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Data Platform-as-a-Service (dPaaS) represents a new approach to efficiently blend people, processes and technologies. A customizable dPaaS with unified integration and data management enables organizations to harness the value of their data assets to improve decision outcomes and operating performance.
dPaaS provides enterprise-class scalability enabling users to work with rapidly-growing and increasingly complex data sets, including big data. Users have the flexibility to deploy any analytics tool on top of the platform to facilitate analyses in different environments and scenarios. The platform provides data stewards full transparency and control over data to ensure adherence with GRC (governance, regulatory, compliance) programs.
dPaaS allows enterprises to reduce the burden of maintenance requirements for hardware and software. Companies can shift IT budgets from capex to more predictable opex, while freeing up IT teams to work on higher-return projects using market-leading technologies in collaboration with business units.
More Data Exacerbates Bottlenecks
Integration and analytics are the top two technologies companies are investing in as they seek to integrate big data with traditional data in their business intelligence (BI) and analytics platforms. Their goal is to make better decisions faster to build customer loyalty, strengthen competitiveness and achieve return on investment (ROI) and risk management objectives.
Yet Tech-Tonics estimates that 75%-80% of BI project time and spending is consumed by preparing data for analysis. Data integration projects alone account for approximately 25% of IT budgets. This is the result of increased cloud and mobile apps, rapid growth of new data sources and formats, fragmentation caused by departmental data silos and ongoing merger and acquisition activity.
Despite this investment, 83% of data integration projects fail to meet ROI expectations. Many projects still get bogged down by a high degree of manual coding that is inefficient and often not documented. IT teams are backlogged with data integration work, including updating and fixing older projects.
The cost of bad data is high. Operational inefficiency, transaction losses, fines for non-compliance and lawsuits stemming from bad data that drive erroneous assumptions and models cost U.S. companies $600 billion a year.
The sheer volume and complexity of big data only exacerbates the workflow bottlenecks caused by a lack of decision-ready data. Traditional practices for discovering, integrating, managing and governing data have become overburdened or incapable of handling semi-structured or unstructured data. But despite advances in technologies to collect, store, process and analyze data, most end-users still struggle to locate the data they need when they need it to allow for more accurate, efficient and timely models and decision-making.
Data Platform-as-a-Service: A New Approach to Better Decision Outcomes
Companies implementing dPaaS can significantly improve success rates and return on data assets (RDA) by allowing enterprises to expand the scope of integration projects and manage larger data sets more efficiently to better leverage their BI investments.
dPaaS promotes a data first strategy for BI initiatives. Data is integrated from multiple sources, harmonized in a consistent state and then managed to end-user requirements. The ability to quickly and easily connect to applications and data sources is critical in handling big data, as well as rapidly integrating new applications. The context end-users gain shortens the path to finding patterns and relationships during data analysis, resulting in faster and more actionable insights.
dPaaS helps streamline the complexity of matching, cleaning and preparing all data for analysis. Data cleansing tools and a specialized matching engine helps find and fix data quality issues. A registry of all corporate data sources maps data to its location, applications and owners. This consistent set of master data – or “golden record” – provides a common point of reference. Versions and hierarchies are maintained to ensure that data remains in sync at all times.
A single, consistent set of data policies and processes also helps overcome the challenges posed by data silos across the organization. dPaaS facilitates integrating big data with traditional enterprise sources, such as transactional and operational databases, data warehouses, CRM, SCM and ERP systems. Interactions between applications that use the data, as well as underlying systems can be monitored to alert for performance issues and user experience. dPaaS also ensures security best practices with stringent policy, procedure and process controls.
A company’s data assets only have value when they can be accessed and used appropriately by employees and customers, and the underlying business processes that support them. A strong data governance program supported by dPaaS can serve as the foundation for corporate data strategy. Reducing costs, enhancing IT productivity and enabling faster time-to-value through improved decision-making all make dPaaS a compelling value proposition for enterprises.