Research states that companies in the top third of their industry that take data-driven decisions are 6% more profitable than their competitors.
However, intuition is often the underrated and ignored aspect. It can play a significant role in shaping a data science career.
Here are four reasons that show why data intuition skills are needed to perform well as a data scientist:
1. Data Science does not factor in change in management practices
External factors like competitors, customer preference, are affected by changing technological development.
As per a report by McKinsey, Big Data has made it easy to carry out real-time updates in prices even at individual business units spread across different regions. This factors in involved many data sets. However, the drawback is that it does not factor in management practices.
For e.g. if a bank wants to customize insurance policies across every branch, big data can provide in-depth data. But, if the big data provides data on lack of trust, then there’s little that data analysis can do to help. Instead, it then goes into the hands of the employees to identify top few reasons for the lack of trust.
Thereby data science can provide a comprehensive breakdown of data, yet; its impact is limited in the case of uncertain outcomes.
2. Relevance of historical data in the age of social media
Sites like Quora, Facebook, Reddit are a landmine for customer data. Millions of valuable data are available across multiple social media platforms. Data science becomes all the more important in this context.
For e.g. Data science is used to check how well a global brand campaign is performing in a particular region. It will also check demographics and different geographies.
On the other hand, an experienced marketing manager will know that data collection is a worthy cause. Butt all campaigns can perform equally well across all regions. It may be because of intuition that points to localization and cultural practices at many places.
3. Intuition helps join the dots
Strategic decisions are still done the traditional way. The only thing that has changed is access to real-time data and insights.
Reports state that a purpose-driven data is meaningful and needs to be supported by insights and intuition.
For e.g. each department in a company have its data sets with the valuable employee or customer information.
Human resource can tap into the right skills sets and marketing and operations into identifying and serving the customer. However, for a successful strategic decision making both the sets have to be merged and decided based on data analysis and intuition to know what works well as a unit.
4. Application of data science goes beyond analytical results
Often it is not the outcome of data analysis rather the manner in which the data is applied that matters. The right questions can often bring out the right way to use the data. Some important questions to ask are:
- Was this data also used in earlier projects and what was the outcome?
- Will use a different, but similar data set analysis to give similar results?
- If the datasets were to be redundant soon can the data still be used?
Analyzing the answers to such questions will help reach an outcome.
Wrapping it Up
Thus the application of intuition to data analysis can yield good results. However, merely relying on data-backed decisions may not always provide the larger picture. Instead, using data science intuition is the key to get more out of data.
In the age of data science usage, data intuition can help you stand apart from the crowd. It can be done by analyzing a situation from many angles.
Data intuition skills can take your data science career ahead.
Are there any points you would like to add? Share it with us in the comments!