Is business intelligence just analytics on steroids, or is there more to it?
That was my initial question when I started to dig deeper into the subject.
It’s enough to read articles like “A beginners guide to BI software” from Software Advice, to understand why seven in 10 UK marketers said they didn’t have the skills required to address big data issues.
It quickly becomes painfully obvious how complicated BI can be, especially for someone who isn’t born with the natural aptitude for mathematics and a strong affection for statistics.
Wikipedia defines Business Intelligence as “a set of theories, methodologies, architectures, and technologies that transform raw data into meaningful and useful information for business purposes“.
Having worked with various analytics- and data based tasks in the recent years, I can’t help myself asking if the Business Intelligence community isn’t a bit wooly and should get a makeover?
Yes, there are some companies that are working on making business intelligence attractive again, particularly through easy to use, SaaS based solutions that do not require long implementation times.
New players have entered the field in the last few years, but for years I didn’t pay attention to the term Business Intelligence, because I didn’t think it was anything that I would be able to deploy and make use of in my work.
I think it’s a problem of semantics. The whole term is just too vague, and not what people outside the BI-industry use when they talk about the exact same problems that BI is trying to solve.
Let us put some data to back that up.
Google trends. Business intelligence vs. infographics vs. data visualization vs. information design
Do you notice how business intelligence is a decreasing trend, while infographics is rising? I doubt business intelligence solutions are actually on a downward slope…
Maybe there is a need to rethink the pitch for business intelligence?
I though I would compare this graphic with the Gartner Hype Cycle for Business Intelligence and Analytics
Let’s look at what Gartner thinks about the situation in Business Intelligence and Analytics.
Observations: Should we expect a new upward trend of BI in the upcoming 2 years? As Business Intelligence solutions are getting more and more accessible, that might very well be the case.
- Dashboards (The core of any BI solution) have entered the Plateau of Productivity
- Interactive visualization has just entered the slope of enlightenment
There is a problem though… Business Intelligence tools have traditionally been very IT-heavy. Setting up reporting views have required server installations and expert analysts to make sense of the data.
The game changers are still in the innovation trigger phase.
Prescriptive analytics will be huge. When the data analysis is intelligent enough to make relevant recommendations, meaning the removal of need of specialist expertise, intermediaries and additional costs for analysts and consultants.
Big Data is also still in the innovation trigger field, despite the enormous media hype around the theme in recent years. Some are saying that the whole term “Big Data” is just a fading definition, and will be replaced by something else a bit more descriptive.
Big Data in itself is no longer such a big challenge. Google Big Query, Amazon Redshift, Hadoop, and other cloud services are bridging the gap and offering relatively cheap processing power to query massive data sets. The big problem is access to data. How many companies actually have access to useful data in such quantities, that they could draw real world benefit from it?
The end goal for most companies with collected data, is to enable customer- and user profiling and targeting for various purposes. The analytical tools are starting to be readily available for the task, but still require structured data in fairly straight forward machine readable formats.
Many are harvesting and stocking data at the moment, but there is significant political and privacy advocate group pressure to regulate data collection.
Although some companies are successfully using big data to boost their businesses, a lot of companies are struggling because of the quality of their data.
That might change when Search Based Data Discovery Tools evolve, and remove some of the limitations that unstructured data puts on analysis and prediction.
In the case of mass customization, predictive analysis is used by companies that try to find ways to automate tasks that previously required resource heavy sales and customer support teams. Even then… prediction based on past behavior might not always be a good indicator of future events.
On one side of the playing field, companies are trying to boost productivity of the old business models. On the other, new technologies are making leaps to make those improvements obsolete with a new shift from push to pull.
Whatever the future may be, the landscape of big data and analytics will only grow in the coming years.
Disclaimer: I am actively working for Bime Analytics to be one of the winners.