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Data Strategy and Machine Learning: How Do They Intersect?

By   /  November 21, 2018  /  No Comments

Data Strategy and Machine LearningWith the tremendous growth of business data in terms of volume, size, and complexity, it is imperative that global enterprises develop a strong Data Strategy to address their core business needs. However, a realistic Data Strategy has to incorporate a clear road map with milestones, so that strategy documents do not end up as digital assets with no real value. The 5 Essential Components of a Data Strategy depicts the tale of an organization’s Data Strategy development that met with surprising success.

Do Organizations Need a Data Strategy?

Businesses need a Data Strategy if they wish to remain competitive in a data-driven, decision-making world. According to Why Organizations Need a Data Strategy, a corporate Data Strategy

“is the comprehensive vision and actionable foundation for an organization’s ability to harness data-related or data-dependent capability.”A designated person, known as the Data Architect, is usually vested with the responsibility of developing a Data Strategy. As someone already knowledgeable about IT architectures and usually responsible for defining all data system capabilities, he or she is uniquely positioned to work on the Data Strategy plan. With a solid understanding of both IT and business needs, the Data Architect can craft executable strategies.

What Has Machine Learning Got to Do with Data Strategy?

With recent value additions like Internet of Things (IoT), Big Data, and Cloud Computing in the global data landscape, organizations have taken a serious interest in Data Governance. Compared to outdated decision-making processes available in many of the Global 2000 businesses, Machine Learning as a core business driver has the power to deliver more accurate, precise, and intelligent decisions. However, while Big Data has strengthened the power of Machine Learning (ML) tools, there is a growing concern about Data Quality and Data Governance among business leaders and operators.

Today’s business operators realize that unless an organization has a solid Data Strategy in place ensuring Data Security, Data Quality, Data Stewardship, and Data Governance, ML tools cannot guarantee superior business outcomes. How Can Machine Learning Affect Your Organizational Data Strategy? makes a case for ML tools in improving organizational Data Strategy.

How Does Machine Learning Affect Data Strategy?

According to Dr. Yoshua Bengio,Université de Montréal:

“Machine Learning research is part of research on Artificial Intelligence, seeking to provide knowledge to computers through data, observations, and interacting with the world. That acquired knowledge allows computers to correctly generalize to new settings.”

Machine Learning provides machines the capability to “learn and improve from experience” instead of being specifically programmed to do so. In the learning process, machines begin by studying available data to detect patterns, and then apply that learning to predict future results. Though the training process initially takes time, machines end up delivering fast and accurate results without any human intervention. ML becomes very useful in processing high volumes of data.

Good Data Quality is an essential prerequisite for all organizations. ML algorithms actually check Data Quality by detecting mismatches, errors, or other anomalies. As Data Quality is a fundamental issue in enterprise Data Management, an executable, organizational Data Strategy requires ML to ensure high quality of business data. This is one of the first points of intersection between Data Strategy and Machine Learning. Increasingly, regulatory requirements like GDPR will require all business data to pass through multiple checks and balances before it is fed into any Analytics System.

The Next Point of Intersection between Machine Learning and Data Strategy

Exploring the Intersection of Machine Learning and Analytics indicates that ML has ushered in a new era of business Analytics by not only automating most Analytics tasks, but by automating the “Data Preparation” phase. During data cleaning and preparation, ML plays a key role in ensuring that the data for analysis is accurate, consistent, and complete.

As business Analytics is the core differentiator in modern businesses, and data is the raw material of Analytics, both data and Analytics are the main assets of an organizational Data Strategy. This is probably the second point of intersection between Data Strategy and Machine Learning.

Machine Learning Is Not Data Science

Though Data Science is widely believed to be an “umbrella field” encompassing many disciplines, AI or ML is beyond the purview of Data Science. On the contrary, ML aids many data processes and tasks to enhance the efficiency and performance of data technologies. Machine Learning is valuable for dealing with enterprise Data Quality and Data Governance, which are two core parts of a Data Strategy. This is the third point of intersection between Data Strategy and Machine Learning.

Separating Machine Learning from Data Science discusses some situations where Data Science does not require ML, though in most cases, data technologies perform best with the assistance of ML algorithms. A good way to think of this difference is to consider Data Science as a discipline for managing data and Machine Learning and a technology for enhancing the performance of data. What Is The Difference Between Data Science And Machine Learning? offers an interesting comparison between these two distinct fields of Data Management.

Why Big Data Analytics Require Data Strategy and Machine Learning

Big Data vs. Machine Learning echoes Forbes’ prediction that by 2020, the global data piles will shoot from 4.4 zettabytes to 44 zettabytes! Thus, dealing with Big Data Analytics is a challenge that only powerful technologies like ML can confront.

In the data-engulfed world of the future, a strong organizational Data Strategy will be the only competitive edge for businesses. Big Data Analytics will need both Data Strategy and ML to deliver timely, actionable decisions. This is probably the fourth point of intersection between Machine Learning and Data Strategy.

Why Business Analytics Depends on Data Strategy

Two main types of Analytics that future businesses will use to remain competitive are Predictive Analytics and Prescriptive Analytics. The success of such complex Analytics processes depends, to a large degree, on the quality of data. As overall Data Strategy will govern Data Quality, and ML will play a key role in data cleaning and Data Preparation, much of future Analytics will depend on the organizational Data Strategy and Machine Learning. In Advanced Analytics, Data Strategy and ML automatically intersect.

ML tools analyze data, make assumptions, and learn to offer predictive intelligence at an accuracy level incomprehensible by human data analysts. Predictive insights will assume more importance for future businesses when digital businesses start engaging in combative marketing techniques to snatch customers from each other.

In a data-driven world, both Data Strategy and Machine Learning are bound to play critical roles in ensuring data delivers competitive value to businesses.

 

Image used under license from Shutterstock.com

About the author

Paramita Ghosh has over two and a half decades of business writing experience, much of which has been writing for technology and business domains. She has written extensively for a broad range of industries, including but not limited to data management and data technologies. Paramita has also contributed to blended learning projects. She received her M.A. degree in English Literature in 1984 from Jadavpur University in India, and embarked on her career in the United States in 1989 after completing professional coursework. Having ghostwritten and authored hundreds of articles, blog posts, white papers, case studies, marketing content, and learning modules, Paramita has included authorship of one or two books on the business of business writing as part of her post-retirement projects. She thinks her professional strength is “lifelong learning.”

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