In a previous blog I defined Business Intelligence (BI) as an organization’s ability to connect people with trusted, relevant information so they can make better decisions, faster. Today, I would like to talk about how to leverage BI within your critical business processes in ways which will springboard your process improvement efforts to greater success levels.
The underlying technology that enables the management of business process is often referred to as the Business Process Management Suite (BPMS). While the primary goal of BPMS is to monitor processes and look for ways to improve processes, those with a mature BI component are able to put decision making explicitly into the processes. This empowers the business to move beyond historical (what happened) and in-progress (what’s happening now) perspectives to a predictive (what is likely to happen) mindset where insightful forecasts allow the organization to optimize recommendations and automate decisions where appropriate.
So what makes up a mature BI solution? Look for the following key features:
Scalable and Unhindered Data Access – While your initial BI implementation may only impact a handful of end users, once the solution has proven its value, more and more users within your organization will want access to it. The number of different reports as well as the number of data sources the BI solution will need to pull from will also expand. Mature BI solutions support growth in all of these areas.
Tip: Look for scalable BI solutions that can easily gather and consolidate real-time information from sources that are proprietary, antiquated, or obscure.
Operational Visualization – A picture paints a thousand words. When dealing with large amounts of data in real time it is critical to have interactive dashboards that let you see how your company is doing and then drill down to analyze the reasons “why.” These highly visual summaries typically include charts, gauges, scorecards and reports all of which can be customized to provide personalized views of the information need to monitor and manage the business. Visualization makes it easier to interpret data and identify vital patterns.
Tip: More sophisticated BI solutions include the ability to transform data into matrixes, scatter plots, histograms, or maps.
Ad Hoc Analysis – For the power users who needs to answer an urgent question, make an “on the fly” decision, or address a pending issue. Mature BI provides features that allow even non-technical users to quickly and easily build and generate their own custom queries and reports.
Tip: Make sure the Ad Hoc user interface is easy to learn and accessible from any Web browser.
Advanced Predictive Analytics – Anticipate critical business trends and events. Move beyond “What If Analysis” and help your business discover subtle patterns and associations that allow you to develop and deploy predictive models to optimize decision-making and mitigate risks.
Tip: A few of the mature BI solutions include features that allow for rapid and highly accurate forecasting and planning.
In today’s fast paced environment of changing business needs and rapidly evolving technologies, I recommend that you partner with an experienced information technology service provider who specializes in process improvement, has hands on experience with the right technologies, a clear understanding of the business issues you face and the experience to lead you to higher levels of BI maturity.
About the author
Esther Mattick has over a decade of experience leading process driven optimization projects that enable organizations to achieve revenue improvement goals in a cost effective manner. She has working experience as a Quality Assurance Technician, Business Analyst, Project Manager, Operations Manager and Director of Technology. Esther has established a reputation for making complex 
technology problems understandable and manageable.
Inspirations
Role of Business Intelligence in Process Improvement
Business intelligence for business users: Insight when and where you need it
What to Look for When Choosing Enterprise Analytics Software
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Esther, thanks for an interesting post. A few observations from a human perspective:
Nature gave humans intuitive capability for decision making, meaning to apply practical plausibility to repeatedly observed patterns in complex adaptive systems without being able to ascertain their true causality. Human decision making can’t be predicted because the actual mindset is intangible. BPM and rule automated decisions are an expensive management mistake where they are meant to manage or control intangibles (Peter Kruse).
Therefore, I consider predefined flowcharts, automated decisions and predictive analytics as unsuitable to improve real-world human interaction and decisions in knowledge work. I do not think that human decisions can and should be automated, because BPM as well as BI depends on assumptions of models, measurements and algorithms. Statistics are fine in large scale oberservations but they should not be relevant for individual contexts and decisions as there is no directed causality but only a cause/effect resonance. Even if models would be resaonably correct, correlation is not causation and stastistical past data do not predict the future. There is no such thing as trusted data for decisions, there is only the excuse: ‘I did what the analytics told me. It is not my mistake!’
IT must support human decisions and not replace them and good decisions need less data and not more. (Gigerenzer et.al.)
Max, I appreciate your point of view. I would agree that there are some business processes where automation is not appropriate. However, I have witnessed first hand how automating processes (with the help of a rules engine and predictive analytics) has made a tremendous, favorable impact on customer service, operational costs and revenue growth. The key is to pick the right processes to automate and to make sure that you do not try to over automate — there will always be exception cases and these are to be handled by humans. When done properly, process automation results in an operation with higher throughput and less errors. The operations staff becomes higher thinking, exception processors. One of my very first process automation projects took place back in the late 90s. I was working in NYC for a major financial institution. The business owners were frustrated that operations seemed unable to keep up with their rapidly growing business and that settlement errors were resulting in significant losses. I spent a few days watching the operations team and it became crystal clear that we needed to automate as much of the operational settlement process as possible. In particular, we focused on the one task that was taking up a lot of time and was where most errors were occurring – confirming settlement instructions. We were able to create a “matching engine” that took electronic settlement messages and used a set of rules to ensure that both parties were in agreement about the terms of the transactions. More than 90% of the transactions went straight through to allow automatic release of payment. The remaining transactions were flag for human review with the fields in question highlighted. This automation enabled the business to increase trading activity by more than 300% without adding operations staff. In addition, the error rate went down to virtually zero.
Esther, thanks for the reply. I am not saying that there is no place at all for automation. If there is a complex set of rules that have to be manually checked and they are in principle all data attributes then there is nothing wrong with automating that. I would however not call that a business decision or knowledge work. Much that can also be check lists. Additionally it may be necessary to allow fairly flexible human processing of the remaining flagged transactions and not putting them into a rigid flowcharted process. I wonder what the predictive analytics would bring in value here. What kind of mathematical function did they perform that improved processing?
Max, thanks for giving me an opportunity to talk a little bit more about how predictive analytics brought value to improving our overall operational performance. Our “matching engine” capability led us to many additional improvements including utilizing predictive analytics capabilities to reduce fraud. In particular the Patriot Act, Title III: Anti-money-laundering to prevent terrorism which came into exist in late 2001 had a profound impact on our business. These regulations tightened the record keeping requirements for our financial institution. We were required to record the aggregate amounts of transactions processed from areas of the world where money laundering is a concern to the U.S. government. We were also required to put in place reasonable steps to identify beneficiaries of bank accounts and those who are authorized to use or route funds through payable-through accounts. Luckily for us, with an electronic stock pile of data (as a result of our automation efforts for settlement) we were able to quickly meet the new reporting requirements and leverage predictive analytics to ensure that we properly monitored and reported any potential money laundering activities. Given the volume of activity it was virtually impossible for humans to processes all this data by hand, much less detect subtle patterns regarding how seemingly non-related transactions actually had a relationship. Thanks to data mining, statistics and proprietary predictive models money laundering detection and compliance reporting connected trading activity became a routine task that required just a few minutes on one persons day to review alerts and escalate any suspicious activity.