Critical Success Factors for Data Accuracy Platforms
The data accuracy market is currently undergoing a paradigm shift from complex, monolithic, on-premise solutions to nimble, lightweight, cloud-first solutions. As the production of data accelerates, the costs associated with maintaining bad data will grow exponentially and companies will no longer have the luxury of putting data quality concerns on the shelf to be dealt with “tomorrow.”
When analyzing major critical success factors for data accuracy platforms in this rapidly evolving market, four are critically important to evaluate in every buyer’s journey. Simply put, these are: Installation, Business Adoption, Return on Investment and BI and Analytics.
Installation
When executing against corporate data strategies, it is imperative to show measured progress quickly. Complex installations that require cross-functional technical teams and invasive changes to your infrastructure will prevent data governance leaders from demonstrating tangible results within a reasonable time frame. That is why it is critical that next-gen data accuracy platforms be easy to install.
Business Adoption & Use
Many of the data accuracy solutions available on the market today are packed with so many complicated configuration options and features that they require extensive technical training in order to be used properly. When the barrier to adoption and use is so high, showing results fast is nearly impossible. That is why it is critical that data accuracy solutions be easy to adopt and use.
Return on Investment
The ability to demonstrate ROI quickly is a critical enabler for securing executive buy-in and garnering organizational support for an enterprise data governance program. In addition to being easy to install, adopt, and use, next-gen data accuracy solutions must also make it easy to track progress against your enterprise data governance goals.
Business Intelligence & Analytics
At the end of the day, a data accuracy program will be judged on the extent to which it can enable powerful analytics capabilities across the organization. Having clean data is one thing. Leveraging that data to gain competitive advantage through actionable insights is another. Data accuracy platforms must be capable of preparing data for processing by best-in-class machine learning and data analytics engines.
Look for solutions that offer data profiling, data enrichment and master data management tools and that can aggregate and cleanse data across highly disparate data sources and organize it for consumption by analytics engines both inside and outside the data warehouse.
