Traditional enterprise systems offer basic reporting capabilities that don’t meet expectations when it comes to exploratory data analysis and visualization, which has been the province of traditional BI tools. But a growing number of business leaders are demanding timely, visual and interactive access to their data. Additionally, they are expecting pre-packaged analytics to be seamlessly integrated into their business systems.
Despite allocating large budgets to implement and manage the enterprise systems, many of them still face the same challenges as they struggle to deliver compelling analytics solutions that meet the information needs of business users. This can be a challenge as any implementation typically accompanies increased expectations of additional business value that can be realized as a result of the project.
In the past, analytics was custom built, requiring a lot of effort to develop. As a result, add-on BI tools were really only affordable for large companies. That has changed over the past few years, as analytics has become more pervasive and affordable. It is expected that ERP systems will provide actionable insights in real-time that can be used to drive business decisions, not just raw data. Business leaders now want financial analytics data in their business system to provide deeper insights into products, processes, customers and more.
Benefits of Pre-Packaged Analytics
Pre-packaged analytics capabilities can not only optimize business operations, but also help to unlock patterns in data and business value. Pre-packaged analytics can help you extract more value from your business data by enabling improved master data management and data quality, business process management and optimization, improved human capital and asset management, and better decision making.
1. Unified View
Pre-packaged analytics enable organizations to deploy analytics on a small scale for a single department and then expand to support other departments using the same model and platform, delivering a consistent view of enterprise information. Pre-packaged analytics can help organizations create an analytics environment on a single, integrated platform across all departments.
However, most organizations, in a rush to meet business needs, build distinct data warehouses for each department, none of which use the same data model or dimensions. This approach usually creates data siloes that must eventually be replaced or consolidated into a single enterprise data warehouse at great expense.
2. Predefined Content
Pre-packaged analytics embed best practices for analyzing data in each domain. This includes predefined metrics, reports, dashboard templates and guided analytics that customers can use to track and analyze the performance of business functions. These metrics and reports are created leveraging the domain knowledge of experts who have implemented analytics solutions for multiple clients and understand the right way to view and analyze information in specific functional areas. Customers can use the default reports and dashboards or tailor them to suit specific requirements.
3.Transactional Updates
Pre-packaged analytics helps close the loop between analytical and operational applications using guided analytics. These are pieces of conditional logic that guide users through a series of reports or actions to address a business issue or anomaly in the data. Some guided analytics instruct users to drill down to related reports, while others trigger alerts or recommend various actions based on values in the data. Vendors that own both operational and analytical packages can tightly integrate the applications with closed-loop workflows, adding value beyond the standalone package.
4. Integrated Data Model
Pre-packaged analytics solutions support an integrated data model that consists of a number of subject areas across multiple business functions and industries. Companies can install one subject area to get started and then add other subject areas later on. The integrated model ensures that new applications integrate with the old ones and data remains consistent across all reports and dashboards.
For example, an organization might install a CRM application and then add a sales performance module later on, or deploy in other functional areas, such as HR, finance or supply chain. The integrated data model ensures that all reports use consistent data and metrics.
5. Reduced Time and Costs
Pre-packaged analytics reduces the time required to complete the four major tasks involved in building an analytics solution – back-end ETL mapping, designing the data warehouse data model, defining metrics, reports and dashboards, and training and rolling out the solution. The biggest gains happen in the ETL mapping step. Pre-packaged analytics can reduce the time required to map source data to a target data model by half, and since creating ETL maps is the most labor-intensive part of building an analytics application, this results in significant savings in time and money.
In contrast, there are many additional tasks required to build and deploy a custom application, such as purchasing a variety of tools, installing and configuring those tools, creating a data model and ETL mappings, and creating reports and dashboards from scratch.
6. Faster Analytics
Pre-packaged analytics applications run a robust analytics platform that enables organizations to deliver reports and dashboards via the internet or mobile devices without additional programming or configuration. They incorporate new interactive visualizations, such as trellis charts, tree maps and bubble charts – that allow users to explore data quickly and publish their findings as interactive dashboards across the enterprise, depending on their level of access and permissions.
It’s evident that pre-packaged analytics can deliver a lot of benefits and should be considered during any analytics initiative. Every organization will need to evaluate different criteria while deciding whether or not to deploy pre-packaged analytics. The best solutions are built using industry best practices and the latest technologies, accelerating time to value and minimizing the risk of a failed initiative. If a large chunk of your operational data resides in an ERP system, you should evaluate the value of pre-packaged analytics for ERP.