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Importance of data quality can never be underestimated

Richard Jones Archive

When it comes to data quality and its importance, it is impossible for companies to underestimate how critical it is to their success. Every IT project, and most business processes today, rely on the functionality of solution design - i.e. how well that design works with the data a company uses. If data quality and its integration aren't included in every project and process, it will have to be retrofitted into the solution, causing delays and causing other potential problems down the road, according to ITWeb.

Beyond the inclusion of data, many businesses need to look toward improving the quality of their data as it is used. So many different aspects of operational success, including the technology being adopted today like CRM, MDM, ERP and business intelligence tools, as well as data warehousing, governance and migrations solutions, rely on high-quality data to deliver results. By cutting these systems short, a business isn't just hurting an individual area of operations, but overall profits.

The trick to avoiding these issues is to implement the proper solutions for data collection in the first place. Email verification and related software will boost data quality from the get go, initializing the entire process and kickstarting other efforts to boost results.

Beyond business intelligence and the specific areas of operations that rely on data so heavily, organizations rely on data for customer service, marketing and even IT. Going further than enterprise operations, schools, hospitals and government agencies all rely on data in similar ways, making these efforts essential for them as well.

Focus on usage
According to the news source, one key way to boost the quality of data is to get the end users involved in the process early on. These are the people that actually have to use the information, and will have a stronger understanding which improvements are necessary. Data quality supports their ability to do their job, and any new systems or supporting software will affect their workflow on a broad scale.

To ensure that data quality meets operational needs, companies should invest in the right tools to focus on this area throughout every process and deliver the proper integration with applications and services at every level of operations. Data has to be collected, analyzed, stored and assessed, and any business that doesn't make this its first priority will fall short in all other areas.

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