In the era of digitization, 2.5 quintillion bytes of data are generated and collected every single day. Alone, this data is useless; but with analysis, it becomes the key with which businesses unlock a treasure trove of information that drives process improvements, sales and marketing, customer experience strategies, and business growth.
But though every organization has an abundance of data to analyze, the ability to derive real worth from the data lies incorrect interpretation. An analysis is complex and influenced by numerous different factors ranging from the type of data, the research and analysis method, and what a company wants to do with that data. While current trends in automation and intelligent technologies have been rapidly adopted to speed up the progression from data collection to applied insights, data analysis, and interpretation by humans remains the core value driver.
Data Collection and Analysis to Beat the Competition
Every day, businesses collect and accumulate data to improve their processes, tailor marketing campaigns, forecast events, and make strategic choices that guide their organization closer to a certain goal. They look at customer data across social media, purchases or customer service queries on their websites, employee interactions with different tools, and analyze and carefully interpret all of this diverse, complex information to make better decisions.
- Text Analysis The automated process of analyzing written text to extract useful information. For example, analyzing customer reviews can drastically improve products and service development, or augment customized recommendation engines.
- Statistical Analysis Uses mathematical statistics for collection, interpretation, and validation to better understand patterns and trends in data. For example, analyzing historical market trends can enable businesses to better understand seasonal product demand.
- Diagnostic Analysis Encompasses collecting and interpreting patterns within data sets to discover relationships within the data or pinpoint any anomalies. For example, businesses can utilize this technique to answer why there is a sales decline in a particular region.
- Predictive Analysis Helps businesses make future predictions based on past data to enable organizations to improve responsiveness, mitigate risks, and take preemptive action. For example, it’s often used to help manufacturers improve conditions on their factory floor, where machine parts are monitored with IoT devices, and data is used to determine maintenance.
- Prescriptive Analysis Involves insights from data to decide on the best solution to take. Big data time series analysis is often used in healthcare to recommend the best course of action to healthcare providers or to predict stock prices.
Importance of Data Interpretation
Data interpretation is a powerful asset for businesses because it elevates the decision-making process with high quality, reliable, and contextual information. By accurately converting and explaining trends in data, companies can avoid the repercussions of misinterpretation and take carefully weighed action.
By analyzing different data sets, companies can make different inferences that pave the way for business progress.
- Data identification and explanation Understanding and giving meaning to the different types of structured and unstructured data in ways that are easily understood and deployed by the business.
- Comparing and contrasting data Involves trying to come to the right conclusions and results by discovering similarities and differences to support competitive intelligence.
- Identification of data outliers Uncovering odd values within data ensure that important findings aren’t overlooked, and results aren’t distorted.
- Future predictions Being able to correctly interpret historical data and make future predictions accelerates business growth, innovation, and strategic development.
How to Interpret Data and the Methods
What is data interpretation? Quantitative data interpretation aims to address questions and problems expressed in numeric form, usually from written or textual content collected in observations or interviews, for example. From the multivariate data analysis, bivariate data analysis, and t-test method of data analysis to SPSS data analysis to the chi-square method of data analysis, data interpretation usually starts with answering business questions. Methods to utilize during the interpretation of data are varied, but include two key processes:
1. Quantitative Data Interpretation
The following are the three key methods of quantitative data interpretation and reporting the inferences:
- Observation Systematic or structured observation acquires numerical data about the “what” of business questions, such as measuring numerical values like the number of customers present at a store opening.
- Document Analysis Sometimes referred to as content analysis, this category-specific themes or content within a document to simplify information and help correlate insights.
- Interviews A personalized research form that requires assigning unique numerical values or codes to interview answers that provide patterns, allowing for more dependable interpretation.
2. Qualitative Data Interpretation
he quantitative data interpretation is made making use of the following methods:
- Regression Analysis Measures a dependent variable with different independent variables, like customer age, financial status, and geographic location, to predict a specific outcome.
- Cohort Analysis A type of behavioral analytics that groups users based on similar characteristics in order to monitor and understand their specific actions.
- Predictive and Prescriptive Analysis Make future predictions based on past and current conditions, events, or trends to figure out how each impact future events.
Data Interpretation and the Role of an Expert
People will always be the deciding factor in data interpretation, though there are data analysis tools that enable users to customize dashboards to present data in a specific way.
Automated systems can never replace an experienced professional who can make abstract, non-linear, and high-level connections during analysis. Human interaction with data ensures that common analysis and interpretation mistakes can be overcome:
- Correlation is mistaken for causation A data interpretation expert knows that correlation pertains to two things being observed at the same time instead of causation, where one thing directly affects the other. For example, an illness being dependent on specific indicators helps improve the viability of patient diagnostics.
- Biased viewpoint Biased interpretation results from inaccurate or inadequate data, among other factors. Human analysts can use fixed formula data analysis to come to more accurate conclusions, such as eliminating gender bias from job recruitment.
- Data relevancy Data quality is essential to getting accurate and consistent results from your data queries. For example, if you want to improve product recommendations to customers, you have to accurately assess their interests and behaviors.
Data Interpretation Requires Human Judgement
The combination of people and data working together brings the most accurate results that align with business strategies and improve decision making. Research Optimus (ROP) is a leading research and analysis firm with decades of experience in data-driven market research, financial research, and data analysis. Our qualified researchers and analysts are tenured in interpreting business data across various industries and helping organizations make connections between their unique data and their business initiatives to help drive desired outcomes.
Let us know if you have any unique requirements, and one of our senior analysts will take you through our process.
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