Tag Archives: business processes
As more and more businesses become fully digitized, the instantiation of their business processes and business capabilities becomes based in software. And when businesses implement software, there are choices to be made that can impact whether these processes and capabilities become locked in time or establish themselves as a continuing basis for business differentiation.
Make sure you focus upon the business goals
I want to suggest that whether the software instantiations of business process and business capabilities deliver business differentiation depends upon whether business goals and analytics are successfully embedded in a software implementation from the start. I learned this first hand several years ago. I was involved in helping a significant insurance company with their implementation of analytics software. Everyone in the management team was in favor of the analytics software purchase. However, the project lead wanted the analytics completed after an upgrade had occurred to their transactional processing software. Fortunately, the firm’s CIO had a very different perspective. This CIO understood that decisions regarding the transaction processing software implementation could determine whether critical metrics and KPIs could be measured. So instead of doing analytics as an afterthought, this CIO had the analytics done as a fore thought. In other words, he slowed down the transactional software implementation. He got his team to think first about the goals for the software implementation and the business goals for the enterprise. With these in hand, his team determined what metrics and KPIs were needed to measure success and improvement. They then required the transaction software development team to ensure that the software implemented the fields needed to measure the metrics and KPIs. In some cases, this was as simple as turning on a field or training users to enter a field as the transaction software went live.
Make the analytics part of everyday business decisions and business processes
The question is how common is this perspective because it really matters. Tom Davenport says that “if you really want to put analytics to work in an enterprise, you need to make them an integral part of everyday business decisions and business processes—the methods by which work gets done” (Analytics at Work, Thomas Davenport, Harvard Business Review Press, page 121). For many, this means turning their application development on its head like our insurance CIO. This means in particular that IT implementation teams should no longer be about just slamming in applications. They need to be more deliberate. They need to start by identifying the business problems that they want to get solved through the software instantiation of a business process. They need as well to start with how they want to improve process by the software rather than thinking about getting the analytics and data in as an afterthought.
Why does this matter so much? Davenport suggests that “embedding analytics into processes improves the ability of the organization to implement new insights. It eliminates gaps between insights, decisions, and actions” (Analytics at Work, Thomas Davenport, Harvard Business Review Press, page 121). Tom gives the example of a car rental company that embedded analytics into its reservation system and was able with the data provided to expunge long held shared beliefs. This change, however, resulted in a 2% increased fleet utilization and returned $19m to the company from just one location.
Look beyond the immediate decision to the business capability
Davenport also suggests as well that enterprises need look beyond their immediate task or decision and appreciate the whole business process or what happens upstream or downstream. This argues that analytics be focused on the enterprise capability system. Clearly, maximizing performance of the enterprise capability system requires an enterprise perspective upon analytics. As well, it should be noted that a systems perspective allows business leadership to appreciate how different parts of the business work together as a whole. Analytics, therefore, allow the business to determine how to drive better business outcomes for the entire enterprise.
At the same time, focusing upon the enterprise capabilities system in many cases will overtime lead a reengineering of overarching business processes and a revamping of their supporting information systems. This allows in turn the business to capitalize on the potential of business capability and analytics improvement. From my experience, most organizations need some time to see what a change in analytics performance means. This is why it can make sense to start by measuring baseline process performance before determining enhancements to the business process. Once completed, however, refinement to the enhanced process can be determined by continuously measuring processes performance data.
Analytics Stories: A Banking Case Study
Analytics Stories: A Financial Services Case Study
Analytics Stories: A Healthcare Case Study
Who Owns Enterprise Analytics and Data?
