Business intelligence (BI) and data analytics are two major parts of custom data management solutions for enterprise businesses and organizations. It drives the process of collecting data from online channels, analyze statistics and raw information and eventually generate insights via intelligent algorithms. These insights help businesses make fruitful business decisions.
Business intelligence and analytics seem two different, often confusing terms that bear separate definitions in business world. The difference is explained with contradictory opinions from data industry experts. The actual difference between business intelligence and data analytics is related to whether the management of data is for future or historic event and what answers you are trying to find.
Business intelligence goes deeper into the event of the past and how it happened. It eventually presents hidden patterns and existing trends of the present moment and does less for the future.
Business analytics on the other hand focuses on reasons of the event under study and gives the idea of factors that made it happen. It helps imply predictions for future possibilities.
The sense of transparency and clarity of AI-based business models is making businesses question the automated data-driven decision making. However, automated recommendations of intelligent models actually help enhance human understanding in many ways. The hesitation around whether or not AI decisions should be trusted demands explainable models with much more transparent algorithms and logics. At the moment, the orthodox AI applications don’t offer a way to thoroughly understand algorithmic functioning behind machine-driven decisions.
This requires the creation of flexible and more permeable models whose decisions can be questioned just like human decisions. This means more pressure on data scientists who get busy developing explainable models with clear contextual understanding. To obtain the strong impact and massive results in terms of simplicity, clarity and penetrability, intelligent analytics must be intelligible.
Natural language processing (NLP) is the mediator unit that helps AI machines understand and process the data in human language. Business intelligence developers will find a way to establish NLP in their platform for an interface built in natural language. For better interpretation of data analytics from AI system, humans have to evolve natural language structure. For instance, in conversational bots, the intelligent system interprets the underlying context to grasp user’s intent and form the responses accordingly, to ensure more natural conversations.
This is advantageous for customer support productivity as the system has pulled in enough data to avoid more efforts on clarification the next time the person approaches with similar questions. Once the evolved NLP gets functional within the AI interface system, the way data is interpreted to generate follow-up responses for inquisitive people will get easier, faster and more precise. This NLP, thus improved, produces more actual conversations and adapts to any analytics to transform modern workplaces and automate data-based operations.
As BI industry is getting more organized, business analytics users will get the advantage of accessing data and actionables from the common workflow. BI developers offer well-cultivated capabilities like mobile analytics, dashboard management, embedded analytics with a single control. Organizations will thus use analytics in the place where it is most needed without any isolation. With tailored APIs and BI solutions, analysts will be able to view analytics data and insights without leaving their station or navigating to other stand-alone applications, dashboards or shared servers. Mobile analytics will make sure people get access to insights on the go. By means of dashboard extension, they can integrate and access other parallel systems right in the analytics dashboard. For businesses that are in need for a powerful, highly synchronized and controlled system, the element of one-stop accessibility of data metrics on demand empowers workspace and meets dynamic requirements of business verticals successfully.
Business intelligence efforts seem to contribute a great deal to existing modern enterprise movement of modernization. The process of developing and then deploying BI solutions to organizations is as important as what comes after that – which is adoption of the solution by workforce. It can be arguably said that access to BI solution involves certain challenges especially when it comes to evaluating how it is adopted for modernized workflow processes. In fact, whether or not your staff get accustomed to using BI the solution defines the true impact on your business.
Companies can look for creating streamlined management among operators and delegate the analytical tasks based on their expertise. This will help alleviate post-installation woes and accelerate adoption rate of BI analytics, reducing the possible scope of maintenance and reporting. Also, users will master their skills at handling intelligence and insights to become subject veterans and share their best practices with others. As a result, it will be easy to extract the value from deploying BI applications in your workspace and experience more efficiency.
Accuracy, simplicity and agility – three major pros where BI and business analytics massively work. Knowing what areas of your business needs more attention, you can choose to implement most suitable business intelligence and analytics and start reaping stunning data-driven insights. Business-friendly and process-specific, these intelligent analytics are tailored by vendors to offer you the dynamic tools that address all your business afflictions and empower you to predict patterns and customer intents. They are useful for both small as well as brand companies for whom gaining competitive advantage in the market is must.
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