Top Business Intelligence Trends You Should Know For 2019

In today’s data-driven world, evaluating emerging trends of smart technologies is essential to shape people’s understanding of data, measure performance, and to take an organization to the next level collectively.

Business Intelligence is a process to analyze data using technological tools and present the data in a useful format to be shared with company executives. It is of much importance these days as business decisions are made using BI analytics tools.

Here are the top Business Intelligence trends you should know for 2019:

1. The rise of Artificial Intelligence:

As businesses rely more on artificial intelligence and machine learning models, the need is to find out the way they ensure their trustworthiness. The increase in technology has led to the growth of AI illustration. It is the practice of understanding and transforming transparent views into machine learning models. AI intelligence assist human expertise and knowledge but not completely replace it.

Business leaders are concerned with the risk like financial services which demands data science teams to use a model that is more illustrated and well documented about how audit trail models are constructed. As data scientists are focusing on explaining these models to business users, they are leaning on BI platforms as an interactive method for exploring and validating conclusions.

Finally, companies have embraced the value of artificial intelligence and machine learning. But to make a disruptive impact in organizations, AI has to be trusted. It must justify its conclusions in an intelligible fashion, and helps humans better understand their data.

2. Communication with data:

Improvements in NLP systems enable all people to have natural conversations with data. Natural language processing brings together computer science and linguistics to help computers understand the meaning behind human language. Today, BI vendors are offering a language interface for visualizations so that users can interact with their data naturally, asking questions as they think of them without in-depth knowledge of the Business Intelligence tool.

Machine learning enables systems to gain more in-depth domain knowledge over time based on a company’s data and the types of questions their users ask. Data communication language will also allow users to ask questions based on a data visualization.

3. Manage data and Performing Analysis in a Single Platform:

People want to manage their data and performing analysis in a single platform in the context of their business processes and workflows, which have become possible with business intelligence platforms that meeting the need by merging with core business operations, workflows, and processes through capabilities like mobile analytics, secured analytics, dashboard extensions, and APIs. As a result, actionable analytics are facilitating the decision-making process for both technical and non-technical roles.

These abilities allow data workers to analyze data and take action after finding an insight, all in the single platform. Embedded analytics puts data and ideas where people are already working so they don’t have to navigate to another application or shared server.

For example, organizations set analytics into customer relationship management (CRM) software like Salesforce. Salespeople can view valuable account data in a context that include product preferences and other information.

The availability and convergence of analytics and action reduce the time and effort between insight and decision-making. It will also make data more broadly available within business workflows, encouraging more people to incorporate data into everyday decisions.

4. Data Management Converges with Modern BI Platforms:

As data sources become more complicated, diverse, and numerous, data management is now even more critical in current BI deployments. Most of the workforce uses data to make decisions; organizations must ensure accuracy within their data and its use in analysis.
Organizations need to address data management and face challenges that come in the data access. Data curation is the way an organization captures, defines, and aligns disparate data. The process of data management bridge the gap between the data and its real-world applications.

Organizations spend millions of dollars on technologies that integrate data with the analytical tools to analyze the data. Data curation tools and processes are converging BI platforms to link data with the business context.

Data curation is the process of identifying the required data sources, using the data in the context of the business to understand and use it for business analysis. Business owners need to understand what the data represents in the real world. Analysts need to verify the data information, and with the change of data, data engineers need to analyze its impact on the business. Combining a data catalog with BI platform streamlines all tasks, providing usage metrics for identifying the most frequently-accessed data sources and dashboards quickly.

5. Increased cloud data migration fuels new BI adoption:

Data is moving to the cloud quicker than ever, driving organizations to rethink their data strategy. Many companies are seeing the benefits of moving their data to the cloud, adding flexibility and scalability for the data management. Most companies are enjoying the benefits of moving their data to the cloud. It combines flexibility and scalability at a lower cost of ownership. Gartner research indicates that the cloud services market is projected to grow by 21.4% in 2018 to total $186.4 billion.

The cloud makes it simpler for companies to capture and integrate different types of data. Now, businesses are moving towards scalable and flexible infrastructure in the form of a full cloud or hybrid solution.

As companies are moving to Google Cloud, business owners rethink their entire data analytics strategy and the impact of cloud on their business. As organizations are assessing cloud-based strategy; companies approaching the cloud infrastructure and systems enables companies to integrate different types of data.

Traditional business intelligence depends on IT departments keeping analytics separate from the business context. Traditional BI deployments are often built on on-demand models that support enterprise reporting.

Cloud analytics gives a variety of benefits including the opportunity to think new business models. The cloud enables secure dashboard sharing with partners or customers, allows to manage the business processes efficiently.

Although all companies are not moving to the cloud solution, many are experimenting with hybrid solutions to take advantage of direct data sources. Companies are assessing modern Business Intelligence platforms on the premise of whether or not they can support a future transition to full-cloud analytics.