QVscribe
QRA’s tools streamline engineering artifact generation, evaluation, and prediction, refocusing engineers from tedious work to critical path development.
Our solutions automate the creation of risk-free project artifacts for high-stakes engineering.
Engineers often spend excessive time on the mundane task of refining requirements, with quality metrics varying across industries. QVscribe, QRA's flagship product, streamlines this by automatically consolidating these metrics and applying them to your documentation, identifying risks, errors, and ambiguities. This efficiency allows engineers to focus on more complex challenges.
To further simplify requirement authoring, QRA introduced a pioneering five-point scoring system that instills confidence in engineers. A perfect score confirms accurate structure and phrasing, while lower scores prompt corrective guidance. This feature not only refines current requirements but also reduces common errors and enhances authoring skills over time.
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DataBuck
Big Data Quality must always be verified to ensure that data is safe, accurate, and complete. Data is moved through multiple IT platforms or stored in Data Lakes. The Big Data Challenge: Data often loses its trustworthiness because of (i) Undiscovered errors in incoming data (iii). Multiple data sources that get out-of-synchrony over time (iii). Structural changes to data in downstream processes not expected downstream and (iv) multiple IT platforms (Hadoop DW, Cloud). Unexpected errors can occur when data moves between systems, such as from a Data Warehouse to a Hadoop environment, NoSQL database, or the Cloud. Data can change unexpectedly due to poor processes, ad-hoc data policies, poor data storage and control, and lack of control over certain data sources (e.g., external providers). DataBuck is an autonomous, self-learning, Big Data Quality validation tool and Data Matching tool.
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DQE One
Customer information is ubiquitous in today's world, spanning across cell phones, social media platforms, IoT devices, customer relationship management systems, enterprise resource planning tools, and various marketing efforts. The sheer volume of data collected by companies is immense, yet it frequently remains underutilized, incomplete, or even inaccurate. Poorly managed and low-quality data can disrupt organizational efficiency, jeopardizing significant growth opportunities. It is essential for customer data to serve as a cohesive element connecting all business processes. Ensuring that this data is both reliable and readily available to everyone, at any time, is of utmost importance. The DQE One solution caters to all departments that utilize customer data, promoting high-quality information that fosters trust in decision-making. Within corporate databases, contact details sourced from different channels often accumulate, leading to potential issues. With the presence of data entry mistakes, erroneous contact details, and information gaps, it becomes vital to regularly validate and sustain the customer database throughout its lifecycle, transforming it into a dependable resource. By prioritizing data quality, companies can unlock new avenues for growth and innovation.
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Melissa Clean Suite
What is the Melissa Clean Suite?
Melissa's Clean Suite (previously Melissa Listware), combats dirty data in your Salesforce®, Microsoft DynamicsCRM®, Oracle CRM® and ERP platforms. It verifies, standardizes, corrects, and appends your customer contact records. Clean, vibrant, and valuable data that you can use to achieve squeaky-clean omnichannel marketing success and sales success.
* Correct, verify, and autocomplete contacts before they enter the CRM
* Add valuable demographic data to improve lead scoring, segmentation, targeting, and targeting
* Keep contact information current and clean for better sales follow-up and marketing initiatives
*Protect your customer data quality with real-time, point-of-entry data cleansing or batch processing Data drives every aspect customer communication, decision making and analytics. Dirty data, which can be incorrect, stale or incomplete data, can lead to inefficient operations and an inaccurate view of customers.
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