AI as a Force Multiplier for Business Intelligence


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What benefits can AI systems bring to Business Intelligence? While this may be of interest to true blue BI Managers and Analysts, business intelligence is making its way into a lot of other roles. Between skill shortages, job title overlap, and a growing interest in cultivating cross-functional teams, BI is pretty much for everyone who has a mind to be more efficient, productive, and profitable. That certainly goes for CEOs and startup founders, but software engineering managers, too.

What is Business Intelligence?

Business Intelligence (BI) refers to the technologies and strategies used to analyze data and provide historical, present-case, and predictive views of a company’s business operations. It started to become popular in the late 1990s, but the prevalence of Big Data is giving it major boost. The goal of BI is to give managers and business leaders a complete picture of the state of a company’s operations. In its simplest form, BI is the analysis of business data.

Global Interest in Business Intelligence on Google Trends (2004 - Present)

BI aims to transform raw data into actionable information. It involves a collection of technological processes to turn structured or semi-structured data into useful information to drive profitable business decisions. A business intelligence system can also help companies manage costs, optimize production and efficiency. The use of BI is growing because the information it provides can help improve decision-making and increase overall efficiency.

It’s also appropriate to point out that BI typically works by finding many small and mid-size ways to improve operational efficiency. Only rarely can it lead to single massive improvements – “If we do this, we’ll save billions!”

The best thing BI can do to improve its effectiveness is to reduce the cycle time needed to generate each insight.

Is Business Intelligence Related to AI?

Business Intelligence and Artificial Intelligence are two distinct, but mutually complementing things. You can do each without the other. BI uses data analysis to improve business operations. AI is a computer system that can perform tasks that would normally require a human.

Computers and AI, however, are able to perform certain tasks exponentially faster than humans, and with greater accuracy. This makes it so BI specialists can spend at least twice as much time finding profitable insights while also 10x’ing the rate at which they can find them.

How Does AI Improve Business Intelligence?

The benefits of AI for Business Intelligence are numerous. Where BI aims to improve operational efficiency, AI helps it to do so faster. AI is especially fast at being able to find patterns and analyze relationships between different data points.

How long does it take a business analyst or manager to sort through all of their data to find a profitable insight? Most use a cyclical framework of analysis, insight, action, and measurement that, all told, can take weeks, maybe months. AI presents the potential to generate insights almost as fast as you can ask a question.

It’s worth itemizing specific benefits to show how that’s even possible:

    • Exponentially Faster Data Processing – AI complements Business Intelligence with its ability to handle the collection, cleaning, validation, augmenting, and analysis of Big Data in minutes and hours for what can take weeks and months with even the best BI tools.


    • Real-Time Data – The value of data is measured largely by how fast you can act on it. A lot can change in a month, so making decisions based on last month’s data leaves more room for error than being able to see the trend of a thing all the way up to the present minute.


    • Real-Time Insights – More than just seeing the data, AI can use predictive analytics to recommend the best course of action to improve productivity, efficiency, and other business KPIs.


    • Skills and Cross-Functionality – An AI Digital Assistant can help with training and making teams more cross-functional so that even non-BI workers can put aspects of BI into practice and align with BI objectives.


    • Ability to Add New Data Points – When needed, organizations can add new metrics without needing to create customized new reports.


    • Single Source of Truth – Your entire organization is able to depend on relevant, validated, timely, and centralized data. Different and conflicting data adds complexity to and slows decision-making and implementation processes.


    • Work on What’s Important – AI can take over many of the tedious, monotonous, time-consuming tasks so people can focus on more meaningful and creative tasks.


What Does AI Automate in Business Intelligence?

According to Anaconda’s 2020 State of Data Science, data professionals spend an average of 45% of their time on “data wrangling” (19% on data loading and 26% on data cleaning). Another 21% of their time focused on data visualization. Model selection, training, and deployment each accounted for another 11-12% each. AI can automate the vast majority of data loading, cleaning, and even visualization tasks consuming two-thirds of their time. That’s time that can be better spent improving and training models, reaching and applying data-driven insights, and training non-technical users on how to make better use of their systems.

BI Data Validation

Data Validation is a major element of Business Intelligence that AI can automate and covers verifying that data is:

  • Right kind (integers, strings, Boolean, text)
  • Proper format (dates, phone numbers, zip codes)
  • Expected range (metrics, prices, hours worked)
  • Correlates to other data (associated with actual accounts, part numbers, codes)
  • Consistent with other data (that one’s hire date comes before their separation date)

Bad data leads to flawed decisions or even strings of bad decisions. Like in Revenue Management, if the data used to determine the “best price” of one product is wrong, it could negatively impact the prices of a range of products (pricing architecture). That’s something consumer packaged goods companies take great pains to avoid leading to data analysts and scientists often spending substantial chunks of time cleaning their data.

Applied to software development, if a developer’s active hours are wrong, their utilization, velocity, and efficiency metrics will also be skewed. If you have confidence in how your data is collected and processed, you can identify outliers much easier.

Data Visualization

As we covered in Static versus Dynamic Dashboards and Digital Assistants, it used to be necessary for companies to create a custom report for users each time they wanted to see data differently. This could take an IT team a week or longer to do if they weren’t backlogged – and maybe months if they were. Conversely, AI can automatically configure reports according to each user’s preferences – line graphs, bar graphs, pie charts, scattergrams, box plots, histograms, and others… even the color schemes they use.

The Growing Talent Shortage

A September 2021 survey by Gartner indicates that talent availability is the main adoption risk for 75% of IT automation and 41% of digital workplace technologies. In the same month, the Linux Foundation and edX also released their 2021 Open Source Jobs Report indicating that a whopping 92% of managers are struggling with hiring and retaining talent, especially as relates to cloud native application development and operations skills. Korn Ferry, a US management consulting firm, also released a study forecasting that the talent shortage could amount to $8.4 trillion in unrealized global annual revenue by 2030

Training and Improving Cross-Functionality

Companies are increasingly relying on training to address the skill shortage. Sam Zheng, CEO of DeepHow and a member of the Forbes Technology Council, makes some excellent points in How to Fix the $1 Trillion Skills Shortage Problem, to quote one of his examples:

“An AI-powered training program can both shorten the training time (and cost) involved in reskilling workers for these roles and recommend an adaptive training program, where the modules are modified to suit the needs of each worker and the skills objectives of a specific plant.”

And while Zheng focuses on manufacturing environments, much the same holds true for tech companies of all sizes, especially high-growth startups. As companies grow, they usually become more inefficient having a more complex organizational structure and administrative overhead.

This is a good basis for companies without a business intelligence component to adopt one – or integrate BI into their roles and processes.

  • The Agile methodology encourages cross-functional teams
  • DevOps works to improve cross-functionality in teams and processes
  • DataOps improves cross-functionality through the sharing of data with all stakeholders
  • AI can accelerate training and cross-functionality

With each successive funding stage (Series A, B, C, etc.), startups usually expand their teams. Hiring new in-house or augmented teams (managers and developers) from scratch with the required skills typically means 3-4 months of delays or elevated inefficiency. This owes to the hiring process and the time for them to come up to speed with the existing codebase.

Mitigating this has been one of the reasons why companies promote from within. Your first developers can become team leads, a team lead may move up to engineering manager or to a more specialized role. Aside from whether they want the promotion, they may have little experience or training in their new role.

An AI-powered Digital Assistant can dramatically ease their landing into new roles and pick up new skills to make it easier for companies to grow. Otherwise… finding a Swift developer with experience in BA, DevOps, and DataOps… locally, is going to be like trying to find a very specific needle in a stack of needles.

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