This article was written as an expansion of our white paper “Choosing Sustainability Management Software for your Business” published in July 2011. If you’re looking for information on how to make your software selection, check out the full article. If you just want to make sense of this particular topic, keep reading. Whether you like this article or not, we want to hear from YOU so that we can continue to provide the best insight for YOU, our readers…
Our series on Sustainability Software continues with “Data Management Concepts for Sustainability”. In this article (Part 2 of 4), we’ll continue introducing and defining key Data Management terms (read Part 1 here). Our end goal with this series is to enable YOU, as the Business Leader, to feel more comfortable in a technical discussion related to the various areas of Data Management, especially as related to the care and feeding of Sustainability Software packages. Being able to “talk the talk” is the best defense in the technology wilderness. Just remember, at the basis of any technical term is a common sense business notion, and staying grounded to this notion will help keep your conversations from drifting astray.
This term is most commonly associated with Data Warehouse design, but is relevant to the construction of any database. If you elect to design and build your own Sustainability Software you will find the design of its underlying database (Data Modeling) to be one of the most labor intensive steps in the process, and because Sustainability is a rapidly evolving concept, it will seem that the database changes are boundless.
Data Modelers are not only IT-savvy, but are required to be subject matter experts in the business functions of the company. Data Modeling usually starts with vocabulary lists which are organized by a discipline called Taxonomy. These lists are then translated into abstractions called Logical Data Models which ideally constitute the rigorous definitions of, and relationships among all the data elements required for the enterprise to function. Then magic happens and database administrators interpret the Logical Data Models into real databases in software products such as Oracle, DB2 or SQL Server. There are software tools like ERWin and ERStudio that assist both the modelers and DBA’s in doing this.
These are lofty goals indeed and can be expensive to implement especially if you purchase expensive tools. Additionally, in a rapidly changing environment it can be difficult for the Modelers to keep pace with the Entrepreneurs, but if your Business requires databases to function, their models (designs) must either be purchased from vendors or created by the home team.
Since Analysis Paralysis can be costly, we encourage you to “buy” vs. “build” the database for your Sustainability Software, especially given the wide variety of SaaS solutions available in the market today. For small to midsized companies, this is by far the most cost effective option. If you elect a SaaS approach, all these issues will be completely hidden from view and their expenses will be shared among all the system’s users as part of the overall licensing cost.
Data Storage & Archiving
This is where the ongoing cost kicks in. Hardware for data storage is at an all time low and trending downward, but the software licenses required are costly to buy and to maintain going forward. Both must be periodically patched and upgraded which requires a sophisticated IT Infrastructure team. These costs and hassles furnish more strong arguments for SaaS.
There are also potential standards clashes with bringing in special purpose software. For example, SQL Server is an excellent database platform for a small to midsized company, but the Sustainability package you love most might be based on DB2 and Cognos. The benefits of the new system could easily be outrun by the cost of this big company software alone. Remember the notion of Total Cost of Ownership, wherein it often turns out that ongoing costs exceed the installation costs dramatically.
This is the area of Data Management concerned with backups, disaster recovery, test environments, complex operational change control, etc. Bear in mind that Sustainability is an emerging venture and that commercial and governmental influences are afoot to undermine your investment, no matter which way you start out. It’s best to adopt the conservative approach unless your industry has specific special needs that package software has not yet addressed.
If you feel you must support your own Sustainability Software on your own premises with your own team, then make platform compatibility one of your highly loaded criteria. If you have a SQL Server shop, try to adapt to a SQL Server-based package if possible.
One final significant consideration: regardless of who maintains the data storage servers, you will be at least partly responsible to assure all data privacy and audit best practices are followed. If these are not contemplated in the initial setup, it is possible you will enjoy fines and audits that will eventually motivate the re-design of the storage systems (or migration to a SaaS solution!)
(TO BE CONTINUED…)
Now that you’ve read this article, tell us what you think! And be sure to check out the full white paper.