Competing on Analytics: A Follow Up to Thomas H. Davenport’s Post in HBR
Thomas Davenport Book “Competing On Analytics”
Solution Brief: The Intelligent Data Platform
Author Twitter: @MylesSuer
Let’s look at the steps in more detail for building a business case for data quality using the bottom-up approach. Where do you start? You need to find a sponsor—someone who instinctively knows there is a problem and wants help in quantifying it. Marketing knows it has duplicate customer records and wants to get a better handle on them. You should look at these systems or business processes that work with the customer data. You must assess how the data in these systems is used within marketing. For example, what is the data used for, what critical decisions are made based on this data, and how many people use it to make decisions? The more users or the more critical the decision, the more likely this data is a candidate for evaluation. Also look at more than the initial decision support system and data. Look at any systems that get data from the decision support system. Data flow diagrams are always helpful in assessing this but usually difficult to find. (more…)
Over the weekend note blogger David Linthicum did a blogpost on eBiz regarding master data management (MDM) and cloud computing. The crux of David’s argument is that while the profusion of cloud computing will exacerbate the need for MDM, the rush to embrace cloud applications could potentially drive MDM into the background at many companies. That, ironically enough, since organizations can save so much money by replacing big enterprise systems with lighter SaaS applications, in the headlong rush to embrace cloud applications “MDM will be an afterthought” and get pushed aside even as the need for it intensifies.
I agree with David that the migration to cloud computing is going to further spark demand for MDM, but I don’t agree that MDM is going to get pushed aside. The reason I make this argument is that we already have a few customers at Siperian who are using MDM with cloud-based applications, and it’s working out very well. These customers are combing MDM with the cloud in the following two ways:
1. Using MDM to create a single version of the truth before enabling the cloud-based applications (i.e., they’re cleaning up data from multiple in-house CRM systems, and feeding reliable, consistent customer data into Salesforce.com)
2. They’re combining customer and other forms of data from the cloud-based applications (e.g. Salesforce.com) along with internal CRM applications to create a single version of the truth to enable operational and analytical business processes.
As organizations grow the number of cloud based applications, they have to control the key data that they will use across those applications as well as internal applications and data warehouses. MDM enables organizations to do just that—either for enabling cloud-based applications or creating a single view of the master data across cloud-based applications and internal applications. Thus a strong foundation of MDM will be the key to successfully taking advantage of cloud computing.
The art of war is of vital importance to the State. It is a matter of life and death, a road either to safety or to ruin. Hence it is a subject of inquiry which can on no account be neglected
– Sun Tzu, the Art of War
As organizations begin evaluating and leveraging MDM beyond their analytical needs and start to view operational MDM as a cornerstone of their overall strategy, the question of governance and workflow becomes a vitally important topic, which cannot be neglected. For many, workflow and business process management (BPM) is a well ploughed road, with established vendors such as Lombardi (a Siperian partner) providing robust offerings. So the marketplace would have you believe in a simplistic view
Workflow Tool + MDM Hub = Victory and Mission Accomplished
but the “Art” of MDM workflow actually lies within the subtle, yet flexible nature of the MDM platform itself, the ability to handle state. Not the State as referred to in Sun Tzu’s quote but the state of the data or record as it progresses through a business process towards its ultimate destination. A significant concern for organizations looking to enable their MDM Hub as a gateway for system of entry of all master data is the ability to enforce the right level of governance through checks and balances as additions and updates to data are made by business users. A workflow tool functioning independently is designed to easily track and route the operations and approval processes to the correct defined individuals or groups. However, a key requirement of the MDM Hub, in concert with such “in-transition” workflow activities, is to be able to manage the changing state of the data.
For example, when new data is created, the MDM Hub should be able to track its state as “pending approval” or any number of additional states before its final destination – becoming the golden master data record. This built-in capability should permit complete control over the availability of the data, framing it as not yet blessed for enterprise-wide public consumption, but continuing to allow the Hub to determine and resolve conflicts that might occur. In our example, if a second addition is attempted for the same record while the previous entry is currently in the approval process, the MDM Hub’s matching capabilities would highlight such a conflict, thereby preventing duplicate effort or accidental overwriting of data. Therefore, in order to succeed, the workflow tool must leverage the unique data state management capabilities of a MDM Hub. Only then can we claim victory and mission accomplished.