Recent years have seen a wide variety of organizations relying heavily on automated collection of data. Most of the critical operations of these organizations depend heavily on this data. There can be instances when the data collected is erroneous or non-trustworthy. Utilization of such data has adverse consequences on the performance of these organizations. Since most organizations do not have any quality evaluation for its collected data, this project aims to create a tool that uses device attributes to help determine how confident we are about the data we are receiving. Every device has some generic and application specific attributes. Generic attributes are common to most devices, for example every device requires servicing. Application specific attributes are unique to a device, for example exposure to vibrations. Devices like electric meters produce misleading results when exposed to vibrations. Several factors such as exposure to vibrations, failure to calibrate device, failure to service device and so on can affect the data quality. These factors can be realized by a detailed research on the device. The tool applies fuzzy logic to compute a quality score for each of these attributes. This score acts as metric for quality evaluation, which is performed by taking a weighted average of these scores. Data with good quality will have a weighted average of scores close to 100. The tool allows the user to specify a range of values within which the data can be trusted or considered good data, every other value which is outside the range needs further probing before it can be applied to a business model. Further it will suggest corrective measures to improve the data quality. The generic tool aims to target data from a wide range of sensors. It will flag erroneous data and help ensure that the data being utilized is fit for the purpose it is intended to be used.