Data quality and forecasting leaders

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The evidence is compelling. Without reliable data , forecasting is not only a waste of time , it is also potentially harmful to business. Data quality is crucial to the success of an advanced analytics initiative, much more so when it comes to making predictions and forecasts that will compromise the future of the organization. Data quality makes the difference between some companies, leaders, and others, still on the road. forecast-2 Photo credits: The scenario: the importance of data quality The failure of predictions is particularly painful when it happens, given the constantly increasing need for forecasting . The dynamism of current markets implies: Reduced production cycles . Considerably shorter product life . Globalization of competition. To be able to face these circumstances, businesses need to rely on consistent information, ensuring data quality , to be able to guarantee: The flexibility of the company and its ability to adapt to change.


His vision of the future, essential to survive. The maturity of organizations, technological advances and the sophistication of tools allow us to experience truly exceptional forecasting capabilities. More precise measurements and better integration translate into renewed planning capabilities , with a high strategic component. Much more than data quality Successful business USA Student Phone Number List performance is closely linked to the application of best practices in forecasting , which involves observing data quality principles . Only in this way can we respond to transformations, be proactive, manage risk and recognize opportunities. But, when forecasts are not supported by a scenario where data quality is assured, implementing change can be dangerous. Tools and techniques alone are not enough to guarantee success in a business initiative. Other elements are needed, which can be reduced to three fundamental ones: 1. End : every forecast must respond to a need. Forecasts must be motivated by an argument, a reason, that justifies the effort invested in obtaining future information to facilitate the making of specific business decisions.



Data : business decisions must be supported by quality information; only then will you be able to acquire knowledge. To do this, you must have precise , relevant and consistent data; registered, acquired and maintained in the appropriate manner. 3. Vision : materialized in the commitment to manage the business prospectively . In a way of considering the evolution of the organization as a result of the strengthening of the forecasting process and the consistent use of forecasting methods as a key element for making strategic decisions . The difference between leaders and those who do not reach that level are these elements. Those with good forecasting capacity and in the best conditions of reliability and alignment do not reach a quarter of the companies that apply predictive analytics techniques. The turning point is marked by the level of data quality , an element that determines the level of effectiveness that the forecast can achieve. Related posts: Data mining models and the most used tools How far does data mining marketing allow you to go? The experts' data quality plan.