How many times have we heard that a company has millions of data and doesn't know what to do with it? Everywhere the message is that data offers great value, although depending on how we formulate it, this is not entirely true. Without a good analysis that extracts information from it, data is practically useless. To extract this valuable information companies can use business intelligence. Business intelligence is the use of data to get information that serves as a solid basis for safer decision making. To do this, companies consume data in different ways. Here are some of them:
Embedded business intelligence is defined as the integration of reports, dashboards and analysis views into an application. Information is displayed and managed on a BI platform and embedded directly into the application's user interface to improve context and data usability. That is, with BI embedded you can have the graphics and KPIs of your BI inside your CRM, PMS, CMS or other, without having to go check your business intelligence software. The advantage of using embedded BI is that it reduces the cost and time involved in creating reports and analysis.
With BI embedded BI joins the application user experience and provides customers with an enriched work context and information within the applications they already use. In this way, users can get better and faster decision making on their own with interactive dashboards and integrated analysis. In addition, these dashboards and reports can be customized by combining multiple data streams according to your specific needs, unlike traditional reporting software.
With the use of business intelligence embedded, users can base their decision making on BI while still performing their normal daily tasks. Embedded BI can also be part of workflow automation, so it will determine certain actions based on parameters set by the user.
Data discovery is a user-directed process by which unknown or unusual patterns and values can be discovered in data. Data discovery consists of collecting data from its various databases and silos and consolidating them into a single source that can be evaluated easily and in real time. It allows you to discover, in a few clicks, the factors that contribute to a trend as soon as it has been discovered.
With data discovery, the user searches for specific elements or patterns in a data set. Visual tools make the process dynamic, easy to use, fast and intuitive. Data visualization now goes beyond traditional static reporting. BI visualizations have evolved and increased to include geographic maps, thermal maps, pivot tables, and more, allowing you to create presentations that faithfully reflect discoveries.
Self-Service analytics allows end users to easily analyze their data by creating their own reports and modifying existing ones without the need for training. For example, if an organization only needs one report per year, it can dedicate IT resources to this task. On the other hand, if this organization has 1000 employees and each of them requires several reports on a daily basis, the IT team will not be able to manage the demand.
Self-service analytics or ad hoc reports offer users the ability to create reports quickly, allowing them to get data analysis in minimum time. End users can analyze their data by dynamically modifying or adding calculation functions to a report. This flexibility lessens the burden on the technical department, freeing up development resources. This gives business users the ability to take control of their own analytical needs and helps them extract maximum value from both their data and their application. In this way, the IT team manages interactive reports that each end user can filter to find the information they need.
Augmented analytics offers automation of data analysis through machine learning and natural language processing. This advanced use, manipulation and presentation of data simplifies data to present clear results and provides access to sophisticated tools for business users to make day-to-day decisions with confidence. Users can go beyond opinion and prejudice to get a true picture and act on data quickly and accurately.
Augmented analytics solves the problem that many organizations still have with the generation of knowledge from data.
What augmented analytics does is to alleviate a company's dependence on its data scientists by automating the generation of knowledge in a company through the use of advanced machine learning algorithms and artificial intelligence.
An augmented analytics engine can automatically process a company's data, clean it, analyze it and turn it into actions for executives or marketing professionals with little or no supervision by a technician.
The consumption of data for use in business takes many forms and each of them can be used alone or with others. Each company, department or specific situation will require one way or another to analyze the data, although the goal of these processes and technologies is similar: to get a good basis for making good business decisions and optimize the processes within the company.