Businesses today deal with complex, massive datasets. If they’re going to use their data as effectively as it should be used, they’ll need either a well-trained IT team or one of the top business intelligence (BI) tools. Through data integration and analysis, this data can reveal accurate, actionable insights that drive the business forward.
This process has traditionally involved a centralized group of data scientists, but business intelligence tools have demonstrated themselves to be more efficient in many areas. From data integration to data analytics, the best BI tools offer a holistic approach to data that allows users to easily access, interpret, and act on what they learn. Augmented analytics interprets huge datasets, while embedded analytics enables users to incorporate their use of the BI tool within every part of their workflow.
Beyond data analytics, there are four basic functions of a top BI tool:
- Data integration gathers information from multiple sources for data replication, transformation, and ingestion. No matter what format the data arrives in, it’s standardized for improved accuracy, then stored in a data warehouse. The users can then access complete, up-to-date information.
- Data catalogs transform and optimize data assets into business-ready, current information. The best BI tools offer an intuitive interface so you can prepare, send, and manage the data for yourself or for other users.
- Augmented analytics implements AI technology to provide trustworthy, fast insights to users. It integrates with all data sources, and provides detection of trends and advanced statistical anomalies. Intelligent alerts notify users of material changes in data, which makes it easier for them to respond quickly when needed.
- Embedded analytics brings actionable analytics features and data to products, processes, workflows, and applications. Users don’t have to visit a certain platform or page in order to view relevant analyses; instead, data-driven insights are brought to the users. The best BI tools provide this functionality with extensible platforms and open APIs.
Augmented Analytics in BI Tools
Before the current BI tools existed, business intelligence was usually handled by a highly trained centralized IT team. Data was processed manually by data analysts, which sometimes resulted in low-quality or slow data. Now that augmented analytics engines have made today’s BI tools possible, the process is no longer administered by a centralized team of people; it’s administered by end users and AI technology.
Augmented analytics also makes the data more accessible through a user-friendly interface. Since users don’t have to undergo a huge learning curve in order to take advantage of the BI tool, they can focus their efforts on developing concrete action plans based on key insights. Since end users don’t have to go through an IT team to access data or analytics, aspects like data governance, data security, and access control are streamlined.
Here are some of the key benefits offered by a business intelligence tool with an augmented analytics engine:
- Better use of time for data analysts – Data analysts and data scientists no longer have to contribute as much to the integration process, so they can focus on complex, deep research efforts.
- More in–depth data analysis – Since the BI tool identifies each factor that could affect your organization by analyzing every data combination, fewer trends or connections are missed because of insufficient analysis.
- Actionable insights – Users can quickly access relevant insights, which enables them to make solid business decisions.
- Faster results – Automatic augmented analytics interprets the data much faster than data scientists are able to, meaning users get actionable data more quickly.
Through machine learning, augmented analytics helps businesses visualize findings and gain important insights from massive datasets. It’s no longer necessary for them to write their own algorithms or build complicated models, and since the data is integrated and analyzed automatically, there’s less of a need for it to be managed or administered by data scientists. If a business is using an augmented analytics engine to drive business decisions, they’ll be acting on information that’s formulated after connecting trends and identifying patterns within the business’s data.
Augmented analytics works to integrate relevant data automatically; the data is cleaned, analyzed, and converted into actionable insights. If stakeholders and company leaders need to make fast, accurate decisions, augmented analytics will supply them with the insights they require.
The Role of Data Integration
Without data integration, augmented analytics wouldn’t be possible. The top BI tools begin with an in-depth data integration process, receiving datasets from multiple sources and transforming them into high-quality, accurate data.
If the output isn’t automatically checked, reports based on that data will be unreliable and inaccurate. There are often duplications among datasets, as well as inconsistencies or errors; if they’re allowed to remain, they’ll create problems when decisions are made based on poor-quality data. With a thorough data integration process, any potential issues are identified and removed as the data is extracted.
Even if there are measures in place to catch reports based on faulty data, this still involves time spent correcting problems that could have been prevented with a BI tool. If the data is standardized and consolidated through data integration, you’ll have more confidence that you’re making well-informed business decisions.
Few businesses can get away with not using a BI tool, especially not if they want to stay competitive. Business intelligence can redefine the way an organization utilizes data, but not every BI tool will be a good fit for every business. The Gartner Magic Quadrant Business Intelligence report provides a thorough comparison of vendors. The Gartner Magic Quadrant Business Intelligence report provides an overview of the BI market and vendors. And as you search for the right BI tool, you might find the criteria below helpful:
- A BI tool should fit in with your organization’s current data strategy, such as an option that uses a platform-agnostic, multi-cloud architecture.
- It shouldn’t cost a prohibitive amount of money for additional infrastructure or resources.
- It should offer an end-to-end solution for everything from raw data to informed insights.
- It should have capabilities that fit the specific needs of your organization.
When a business is paired with a BI tool that matches their requirements, the results speak for themselves; the business can make data-driven decisions that encourage growth and lay the foundation for future success.