Risk management in the age of big data
Thursday, January 16, 2020
There’s no doubt that we’re living in the age of big data. The numbers tell the story:
- 2.5 quintillion bytes of data are created each day.
- 90% of all data in the world was created in the last two years.
- 50 billion connected devices and sensors are expected by 2020.
- 82% of executives say their organizations are increasingly using data to drive critical and automated decision-making, on an unprecedented scale.
- 89% of companies believe that big data will revolutionize business operations in the same way that the internet did.
Of course, there are many risks associated with managing an organization and its projects in the age of big data. Risk is inherent in all human endeavors, and we need to identify and understand big data risks and know how to manage them effectively.
Two risks currently appear to be the most critical, and they demand focused attention from any organization that is serious about surviving and thriving in the age of big data:
1. Data Governance
Today’s organizations recognize that managing data is central to their success. They recognize the value of their data and seek to leverage that value. As the human capacity to create and exploit data has increased, so, too, has the need for reliable data management practices.
This makes data governance really essential. There is a major risk if we are trying to exploit the benefits of big data without having data governance that is aligned with business strategy. If your organization currently doesn’t have data governance in place, then now is a good time to start.
Defines a set of guiding principles for data management and describes how these principles can be applied within data management functional areas.
Provides a functional framework for the implementation of enterprise data management, including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics.
Establishes a common vocabulary for data management concepts and serves as the basis for best practices for data management professionals.
To manipulate, analyze, and leverage the insights available from big data, companies must hire people with skills and knowledge in data science, mathematics, statistics, artificial intelligence (machine learning and deep learning), and storytelling.
The demand for data scientists, data engineers and programmers can only grow stronger. It will not be possible to exploit the value that big data can generate for your business without having these professionals working in your company. Some businesses are forming talent teams, investing in courses to develop the skills that are needed.
Smart companies are already reaping the benefits of guiding their business from the valuable insights available from big data. They are now investing in business intelligence to analyze historical data and are developing advanced artificial intelligence algorithms to enable predictive analysis.
Companies without a data-driven culture in this age of big data will be increasingly exposed to the two risks of inadequate data governance and lack of available talent, making them less likely to survive and thrive than their competitors.
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