Data collection, review and analysis are underutilized tools and activities available to employment programs. Job developers operate best by collecting better and timely information and learning from experience. The more labour market aware, the better off job development will be. 

Job development takes place in the present tense. A job opportunity exists, gets filled and the opportunity disappears. Success or failure in this immediate guides and predicts all present and future activities. Job developers must know current information on a regular bases to act promptly and effectively in this rapidly changing job market. Good and timely data collection is crucial to effective job development strategies. 

Problems in job development data collection and analysis: 

1. Data is collected in an unstructured way and analysed too late to have any significant impacts on action. The same ineffective activities continue to be repeated.
2. Learnings from failures are identified too late and are seen as things in the past and not directions for the future.
3. Lack of data leads job development strategies to be driven internally by organizational needs, not externally by employer needs. Employer needs govern employment, not our needs.
4. Too often, there is little trend analysis being undertaken. Consequently, the only thing known is where the labor market has been, not where it is headed.
5. Job development techniques are not assessed as to what is effective for the market. Consequently blame is transferred to the candidates as “not ready or good enough”, the economy in general, or the local employers, as opposed to job developers accepting responsibility for performance.

With poor data collection, we often see an accumulation of harder-to-serve populations waiting for jobs, never attaining employment. Waiting instead of job development strategy is being used to address these people unemployment issues.
 

Good Data Collection: 

Good data collection from our experience needs the following elements: 

1. Data is collected and analysed in real time, not days or months later.
2. User friendly, easy-to-input data collection methods reflect the least amount of time to prepare and input the data.
3. The data and analysis is readily accessible to anyone. Generally this means web based access to input and report data.
4. Effective data analysis indicators highlight trends, timeframes and profiles. Tracking the “length of time waiting for jobs” and levels of employment barriers are important indicators.
5. Linking data collection, between potential candidates’ profiles who will want jobs and the employers available who will accept these types of candidates, is significant. (The gap analysis between these two determines future job development efforts).
6. Data collection that tracks the link between interventions and outcomes gives us the knowledge of what actually works.

There are many good data collection systems available and companies that can build them. DTG-EMP does not build data collection system but works with you to ensure the right information in the right way is being collected.