Long before the days of computers and the Internet, gaining intelligence on rival businesses, current or potential customers, and the value of investments and new strategies have been fundamental parts of business operations. The ability to accurately assess and predict trends in a particular industry is key to success in competitive corporate landscapes, and generally the businesses with the best intelligence tend to come out ahead — and stay ahead.
The landscape of business intelligence was significantly changed by the arrival first of computers, and even more so by the Internet. Now, companies could gather data in real-time, coordinate various locations and facilities for better real-time pictures of operations and trends, and collaborate more effectively between departments to make strategic decisions and shifts exponentially more quickly than ever before. Over the past decade, these shifts have been supercharged further by cloud computing, automation, and AI, which now let companies leverage algorithms and machines to do intelligence gathering even faster than any human ever could.
But what is business intelligence at its basic level, and why is it such a critical component of modern corporate strategy? The truth is that for many developers, who may have begun their coding careers with eyes on more flashy industries like gaming or AI, business intelligence actually offers one of the most profitable and rewarding career paths out there. Let’s take a deeper dive into what business intelligence is, and how programmers and developers are becoming a fundamental part of driving advances in business intelligence every single day.
Basics Of Business Intelligence
Business intelligence (sometimes known in short-hand as BI) integrates business analytics, data mining, data visualization, data tools and common feedback practices to help organizations make accurate data-driven decisions. In the modern business environment, this includes a comprehensive view of a particular organization’s data, which is aggregated and shared in order to help various management and project teams drive operational changes, eliminate inefficiencies, and nimbly adapt to market or supply changes as they happen.
It’s important to note that this is a very modern definition of BI, one that has evolved alongside the technologies noted above. BI actually originally emerged during the 1960s as a method of sharing information across organizations. Over the next few decades, as computer models for decision-making and turning data into insights emerged and evolved, BI moved beyond simply sharing data to become an actual in-house element of most major companies. Modern BI solutions emphasize flexible self-service analysis, governed data on trusted platforms, empowered business users, and speed to insight.
While this is what business intelligence looks like, it’s also worth noting some of the key reasons that business intelligence is so important to companies:
- In a globalized modern economy, with competitors potentially located in every time zone around the world, being able to react in real-time means having intelligence structures that go beyond the traditional “9-to-5” business hours of the past.
- Since data science has become the main driver for modern business decision-making, building in-house solutions for business intelligence are even more important than before.
- Competitor monitoring has always been a part of successful business, and some business intelligence tools now offer the ability to see price changes, new initiatives, or changes in offerings in real-time also.
- With many investors focused specifically and intentionally on ROI (return-on-investment) metrics for committing their dollars, business intelligence can provide actionable data to support requests for more funding.
Software Engineering And Programming Behind Modern Innovations iIn BI?
Now that you know the basics of business intelligence, you probably are wondering why we hinted that developers are in such demand in the field. The fact is that the vast majority of modern business intelligence is done through software and algorithms — similar to how finance and trading rely heavily on algorithms to make trades and conduct business, intelligence is handled more efficiently by modern hardware and software than any human.
If you are a new developer wondering what you’d need to learn to break into business intelligence as a potential career, here are some of the main tech specialties and sub-categories that fall under the business intelligence umbrella today:
Analytics Software
The most widely known example of analytics software is Google Analytics, the ubiquitous dashboard that allows website owners and operators to track visitor data and engagement to a level never before thought possible. GA and other analytics programs offer real-time tracking of site visitors, the ability to see how individual visitors interact with pages and forms, location tracking, time on site, bounce rate, click-through rates, conversions of specific campaigns or content — and hundreds of other data points.
For companies, this type of real-time insight into customer behaviors and preferences is invaluable. When before, companies relied on customer feedback surveys or word-of-mouth to figure out preferences and pain points, now customers don’t even need to be bothered for companies to gain the operational data they need to make significant changes to website flow, offerings, or content. Beyond this, analytics software can also serve a predictive component — by showing companies what ad campaigns or techniques are leading to the best conversion rates, future marketing and advertising buys can be tailored more effectively to maximize ROI. No more spending tons of money on a billboard near a highway and hoping it’s doing the trick to bring in new customers.
Data Collection And Visualization
While analytics software performs some data collection and visualization functions itself, working within data collection involves much more when it comes to business intelligence needs. Beyond simply measuring existing or new customer engagement and behavior, businesses also need intelligence on other key factors that affect their bottom lines and operations. Some of the most common include:
- Real-time tracking of competitor engagement, initiatives, price changes, promotions, hirings/firings, and mergers to inform long-term planning
- Data on shifting market trends or potential disruptions to existing operations, the best example being something like a global pandemic
- Comparative data between parallel initiatives to help evaluate the success or failure of one strategy over the next
- Employee performance data (customer service reps, sales reps, managers, HR employees etc) to figure out areas for growth, potential hiring, or needs for training or trimming of staff
- Tracking supplier pricing to evaluate the best vendors to source your goods or services from, and allowing your business to negotiate the most competitive prices
Beyond simply collecting and aggregating this data, visualizing it into charts or graphs to identify trends and easily communicate those trends to other departments or teams is another important job that developers and software engineers often fill. Python and R, especially, have become particularly popular for data visualization in business intelligence, so becoming an experienced programmer in either language promises plenty of career opportunities.
AI And Automation
The most recent (and arguably most important) trend in business intelligence has been the use of predictive algorithms and automation to allow business decisions to be made in an instant, without needing to be discussed by teams and thus delayed. For example, if a supplier experiences an outage due to supply chain issues, automation tech and AI can immediately shift a purchase order to a new supplier to fill the gap and ensure customers don’t have to wait longer for goods than they were promised.
Similarly, AI and automation can measure employee performance more effectively in many ways than human managers can. From noticing subtle trends in performance (increases in turnaround time on work, decreases in customer satisfaction from automated surveys, social media trends etc), these tools also offer internal intelligence that can better inform hiring and training decisions than ever before.
