The most successful businesses have learned how to leverage their internal data to improve their technical processes and deliver better products and services. The use of raw or warehoused data to assess and make reports regarding business practices offers actionable insights that you can take advantage of to maximize your growth and minimize costs. Though there is a wide variety of methods for collecting valid information, they each require considerable time and resources. As such, it is difficult for organizations to operate at scale, but through business intelligence (BI) automation, they can reduce their workloads while still taking advantage of existing data resources.
In this article, we will cover:
What Is Business Intelligence Automation?
Today, business intelligence is more important than ever, but with so much data to process, organizations can have a hard time keeping up. It’s natural to ask, “What is business analytics?”
Using automation technologies to take on some of these workloads can lead businesses toward better and quicker decision-making while also allowing for the gathering of key metrics that can inform them of how to operate in the future.
Examples of BI automation platforms and applications include:
- Power BI
- Tableau
- Databricks
- Snowflake
- Azure Data Factory
Business intelligence automation pairs existing strategies with modern data management techniques to reinforce market research. In other words, BI automation is the result of decision-makers leveraging the IT resources available to them to enhance operations across the board.
5 Tasks You Can Automate in Business Intelligence
Good leaders understand how to derive actionable information from existing business intelligence strategies. Whether forming budgets or generating reports, BI can give you the needed data. Be that as it may, there are limitations to how much a person can do on their own.
While there are various benefits to using business intelligence in your organization and business intelligence analysts capable of delivering important insights, the automation of software, hardware, and other pieces of infrastructure for collecting data can ease their workloads and limit their potential for mistakes.
Five business intelligence processes that can be automated include the following:
- Auto-Discover Insights
- Automate the Ranking of Insights
- Embedded Insights
- Extract Bias
- Universal Accessibility & NLP Queries
1. Auto-Discover Insights
Businesses can only ensure sound decision-making if they have the correct information to apply to the process. Understanding what information means and how it can be best applied requires actionable insights based on real-time occurrences. A person might be able to find some pertinent data when doing business intelligence research, but there are always things that can likely be left out.
Aggregating and analyzing information requires ongoing effort, and there’s always more to collect. Still, by automating business intelligence, companies can automatically discover valuable information about their operations to make adjustments as soon as possible.
2. Automate the Ranking of Insights
Knowing and acting on the most important insights can still be complicated, even if you have the correct information. Sifting through vast amounts of data to find what you’re looking for can be a nearly impossible task, even for large teams, making BI automation a necessity.
For companies that want to find the most important information without wasting resources, automation tools make it easier than ever to rank insights according to your needs. Determining the most important information will allow you to enact better plans in the future.
3. Embedded Insights
Having the right tool for the job is vital in any industry, but it’s hard to switch to something new when integrating business intelligence into existing operations. For most organizations, that can lead to limited knowledge-sharing across teams.
Making information available within employee workflows can be tricky, but automated solutions can help reduce the burden on IT. Companies that want to improve their key business intelligence technologies without disrupting their workforce can benefit from embedded analytics capabilities. Data visualizations, cloud-based dashboards, and other capabilities make it easier to use native apps for market research.
4. Extract Bias
Human bias exists at every level of the decision-making process, and it’s something that can go unnoticed if it’s not accounted for at all times. Proper business intelligence necessitates the use of predictive models that can be severely disrupted by unexpected bias, so the classification of specific data as “off-limits” can help ensure the integrity of the final result.
BI automation allows businesses to reduce the amount of bias within their models. Without such capability, it can be hard to tell what information is actionable and which results from human error. While they’re far from perfect, automated business intelligence systems can give companies a better understanding of their biases so that they can act accordingly.
5. Universal Accessibility and NLP Queries
One of the best aspects of automated business intelligence is that it’s more accessible to people who otherwise wouldn’t have been able to take advantage of the information available to them. Automated systems are easier to use because they’re built using NLP queries, and the capacity for character recognition being built into more computing systems could be a substantial development.
Accessibility testing is essential for improving your business, making automation the perfect match for the quality assurance (QA) cycle. Using NLP queries for improved automation testing will allow your organization to determine which systems work for specific groups or individuals and how they can be adapted for better accessibility.
If you want your services to be usable by everyone, you’ll need to be able to leverage BI automation to ensure stated guidelines are met.
