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Business intelligence is advancing and telling us more of what we need to know.

How, one asks? Machine learning has improved speed, reliability and ultimately the value of the most commonly used BI and analytics solutions, leveraging immense volumes of disparate data at close to the speed of thought.

Augmented data analytics is the use of ML and natural language processing (NLP) to enhance BI, data analytics and data sharing. This extends the value of an organization’s data foundation and provides near-real time business information. Traditional decision support systems struggled to process data in a timely manner, whereas today, petabytes are being processed faster than ever, offering quicker time to value.

Augmented data analytics is already becoming a dominant driver in new technology platform purchases.

In Q4 2019, Microsoft released Automated ML in Power BI. According to Microsoft, “With Automated ML, business analysts can build machine learning models to solve business problems that once required data scientist skill sets. Power BI automates most of the data science workflow to create the ML models, all while providing you [sic] full visibility into the process.”

A recent Gartner study found human decision making is increasingly inadequate given our expanding digital world. According to Gartner, “Data science and especially ML excel in solving highly complex, data-rich problems.”

Forward-looking data leaders should plan to adopt augmented data analytics as platforms mature. Below are some key thoughts and considerations on the value and importance of such adoption.

Augmented Analytics will enhance the human experience but will not replace the human

Augmented analytics solutions cannot fully replace critical human thought. Technology and business leaders should be thinking more strategically about the use and value of their data.

A recent Forbes article profiled Grammarly, Inc. and its use of augmented AI. The article shared the value and benefits of augmented AI while reinforcing the belief that AI enhances the lives of humans more than it displaces or replaces them.

Grammarly is a good example of augmented AI because it uses very sophisticated (possibly recursive) algorithms to make determinations. An example being, improper use of grammar, resulting in recommendations on how to re-phrase. Rather than replacing the writer, it points the author in a particular direction, providing a degree of editorial expertise so he/she can publish with high confidence and/or reduce an editor’s burden of basic grammar checking.

Augmented analytics is also improving the healthcare industry. Studies show that for every hour spent with patients, physicians spend two additional hours researching and documenting clinical interactions. Technological advancements will enrich people’s lives, improve disease prevention and identify more effective treatments. ML and AI will not replace the doctor. Rather, it enables doctors to spend more time on patient care.

A well thought out, socialized and leadership-sponsored use case enables organizational adoption

Seeking new ways to utilize data to identify business value is tantamount to business success. Leading practices show that the journey begins with a relevant, clearly defined and well-understood use case. Companies are seeking new ways to leverage internal, external, structured and unstructured data to advance operational efficiency, improve profitability and increase market share.

  • What are the insights you hope to obtain?
  • What are you looking to measure?
  • What do you hope to achieve?

These questions should be addressed at the start the journey. Establishing a set of prioritized use cases at the functional level will ensure clarity and strategic guidance for each initiative and provide criteria for measuring success. Use cases help create business cases that provide both IT and the business a common framework to deliver maximum value.

Data governance capabilities are critical to success

Data governance optimizes the value of data to meet an organization’s objectives while enabling adherence to regulatory requirements and managing related risk. Efforts to enhance data as a critical corporate asset should focus teams on holistic approaches that incorporate ethical and behavioral governance as a core component.

Implementation of a governance model will provide a framework for industrialization of current and future capabilities and help the organization capitalize on the value of their data. Innovative organizations will ensure proper governance capabilities are in place to harness the true potential of monetizing data assets. Successful governance programs require a true collaboration between business and IT with proper accountability.

In the example of augmented analytics improving the delivery of health care, AI must be designed in a trustworthy manner and create solutions that reflect ethical principles: fairness, reliability, privacy and inclusivity. Transparency and accountability are key to building trust in AI within healthcare, and across all applications and industries.

Looking to the future

In summary, data management and AI technology will continue to advance and the solutions will help us live more productive, healthier and happier lives. Organizations should embrace change, stay vigilant, collaborate and watch for opportunities to harness data to improve profitability, reduce costs and increase revenue. Continuous measuring, monitoring, governing and adjusting the data, analytics and AI solutions are paramount to success.

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