To implement predictive analytics systems based on artificial intelligence, such as those developed by Alaya, it is essential to have adequate data. For this, there is data governance. Francisco Guiñez, President of DAMA Chile Chapter and CEO of Data Management Consulting (DMC), talks about this topic.
Today, data is a strategic asset in companies and organizations. They are used for analysis, prediction and decision making. But to carry out data-based management, it is necessary that they be updated, standardized and reliable, explains the Latin American consultancy NAE.
Inadequate data, instead of helping, can cause losses. According to Gartner's Data Quality Market Survey, poor data quality was responsible for an average loss of $15 million per year as early as 2017.
data governance
In this context, data governance is essential to take advantage of them. Technologies such as artificial intelligence or Machine Learning completely depend on the quality of these and, with data governance, companies can turn them into a strategic asset.
DAMA (Data Management Association), an international data management association, defines data governance as the exercise of authority and control (planning, monitoring and execution) over the management of data assets.
"Data governance arises as a need after companies discovered the opportunity that analytics represents as a tool to make more valuable decisions," says Francisco Guiñez, President of DAMA Chile Chapter and CEO of Data Management Consulting (DMC). Through analytics, “the value that the development of calculation models that effectively allow more valuable decisions to be made is demonstrated,” he adds.
The professional explains that the maturity and massification of certain technologies, such as the cloud, allowed analytical technologies to become more accessible and with this the interest of organizations in this subject was spread. When using the data, the companies noted that "in the state in which they are found in their source records, they are not useful as an input to feed the analytical models," says Guiñez.
On the other hand, data governance is also necessary to manage data as an asset within organizations. Data governance "installs the authority and control that allow data to be placed as an manageable resource and capable of being converted into the fuel that analytics needs," says the President of DAMA Chile.
Challenge
When establishing data governance programs, in addition to practical obstacles, the main challenge for organizations is cultural change. “The challenge is always the cultural barrier. People are used to operating this way. Breaking the inertia, taking people out of their comfort state, is like moving a mountain” says Guiñez.
It is also important that people understand and concretely see the value of data. “There are many people who talk about data driven or decisions based on data, but it is not understood how this materially takes shape. As much as one does a very good data governance, that they are very refined and that the analytics is atomic, if this does not materialize in more valuable decisions, it does not make sense ”adds the expert.
On the other hand, it indicates that making decisions based on data does not mean leaving people aside: "the process must be understood as the search for a balance between expert judgment and evidence when making a decision."