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Developing information analysis involves setting clear objectives, gathering qualitative and quantitative data, using tools like Tableau for visualization, and regularly verifying results. For instance, utilize GDP and unemployment rates to measure economic policies’ impacts.
Clear Analysis Objectives
Having a specific goal will direct the analysis, and in turn each step of it. For instance, in strategic intelligence analysis one might be interested to analyze the political stability of a country (testifying shadow economy): that demands precise information on politics: election results, policy changes or even public opinion survey result.
Analysis Objectives: Quantitative data Analysis objectives are central to analysis requirements. For example, to analyze the impact of a kind of policy on the economy you need to provide some economic indexes such as GDP growth rate or employment rate and so forth.
By quoting the consequences of the 1979 Iranian Revolution on contemporary Middle East, we can analyze how such events may have an impact upon modern politics. Further, “those who forget history are condemned to repeat it” highlights that analysis can spare countless iterations unwise choices if historical context and precedents are fully integrated into the reasoning.
Illustrations from real cases can also contribute to the force of an analysis. For instance, while examining the political stability of a country, one can refer to similar cases in other nations – like the way different countries reacted and ended up after Arab Spring.
Analysis Objectives: The time/cost Factors Determining objectives in the business intelligence field, for instance, evaluating a strategy to enter into a new market can take several months and cost up paying hundreds of thousands of dollars.
When specifying analysis objectives keep asking: What are we trying to accomplish? How will these results affect our actions? This simply boils down to, What data and resources will it take for us to accomplish these goals? Both regular feedback and discussion can guide analysis objectives to be adjusted and optimized, in align with more focused/feasible outcomes.
Data Collection and Organization
The reliability of the analysis results in strategic intelligence is essentially based on two parameters: sources and quality of data. Specify Data Requirements: Be it Election data, Government policy, or public opinion surveys.
Primary data sources for strategic analysis would include government statistical information and reports released to the general public. For example, if we are scraping the economic data of a country then one can read the annual economic report of that country and understand what is their GDP growth rate, inflation rate, and unemployment rate, etc.
Secondary sources of information come from newspapers, news reports, and social media. An example would be to analyze discussion intensity and sentiment tendencies on social media platforms when collecting public responses about a policy.
Then, historical events can be sketched out in which data analysis will become expanded and deepened even more. For instance, if your international relations analysis is on current events, perhaps that piece references data from the arms race during the Cold War. The truth is Winston Churchill famously said: Those who fail to learn from history are doomed to repeat it.
Data Cleaning and Normalization – Data quality is vital. For example, collected election data that you want to aggregate might come from various sources and need to be formatted in a uniform way and de-duplicated.
The categorization and filing of data are also important phases in structuring your documents. For example, intelligence analysts may categorize collected data by source, time, and content in relational databases (e.g. MySQL) or non-relational databases like MongoDB.
It starts with some data organization that ends up looking like the previous pie chart. Data visualization is the last step in this process, where it visualizes your structured work. Complex data can be turned into easy to understand charts and reports by using visualization tools like Tableau or Power BI, for instance: pie charts to show voter structure and line charts for the changes in economic indicators before or after a policy was implemented.
Verification of Analysis Results
Verification, as a part of strategic intelligence analysis and political intelligence analysis, requires the support verification of various levels and angles.
Quantitative Research: It is crucial to verify the results using quantitative data. At the same time, when checking an analysis conclusion such as estimating economic influences of a policy (where GDP growth rate, inflation rate, and unemployment will be known before and after), it reminds us to compare data.
Political stability index and government efficiency assessment are examples of terms that can be used in political intelligence analysis to measure a given country’s politics.
Another useful method is to refer to news events from history and industry that have higher significance. An example is the recent international trade policy analysis in which NAFTA implementation effects of the 1990s were referred to. George Santayana: “Those who cannot remember the past are condemned to repeat it.”
When actual cases are referred to empirical validity of the result verification can be strengthened. So that in assessing the level of impact afforded by an anti-corruption policy implemented in a country, one can lean on similar policies results as applied somewhere else.
Verify With Your People: Data Quality & Completeness Matter Similarly, if you want to verify the impact that a policy has on somebody’s ability to live his or her life normally, expect not only official data but also news from other independent institutions and media reports.
Cycle timing analysis is also an essential part of verifying results. As an example, a long-term policy will clearly require years of data collection and analysis not only to determine if the intervention is sustainable but indeed what its trajectory looks like.
Furthermore, the comparison of actual costs and budget values can represent important benchmarks for verifying results. For example, the actual expenses of a public project can be compared with budget costs to verify the economic effects and control costs directly related to operation.
Graphs and reports that are used to depict comparisons and analyses can display results visually. Such as employing line charts to display changes in major economic data before and after the implementation of a policy, or using pie-charts to show the change in voter groups and voting rates.
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