Analytics is a red hot topic at the moment. Explosion of digital data everywhere and programmability of almost anything have resulted to unforeseen interest in analysis methods and analytics-based decision making.
Artificial intelligence alias smart algorithms are the cream on the top of the analysis cake, be it the case of advertising in order to persuade customers to purchase and users to participate, or measuring business performance.
One could claim that through analytics we are becoming free from one-directional value chains and distinct supplier-intermediary-customer roles, because the search, (re-)production and delivery expenses of all-digital offerings are in practice zero. The only cost to cover is our trust and reputation. We can focus on analyzing and making use of our customer-related business opportunities.
To do or to analyze
In the good old early days of digitalization we researchers were often warned from not to become paralyzed by analyses, but to go on and develop, demonstrate and productize something new and meaningful. All types of technologies and solutions were shipped to the market pretty boldly. New things were requested and developed closely together with industrial partners. Later on, some of them were more thoroughly analyzed and modified to fit for evolving purposes, but many also gave room for competing alternatives. Innovation-based business as usual?
The point to make is that many of at that time advanced products and services did not scale up, because they became prisoners of their original ideas, technologies and contexts in which they were used. Could this be avoided by more timely and effective analysis?
What to do to analyze
First, we clearly need better means especially for predictive analytics, because the speed of businesses is quite dramatically increasing. Analysis for the future must be accompanied with decision making, be it dealing with this afternoon’s modified earning logic or the future seventh generation’s wireless offerings. A plethora of methods and tools is available, but few are used for actual business management. In other words, we need quantified business strategies and models in addition to quantified ourselves.
Big data are often mentioned in connection with analysis, but MyData and other small, heterogeneous, fragmented and incomplete data may prevail especially when it comes to new and growing business opportunities and emerging markets. Moreover, the present digital platforms may hide the fact that the most meaningful data are neither balanced nor readily aggregated for use. We must be capable of doing that for our business.
Required – skills to analyze
It is necessary and even urgent to start training business analysts, or preferably experts with analytics and data and business management capabilities. In the 2019 ranking of the world’s best business analytics education programs altogether 74 programs were recently listed. MIT’s Master of Business Analytics was again ranked the best, but Imperial College’s (London, UK) Master of Science in Business Analytics was the second. As many as 31 programs were European, but none of them was from Scandinavia.
It is time to avoid paralysis by not having analysis skills around.
Last updated: 1.11.2019