Various decision support systems are available that implement Data Mining and Data Warehousing techniques for diving into the sea of data for getting useful patterns of knowledge (pearls). Classification, regression, clustering, and many other algorithms are used to enhance the precision and accuracy of the decision process. So, there is scope for increasing the response time of the decision process, especially in mission-critical operations. If data are ordered with suitable and efficient sorting operation, the response time of the decision process can be minimized. Insertion sort is much more suitable for such applications due to its simple and straight logic along with its dynamic nature suitable for list implementation. But it is slower than merge sort and quick sort. The main reasons this is slow: firstly, a sequential search is used to find the actual position of the next key element into the sorted left subarray and secondly, shifting of elements is required by one position towards the right for accommodating the newly inserted element. Therefore, I propose a new algorithm by using a novel technique of binary search mechanism for finding the sorted location of the next key item into the previously sorted left subarray much quicker than the conventional insertion sort algorithm. Performance measurement in terms of the actual running time of the new algorithm has been compared with those of other conventional sorting algorithms apart from the insertion sort. The results obtained on various sample data show that the new algorithm is better in performance than the conventional insertion sort and merge sort algorithms.