may contain sensitive or confidential information. Therefore, privacy concerns should be addressed before conducting KDD.

In conclusion, KDD is a potent tool for organizations needing to analyze large data sets for insightful decision-making. It helps to uncover hidden patterns and trends within data, thereby offering a strategic edge over competitors. However, like any other tool, it comes with its own set of challenges that need to be smartly and effectively addressed for successful implementation.