With the development of various digital systems, the amount of available data has grown exponentially in the 2010s. Enterprise resource planning and customer management systems have become a part of everyday life, as have online stores, mobile devices, social media and, through the Internet of things, various sensor data. However, we have ended up in a situation where the utilisation of data has taken back seat to data collection. More and more data is being collected, but the real challenge is transforming that data into an understandable form so that it may be used for purposes such as improving services or enhancing business operations.
The answer to this problem is analytics, which has developed as the amount of collected data has grown. In the past, we used to focus on understanding what happened. The goal of analytics and data visualisation is to communicate why something happened, in a format that is easy to understand. As machine learning and artificial intelligence continue to develop, we may also begin figuring out what will happen in the future, as well as predictively influence events.
Efficient analytics makes data understandable
Although there are massive amounts of data available, the prevalent paradigm of “the more data, the better our understanding of things” should be challenged. The quality of the data also makes a difference. That is why, when building an analytics solution, it is good to focus from the very start on what data is being collected and how, as well as what portion of the data is relevant enough to preserve. Modern dashboards should easily communicate the information that is essential to decision-making. With an onslaught of information, it is easy to not be able to see the forest for the trees, making relevant data no longer easy to make out.
Efficient analytics highlights information that is relevant to the target group. Even with multiple information sources, the view of the results should be sufficiently clear and as unambiguous as possible.
When building analytics, it is also good to ensure that your goals for each time period are realistic. Analytics can be thought of as a process that evolves with data and the possibilities provided by artificial intelligence.
Revolutionising business with analytics
Analytics changes and revolutionises business models. In the media industry, for example, speed is everything: notable news must be reported as quickly as possible. However, the increased amount of information poses a challenge. The Internet is the largest archive of information ever created, and it is still constantly growing. Media companies themselves are creating and sharing more and more information every day: The Washington Post, for example, publishes about 1,000 articles per day. It is impossible for users to pick and read the important articles out of such a huge selection – let alone the approximately 2 million blog posts that are published online daily. Artificial intelligence can help process as many as millions of articles per day (in several different languages), thus giving faster access to relevant information.
Analytics also makes it possible to make current business models more efficient. For example, weather information and positioning data can be used to calculate the optimal moment to send a snow plough to remove snow off the roads. When the machine learning algorithm begins to also make adjustments based on feedback directly from the plough operators, we can quickly reach a situation where we can reap significant business benefits and improve the road users’ satisfaction with the road conditions.
In addition to benefiting businesses, analytics provides completely new possibilities for improving the everyday lives of regular people and consumers. Making cities smart makes it possible, for example, to reduce the amount of traffic jams, as people will have quicker access to information on where to find the closest parking space. Analytics also introduces more security into elderly care with various sensors and movement detectors. Analytics can also pave the way for more responsible consumer practices, because it directs users towards a more ecological use of power and water by telling them what their consumption is based on and how their consumption compares to, for example, the average consumption in the area.