This article originally appeared on the BeyeNETWORK.
Today’s business leaders are more aware of the positive impact that reliable data, including customer data, can have on critical business functions. Most have a clear understanding of the value this data can have on customer-facing functions such as marketing and sales. With federal compliance regulations taking center stage – particularly in the financial services and pharmaceutical sectors – having a single view of clients and healthcare providers has become a major requirement in meeting industry regulations. Yet, despite acceptance among various line-of-business users, an organization must have a viable business case if it hopes to convince both IT and executive management of the overarching benefits of enterprise data management (EDM).
Most of us have come across good information about deriving return on investment (ROI) and justifying an EDM discipline, whether it is within an integration competency center (ICC), business intelligence competency center (BICC), or managed as a central portfolio for master data management (MDM). Many have successfully secured funding for the first-year effort in these initiatives – the ideal situation for no-chargeback. But to sustain and continue ongoing efforts around enterprise data management, continued funding is needed. Unfortunately, not every organization can provide ongoing central funding. EDM should eventually charge the applications or projects that use and benefit from its services.
Basic Chargeback Methods for Enterprise Data Management
The value of data is difficult to realize until it is highly integrated and manages to provide timely and accurate information for business processes and decision making. If enterprise data management is to be perceived as valuable for initial and ongoing funding, it must have a price. Usage-based chargeback is the best way to build a price-to-value relationship for data management services, and it is one of the cornerstones for running data management portfolio (ICCs and/or BICCs and/or MDM) as a business within a business.
As long as there is a solid understanding of the portfolio’s operating costs, you can use pricing as a strategic tool for improving alignment with the business by providing executives a better understanding and control over data management resources. Different models with different classes of service can be used to drive more cost-efficient consumption of data management portfolio and technology and to achieve more effective matching of service to business need.
Basic Methods for Pricing Enterprise Data Management
The simplest chargeback model, subscription pricing, is a pay-per-use model in which pricing is based per unit of time. The operational cost of data management facilities is calculated and amortized across a subscription period (i.e., one year) and then divided between all users of the service. Depending on the operating profitability goals applied to the data management organization by the business as a whole, an element of gross margin may be added – perhaps to create a pool to fund data management research, program governance or future projects.
- Simplicity: For example, if five lines of business were subscribing to a project that will launch a master data synchronization service that will cost $50,000 per month, the subscription charge (assuming a break-even business model) would be $50,000/5 = $10,000 per business unit per month.
- Cost-sharing eases the funding approval, as multiple lines benefit.
- It is much easier to monitor and measure than consumption-based pricing.
- This model works well in project-based funding for resources.
- No usage monitoring or penalties: It assumes all parts of the business will use the service at the same level on a constant basis, with no penalties for excessive consumption or peak-time usage.
- No cost justification: There are no metrics by which the actual level of consumption can be measured, calculated or justified to skeptics.
The peak-level approach takes the subscription model and adds a mechanism to monitor and record peak consumption. Consumers are billed according to their peak use, not according to average use.
- Simple to meter: Only peak-level usage needs to be monitored and recorded.
- Clear cost justification: It is easy to show when consumers are using more than the base-level resources.
- Penalizes variability: If there are just a few usage peaks during a given period the scheme can seem unfair. However, shortening the analysis period (from six months down to one) and the measurement intervals (from weekly to daily) can help solve the problem.
If user management is a bigger cost issue for data management than hardware usage, it makes more sense to meter data management by the person rather than the machine. If users are connected to their computers for fairly similar periods of time and have relatively well-understood transactional profiles – for example, bank customer service representatives who work on web portals – this can be a fair and easy way to charge for usage.
- Easy to implement: Tracking the authentication of individual users to data services is relatively simple, especially if a single sign-on system is in place.
- Clear cost justification: The authentication records provide the basis for cost justification.
- Ignores system load: If users make heavy demands on systems when they log on, this model shortchanges IT.
In data management environments where quality of service is critical for transactional systems, a data management program can tightly meter and control usage using electronic "tickets" for short validity periods (for instance, four hours).
- Consumption regulation: Ticket-based pricing allows IT to control system load, helping to eliminate usage peaks and ensure business continuity.
- Simplicity: All that is required to monitor ticket pricing is a low-latency (i.e., fast-responding) portal, most likely constructed as a web service. Tickets provide permission to use the data service multiple times during the ticket validity interval (a "multiple right of re-entry" solution).
- Strong cost justification: Of all the models, ticket-based pricing is the most powerful in terms of cost justification.
- Pinpoint monitoring: Tickets can be specific, allowing both sides to monitor exact usage down to the specific application level.
- Ticket hoarding: For the ticket-based model to operate effectively, it’s often necessary to implement "use-by" dates on tickets to avoid stockpiling.
These chargeback models have been found useful for enterprise data management leaders when justifying value and, most importantly, securing initial and ongoing funding.
- Many larger IT shops have a due-diligence culture of charging back for each IT portfolio irrespective of its value or impact. In this case, having chargeback becomes mandatory for data management discipline.
- As a result of recent recognition by analysts and data management magazines, most everyone running business and IT for profit and compliance understands the value of enterprise-wide business intelligence and data management. But there is no single department that can buy the whole bus with restricted funding. In these cases, each department currently spending on siloed business intelligence and data management work can spend a major part of it wisely on enterprise effort and track its cost share using chargeback. This adds more responsibility to the program governance of enterprise data management efforts. Over time, each department realizes they are spending less gradually, compared to siloed work.
- If data management services are centrally funded but serve multiple departments, the risk of a monopoly exists. It might operate in a central governance model not necessarily driven by consumer demand and innovation for continuous improvement. Introducing a chargeback model in this case sustains monopoly to a certain extent; but, most importantly it institutes a customer service culture focused on continuous innovation for ongoing improvements in process, cost and quality.
First Class or Coach
There are other, more complex models for chargeback that bring even more depth to monitoring and cost, but these four models provide a start without making it too cumbersome or meaningless for accounting. They can become more meaningful by layering a system of service levels (and varying costs-per-unit of service) on top of each model, similar to the airline fare-class pricing model. For example, ad hoc data or information access (whether it is for a BI report or MDM service) could be offered under the ticket-based chargeback model at three price levels, each with varying degrees of bandwidth, service level guarantees and peak usage guarantees. Furthermore, in any model, companies should ensure the EDM tools and processes implemented can capture the minimum required metadata for chargeback inputs.
The goal in any of these consumer-centric chargeback scenarios should be to deliver data integration and management services in ways that present the highest degree of visible perceived benefit to consumers and price accordingly. For example, if one of the shared data management services is “Data Quality Audit” with a three- to five-week response time, you may offer realistically expedited service with a premium charge. This approach also proves valuable in investments for infrastructure, those funded centrally versus subscription-based, to continue offering highly efficient services. With this business world approach, chief information officers or chief data officers usually have the opportunity to manage their own cost structures away from the prying eyes of consumers.
Chargeback is a way to put enterprise data management services in terms that business people understand and value. When enterprise level data is bought and consumed like other services, EDM can become a business within the business – and that is the path to true enterprise data management value, real as well as perceived.