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Data Driven Society
An information-driven society is one that uses data for additional economical, decentralized, Decision-making, Driven by innovation and developments like IoT, the societies now a day’s  produces  large  volume  of the information from the wide spread of sources.

What is data Driven Society?
A data-driven society is one that uses data for more efficient, decentralized decision-making. Driven by innovation and developments such as IoT, society today produces massive amounts of data from a range of sources.

What is still not done very often, however, is sharing these different data sets to create new services, and enable faster, better quality and more efficient decision-making. Doing so on a large scale would create a data-driven society that experts believe could become a reality in a matter of years.

Examples could be efficient waste management in cities based on real-time and historical data, or providing tailor-made advice for crowd management, guaranteeing safe and enjoyable mass sporting and music events.

Better decisions making:
With the appearance of technology, big-data has become progressively vital. So, creating selections on intuition alone will result in biases or errors. Taking advantage of insights from the information generated from each internal sources – operational and money systems, and external sources – Glassdoor and social media platforms – will alter managers to cut back risk and build higher selections.

Embracing data-driven selections is simply the beginning, for firms to completely adopt this apply the culture of the firm should support it. Ultimately, this effort can alter the personnel to know the importance of mistreatment knowledge to create higher selections.

Enhances Communication:
The future of labor revolves around organizational wide access to relevant knowledge and insights. Making a data-driven culture enhances communication because it facilitates higher cognitive process and promotes confidence on ‘why’ such choices have taken place.

Providing everybody with organizational wide access to knowledge promotes transparency and trust. This sends a message to key stakeholders of your commitment to a culture of openness and knowledge sharing. However, one must use caution of what level of knowledge to share with workers. Ultimately, this helps enhance communication, worker engagement and productivity.

Improves Productivity:
When knowledge is accessible to everybody it will increase the chance to know and gain new insights from all levels of the organization. Such insights open a systems-thinking approach and ultimately change opportunities for learning and growth for the organization. Another necessary side is to share Key Performance Indicators (KPI) which will change workers to grasp ‘what’ to realize.

KPI’s are measuring values that demonstrate however effectively a corporation is achieving its key business objectives a decent KPI acts as a measuring instrument facultative the work force to know whether or not they are taking the proper path towards their strategic objectives. Consequently, making a culture of responsibility and holding the work force responsible wherever necessary.

When the complete organization has access to relevant business performance knowledge and key metrics they're going to ultimately understand what direction to try towards. Therefore, having measures in situation, will allows the business leaders to measure success. To be Sure of, knowledge may be a good resource for creating higher choices, enhancing communication and up productivity; however, it may also cause issues if used inadequately. So, let’s have a glance at a number of the pitfalls to avoid.

Elicits Blind Trusts:
A data-driven corporate culture can lead employees to overly doubt their own judgment and experience. Believing that something must be true just because the data says so, without any further investigation or thought, can be very harmful.

During data analysis, it’s important to keep some healthy skepticism around numbers that seem too good to be true, or conversely catastrophic. If something looks off, that’s a good sign that it might be. When looking at your data, you want to ensure that everything aligns and nothing is out of the bounds of logic. Mistakes happen, and data isn’t exempt from it.

Data Overload:
Big data is massive and currently it is estimated that by 2020 the digital universe will hold up to 44 trillion gigabytes of data. It is easy to get sucked into the plethora of information that can be collected via digital platforms such as customer feedback, employee engagement data, wireless sensors, mobile phones etc. that we end up collecting more data then we can analyze and make sense of the problem is that we not only waste resources but also this data according to GDPR once collected for a particular cause cannot be reused for another issue.

Low-Quality:
Insights from the information collected depend on the standard of the information. Poor knowledge quality will cause less dependability, eventually destroying business price. Recent analysis from Gartner shows that poor knowledge prices organizations on a mean $15 million in damages per annum. a number of the main reasons might embrace human error in knowledge entry and errors through computer code and data format. So, tons is at stake once selections square measure created victimization poor knowledge.

Conclusion
Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow.