The impact of quality of data in environments Business Intelligence

The impact of quality of data in environments Business Intelligence

The importance of data on projects Business Intelligence (BI) exceeds any consideration we can do about other key elements in business intelligence environments.

His role, more than important, it is absolutely essential: the calve on data of any successful BI project lies. However, although this remains so today the number of sources of origin that constantly bring new data bases or data warehouses corporate is so big and so huge volume and variety of these same data, It is necessary to consider other quantities and quality, in addition to the amount, for a project Business Intelligence offer the expected results: primarily a solid and reliable decision support .

Data Quality and Business Intelligence

Relatively little (if we consider the historical periods of time, and compared with the magnitude and importance acquired data for organizations) that the data are considered one of the most precious assets are there any corporation. And it’s even less time that separates us today from the time when the amount of data housed in the foundation and corporate systems was a priority, a must have appealed in any Business Intelligence project.

Currently, however, the proliferation of mobile devices, the rise of social networks and the resulting multiplicity of channels through which information flows have become the number and variety of data parameters insufficient by themselves.

A greater volume and variety of data, more difficulties and challenges facing the analysis and management of them, which we know are especially critical aspects of BI projects.

The insecurity, the unreliability, the poor integrity and dubious veracity presenting much of the data provided through the new channels strongly influence the success or failure of a corporate strategy designed under a business intelligence environment.

It is therefore not surprising that the concern for quality has become a matter of great significance, a rising and unquestionable magnitude tackling any project analysis and data management .

For data quality must understand the level of adjustment thereof to major corporate needs, essentially determined by the type of decisions to be taken in the field of leadership and management. In any case, the common denominator that provides the quality of corporate data can be presented as the sum of three basic factors : integrity (the data are complete, duplication is avoided and the necessary measures are available to prevent crossings and interference ), operation (the data are sufficiently homogeneous, solid and consistent to allow proper operation thereof) and accuracy (which have a proven reference value).

Technology leader in Data Quality solutions, IBM InfoSphere QualityStage, helps companies create and maintain consistent view of key entities such as customers, suppliers, locations and products. In this mode, investigate, clean and manage data. The solution provides quality data for Big Data environments your business intelligence, data warehousing, migration of applications and projects master data management.

No doubt the great impact it has had the appearance on stage of quality as new vector, as indispensable in magnitude to estimate the chances of success or failure of a project Business Intelligence.

One issue that we will review here pointed and sometimes succeeding, encouraging that, until then, the interested reader will turn to the guide (completely free) 10 keys to define your corporate data management strategy , a resource without doubt imperative to deepen in this issue.

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