How to Get Started with Business Intelligence Automation
Getting the most out of your data requires a set of best practices that can guide you toward your stated goals. For companies that want to build a fast, efficient, and powerful data analysis pipeline, there’s no better solution than automated business intelligence, as a qualified business intelligence analyst can do more with automated systems.
Getting started with BI automation is relatively straightforward for most companies. However, you’ll still need a good strategy in place and the capacity to stick with it and make improvements over time for it to really take off.
Do Research
The first thing you’ll want to do when starting out with automation business intelligence research is to find a gap that available technologies can easily streamline. If you have access to ML and AI capabilities, you’ll want to ensure you know how to leverage them to their fullest.
Take the time to schedule brainstorming sessions and communicate the most important information so that nothing is left out. You can also conduct surveys or interviews to determine which areas need the most attention.
Create a Plan
Your business intelligence strategy won’t get very far without a sound plan of action. If you’re starting out with the automation process, you’ll need to set goals and design a plan capable of achieving them. Once you’ve figured out what you need, it’s time to actually build a plan. Create mockups, set timelines, and state expectations before finalizing so you can assess the results.
Gather Resources
With a plan prepared, the next step involves gathering available resources and ensuring enough are available to meet the needs of your ongoing projects. Promising business intelligence starts with understanding your capabilities and designing strategies around them that can be realistically put into action.
Whether you’re using consultants or doing everything yourself, you’ll need to have a clear picture of your operations if you want to be able to integrate current business intelligence practices with automated services.
Finalize
Once everything is implemented, ensure you’re ready to support your vision for the foreseeable future. After executing your strategy, there’s no going back, but even with that being said, business intelligence doesn’t end there.
You’ll want to evaluate the results of your business intelligence strategy to see how they align with your initial expectations. Make sure you get user feedback for your automated systems and adjust things accordingly so that future deployments can go more smoothly.
Risks Associated with Business Intelligence Automation
BI automation can transform your business, offering huge advantages to you and your workforce. However, proper risk management is necessary when making decisions that will impact your operations on such a fundamental level.
Business intelligence reduces uncertainties associated with ongoing operations, but poor intelligence can have the opposite effect. Ensure you understand how automation can amplify these problems before deploying it to collect intelligence for your organization.
- Data Quality or Data Loss
- Metric Consistency and Understanding
- Misinterpretation of Results
- No One Using It
Risk 1: Data Quality or Data Loss
The job of a business intelligence analyst is filled with uncertainties. You can never be certain that the data you’re collecting is relevant until it is put into action, a point at which it might be too late to take corrective action should problems arise. There’s also the risk of data loss if the software you’re using isn’t properly vetted.
Automation is great for searching through large volumes of data, but the quality of that data will rely entirely on how good the tools you use are. Knowing which tools you should use for specific tasks will make finding good data easier and keep it safe from harm.
Risk 2: Metric Consistency and Understanding
Another risk you take when using automated business intelligence is the loss of context. Automated tools are great for scraping large volumes of information, but raw, unprocessed data can be misleading, making it harder to understand ongoing processes.
Properly integrated systems are better at maintaining accurate information, and consistent collection and storage strategies can make it easier to get objective data that offers real insights into your business.
Risk 3: Misinterpretation of Results
Collected data must be adequately understood to be used to its fullest potential, but like people, computers can sometimes misinterpret information, leading you to the wrong conclusions about your business. Situations like these are especially problematic in business intelligence, where automated systems can become confused by mislabeled or unorganized data.
Improperly maintained data isn’t the only risk, either: Automated systems with poor or unintelligible interfaces can make it harder to get a clear picture of what’s being presented.
Risk 4: No One Using It
Even the best-automated systems are worthless if they aren’t properly used, and it is no different with business intelligence. Unfortunately, many people avoid using automated systems because they seem too complicated.
If you want your automated business intelligence to bring results, you’ll have to train your employees so they understand how to take advantage of the tools available to them. With the right mindset, anyone can get started using BI automation systems to improve their workflow.
How Did Business Intelligence Automation Begin?
The concept of business intelligence has been around since the late 1980s and was often used to describe the strategies used to inform important decisions with real-world knowledge.
Building off that, business intelligence automation is a more modern term that’s become popular over the past few years, describing the marriage between traditional business intelligence with advanced computing and software systems.
As companies have continued to search for and find new ways to maximize their ROI, they’ve uncovered the need to adapt to changes in the market, resulting in a fundamental shift in how business intelligence is viewed.
