As the world of data science expands, becoming data-driven has become a common objective by several organizations today. Be it to achieve larger goals in digital transformation such as competing on analytics or becoming AI-first, establishing clear goals in all its forms are mandatory.
Forrester’s report says that organizations started to harness insights creating competitive advantages that are increasing at a rapid pace of over 30% annually and is predicted to earn around $1.8 trillion by 2021. Although many organizations are consistent on their goals, the progress is rather painfully slow. Several companies seem to fail in their efforts in becoming data-driven.
Here are some of the alarming results:
The state of organizations with the data-driven culture around the globe
• In 2017 – 37.1%
• In 2018 – 32.4%
• In 2019 – 31%
As per Gartner,
• At present, there are around 87.8% of executives who says they’re in dire need of investing in data-driven initiative.
• By 2020, 80% of organizations should deliberately start initiating competency development in data literacy to acknowledge the deficit in data science knowledge.
According to 2019 Big Data and AI Executive Survey,
• 72% of the participants reported that they are yet to forge data culture
• 69% reported stating they haven’t started creating a data-driven culture
• 53% says data is yet to be treated as a business asset
• 52% are not even competing on data and analytics
Irrespective of several organizations adopting to data-driven culture and have hired data science professionals, they’re still way behind performing considerable tasks to become one. To be precise, there isn’t a single correct pathway in becoming data-driven, however, there are practices and data science frameworks for one to start over.
Here’s an infographic depicting the essential knowledge framework (ekf) one must grasp as data science professionals, big data engineer, and big data analysts:
• Big data value chain industry applications
• Critical elements of big data strategy and management ecosystem
• Data science programming engineering
• Big data analytics visualization and reportage
• Data science professions workplace career environment
Glassdoor recently ranked data scientist among the top three jobs that have the highest satisfaction in 2019. The salary compensation of a data scientist is near about $108,000, a big data engineer is around $100,000, and big data analyst $60,000. Yet there is still so much debate going around regarding the skillset and the role of these individuals.
Whatever may be the reasons for these companies to achieve slow data-driven culture, the amount of data will continue to rise. In short, the need for a data-driven culture isn’t going anywhere. Organizations need to dig deeper, educate through data science professional certification to gain traction in the world of business.
To embrace these challenges and work toward a data-driven culture, the entire community and stakeholders should work collaboratively and effectively.
