From the flood of data to the business intelligence revolution
Posted: Wed Jan 22, 2025 6:14 am
Learn how to work with data in such a way as to achieve a true business intelligence revolution
Having large amounts of data can be an important first step in supporting decision-making processes in companies . But if this torrent of big data cannot be transformed into real knowledge – that is, into data that adds value to the business – a great potential will be wasted.
The big problem for companies today is that they are often overwhelmed by huge amounts of data – much of which comes from new sources such as mobile devices, applications, wearables and the Internet of Things – that they are unable to take advantage of. Companies are “ drowning in their own data ” because until now they have had no way to classify it. This way, the data runs the risk of being incomplete, inconsistent and inaccurate, and also of lacking protection and not complying with internal controls or external regulations.
Many analysts are forced to chase fragmented data across their stockholder database to gain insights. This situation not only prevents key business intelligence insights from being discovered, but slows the adoption of innovations.
Despite the importance of data, only 14% of companies make it widely accessible to employees. And while the benefits of big data analytics are well-known, 63% of employees report that they are unable to gather insights in the time frame required. For most workers, it can take hours or even days to find the right data they need – only 3% of employees can get the data to answer their questions in seconds.
To achieve a 360-degree view of the business and ensure that the entire company has access to data, it is essential to have effective data management, as well as to develop a data-driven culture. This judicious management, supported by technological tools, is what allows us to move from the flood of data to the intelligence revolution.
Data management and governance for data-first companies
Democratization of data
To move forward in this direction, organizations must free data analysts from repetitive and time-consuming tasks. Data sources and tools must be self-service. This way, users can perform their analyses autonomously and freely and follow new lines of inquiry.
Having large amounts of data can be an important first step in supporting decision-making processes in companies . But if this torrent of big data cannot be transformed into real knowledge – that is, into data that adds value to the business – a great potential will be wasted.
The big problem for companies today is that they are often overwhelmed by huge amounts of data – much of which comes from new sources such as mobile devices, applications, wearables and the Internet of Things – that they are unable to take advantage of. Companies are “ drowning in their own data ” because until now they have had no way to classify it. This way, the data runs the risk of being incomplete, inconsistent and inaccurate, and also of lacking protection and not complying with internal controls or external regulations.
Many analysts are forced to chase fragmented data across their stockholder database to gain insights. This situation not only prevents key business intelligence insights from being discovered, but slows the adoption of innovations.
Despite the importance of data, only 14% of companies make it widely accessible to employees. And while the benefits of big data analytics are well-known, 63% of employees report that they are unable to gather insights in the time frame required. For most workers, it can take hours or even days to find the right data they need – only 3% of employees can get the data to answer their questions in seconds.
To achieve a 360-degree view of the business and ensure that the entire company has access to data, it is essential to have effective data management, as well as to develop a data-driven culture. This judicious management, supported by technological tools, is what allows us to move from the flood of data to the intelligence revolution.
Data management and governance for data-first companies
Democratization of data
To move forward in this direction, organizations must free data analysts from repetitive and time-consuming tasks. Data sources and tools must be self-service. This way, users can perform their analyses autonomously and freely and follow new lines of inquiry.