Terms like “business intelligence” (BI) and “data analytics” (DA) abound in today’s data-driven corporate scene. Where do they differ, even if both entail learning from data?

We’ll investigate business intelligence and data analytics in this blog, investigating their unique qualities, uses, and features.

Knowing these differences will enable companies to decide which strategy best fits their situation.

What is Business Intelligence?

Business intelligence, or BI, is about turning your data into valuable insights to guide choices.

BI helps companies make data-driven decisions that result in improved outcomes rather than depending on gut emotions or presumptions.

BI is turning challenging datasets into aesthetically pleasing and easily consumed forms, including dashboards, charts, and reports.

These visual aids help companies to grasp why and what is happening in their operations.

Example:

Imagine yourself owning a worldwide online retailer of fashion goods. You wish to monitor monthly sales and find the sources of those increases. BI tools let you create a report showing your monthly sales patterns.

Monthly sales trends:

This report shows a notable increase in sales. The BI tool clarifies “what” is happening—increasing sales. But by looking at the source of the sales trends, you can better understand “why” they’re growing.

Monthly sales from source:

Examining this information reveals that your email marketing has been relatively successful, which helps explain the rise in sales.

Here, BI closes the distance between raw data and valuable insights by integrating data, reporting, visualization, and analysis.

Business intelligence allows businesses to:

Track performance against standards and goals using data visualization and analysis. For instance, you can track monthly sales, orders, and earnings.
Discover trends and patterns in big data using BI, fostering a better knowledge of consumer behavior and corporate performance.
Comprehensive reports and clear visualizations enable corporate leaders to make data-driven choices. Knowing that email marketing went successfully from the above, you can maximize and give them more attention.

By using data to drive proactive decisions, BI helps companies to keep ahead.

Though BI shines in descriptive analysis and reporting, it lags in predictive and prescriptive analytics—where data analytics is most beneficial.

What is data analytics?

Data analytics is the application of computer techniques, machine learning algorithms, and statistical analysis to derive insightful data information.

There are four primary forms to classify data analytics:

Summarizing and visualizing past data, descriptive analytics helps one to grasp “what has happened.” It pays especially attention to prior performance, trends, and patterns.
By spotting the underlying causes of trends and patterns, diagnostic analytics helps one to grasp “why something happened”.
Predictive analytics addresses the “what will likely happen” issue by using statistical methods and past data to project future results.
Prescriptive analytics addresses “what should we do” and provides advice on reaching particular objectives, guiding companies in their decisions.

Example:

Using our e-commerce example, BI found more revenues from recent email marketing. Data analytics lets you now address “how” such initiatives are performed.

For instance, the success could be due to:

Customizing emails by audience segmentation.
Including appealing offers, client comments, and quotes.

Future performance can also be projected with data analytics.

Analyzing past data will help you forecast sales trends for the following several months, enabling you to make preemptive decisions, including inventory building or increased marketing activity, should sales decline.

Comparing business intelligence and data analytics

Comparing data analytics and business intelligence helps one see the overlap and differences between the two areas.

Both fields make use of statistics to derive understanding and backup for decisions. Still, they have various uses and appeal for different company departments.

Here’s a detailed table of the key differences between business intelligence and data analytics:

Understanding these differences can help you determine which approach aligns best with your business goals.

Which one for your business: BI or data analytics?

Your company’s needs, goals, resources, and skill requirements will determine whether business intelligence or data analytics best fits you.

Business intelligence could be the best option if your primary concern is measuring and observing performance using well-organized, consistent data.
Data analytics would be more appropriate; however, if you manage significant volumes of unstructured data, predictive modeling and advanced analysis—are needed for strategic decision-making.

However, BI and data analytics are not mutually exclusive, and this should be remembered. Many companies use a hybrid strategy, combining data analytics and BI to satisfy their particular needs.

But you could wonder—will using a hybrid strategy require considerable time and financial outlay? In reality, it’s not necessary.

Modern top-rated data analytics products combining BI and data analytics features let you advance your data-driven path without breaking.

Wrapping Up

Data analytics and business intelligence are essential in the changing terrain of data-driven decision-making.

Data analytics provides deeper insights into future outcomes and prescriptive actions, whereas BI shines in clearly, visually comprehending past and present performance.

The proper strategy for your company will rely on your particular objectives and requirements.

Whether your inclination is towards BI, data analytics, or both, the secret is appropriately using your data to propel company success.