In the past, business intelligence was seen as a more human-focused approach, whereas today, it’s largely done by machines. The change in perspective has led to an increased focus on advanced technologies capable of handling increased workloads with little to no user input.
What Technologies Are Used in Business Intelligence Automation?
Business intelligence automation relies on key technologies that empower users to interface with transformative capabilities. By introducing automation into their workflows, enterprises and organizations stand to reduce tedious labor for digital workers while improving their capacity to leverage digital information.
There are quite a few options available to companies that want to leverage business intelligence automation. When armed with the right tools, companies can improve their internal and external processes without investing in costly solutions.
However, depending on the desired applications, business leaders must be ready to employ the following technologies capable of properly doing the job.
Artificial Intelligence & Machine Learning
Artificial intelligence (AI) and machine learning (ML) technologies are already transforming how companies work on a daily basis, so seeing businesses leverage them for automated business intelligence was only a natural outcome.
AI that is capable of character recognition, for example, makes it easier for businesses to scan digitized documents and other materials that would otherwise have to be manually entered by hand.
Similarly, machine learning software can empower organizations by helping them sift through large volumes of previously undiscovered information quickly and effectively without the need for human intervention. As the computing capabilities available to businesses continue to grow, more opportunities open for what companies can do with these technologies.
Natural Language Processing
Natural Language Processing (NLP) refers to the use of computing technologies as a means of better understanding how people communicate through voice and text. Language-related tasks, such as reading, writing, and computation, are easy for computers to perform repeatedly due to their fast processing times.
Character recognition and NLP are so appealing to many organizations, given that business intelligence relies on many of the applications offered by NLP, which makes the two a perfect pairing. Whether performing translation, spam filtering, or editing processes, NLP can ease employee workloads and improve the overall quality of the work.
Digital Analytics
The age of digital transformation has radically changed how organizations seek and obtain their knowledge, and optimizing business practices requires tools capable of processing and leveraging relevant information.
Companies need to collect data from various sources when performing business intelligence audits. Ads, websites, and social media are all potential sources of information that companies can leverage to inform their decision-making and stay competitive.
With that being said, what is business analytics used for? With digital analytics, companies become better equipped to deal with ongoing changes in the market and perform quality web market research.
Process Intelligence
Businesses must have a clear picture of their systems, infrastructures, and capabilities at any given moment to ensure that their decision-making processes are as efficient as possible. Finding and addressing problems in your pipelines is only possible if you can accurately assess what’s going wrong and how to fix it.
With that in mind, you need to ask yourself: What is business analytics useful for if it isn’t paired with the proper context? If a specific strategy doesn’t properly scale, you need to be the first to know. Process intelligence will allow you to find out why and fill any gaps to ensure operations aren’t interrupted by unexpected failures.
Robotic Process Automation
People are capable of many things, but they’re often better suited to analyzing processes and solving problems instead of repeatedly performing monotonous tasks. Robotic Process Automation (RPA) makes it easier for businesses to develop and deploy bots capable of easing workloads and providing important information to users.
RPA is often used in business intelligence to monitor multiple systems for pertinent data and extract it on the fly. The software can be especially useful in governance, accounting, and manufacturing sectors as they are applications central to good business intelligence.
Computer Vision
Although finding and analyzing audio and text-based information can provide several key insights, a vast amount of data can be extracted from visual media. Computer vision aims to make it easier for users to comb through pictures and videos in order to acquire highly valuable data from the real world.
The use of computer vision can give your organization important insights into itself, as well as other businesses. Since many modern technologies are capable of sophisticated tasks, companies can deploy computer vision services to gain a competitive edge without spending much in the process.
Stay on Top of Your Business Intelligence with Automated Services
Making the right decisions for your business isn’t always easy, but with the right tools, you can improve your intelligence and gain a competitive advantage. Whether you’re looking at consumer outreach or product development, business intelligence is essential for getting products and services to market. It’s easy to improve your profits, assess consumer behavior, and improve your strategic management by gathering the right information.
Businesses that want to create scalable project pipelines while communicating important information across the organization can use business intelligence for generating reports, enhancing training, and restructuring teams for maximum efficiency. With so many opportunities, it’s not hard to see why more companies than ever are leveraging BI to improve their internal strategies.
There’s no end to the improvements to productivity that BI automation can bring to your organization. If you’re interested, you can find out more by reaching out to our team on how you can implement BI automation in your organization.
