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Georgia Governor’s Office Mines the Web to Analyze Pending Legislation

Published
  • July 01 2005, 1:00am EDT

QL2 WebQLREVIEWER: Steve Elmore, president of Steve Elmore & Associates, Inc.

BACKGROUND: Steve Elmore & Associates, Inc. is an Atlanta-based technology consulting firm that assists governmental and quasi-governmental organizations in maximizing the impact of their technology budgets.

PLATFORMS: Dell Precision Workstation 370, Intel Pentium 4, 3.0 GHz, 1GB RAM, Windows 2000 Pro.

PROBLEM SOLVED: Thousands of new bills are introduced in the Georgia General Assembly each legislative session. As a bill makes its way through both chambers of the General Assembly, it may be amended repeatedly, both in committee and on the floor, resulting in formatting that is often more creative than consistent. With hundreds of new bills affecting Georgia's existing statutes, multiple bills sometimes unwittingly modify the same section of a statute - with potentially chaotic results. The full text and status of each piece of legislation is posted on the General Assembly's Web site at the end of each day. The site contains thousands of Web pages. Each Web page may contain hundreds of pages worth of printed text. The Office of Planning and Budget (OPB) tracks new legislation as it winds through the General Assembly. The OPB needed an efficient way to extract very specific data, automatically and accurately, from the General Assembly's Web site and export it to the Governor's Legislative Information System (GLIS) for further analysis. Budget and policy analysts and state agencies then add to GLIS analytical comments that critique the legislation and its impact on agency budgets and programs. Ultimately, the extracted data and analysis is reviewed by the governor's staff in deciding whether the governor should sign or veto a bill.

PRODUCT FUNCTIONALITY: QL2 Software WebQL's SQL syntax and implementation of data translators provided the power needed to extract the desired data no matter how deeply buried or inconsistently formatted. What used to take 16 hours and a lot of manual intervention to process with previous software, WebQL accomplishes in minutes. This makes daily rather than weekly database updates practical. The governor's office is delighted with the accuracy and timeliness of the data. The process of data extraction and export is almost entirely automated. The labor savings alone will be substantial over the anticipated life span of the project.

STRENGTHS: Because of the significance of the governor's decisions, data accuracy is of utmost importance. WebQL's extraction engine is consistently accurate no matter how complex the context. The data must also be timely, tracking changes to evolving legislation and warning of potential legislative conflicts. WebQL delivers the data in a fraction of the time taken by a previous solution. The concise syntax is also easy to maintain and reuse for similar projects.

WEAKNESS: The OPB operates on a constrained budget, so cost was initially a big problem. WebQL is not inexpensive. Fortunately, QL2 Software was very accommodating.

SELECTION CRITERIA: The ability to try WebQL and the responsiveness of QL2's staff to questions was influential in the final decision. During the trial period, they helped with initial queries even before the contract was signed. They took the time to understand the business requirements. That level of service means a lot to a small company with little expertise in unstructured data. WebQL's actual features and performance ultimately convinced us, exceeding expectations during the free trial period.

DELIVERABLES: New bill data, recorded votes, bill status and referenced code sections are extracted to the GLIS database and analyzed by the governor's staff and state agencies. By the time a bill has been passed by both houses, a complete history as well as comprehensive analysis and recommendations are available to the governor, who then signs or vetoes the bill.

VENDOR SUPPORT: QL2's support of our project was superb. The queries developed were thorough, well tested, and showed an understanding of business requirements. The level of detail in the code showed that the queries had been run against actual target sites and that many of the "real-world" problems had already been resolved.

DOCUMENTATION: WebQL's documentation was surprisingly good. It not only covered the software, but also a few tricks on extracting data from unstructured data repositories.


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