But how can organizations arm themselves with useful and usable data?
Organizations often find themselves juggling two complexities: speed and data trust. That’s why in this article I want to talk about these two factors.
We start by pointing out that we said this concept several times during our webinars on data integration, but I think they are never too many: Information must be timely to reduce time to market and be available for the digital transformation process.
According to a Forrester research, however, only 40% of CIOs provide the required speed: this translates into a lack of dynamism, whether it’s answers to provide to business teams or customer experience.
But not only that. There is also another factor to consider when you want to be a data driven company: people who use data must be able to trust them. What does it mean?
According to research by HBR, 47% of the data is created with critical errors that affect the operation and quality of work. Speed is essential, but it must not affect the correctness of information.
Those who aim only at speed usually find themselves having to satisfy their users, within time or budget. To do this, sometimes developers integrate the code manually in order to get short results. This approach may be wrong in the long run, as it would not scale and grow with the company.
On the other hand, those who aim only at trust, and therefore at quality, often put in place too stringent, strict and inflexible controls, which can hinder the fluidity necessary to compete in the modern economic context: organizations too slow, risk losing opportunities and staying behind.
But is it necessary to choose between these two paradigms?
No: you can combine speed and quality without compromises that can damage the company. The first step is to bring transparency and accessibility to all data assets everywhere: whether in business apps, in traditional data or in the cloud, you need to set up proper screening. In this way, we will obtain an overview of the origins and the incoming and outgoing flows, making the data identified, documented and reliable. Once the data is under control, it’s time to exploit its value and provide access to all stakeholders, involving them and avoiding bottlenecks.
The creation and maintenance of quality data requires a holistic management that supersedes the entire life cycle in order to ensure both compliance and the level of security, but without intercepting and restricting access and use of data excessively.