1. Artificial Intelligence (AI) is back on the agenda
An old term from 1960 has been revived. It’s developing very quickly, powered by all applications of major data masters such as Google, Uber, Amazon, LinkedIn, Instagram, and Facebook. The algorithms were there, but the large amounts of data were missing. Artificial Intelligence never managed to get rid of this. And forecasts were not always as reliable. Now, since more and more pictures, videos, blogs, posts, and sensor data gets available, AI acquires its actual added value.
How does AI run with computers? Are they able to comprehend depth based on photos? Are we able to program and feed AI so that they are unbiased? Shouldn’t we avoid deeper cooperation with them? Wouldn’t we miss the essence of our (working) existence? Aren’t we taking irresponsible risks?
2. Big Data and IoT experience a dramatic growth
The public space, means of transport and humanity, can become completely dependent on sensors and cameras, in theory. This space is immense. It includes everything from door to door. And everything in between, underneath, and above.
There are numerous possibilities, to the extent of the applicable privacy laws, to apply Big Data and Internet of Things (IoT). Think of cameras installed on cars of 10% of the population that make continuous footage of the surroundings while driving. Or small drones buzzing around with a camera.
Based on the collected images, a Big Data processing center can detect, for example, sick trees or storm damage. Or forecast when the first leaves will fall. Another small step for Big Data is to determine the capacity planning and optimal routes of the blowers, sweepers, and the tree doctors. A totally different way of organizing than usual.
This allows for a completely different business model. The administration is going to be transformed to the left or to the right. Big Data moves the government from being reactive to being proactive, from annual evaluation based on surveys to real-time feedback obtained from the field. Digital disruption enters the public sector.
3. Mobile BI will not suppress regular BI
A poll from 2012 asked visitors of our website or mobile BI whether BI on laptops and PCs would be suppressed in the near future. More than 75% of the 2,000 respondents answered positively. They proved to be wrong. This turned out to be completely the other way around, despite all the investments that the Business Intelligence vendors made to make mobile BI very user-friendly. Five years later, the majority of BI users still use their laptops or PCs. Or they do not use BI at all.
The idea behind mobile BI was that it should simplify the market. After all, the users of mobile devices substantially outnumber PC users. However, that hasn’t happened. A study by Howard Dresner, also sponsored by the company Tableau, shows a declining trend in the interest in mobile BI. The expectations should not be raised too high. Only 19.2% is really convinced that the organization is ready for mobile BI. The current level of using mobile BI remains modest. Within three quarters of companies, 80% of employees do not use mobile BI.
The Dresner study reveals that the main target group of mobile BI is the management. Intelligent organizations work with self-managing teams that use data analytics daily for control. There is no longer a place for a traditional manager. Perhaps the point here is that the adoption of mobile BI has not taken off. Mobile BI for the management may be asking for problems. Greater chances of success for continuous improvement: use the information to drive directly to or to intervene in the workplace. Especially in places where PCs and laptops are not convenient or possible.
4. Decision makers become unnecessary
In the US, the computer calculates the recurrence rate of heavy crimes. Then, the decision is made whether a convicted criminal can walk free prematurely or should remain behind bars. There is no more need for a judge. This results in major consequences for the convicted: they are either granted or deprived of his freedom. Machine learning makes this possible.
Analytics and predictive models need to be self-learning. And evidence must be supplied so that the models can make substantially better decisions than people. Machine learning, thus, will make deciders in many areas unnecessary. This has social and economic importance. Acceptance is only a matter of time. As soon as an organization is successful in machine learning, the rest will either want to follow the example or will ultimately throw it down.
5. Data Agility distinguishes winners from losers
Only possessing and playing with data is not enough. Nowadays, with change is piling on change, it has become necessary to make decisions faster than ever. And ‘they better be good!’ We’re technically capable of doing everything together.
But isn’t the timely and precise use of the available data the essence? Isn’t rooting Business Analytics deeply in the primary process a big advantage? As a result, companies implement reflex intelligence: the data is manually or automatically used to make (operational) decisions as soon as possible. Data collection and application are very close to each other. The feedback of the action and what happens becomes clear quickly.
Heyday for agile organizations
Where traditional organizations sporadically make a decision, frequently based on spreadsheets or gut feelings, intelligent organizations make continuous computer-powered decisions. They fine-tune everything based on data.
But Data Analytics does not make the difference by itself. Obviously, this has already become the top priority in research into the pitfalls and success factors of Business Intelligence. Analytics technology makes the BI process efficient, with organizational skills like agility and alignment making good on the added value. It is a heyday for both agile employees and organizations.