The Road to Augmented Analytics

Posted by Jay Milligan, CEO KPISOFT-Americas on Oct 10, 2018 7:36:36 AM
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Has your organization explored the principles of augmented analytics? As data increasingly becomes available to users across organizations, now’s the time to become fluent.

Analytics, it seems, are everywhere. Many companies may find the flood of analytics as overwhelming as the flood of data that preceded it. But if your organization wants to make sense of all that data and make the most of opportunities in strategy, operations and people, it needs to understand how augmented analytics can enhance its business.

Augmented analytics use tools such as machine learning and natural language generation, providing the most significant insights to make better, unbiased decisions. This is critical as data volumes become increasingly complex. Organizations are struggling to get a handle on the sheer amount of data available, and sorting through it to make sound decisions is increasingly difficult.

Fortunately, the technology is responding. By 2020, natural language generation and artificial intelligence (AI) will be standard features on 90 percent of business intelligence platforms. Also, by 2020, half of analytics queries will be generated through technologies such as natural-language processing and automatic generation. These tools will help organizations process and analyze data, making the flow of information more manageable and, ultimately, improving business performance.

Then there are the applications that will “speak” to users. Conversational analytics will comprise a key feature of these technologies within the next two to five years. Already, there is an emerging set of applications that allow users to search or receive and act on insights in natural language, such as voice assistants. We expect to see vendors apply these technologies and build applications with advanced analytics capabilities in the near future.

Why are these innovations so important? For one, augmented analytics can reduce time-consuming exploration of misleading or less valuable insights. By applying a combination of algorithms and ensemble learning, the technologies can lower the risk of missing key insights compared to manual data review.

Part of the solution involves data preparation, improving quality and modeling data to make better use of the information. There’s also augmented data discovery, which permits machines to automatically identify and visualize correlations, clusters and predictions without the need to write algorithms. This can save both time and money.

Machine learning can further refine augmented analytics by automating key aspects of modeling, such as feature selection – reducing the need for specialized skills to generate and manage advanced analytics models.

Some organizations need to think of their AI capabilities along a readiness curve. At the low end of the curve are organizations that focus efforts on business dimension modeling, while those at the top of the curve are able to use augmented analytics to predict, recommend or even evade risks that could affect their organizations.

While this, too, may sound overwhelming, preparing your organization for the democratization of data doesn’t require the levels of data warehousing investments that companies needed in the past. Enterprises still need a comprehensive policy on governance – one that creates a risk profile for all employees and sets guidelines for access. Organizations also need a strong framework on which corporate strategy, operations and people performance can thrive. That’s why KPISoft’s proprietary technology works with a company’s existing data to create a single intuitive platform, creating seamlessly integrated business performance data.

Organizations that intend to ride the wave of the trend need to take a hard look at their investments in data literacy. After all, data insights cut across functions and employees.

For more from KPISoft on augmented analytics, see our latest report.

Get in touch: Jay.milligan@kpisoft.com

augmented analytics

Topics: augmented analytics, machine learning, business analytics, artificial intelligence, augmented data

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