Business impacts of data science and machine learning are beginning to unfold and only the well positioned businesses will take time to analyse and tap into the insights derived from analysing the business impacts of data science and machine learning.
There are other game changers like Blockchain, DevOps, Agile Computing, SaaS, Edge Computing, Analytics, IOT, big data, Fintech, Cloud accounting and more that are rapidly advancing and creating numerous digital transformational landscape . These are creating more opportunities and new technology ecosystem.
However, my focus in this post will be on Data Science and Machine Learning.
Successful businesses are more often than not led by data driven leaders, who understand the importance of proper categorization and communication of the potentials of data science and machine learning.
The rest of this article will be devoted to looking at the impacts of data science and machine learning. For those interested in preparing business cases, the ideas discussed here are very important so enjoy.
6 Ways Data Science and Machine Learning Impact Businesses
Data science and machine learning are having profound impacts on businesses, and are rapidly becoming critical for relevancy and survival. We will be looking at the business impacts of data science and machine learning under the following 6 headings.
- True Innovation:
With true innovation, no business height is too much to achieve. The sheer power of data science and Machine Learning, combined with the integrated thinking of high profile data scientists has made finding new ways of solving eternal problems very easy. The disruptive power of data science and machine learning is so powerful that disruption has now become the new buzz word that keeps most C-suits executives awake at night.
With their ability to frame complex business problems as machine learning or operations research problems, data scientists hold the key to unveiling better solutions to old problems.
Data science and machine learning may even reveal problems that we never knew existed and approaches that were previously unknown. Sensitivity analysis will be more efficient with machine learning and data science analytical tools.
2. Experimentation while Exploring:
Data scientists should be given freedom to ‘test the waters’, make “big data expeditions” and come up (with crazy) ideas that may one day save the world. Unexpected relationships between data are easily discovered by data science and machines that have learnt enough that they can predict things based on data sets.
3. Prototyping and trying out novel ideas: the whole idea of probing into data is to have enough factual insights that can enable one challenge the status quo. It is no longer news that human decision making capability cannot take us all to the promise land that we all are dreaming of – to achieve higher level of digitalization. Data science and especially machine learning excel in solving the kind of highly complex data-rich problems that overwhelm even the smartest person.
4. Continuous change and Refinement: Continuously improve on existing processes and products. The invention of Blockchain has give hope that record keeping and its integrity, especially in accounting will be greatly improved.
This is perhaps the most common application of data science. Most data scientists work in the production part of their business, and have established models for refining processes and products according to the data their organization collects.
- Remediation: Identify the drivers of certain undesirable situations and proffer solutions. This category is very similar to the exploration category in terms of its methods, but is applied in a different context. Sometimes organizations trigger a data science initiative in response to crises where the symptoms are obvious — for example, a rise in customer complaints or a rapid drop in profitability.
- Lawsuits: a lot of businesses gave gone underground as a result of expensive lawsuits. Given the right kind of information, most lost cases would be won. Data scientists and machine learning can help produce information that when utilised would help ensure that companies would not be at the mercy of some smart lawyers who prey on the lack of convincing evidence to nail companies that would otherwise still be in existence.
Business impacts of data science and machine learning can also be negative however; all efforts in the business technology world are being channelled to ensuring that the positives outweigh the negatives.