How to Implement Augmented Analytics for Data-Driven Decision-Making
Data analytics is integral to modern business, but many organizations’ efforts are starting to fall flat. Now that virtually every company is capitalizing on data, analytics alone isn’t enough to surge ahead of the competition. You must be able to analyze data faster, more accurately, and within context.
It’s often hard to extract value from predictive models because they lack explainability and can be challenging to implement effectively. Augmented analytics — which automates more of the analytics journey through AI — can address conventional obstacles to make it easier to turn data into relevant, accurate and actionable insights. Here’s how to execute it effectively.
1. Clean Your Data Before Analysis
Overlooking the need for data cleansing is one of the most common mistakes hindering analytics accuracy. As it happens, this area is also an ideal application for augmented analytics. Using AI to clean and organize your data before you analyze it vastly improves your model’s reliability without adding too much time to your workflow.
Manual data cleansing gets the job done but is slow and error-prone. Letting AI do it instead saves time and reduces the risk of human error. You can even use generative AI to supplement your data sets with synthetic data for privacy or accuracy.
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2. Reduce Tedious Work
Similarly, augmented analytics should reduce human involvement in the most tedious parts of the process. Almost two-thirds of people say they don’t have the time or energy to do their job, making them far less adept at innovative and strategic thinking. You can avoid those outcomes and ensure you interpret your analytics effectively if you prevent burnout through automation.
Most businesses already recognize the need to automate the actual analysis of data, but you can go further. Automating the data preparation and interpretation phases will take much time and effort out of the equation, too.
3. Address Bias
As you automate more of the analytics process, you must consider potential biases in your interpretation. Augmented analytics can reduce bias by taking the human aspect out of the equation when analyzing data. However, AI can feature prejudices of its own, even increasing human bias in some cases as people use biased models.
Removing identifiers in training data that may produce bias is a good start. Ensuring explainability in any AI models you use is equally important, as is reviewing them to ensure you can trust their results. Regardless of how much you automate, humans should always have the final say.
4. Emphasize Visualization and Storytelling
Visualization is another key use case for augmented analytics. Data-driven insights are only useful if decision-makers can understand them, and visualization improves decision quality and speed. Consequently, using generative AI to create engaging charts, graphs and other graphic demonstrations of AI insights makes analytics more practical.
Storytelling has a similar effect. Generative AI can help you present analytical findings as a story, providing crucial context while highlighting the practical implications.
5. Implement Augmented Analytics Thorough Governance
As you engage in augmented analytics, you must develop and implement a formal governance strategy. Augmented analytics involves extensive AI and automation use, so being careless can lead to regulatory and cybersecurity issues.
Protecting sensitive data lies at the top of these governance concerns. Augmented analytics can help in this regard, too, as AI-generated synthetic data can replace real-world data sets that may contain identifiers. Ensuring model explainability, protecting training data sets from data poisoning attacks, and regularly reviewing these technologies are similarly important.
Augmented Analytics Takes Your BI Further
Business intelligence alone isn’t enough to remain competitive in today’s accelerated, tech-centric environment. Data scientists must refine their analytics processes through AI and automation to deliver insights faster, more accurately, and in a more easily understandable manner.
Every augmented analytics journey looks different, but these five steps lay the groundwork for any effective endeavor under this umbrella. Start here to take your analytics workflows further today.
Originally posted on OpenDataScience.com
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