10 Traits of Modern Data-Driven Applications
It takes far more than data to have a credible data-driven application that's compelling to a wide range of business users.
- By Philip Russom, Ph.D.
- April 21, 2015
The term data driven is bandied about nowadays by many people in many contexts. For example, more business managers need to make fact-based decisions and run a business "by the numbers" based on reliable data and metrics that are integrated into business processes. As new data sources come online -- from third-party sources, social media, and even machinery, devices, and smartphone apps -- business teams need an efficient and effective way to relate new data and leverage it to achieve organizational goals.
For managers, processes, and organizations to be truly data driven requires going beyond the hype in the press and siloed master data management processes. The success of consumer data-driven applications -- such as LinkedIn and Facebook -- has shown that frontline users can easily access, improve, analyze, and share their data in a seamless, unified environment. The problem with traditional process-driven applications such as legacy CRM and ERP systems is that they place the burden of capturing analytical insight, making decisions, and taking action on the business user. To correct that problem and offer other benefits, modern enterprise data-driven applications (DDAs) predict and prescribe what to do next with reliable data, relevant insights, and recommended actions.
In addition, a data-driven application:
- Automates business processes, problems, and opportunities that can only be driven forward, solved, and leveraged via ample volumes of diverse data.
- Operates on diverse data from many multichannel data sources, typically through data-as-a-service (DaaS), both inside and outside the enterprise.
- Makes information universally available across the organization within a single application. The ideal DDA is an out-of-the-box contextual application, with data included, that customizes each view with accurate, timely, and relevant information based on the role and goal of the user.
- Seamlessly combines operational and analytic capabilities, breaking down traditional silos. Analytic insights are linked directly to the execution of specific business tasks.
- Automatically generates master data, metadata, models, schema, and graphs as the user searches, queries, and collects data. This new, modern approach to data management keeps business users working without burdening IT. An enterprise DDA makes data reliable, instantly accessible, easily audited, and relevant to business objectives.
- Continuously correlates data entities (e.g., customers, partners, products, locations) into 360-degree views that can be pivoted from any perspective. The DDA enables business users to make decisions and take action based on complete and fresh data, with predictive insights made evident by revealing relationships among multiple entities, facts, and data points.
- Depends on consumer-class ease of use, as seen in LinkedIn and Facebook, requiring little or no user training.
- Supports real-time operation, collaboration with colleagues, and broad flexibility and scalability.
- Closes the loop by providing recommended actions and aligns the results of those actions to continuously improve the next set of recommendations.
- Reveals business value for unquestionable ROI by continuously measuring outcomes to show metrics such as cost savings, which customers are the most profitable, and how your business decisions are yielding results.
For more information about modern data-driven applications (DDAs), their use cases, and how to prepare for their use, read the 2015 TDWI Checklist Report: Bringing Modern Data-Driven Applications to the Enterprise, available for download by TDWI members, here.
Philip Russom, Ph.D., is senior director of TDWI Research for data management and is a well-known figure in data warehousing, integration, and quality, having published over 600 research reports, magazine articles, opinion columns, and speeches over a 20-year period. Before joining TDWI in 2005, Russom was an industry analyst covering data management at Forrester Research and Giga Information Group. He also ran his own business as an independent industry analyst and consultant, was a contributing editor with leading IT magazines, and a product manager at database vendors. His Ph.D. is from Yale. You can reach him by email (firstname.lastname@example.org), on Twitter (twitter.com/prussom), and on LinkedIn (linkedin.com/in/philiprussom).