Your results depend on the quality of data in your data lake

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Data has a direct impact on business results. And the quality of data in your data lake determines the information available.
Today it is common to hear that data is the fundamental asset of organizations. In these times of analytics, data is the raw material that will allow us to obtain knowledge about customers and the business . And this latter in turn will enable us to offer differential experiences to users or consumers, as well as to better guide business decisions.


Today, data has become a vital component of interactions with customers , partners and other key stakeholders. Data can now have a direct impact on business results – facilitating increased revenue, reduced costs and minimized risk .

But does all data have this potential ? The truth is that it does not: when data is of poor quality, its value deflates. And what makes it recover its potential value? Adequate management ( data management ) and data governance are what ultimately ensure that data will be available and of good quality.

Data Lake: Overcoming the Limitations of Data Warehouse

Quantity and quality of data
These days, data is an abundant commodity . In fact , companies today are often overwhelmed by the huge amounts of data they need to harness and manage. However , the quality of this data still overseas chinese in uk data much to be desired. One study found that only 3% of data quality scores assessed met the "acceptable" criteria.

Poor data quality undermines productivity and wastes valuable hours. Research conducted by Forrester showed that nearly a third of analysts spent more than 40% of their time validating analytics data before it could be used. And another survey found that an average of 30% of total company time was spent on non -value-added tasks due to poor data quality and availability .


5 key aspects for good data governance
The latest in data storage is intelligent data lakes in the cloud . Well managed , they allow you to identify and exploit data- driven insights . Without proper management , however, they can become a “ data swamp .” So what companies really need is to optimize these repositories through consistent data governance practices . They must develop practices and processes “ that ensure the quality , availability, ease of use, integrity, and security of enterprise data assets , both on-premises and in the cloud . ”

Governing data involves, among other things, collecting and managing metadata or "data about data." But good data governance must also ensure five key aspects :

Make data relevant to people across the business (from end users to data analysts).

Keep workflows and  processes simple .

That there is flexibility to address the different management requirements depending on the type of data stored in the data repositories (for example in data lakes in the Cloud ) .

That the data is reliable.

That AI-based automation can be incorporated to streamline manual processes .






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Agile and profitable businesses with cloud-native data management





Today, organisations need to ensure that their data strategy supports the business. Poor data quality has a real financial impact and there is no longer room for managing this issue haphazardly. This would expose companies to poor performance , non - compliance risks and loss of reputation .