In just a few short years, small internet-enabled devices, known collectively as the Internet of Things (IoT), have transformed how we live in ways that continue to grow and evolve.

The IoT revolution really began with consumer-facing products – smartwatches, thermostats, and television remotes. While today your IoT-enabled refrigerator might tell you that you need to get more milk or your smart garage door opener may send you a text asking if you meant to leave the door open, the IoT is now set to transform and revolutionize business processes and operations.

While the roots of Industry 4.0 started in the 1990s, it is the rise of interconnected systems communicating via the internet that is truly leading to a revolution in manufacturing. Not only can machines on factory floors communicate more intelligently with each other across physical and geographical barriers, but these smart machines are also able to monitor, detect and predict faults, suggesting preventive measures and remedial action before downtime occurs.

The IoT also allows manufacturing processes to be completely virtually visualized, monitored, and managed from remote locations. Industry 4.0 puts machines, people, processes, and infrastructure into a single, connected manufacturing process, which provides businesses with full disclosure over the entire workings of their manufacturing and production. Using this information makes overall management highly efficient.

Supply Chain Management

Just one way that the IoT will create efficiencies for the world of business, be it B2B or B2C, is in supply chain management.

From factory to shelf, the IoT cannot only make existing processes more efficient, but it can also detect potentially expensive problems in advance of them impacting your business. Picture a small component in a factory. As a ‘dumb’ cog in the machinery, the part could fail without notice, shutting down an assembly line for several days. As a ‘smart’ cog, a similar part could tell engineers that it was set to fail days in advance, automatically place an order for its replacement, and direct staff to its location in the factory to replace it.

The same technology makes it easier for businesses to track inventory around the world, creating opportunities for supply chain and logistics optimization. Connected manufacturing and the IoT provide employees with visibility over company assets worldwide. Standard asset management tasks such as transfers, disposals, and adjustments can be streamlined and managed centrally and in real-time.

Similarly, the IoT has been used to handle logistics for the maintenance of a shipper’s fleet. With equipment requiring regularly scheduled maintenance, knowing where a given truck is on a given day, scheduling it for service, and making sure that there are enough trucks in the right place to cover customer needs. Easier scheduling, reduced downtime, and balanced fleet usage all translate to savings.

Asset Tracking & Waste Reduction

IoT sensors on both vehicles and product packaging can provide insight into where a company’s inventory is at any given time. When a company always knows exactly where their inventory is, they can be agile with moving it to where the need is the greatest. Furthermore, the same sensors are able to detect changes in temperature, light, and other environmental factors, ensuring that potentially perishable items do not go to waste.

With real-time insight into buyer behavior, retailers can stay up to date not only with on-shelf product stocking, but also by tracking which goods are most popular in a given setting, allowing them to increase their profits with efficient sales and stock management.

Advanced Workforce

Frontline workers need access to accurate and up-to-date information in order to solve problems and increase productivity. Access to information, guidance, training, and support which was previously delivered in person can now be delivered directly to the shop floor.

Manufacturers and industrial companies of every size can now access digital transformation initiatives in order to maintain business operations and business continuity. Technologies such as artificial intelligence (AI) and augmented reality (AR) can now be a part of the toolkit available to workers on the shop floor and are crucial to making the most of a workforce that is increasingly spread across the world.

Advanced Analytics & AI

With information coming in from sources as disparate IoT sensors on factory floors, lighting systems, sales data, supply chain, and customer demand, there are massive amounts of data to sift through. Thanks to advances in data and analytics, manufacturers are more capable than ever of using that data to make informed decisions to improve internal processes.

Taking it to the next level, Artificial Intelligence (AI) and machine learning can be leveraged to further process your data, using it to reach conclusions that go beyond the obvious. Some examples include forecasting market changes and predicting machine downtime.

Customer Engagement and Real-Time Insights

Stepping outside of the shop floor, as the IoT and its associated cloud platforms become more prevalent, companies will be able to gain deeper insight into how their products are being used, potentially leading to new marketing channels, improved customer service, product improvements, and ultimately, increased customer satisfaction.

Transform Your Business

As the IoT landscape continues to evolve, market research from Gartner and Cisco predicts that the Industrial Internet of Things (IIoT) will grow even larger, improving asset management, operational visibility, safety, and security. With all of these interconnected devices, the amount of data grows exponentially. By partnering with a technology company experienced in supply chain management, big data, machine learning, and data visualization, you can transform your business for the 21st Century.

Category: Data & Analytics

Business Intelligence vs. Data Analytics: Understanding the Key Differences