Making a case for data maintenance outsourcing
Building an Outsouring Case
Data maintenance must be seen as a sustainable priority. What is the cost of outdated
information? In the field it can mean missed repairs, incomplete regular maintenance, costly
reworks, and inefficient scheduling and dispatch of field crews. In the office, it can lead to lack
of confidence in the database, increased engineering and design costs, and other costly
operational errors.
In evaluating the viability of an outsourcing strategy for data maintenance, utilities must consider
several criteria. The objective is not just to control costs, but to optimize the use of resources to
sustain data maintenance as a priority in an overburdened operational environment.
Identify True Data Maintenance Costs
Upon first review, many utilities encounter a kind of sticker shock, The long-term cost of
outsourcing data maintenance may appear prohibitive. Based on traditional approaches to
budgets, departments responsible for maintaining GIS data may feel they are better off absorbing
those costs into their department and conducting data updates in-house. But the true costs of inhouse
data maintenance may not be filly understood. Decision-makers must evaluate their
decisions based on more thorough criteria, including:
- The fill costs
- The efficiency of the process
- The optimal use of in-house resources
Components of Costs
Like all corporate activities, data maintenance costs include more than just an hourly labor. They
must also include:
Employee benefits - Utilities' benefits may be equal to 30 to 50 percent of basic salaries.
Even if the utility hires lower wage or temporary workers for data maintenance activities,
their organizational structure adds higher costs to basic wages that cannot be easily avoided.
Administration - It is difficult but necessary to calculate the administrative functions that go
along with data maintenance activities. Someone has to manage the process and that
management requires constant attention.
Equipment - Hardware, software, LAN connections, IT support, and other equipment costs
must be calculated into the cost evaluation. Cost of ownership is a major IT issue for all
corporations. Estimates run between $2,000 and $5,000 per year to maintain a personal
computer within a corporate environment. For the engineering-grade workstations and PCs
required to operate the GIS, these costs tend toward the higher side of the estimates.
Facilities - Finally, utilities must also consider the costs of office space, energy consumption
and other physical support for their data maintenance activities. wile these may be dii%cult
to identifi and calculate, the cost of facilities can be important.
When in-house costs are filly accounted, the actual rate may be double, possibly triple, the basic
hourly wage. In this light, the fee for outsourcing of data maintenance may prove to be
substantially lower than a utility's true in-house cost. Service providers incur much lower real
labor costs than utilities, especially when benefits are considered.
Additionally, service providers are able to match staff size to work volume, instead of laying off
or carrying unneeded staff. By outsourcing, utilities only pay for the time spent directly on their
project. Because the service provider can shift people on and off a project as the volume of work
changes, utilities avoid the fixed cost of supporting an internal system that will be underutilized.
Increased Efficiency
By calculating the labor costs in this manner, utilities gain a more complete perspective on the
amount and nature of resources they require to perform data maintenance or any other internal
activity. But direct cost is not the only consideration when evaluating whether or not to outsource
data maintenance. An outsourcing solution generally will provide a better quality service in a
shorter amount of time.
This is probably the most difficult point for utilities to accept. However, consider the operational
differences between an in-house and outsourced solution. First, a service provider has only one
job, one reason to exist: convert and maintain geospatial data. The utility on the other hand, has a
very different reason to exist: to create and deliver energy or other services to its customers.
Whose resources will be most focused on developing and using the best, most efficient and most
accurate data maintenance processes? If the service provider fails to deliver its services with
optimum speed and quality, chances are it will go out of business. The service provider is
completely focused on providing efficient data services as a matter of survival.
To that end, the service provider will have extensive quality assurance/quality control (QA/QC)
procedures in place. It willingly invests in new tools, new technologies and new techniques that
increase speed and reliability -- investments the utility would be better served to make in its own
core competencies. By virtue of its experience gained on dozens of previous projects, the service
provider has developed processes that reduce the time it takes to update records, increase
operator accuracy and reduce clean up at the QA stage. Its staff is filly trained and singly
focused on accurate, timely data services.
