Real-Time Data: hyper-personalization to make decisions
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The business ecosystem is moving towards a competitive, digital and connected paradigm, which creates the need to make intelligent decisions based on data quickly and efficiently. Advances in Big Data and Data Analytics have reduced the time needed to process them with guarantees. The challenge is to streamline times, to get as close as possible to obtaining and analyzing results in real-time.
The evolution of Big Data and advances in Data Science have made data one of the most valuable assets of a company. But, today, in a context marked by the need for immediacy and hyper-personalized insights, it is worth reflecting on what type of data generates a greater impact on decision-making. In this sense, the strategy must focus on defining a data architecture capable of obtaining and analyzing it at critical moments and, of course, processing it in real time.
The capital importance of the Real-Time Data approach
Since 2020, as a result of the exponential growth of online activity in all types of businesses and organizations, there has been a huge increase in the volume and speed with which data is recorded online by consumers. Companies have found a huge opportunity to capitalize on all that information generated by users. But on the other hand, this information is generated faster and faster, and also loses value faster, so it is necessary to process it at the same rate at which it emerges.
The challenge is to streamline times, to get as close as possible to obtaining and analyzing results in real-time
Due to this trend, being able to process data with agility and obtain actionable decisions through it has become an increasingly important challenge for organizations. This capacity allows us to gain a great competitive advantage since the most advanced companies use this information to offer products and services that are increasingly personalized and contextualized.
Adopting tools for real-time data processing allows you to obtain an on-demand view of what is happening at the present moment, without time gaps. The teams can thus access actionable information on consumer behavior, in the context in which it occurs at all times, and in relation to the critical processes of each business in question, whether they are sales, marketing, operational processes. or financial, among others.
Data, a matter of precision
Although it may seem obvious, the importance of having data information immediately must be highlighted. Being captured at the source, processed and displayed in real time, they allow an organization to use the information very quickly. Customers expect companies to make decisions based on real and accurate data, so if companies report on outdated information, decisions and forecasts can be negatively affected. Improving access to timely, real-time data enables more effective and efficient business decisions.
Also, having access to real-time data can uncover certain problems before it’s too late to fix them. Obtaining them precisely at key moments can help any area of the organization to improve its processes and establish data-driven models.
But that’s not all, they also allow companies to see what their customers prefer or what problems they face at any given time. In addition, real-time data can help anticipate future needs, making operational processes more efficient and freeing employees from routine workload.
The benefits of Real-Time Data
In this sense, the main benefits are to improve reaction time and reduce risks, promote the democratization of data, obtain a 360º view of customers and optimize business processes.
Although organizations record more and more volume of data and the variety of devices and channels through which they are generated is also greater, there are still a series of barriers that make it difficult to access them in real time. It is necessary to solve all those that may prevent the proper functioning of a data analytics program.
In the first place, it is necessary to understand what are the objectives and the concrete benefits that the company can obtain through the processing of data in real time; use cases depend on the sector and the specific activity. It is also important to know the uses that the competition takes into account and what our differential value proposal will be.
Depending on these purposes, whether they are to improve marketing strategies, improvement of CX, study of consumer habits, evaluation of risk profiles, monitoring of machinery performance, predictive modeling, optimization of operating processes, etc., Different data sources will be identified and different approaches can be applied.
By Gema Ruiz Díaz-Mariblanca, Head of Digital Data & AI at Softtek EMEA