https://www.infogix.com/using-quality-data-retain-customers-banking/In business today, everything is about speed. How fast can a business deliver products and services to customers? How quickly can an organization develop analytical insights from enterprise data? The sooner a company can deliver, the greater the competitive advantage.
One industry that demonstrates the importance of speed is the banking sector. In the competitive world of financial services, companies are constantly looking for ways to make banking faster, better, and easier for customers. They want to use the power of advanced analytics to derive insights on customers and processes to understand past performance and predict the future for better business decisions, all in an effort to gain a step on the competition.
Why Speed Matters in the Banking Industry
Every large, multi-national bank has collected significant amounts of data. Different lines of business require access to data for various reasons. Marketing needs data to better understand banking behavior and how to connect with both current and potential customers. Sales teams require data to deliver personalized offers to individuals for products or services attuned to their needs. The legal team needs data for ongoing regulatory compliance efforts. And business leaders across the enterprise need data quickly to identify opportunities for innovation and operational improvement. Today’s on demand world requires real-time data analytics to compete.
Regardless of how a department leverages data, it is often the IT staff who’re tasked with preparing information for users. They’re the only ones with the technical expertise to harness legacy extract, transform and load (ETL) tools. Traditional ETL tools struggle to keep pace with the scale and demands of today’s fast-based environment. Their highly technical nature also means that only a few resources have the skills to effectively use them. When the IT staff receives numerous analysis requests from multiple departments on a daily basis, these requests pile up and are sometimes left unfulfilled for weeks. As new data requests accumulate, both IT and business frustrations build as the backlog increases.
Banks can no longer rely solely on yesterday’s ETL tools if they want to speed up the data preparation process and improve analytical insights. Financial institutions require new tools and technologies built for enterprise capabilities to handle the demands of big data and build a sustainable and streamlined approach for analysis. Tools that democratize data analysis by empowering more users will eliminate the reliance on IT for data preparation and analytics responsibilities and increase data utilization.
Modern Technologies and Procedures
Business users in areas such as marketing or sales rarely have the expertise required for typical tools used in data analysis.
Switching to modern, self-service, business-friendly data analytics technologies enable banks to provide greater transparency and flexibility for advanced analytics. With a comprehensive data intelligence platform, financial institutions can deliver data to business users immediately without relying on IT for time-consuming, manual data preparation.
Modern data intelligence platform offer banks the added benefit of integrating analytics with data quality and data governance efforts for a streamlined data management solution. The best tools feature intuitive, self-service options that enable business users with limited technical knowledge to take a front seat in data analysis, along with automated data quality monitoring that lets those users trust in their data and the insights it produces. The cumbersome and lengthy process of using slow ETL tools with rigid development cycles is replaced with an agile alternative. Users can access any data source easily, and simply acquire and parse that data in a fraction of the time. In addition, business users can easily collaborate across departments as they extract, prepare and analyze data from unrelated sources.
Complete transparency into the data preparation process, and the automation of data blending and cleansing to enable users to profile, aggregate, correlate and transform selected data, ensures that every department benefits from power of real-time analytics.
With unified technologies for preparing data, creating models, and embedding those predictive analytics within any business process, banks can prepare data immediately to uncover new analytical insights.
The Power of Fast Analytics in the Banking Industry
When it comes to choosing a bank, customers have plenty of options. One leading multi-national bank wanted to leverage data as a competitive advantage over their competitors, but long IT wait times for business user requests proved too big of an obstacle. Wait times were as much as six weeks, and by the time IT was able to fulfill requests for users, the information was outdated and no longer valuable for marketing or sales purposes.
By implementing a data intelligence platform with self-service data analytics, the bank automated the data preparation and analysis process, and reduced data request wait times to one day instead of more than a month. Eliminating time-consuming, manual processes and automating data preparation and analytics freed up $500,000 in resources for other projects within 18 months of implementation.
Data preparation and analytics have a major impact on a bank’s overall growth. With a modern approach, financial institutions empower business users across departments to divulge impactful knowledge into the customer experience, improve operational efficiencies, and provide a regulatory landscape to grow revenue and develop data-backed decisions.
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