The power of strategic information and insights in generating growth has only recently been harnessed due to availability of data and high-speed computing.
The algorithms and statistical tests that are routinely employed in building models and validate its accuracy are not necessarily recent research findings. Economic reasons apparently are the primary motivation in employing Data Science as new engine for growth.
#1- To support business owners and organization leaders formulate strategies and policies
With accurate prediction and forecasting, ‘crystal balling’ in business, management and governance is now a reality – and more importantly, it can be practiced by any business owner or just plain anybody.
In data-driven economy strategic information improves productivity and profit margin by eliminating wastages and reducing cost. So, utilizing data analytics in business cannot add unrealistic cost burden to business. Rather, it must achieve the opposite – bring more opportunities with less cost. But how can this be realized? – this is the real challenge.
Clear direction, genuine intent and sound economic policy that benefit from data, at the federal and state levels, are the most impactful initiatives in building a new economic engine.
It is essential that economic policy is supported by business owners and executives who must formulate their own strategies and policies in utilizing data.
#2 – To reskill talent in organizing datasets using advanced programming, mathematics, statistics and neural networks
Now, some questions need immediate attention – who are the new doers whose roles are critical in this new economy? Do they require new skill sets? Fortunately, not all are entirely new in data-driven economy.
First big data need to be organized into datasets that can be modeled. It also involves capturing and storage of data, as well as converting them into desired format of data structures.
This sounds like IT job to most people, or it should not be surprising to consider transforming some of these personnel to data engineers. Competency in advanced programming is essential in this role and in this manner the doers are also developers.
Entirely different skill sets involve the use of mathematics, statistics and neural networks in solving classification, clustering and forecasting problems. This role seeks to unravel useful new information that were initially buried under seemingly meaningless numbers.
#3 To utilize the support from open-source community and relevant parties
Support from open-source community and relevant parties has significantly accelerated the realization of big data analytics. Accessibility of key components in Data Science tool set allow developers to experiment vanilla versions of these technologies, share new examples, fix common bugs and even improve and customize certain parts of it.
Other facts also support this position – Google ‘s recent release of TensorFlow to the public and its inclusion of Keras on top of its core kernel greatly benefit deep learning researchers, developers and scientists.
Now, relatively advanced neural network classification and forecasting experiments can be completed quickly and performed on affordable laptops and bandwidths.
Recent study by Deloitte Data Analytics reveals that if we take company policy on utilizing data for growth as key indicator for data-driven economy then the commitment at top level is still relatively low.
Therefore, the focus now should be on providing awareness that help push the idea that data can create real values – and not just another hype.
Future job seekers are now still in schools and colleges. They must be informed on the value of utilizing data and must be properly educated to be competent practitioners in this new profession.
While helping the economy, we also help our children in assuming their roles – and it is now.
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