In the hype and excitement surrounding artificial intelligence and big data, most of us miss out on critical aspects related to collection, processing, handling and analyzing data. It's important for data science practitioners to understand these critical aspects and add a human touch to big data. What are these aspects?

1. Caring for data

Every piece of data is typically linked to humans in some way or another. It can be someone’s date of birth, social security number, address, income, medical information, etc. Organizations dealing with customer data have more responsibilities than ever to protect data by taking great care of it. Organizations neglecting to take proactive steps to protect data may pay a heavy price. It can even impact the survival of organizations. The recent Equifax data breach and aftermath have important lessons for all of us.

2. Understanding data context

Big data alone will not always provide the magic results you're looking for. It's important to understand the context of the data. Before the 2016 US Presidential election, incorrect poll predictions about the election outcome in Midwestern states is a good example. Most polling organizations neglected the social and geographical complexities involved in collecting data in those states, thus completely missing the hidden forces that eventually shaped election outcome. Another example is the way a company's sales figures need to be analyzed. Typically, the changes in sales figures must be analyzed from a social, political, and economic context to gain useful insights.

3. Telling a story from data

It's important to be able to tell a story based on the analytic insights gained from big data. A huge skill gap exists in interpreting analytic insights and explaining it in simple, compelling language that's easy to understand. Data storytelling is not a fantasy job anymore. It's an essential skill that every person dealing with data should possess!

Why is it important to add a human touch to big data?

A lot of big data projects fail in part because organizations fail to add a human touch to big data. By improving the quality of analytic insights, we can reduce the failure rate of big data projects. Through data storytelling, we can ensure that the analytic insights will have a better, deeper impact on people and organizations. Moreover, by taking steps to protect data, organizations eliminate a huge risk caused by data breach.

Thinking about making your organization a data-driven organization? Check out this e-book on "Becoming a data-driven organization" and this white paper on "5 Data Management for Analytics Best Practices".