Find out how you can respond to the needs of your customers by implementing real-time analytics
In any business, at the operations level, we are used to having access to real-time reports from the applications we work with on a daily basis. These reports are generally very detailed and work very well when we want to see the transactions that took place in a limited period of time and perhaps on certain specific data, however, they are not suitable for detecting behaviors or trends, nor for making decisions that can drastically change the course of the results.
When we want to have greater visibility or when the data generated is of high volume in a very short time, generating a report is impractical or gives us very little idea of whether they are really important events or not.
Considering that today, the generation of new data is extremely fast, not only through human interaction, but to a large extent generated by systems or machines, it is very important to evaluate other ways of approaching its use.
Real-time analytics offers us new possibilities.
Let's consider the following scenarios...
A digital marketing manager who needs to be able to adjust his campaigns, the information deployed to social networks or the content of his different landing pages depending on the interactions achieved in each channel and with each audience during the day.
A factory maintenance manager who needs to predict, based on the behavior of each piece of equipment on an ongoing basis, when to stop lines and change parts without affecting production and minimizing cost.
An ecommerce manager who needs to detect when a customer did not find a product and based on that, send them a personalized promotion or a suitable substitute based on a market basket and measure the impact that this is having minute by minute on their revenue to make adjustments on the spot.
The same marketing manager needs to detect when there is a trend in networks that can trigger a massive purchase on your site and needs to anticipate with promotions or promotions change, or simply to detect trends that are presented during a day and based on this to change strategies at the time.
An infrastructure manager who has to foresee and anticipate, with respect to the amount of transactions that are being generated during the day and the load on his servers, whether or not it is necessary to enable one more node or one more cluster, considering the cost involved, whether or not to do so.
These come to mind, but there are many, many more use cases, real today, that we could imagine and discuss in this space.
The problem is that generally all these scenarios have as a common denominator that the information that is generated is huge and changes very quickly and analyzing one by one or in small groups the transactions that comprise it is practically impossible or, as I mentioned before, will not tell us much.
This is where real-time analytics makes sense.
What would happen if it were possible to integrate, clean, consolidate this information and generate alerts and indicators practically in real time, ingesting and analyzing all the data as it is generated and being able to detect trends that allow us to make decisions on the spot, aligning the entire process on the basis of real behaviors based on data and statistics.
This is possible today with a modern cloud data architecture and it can be implemented with different technologies depending on the situation and need of each company and each business case.
Bearing this in mind, we created our 5 recommendations for considering a real-time analytics solution:
Choose the business cases.
Determine how quickly you can implement actions based on the data you can get minute-by-minute and on the detection of trends and correlations between them.
Establish the value generated to the business if you manage to adjust your strategy during the day.
Choose a modern cloud analytics platform that is compatible with your current architecture.
Implement a pilot project to test the results and thereby justify an organizational project.
Why real-time analytics?
Because it is not only about data, but also about behaviors and trends that we can detect today and every day.
Because historical data are no longer enough.
Because users are faster and we want real time answers.
Because whoever dominates today, will dominate their competition. The speed has been the key for a long time, but to move with data is to do it in an intelligent way.
Because it is necessary to take advantage of the available data to generate competitive advantages in a matter of minutes and not hours or days.
In the following days, we will be analyzing real-time analytics architectures and business cases that can inspire you to implement change in your own Company. I recommend you to check out our posts!