Undertaking a process reengineering initiative without a business intelligence team introduces a significant risk to the project’s success. Business intelligence refers to technologies and practices for the collection, integration, analysis and presentation of business information; sometimes, it also refers to the information itself. The purpose of business intelligence is to support better business decision-making, especially during process improvement efforts.
The Need for Business Intelligence
Any process improvement program can be overwhelming to an organization if it is implemented with no regard to that organization’s change management history, culture and readiness. During process reengineering, organizations “soul search,” uncovering the traditions and inefficiencies that cause the company to stumble. Then the focus turns toward what the company should become and how it is going to get there – with the ultimate goal of emerging with less complicated and leaner processes. The starting point for any organized approach to process improvement aimed at enhancing customer experience and impacting profitability is ready access to both customer information and business process information. Business intelligence capabilities answer that need and move the process improvement initiative to a new level.
Remember, you can’t improve what you don’t measure. Because process engineering is all about a change in behavior, the measurement of that new behavior is absolutely central to its implementation. Measurements and metrics are key tools to understanding the behaviors, successes and failures of programs, projects and processes. Reports and reams of data are not metrics. Metrics are actionable, single measurements and help in making decisions about the future – not just quantitative reflections of the past Therefore, having an effective team around information decimation — business intelligence — that will monitor and analyze these metrics is absolutely critical.
Finding Key Performance Indicators
Business intelligence often uses key performance indicators (KPIs) to assess the present state of a business and to prescribe a course of action. Examples of KPIs are things such as lead conversion rate (in sales) and inventory turnover (in inventory management). Prior to the widespread adoption of computer and web applications, when information had to be manually inputted and calculated, performance data was often not available for weeks or months. Recently, however, banks have tried to make data available at shorter intervals and have reduced delays. The KPI methodology was further expanded with the introduction of Lean Six Sigma, which incorporated KPIs and root cause analysis into a single methodology.
The challenge is producing a strategy that facilitates the implementation of this future process information. How can a team identify KPIs when they might not even have applications to support new processes and behaviors? It is possible to do this by starting with business intelligence practices in a delivery methodology that meets the unique requirements of a process engineering effort. In order to provide a method of measuring the newly implemented processes at any reengineered organization, a mixed approach for meeting both short- and long-term needs is required. The following three steps help to integrate business intelligence into the process improvement approach from the beginning.
Step 1: User-interface Modeling
When the organization is doing its soul searching and planning, this is precisely when information resources should be at hand. The future vision of what will be measured, and its mock-up, are a source for more process improvement ideas. Additionally, the resources who are modeling the user interfaces are exposed to the overall vision, further enhancing their ability to interpret the usage scenarios. There are many specialized business intelligence tools that allow for modeling to be done in a long-term fashion. Hence, the mock-ups do not become just eye candy, but rather a usable tool that can later be plugged into live data. This gives the organization continuity between the mock-ups and live dashboards.
Step 2: Semi-centralized Sourcing and Modeling
A process reengineering effort presents a unique problem in the data world. The organization’s applications are often built or customized to support broken processes, so the data coming from these sources is not reliable for measuring the new, more efficient processes – at least not until they are changed.
To address this problem, business intelligence resources on process reengineering teams may implement an interim location where data can be centrally gathered for process measurement. This central information becomes the data source of the user interface for business intelligence data is produced in Step 1. The database model is a best-of-breed design produced by the business intelligence team for rapid information queries. This step allows the organization to store its critical information about performance, while the applications and infrastructure catch up to the new process rules.
Step 3: Application Connectivity
Once applications have been updated, a proven data model is in and dashboards are delivering from the data, the organization can now extract, transform and load (ETL) its application data.
In an ETL process, a repeatable load of data is mapped from one database location to another. In this case, the application data would be the source for extraction, and the database model that was designed in Step 2 is the target for the load. Once this continuous load is fully developed, the KPIs are completely connected. The result is a best-of-breed BI-based process improvement implementation.
Use Business Intelligence from the Beginning
Business intelligence must be part of any business process reengineering effort from conception – not simply after final specs are produced. It is a driving force for determining the efficiency gains and final specs of an organization. This is accomplished through specialized tools and business intelligence team participation in the engineering sessions. Fortunately, technology, coupled with proven methodology, allows business intelligence teams to do this without having to re-implement once the application data is connected.
In the end, strategic and tactical business process improvement occurs by establishing key success metrics that enable the business model and its supporting processes to dynamically listen, respond, anticipate and adapt to the voice of the customer. It is necessary to develop appropriate metrics to measure the performance of the new processes, subprocesses, activities and tasks. These metrics must be meaningful in terms of inputs and outputs of the process, and also in terms of the customers of and suppliers to the process. Data and information that is collected and analyzed is reviewed and recommendations are made for the improved processes that leverage business intelligence tools.
