Moving to the cloud is often considered a way to obtain valuable information from our data. However, first we must plan and dedicate time How the cloud can help you understand your data
Organizations can be drawn to the cloud by the lure of gaining new information and insights from the data they own. They may learn to retain more successful customers or have a better understanding of profits or costs.
How the cloud can help you understand your data
The benefits of Artificial Intelligence and Machine Learning (AI and ML) implemented in the cloud can work wonders. However, They need “food.” They need raw data to work on and they need it to be in top condition. For many organizations, preparing this “meal” requires time, effort, and attention to detail.
Clear data strategies and priorities
There is no doubt that AI and ML can provide information that organizations they cannot obtain in any other way. Data processing can, if done right, help organizations increase productivity and profits. However, optimal success does not come overnight.. As Yann Lepant, MD of Accenture Technology He says, “It’s easy to be tempted by the vast set of new cloud-based data and AI technologies, as it’s an exciting new playing field to venture into. However, it is also a place where it is easy to consume, waste and get lost ”.
A key factor in avoiding cheating is have clear priorities and a solid data strategy. Lepant says that having them like this will mean that “each initiative is carried out with a purpose and a result, contributing to a maturation journey through the cloud ”.
Ingrid Vershuren, head of data strategy at Dow Jones, whose global news database grows by a million articles a day, says the following. “It doesn’t matter how good the technology is if the data that powers it is of poor quality. The first and most important step in getting the most out of our data is to make sure that we are using the correct data. Also that they are structured in a way that they answer the questions we want to ask.“.
Cleanliness and refinement
Another couple of related tasks that may need to be done before AI and ML can work their magic they are cleaning old data and refining new data that is collected.
Part of the cleanup task will involve getting data out of the silos. This can be complicated, but it is worth the effort, and organizations undertaking this task can take comfort in the fact that they are not alone. Often times, data silos are the result of historical practice and how things have grown organically over time. It takes a concerted effort to undo in a relatively short time what was accumulated over a much longer period. But as Lepant points out, there really are shortcuts. “From getting a 360-degree view of the customer, to end-to-end supply chain management, to fraud prevention and smart forecasting, the list of business results that enables the elimination of data silos is endless. “, He says.
Cleaning data is another task that can take time, but eThe effort is definitely worth it in the long run. Vershuren provides some helpful tips to help keep your cleaning task focused. “Before you start cleaning data, you need to determine how this data will be used and what insights you need to generate. Ask yourself, what does perfection look like? From here, we must define the data fields thatand will be part of our set and the entry for each of these fields ”. Indeed, you have to work retrospectively from the results we want to obtain and then determine what data we need to obtain them.
Analysis and information finally arrive
With clean data, a clear data strategy in place, and perhaps some new data collection streams as well, an organization is finally in a position to start using AI and ML.
However, This is not the end of the story. To continue to gain insights over time, data must take its place, without silos, front and center. Or, as Lepant says, “Implement a program to update ways of working, culture, and data literacy to help the business become more data-driven and self-service.”