How to Translate Big Data into Big Business Value

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Many organizations are on a quest to become more data-centric and, as a result, a significant part of its IT budget is invested in a big data strategy. Beyond implementing a data lake strategy or mastering associated technical hurdles to collect and store data more systematically, however, the challenge to leverage data for true business value often remains.

A Strong Foundation

A data lake is a centralized repository that allows a business to store structured and unstructured data on a large scale. It’s an important, although not necessarily sufficient, prerequisite for embarking on a digital transformation roadmap. Once the organization becomes more sensitive to the potential value of its data and executives recognize it as a competitive advantage, the real journey begins.

If we consider data to be the new oil, then data science serves as the refinery that turns raw data into useful, valuable information for business stakeholders to act upon. While other technologies—like business intelligence dashboards and reporting—benefit from big data, data science will unlock the its true value. The goal with more (big) data should not be in creating more dashboards or reports, rather it should be to enable more automated, intelligent, data-driven decisions. Artificial intelligence (AI) and machine learning algorithms reveal correlations and dependencies in business processes that may otherwise remain hidden in the organization’s data. These algorithms then form the baseline for smarter IT solutions.

Generating Business Value

Predictive models help optimize existing processes and smarter applications are able to augment human decisions, especially in highly repetitive tasks. Today’s deep learning algorithms are already capable of super-human accuracy in certain tasks like computer vision and pattern recognition applied in the context of predictive maintenance or quality control. As a foundation of the industry 4.0 vision, these algorithms deliver on the promise of digital transformation. Driven by insights that data scientists gain from analyzing the organization’s data, smarter IT applications can transform how the enterprise operates, implement more precise automated decisions while freeing human experts to focus on higher value tasks.

Buzzwords like AI, machine learning and data science are on every executive’s mind. But the AI journey almost always starts with the organization’s raw data. Organizations with prior investments into more (big) data, real-time data and high-quality data have a strong big data infrastructure, which is now ready to deliver on the promise of predictive models for better decision making and will serve as the invaluable foundation of a truly data-driven enterprise.

For the executive team, the key to maximizing this technology is to transition from an IT-centric view (big data IT infrastructure) to a business value-centric approach (how to leverage that data collected to optimize, i.e., a manufacturing process). Teams should seek data for use in solving key business questions or improve critical business processes to reduce cost, increase efficiency and improve quality. In short, you should be looking at how to transform the business and gain a competitive advantage in the market. You may be surprised by the innovation an organization can unleash simply by breaking down data silos and allowing more open accessibility to all of the organization’s data.

For businesses looking to turn big data into big value, its best to follow these steps:

  1. Strategize: Any changes in data strategy will require commitment from the executive team for up-front investment and room for experimentation through a few initial projects. Not all projects will succeed, but an agile project approach will allow you to fail fast and correct course quickly.
  2. Prepare: Create a joint task force of business domain experts and data scientists to identify and prioritize the highest value projects. If you don’t have the data science talent in-house, find a trusted partner to conduct the first projects hand in hand with the business stakeholder.
  3. Execute: Once you identify the high-value AI-enabled solutions that can transform your business, then it is time to be bold. Prioritize time-to-market over perfection and empower the joint task force of business domain experts and data scientists to run the project from beginning to end with strong support from the executive team.
  4. Augment: Afterwards, share the use cases internally for other teams to learn, invest to accelerate adoption. Remember, the operational deployment of AI as the ultimate goal since that is where the true ROI of AI will emerge.

While (big) data serves as the foundation, smarter, data-driven decisions deliver the business value.

About the Author

Dr. Michael Zeller serves as the secretary and treasurer for ACM SIGKDD, organizing body of the annual KDD conference, the premier interdisciplinary conference bringing together academic researchers and industry practitioners from the fields of data science, data mining, knowledge discovery, large-scale data analytics and big data. KDD 2020 will take place August 22-27 in San Diego, California. Zeller is also the current CEO of Dynam.AI, a provider of end-to-end AI solutions for your business.

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  1. Joseph Yacura says

    Excellent article.