Making Information Meaningful
- It is important that current and potential employee issues are addressed in the survey so that data can be used for tracking progress, ie, longitudinal analysis. This means taking a broader view and not just working with "hot potatoes." Working off existing initiatives, conducting focus groups and working within the framework of your organization's strategic plan all help shape the survey.
- Literacy - both language and computer - are important considerations for data collection. Alternative methods/activities may need to be used to deal with this issue.
- The level of trust within the organization is a critical issue. How willing will employees be to respond and to respond truthfully. This will affect your communication strategy.
- Decide the purpose of the feedback early in the planning process . This will significantly shape all other decisions in the survey process.
- Engagement (briefing respondents; having others prepared to give feedback and having employees prepared to receive feedback) is a critical success factor in this type of survey.
- Logistics - getting the right details, respondent relationships, distribution and nearly 100% response rate - are all important.
- Distinguish between customers and stakeholders - they are usually not the same and will have quite different issues.
- Decide on the best way to contact your customers - self-completed paper or web questionnaires, face-to-face or telephone interviews, focus groups, etc.
- Focus on where you fit in terms of your customers' delivery frameworks, not where customers fit within your organization's process structure.
- How will results be benchmarked and interpreted in light of performance contracts?
- How to reflect performance indicators in a valid, reliable and simple questionnaire.
- Deciding whether to weight responses of critical users.
- What information will you collect out of basic pay, bonuses and benefits, annuities (superannuation/pensions), vehicles, equity plan, performance pay and government taxes?
- Continuous, biannual or annual polling.
- Validation during data collection, eg, 8% annuity to be entered as 8 and not 0.08 or $1,600 (of $20,000), etc
- What calculations do you need to perform before and after data collection?
- Data collection method - interviews or questionnaires (paper, web or spreadsheet)?
- Will company size, location, industry, and turnover influence your results?
- What are you really benchmarking - financial performance or service delivery?
- For sampling across organizations, ensure you use language and terms familiar to all.
- How much derived data can you ask for, ie, how much calculation is it reasonable to expect respondents to do before answering your questions, eg, cost/area, NPV of investment after 18 months, average rate of return, etc?
Inventory and asset tracking
- Are the items coded consistently, eg, furniture is 6 alphanumerics, car parts are 16 numerics, and consumables are 8 alphas?
- Distinguish between short-term inventory and raw materials.
- Are the assets truly tangible?
- How are assets with no value/fully depreciated handled, ie, where is the cut-off decision made?
- Is data obtained from people or machines?
- What is being measured - qualitative or quantitative data?
- How will out-of-process range data be handled?
- What type of trend analysis will be used to identify process runs?
- Consider the issues of sampling frequency, accuracy and precision of measurement. Are these issues affected by multiple variables?