Recent technological advancements in data collection and analyzation have opened up the world of intelligent testing to businesses in all industries regardless of their size or high-tech capabilities. Learn more about the process of business intelligence (BI) and intelligent testing works and what it can do for your online businesses.
The Basics of Intelligent Testing
Intelligent testing arises from the tech-driven process known as business intelligence and reporting. BI and reporting refers to the gathering and analyzing of large swaths of data from a business in order to extract meaningful information that managers and executives use to make better company decisions.
Both BI and Pathwwway intelligent testing employ a multitude of tools, methodologies, and applications to collect the data from both internal and external sources, prepare the data for analysis, create and perform queries against the info, and then develop reports, data visualizations, and dashboards to communicate the results.This is important for people new to the game.
These results help your business’s decision makers and operational workers improve and accelerate their decision-making process, increase operational efficiency, optimize internal processes, drive new revenues, and gain significant competitive advantage over the competitors in your industry. They help a business identify risks, potential issues, and market trends that all contribute to better decision making.
The data gathered during BI and used for testing includes information already cataloged in your customer management and contact storage programs, as well as collecting new info upon it entering the system. Thus, the results can support both tactical and strategic decisions. As more and more business owners turn to intelligent testing, the easier the BI software becomes to use thanks to the development of self-service data discovery tools.
Important Elements of Intelligent Testing
Intelligent testing for businesses incorporates a variety of data analysis applications and advanced analytics vital to the processing of the collected information. Even non-techy business owners benefit from knowing the methodology behind BI and Pathwwway intelligent testing. Familiarize yourself with the definitions for standard terminology associated with BI, reporting, and testing to get the most out of them.
Ad-hoc analysis means setting up the BI process to answer only one, specific question related to the business. The test results generally include an analytical report, statistical model, or some other kind of data summary. It reveals more details about records, accounts, and transactions found in static reports.
Database query is a general term describing any request for information from a specified database. It involves choosing the parameters for the query (the least flexible method), searching by an example query, or through the use of a specialized query language (the most exact method). During testing, the tools or applications perform thousands of queries throughout your system to find valuable information.
Enterprise reporting involves extending the analysis and reporting capabilities of your BI beyond the realm of the IT staff, analysts, and other immediate users. It enables anyone with impact on the business from managers and executives to frontline workers, to easily access the collected data to better perform their jobs. Some companies even let suppliers, customers, and other outside business partners review the vital information.
Online Analytical Processing (OLAP)
OLAP is a type of computer processing that allows the user to selectively and quickly extract data and examine it from various points of view. The data is stored in a multidimensional database making it possible to consider each data attribute as a singular and separate dimension. This can help you find the intersections between the data to help with things like your marketing strategy.
A type of BI tool, location intelligence presents your business data within geographical contexts. This kind of software works great for in many different industries because it lets you draw on many different data sources like aerial maps, geographic informational sources, demographic information, and even your business’s own databases.
A general term that applies to more than just BI and intelligent testing, data visualization encompasses any effort made to more clearly communicate the significance of data by putting it in a visual context. Trends, patterns, and other correlations might go undetected if you only viewed the data in a text-based form. You can buy data visualization software that recognizes and exposes these patterns easier than doing it yourself.
A performance scorecard is a type of graphic representing an entity’s progress over time toward some predetermined goal or end game. The entity could be a business unit, a specific employee, or a certain enterprise. These scorecards appear in both the private and public sectors.
Key Performance Indicators (KPIs)
KPIs are business metrics for the evaluations of distinct factors vital to your business’s success. KPIs help focus attention on the processes and tasks critical to making progress towards set targets or goals as decided by you or your managers. Your KPIs vary depending on your industry and people use them for different purposes within the business hierarchy, making them very valuable.
The process of combing through those large sets of data to establish patterns and identify relationships between the information to correct issues with data analysis is known as data mining. All this info allows a business to predict future trends, improve conversion rates, or detect fraud to stay at the top of their game.
A type of advanced analytics, predictive analytics use both historical and new data to forecast future trends, activity, and behavior within the market and your industry. It uses statistical analysis, automated machine learning algorithms, and analytical queries to create the predictive models that indicate the likelihood of certain events happening with a score.
A basic form of data mining, text mining applications only look at text-based content like emails, postings on social media sites like LinkedIn, Facebook, or Twitter, and word documents. The unstructured data is mined with natural language processing, machine learning techniques, and statistical modeling.
An integral part of intelligent testing and data analytics is statistical analysis. It involves the collection and examination of every data point from a sample set. Statistical analysis helps describe the nature of the data being analyzed, the relation of the points to the underlying population, to create a model summarizing the relation, and then proving the validity of the model based on the data.