To give Market Research a meaningful purpose, Ace Research’s primary objective of carrying out any study is to draw relevant, up-to-date and decisive information. Whether it is a quantitative or qualitative study the spirit lies in the analysis of the data which derives business Intelligence. For an acute understanding of market dynamics data analysis is actually a product of clearly demonstrated business objectives of doing any particular research; problem statement that needs to be addressed like for e.g. why do the clients after taking loans, are not turning up next time, precisely, what is the churn rate?
The next phase is to collect the data form sources which are data hub for any organization. Data could be collected from primary sources or secondary sources depending upon the type and need of research. If there is data available and our requirement is to draw a relationship or make what if? Analysis so, we rely on secondary data but if our objective is to carry out an exclusive study that has no existing data available or data available is not sufficient enough to base our findings on it so, in that case, we create a questionnaire, covering all information areas that we want to explore.
Once data is collected from sources, we need to arrange that data in a format that is sortable, clean and converted into one format. Cleaned data which has no conflicts, errors and formatting issues is now ready for data analysis. In data analysis we determine the relationships, causal effects, find patterns and explore any aspect relevant to the research objective.
After thorough data analysis, the conclusion can be drawn as if what are the possible outcomes and predictions of the research and this is ultimately summarized in a report which is a compact and comprehensive document carrying all research findings.
Data analysis is the process of evaluating data using analytical and statistical tools to discover useful information and aid in business decision making. There are a several data analysis methods including data mining, text analytics, business intelligence and data visualization.
Data mining is a method of data analysis for discovering patterns in large data sets using the methods of statistics, artificial intelligence, machine learning and databases. The goal is to transform raw data into understandable business information. These might include identifying groups of data records (also known as cluster analysis), or identifying anomalies and dependencies between data groups.
Anomaly detection can process huge amounts of data (“big data”) and automatically identify outlier cases, possibly for exclusion from decision making or detection of fraud (e.g. bank fraud).
Text analytics is the process of deriving useful information from text. It is accomplished by processing unstructured textual information, extract meaningful numerical indices from the information and make the information available to statistical and machine learning algorithms for further processing.
Examples of text analytics applications:
- Analyzing open-ended survey responses. These surveys are of an exploratory nature and include open-ended questions related to the topic in question. The respondents can then express their views without being constrained to a particular response format.
- Investigate competitors by crawling their websites. This could be used to derive information about competitors’ activities.
- Security applications which can process log files for intrusion detection.
Business intelligence transforms data into actionable intelligence for business purposes and may be used in an organization’s strategic and tactical business decision making. It offers a way for people to examine trends from collected data and derive insights from it.
Some examples of business intelligence in used today:
- An organization’s operating decisions such as product placement and pricing.
- Identifying new markets, assessing the demand and suitability of products for different market segments.
- Budgeting and rolling forecasts.
- Using visual tools such as heat maps, pivot tables and geographical mapping.
Data visualization refers very simply to the visual representation of data. In the context of data analysis, it means using the tools of statistics, probability, pivot tables and other artifacts to present data visually. It makes complex data more understandable and usable.
Increasing amounts of data are being generated by a number of sensors in the environment (referred to as “Internet of Things” or “IOT”). This data (referred to as “big data”) presents challenges in understanding which can be eased by using the tools of Data visualization.