Data without a label is useless. Business owners often neglect a very important aspect of data management and operations when they begin their Big Data projects. Yes, we are referring to “data interpretation.” In today’s high speed data science environment, it is very easy to lose focus of collected information that is assigned with no labels and therefore risk of getting cleaned up during data wrangling stage, leaving serious implications on the big data management and analytics processes. In order to succeed with any data science project, you have to focus extensively on cleaning up our data interpretation workflows.
So, how do you do that, especially if you are doing it for the first time?
In this article, we have explained the reasons backing the need to dedicate more time and resources to your data interpretation goals.
Data interpretation improves iterations
If you are working with supervised Machine Learning algorithms, is always important to do as much iteration as possible with the shortest span of time involving analysis and interpretation.
Data analytics is highly contextual in nature. The same set of data that can be used for one project says in healthcare, may not be as useful in employee data analytics projects. Likewise, for marketing and sales data used in the financial setup. In order to extract the highest possible value from data streams, it is recommended that you interpret data for your distinct applications. The multiple dimensions involved in data interpretation allow analysts to perform data labeling and deliver transparency to validate or test as you perform analysis in real time.
Ability to improve predictive intelligence
Ask data analysts about the biggest challenge they come across in their data management lifecycle, and they are more likely to state that “data blindness” impacts their ability to deliver predictive intelligence to stakeholders involved in a data analytics project. Despite extensive work in predictive intelligence, it remains a “holy grail” for a large number of data analysts who seldom back their skills to determine outcomes of their analytics in advance.
With data interpretation, the process of predicting outcomes becomes easy and reliable. Data analysts who focus on data interpretation are indeed found to be more successful with sophisticated algorithms that are developed to deliver ‘predictive intelligence ’ as a service to customers who require real-time insights on actionable information.
Detect Potential Pitfalls
When interpreted accurately, data analysts are able to identify and mitigate pitfalls in their data wrangling and labeling workflows. When done correctly, this prevents the proliferation of ‘bias’ in the analytics that could lead to serious complications if not addressed immediately.
Interpretation of data removes absurdity, ambiguity, and irrelevance from the scenario, enabling analysts to make the quantitative and qualitative judgment of patterns and themes associated with the development of advanced Big Data projects for Artificial Intelligence, Machine Learning, and Robotic Process Automation or RPA.
Good data visualization is a story well narrated.
There is no denying that data analysts that fall short of expectations from their visualization standards have their interpretation techniques all messed up. Data entanglement can cause this error of application when it comes to reporting outcomes in an interpretative manner using dashboards and visualization tools.
Without proper data visualization, your work in data analysis runs the risk of getting nullified and stands void if stakeholders reject the reporting metrics. Data interpretation not only simplifies data analysis but also imparts a sense of sanity to visualization techniques that infer that analysts have worked with high quality information and used best in class techniques to display data in a meaningful manner.
Overcome limitations in analytics
We all have our strengths and weaknesses. In data science, more often than not, the success story is governed by how well you have managed to unwrap your limitations and utilize data interpretation to allow some level of regularization, optimization, and generalization. Structural interpretation of data can untangle the problems associated with disaggregation, improving the overall quality of data by bringing in different perspectives using data visualization, iteration techniques, and predictive intelligence.
These are extremely useful in data analysis and interpretation of raw data used for developing CRMs, automated communication channels, and cloud automation platforms for call analytics and customer experience management.