Data collection is the process of obtaining and organizing primary and secondary data.
Data analysis is the use of tools to establish patterns and relationships in collected data by discursive or statistical means.
Our objectives when collecting data are to obtain original data first-hand through a number of methods where we communicate directly with one or more people who are the source of the required information (Primary Data), and to collect and organize in original ways data obtained by others sources such as financial reports and articles produced by and about the businesses in question (Secondary Data).
Methods of obtaining Primary Data may include observations, surveys, interviews, conversations, meeting with sources, written exchanges with respondents, etc.
We also collect Secondary Data produced within and about the organizations being studied. Then we sift, select and sort the data in ways that match our customers research aims.
Sources of Secondary Data may typically comprise reports & accounts, publications, including publicity material, reports about the targets written by business analysts and other academics, general Internet data (with suitable caution in selecting reputable sources).
We provides statistical data from a wide variety of public and private sources, and we validate and explain if they are from large international organizations such as the World Bank or other Organization.
In terms of data analysis, we analyze primary and secondary data by drawing on established analytical tools.
Different analytical tools may be used for different research methodologies.
Qualitative research mainly involves textual analysis of orally communicated data, analytical tools may be drawn from discursive methods, including narrative analysis, to study speech in the recorded text. Sophisticated data analysis software may be used for particular qualitative data analysis functions like thematic analysis.
Statistical analysis in both qualitative and quantitative research may draw on a range of elementary and advanced tools to test relationships between variables. Selection of suitable statistical tools depends on the complexity of the relationships between variables that are being tested.
Larger data sets, for example of financial data, are often analyzed with software programs that can apply both elementary and advanced (multivariate regression analysis, forecasting) statistical tools.
Drawing on a range of statistical techniques can increase the sophistication of findings, where selected techniques should directly test specified hypotheses.