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Make informed, data-based decisions with business intelligence, reporting, and analytics

  • Article

In this section, we explain how to use data to make smart decisions with Dynamics 365. We explore the evolution of business intelligence solutions, how to build a modern data estate, and how to use reporting and analytics strategies to get deeper insights from your data.

Key objectives

  • Learn why data is important, how to break out of silos, and how to use the digital feedback loop.
  • Understand the evolution of business intelligence, reporting, and analytics.
  • Find out what a reporting and analytics strategy looks like.
  • Create a data strategy to modernize your data estate.
  • Learn about the components of a modern data estate.

The importance of business data

Data is the key to reporting and analytics, and business intelligence is the key to success. It's all about actions and outcomes, measuring what matters most, and giving users the right insights at the right time.

Business intelligence solutions have changed to keep up with more data sources, as organizations seek ways to reach more customers and offer them more content, channels, and choices. In this context, turning data into business knowledge is crucial.

While AI and machine learning keep improving—and are now used natively by apps—your organization's intelligence strategy should be based on a modern data estate, with a focus on how to strengthen the organization.

As this Microsoft Dynamics 365 Blog article says, "There is a fundamental change occurring across industries and organizations: Data now comes from everywhere and everything." Data is an essential part of a business solution. The solution processes and analyzes the data to produce information, which helps an organization make informed decisions, and determines actions that can come from it.

Users generate a lot of data using any application—structured and unstructured. For example, customers generate data by visiting a website or using a product. Businesses generate data about their products or services and how customers interact with them.

And the amount of data is growing fast. Not only does user activity create data, but new data sources such as the Internet of Things (IoT) need to be captured, organized, and secured.

The main role of data is to generate information that helps businesses and customers make informed decisions. For businesses that want to stay competitive, the data also must trigger actions that show they understand and value their customers.

Resilience—the ability to adapt quickly to change—has become a mark of business success today. Competitive, resilient organizations invest in turning the data they collect into business knowledge, build the right data estate to get a 360-degree view of their users, and use business intelligence to find the best actions and outcomes.

Organizations that have already started their transformation journey to improve how they use data are better prepared to respond and adapt to change.

Break the silos and use the digital feedback loop

Organizations face challenges with how to deal with more data—and how to generate value with it—especially when the data gets siloed by the systems that create or collect it. Data silos make it harder to have a 360-degree view of each user. Successful organizations digitally connect every part of their business. Data from one system can be used to improve the outcomes or processes in another. By creating a digital feedback loop that puts data, AI, and intelligent systems and experiences at the core, organizations can transform, become resilient, and unlock new value for users.

Graphic showing the digital feedback loop between customers, people, products, and operations, with data and AI in the middle to help fuel intelligent systems and experiences.

Evolution of business intelligence, reporting, and analytics

The ways of gathering, analyzing, and acting on data have changed a lot over time. Traditional methods of making and using static reports don't give businesses the agility to adapt to change. New technology and secure, reliable cloud services—which can meet the needs of organizations that have to quickly manage more data—have started a new era of digital intelligence reporting.

Traditional reporting

The first generations of business intelligence solutions were usually centralized in IT departments. Most organizations had multiple data repositories in different formats and locations that were later combined into a single repository using extract, transform, and load (ETL) tools, or they would make reports within siloed sources and merge them to get a complete view of the business.

Once the data was unified, the next steps were removing duplicates and standardizing, so the data could be structured and prepared for reporting. Business users who didn't have the skills to do these tasks had to rely on the IT team or specialized vendors. After a while, the business would get static intelligence reports, but the whole process could take days or weeks, depending on the data and the processes. Then, the data would be further changed, if needed, and shared across different channels, which could create multiple versions that would be hard to track.

This approach, which also depended heavily on the business owner's powers of analysis to generate business insights and actions, was often slow and error-prone. And if an error was found, it had to be fixed in the central repository. The process to make the report would need to be done again—and repeated many times if the same fix wasn't applied at the source. This method of reporting was called "closing activities," because it usually happened at the end of a month, quarter, or year, which meant that organizations were slow to respond to opportunities because they waited until the end of a period to analyze their data and make decisions.

Self-service reporting

The change to a more agile approach favored self-service features to empower users. More user-friendly solutions reduced the dependency on IT and focused on giving business users access to data and visualization tools so that they could do their own reporting and analytics. This sped up the data analysis and helped companies make data-driven decisions in competitive environments. However, in this model, reporting was unmanaged—increasing the number of versions of reports, as well as different views of the data—which sometimes stopped organizations from using a standardized way to analyze data and make effective decisions.

This updated approach didn't completely replace the IT-centric model, as many organizations started using a mix of traditional and self-service reporting. However, it did provide quicker access to information, so organizations could react faster to business challenges and opportunities.

Reporting from anywhere

The growth of IT infrastructures, networks, and business use of devices such as mobile phones and tablets started a digital transformation from old systems into more modern solutions for most organizations. Business intelligence apps allowed reporting from anywhere at any time, giving users access to data while they were out of the office or on business trips. These apps improved how organizations could respond to customers, and gave a 360-degree view of each customer interaction. They also provided better visualizations of data, with better features to understand it.

The new era of business intelligence solutions

With data now coming from everywhere and everything, organizations must be ready to turn that data into business knowledge so that users can make informed decisions and take actions. Many organizations have highly skilled workers, such as data scientists, who are in charge of analyzing the data and producing the insights that can affect the business. This approach is expensive and adds dependency on specialized roles to do tasks that usually can't be done by typical business users.

An approach to reduce this dependency while also increasing easy access to data is to use augmented analytics, a feature of modern business intelligence solutions. According to Gartner, Inc., a research and advisory firm, "Augmented analytics is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation, and insight explanation."

Embedded intelligence solutions that use augmented analytics let processes, services, or products deliver their own insights to improve quality, efficiency, and customer satisfaction. These are new types of reporting and analytic experiences where the intelligence is by-design, and the solution itself analyzes the data to provide insights. While the data may not yet be unified with such an approach, customers and organizations can use insights provided by products and services from the earliest phases of usage or production, which increases their return on investment (ROI).

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