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TDWI Upside - Where Data Means Business

The Word of the Year in 2020 Will Be Augmentation

The upcoming decade will see revolutionary changes to business across industries. Much of that change will be driven through analytics; 2020 will be a launching point and will have a heavy focus on augmented analytics.

As we come to the end of a decade, we have time to reflect and look back at the significant changes that have happened in the last ten years in the area of business intelligence and analytics. Bill Gates once said that "we always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten."

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Guided Analytics and the Future of Data Science in the Enterprise

Choosing Augmentation Over Automation in AI

At the beginning of the decade, BI was focused primarily on reporting and metrics, but in the last ten years we have seen the rise of big data and its associated technologies, the end of the dominance of SQL databases as the only place to store an organization's data, and the widespread adoption of new languages, methodologies, architectures, and infrastructures to support a broader analytics domain more closely aligned with organizational strategy. CIOs and CEOs repeatedly put analytics and business intelligence at the top of their list of areas for investment for the coming years due to its transformational impact on the organization.

When we look ahead to 2020, the word that comes up over and over again is augmented. When we look to the coming decade, it is highly likely that the change we will see will usher in a new generation of artificial intelligence and will have a significant economic impact on society and the job landscape. We will probably see an innovation that is in its infancy today or that hasn't yet been envisioned that will have a major disruptive impact on the analytics space. However, 2020 is destined to be a stepping stone to that future with increased adoption and transformation through the use of augmented analytics.

In the next 12 months, it's highly unlikely that robots will replace large segments of the workplace, but what is very likely is that analytics will be incorporated into more business processes, more decision-making activities, and more work plans than ever before. The goal of these analytics processes will be to augment workers' capabilities, taking them to the next level of incremental efficiency. This goal is accomplished as automation is used to take over the mundane, repetitive aspects of a job and allow the worker to focus on those parts of his or her job that have the most value to the organization. Augmented analytics is the pairing of humans and machines to make work as effective as possible. It is about human amplification, not human replacement.

When we look at the analytics space, the word augmentation generally shows up in three main areas: data preparation, data discovery, and data visualization.

Augmentation and Data Preparation

In the data preparation space, we see that machine learning is being utilized to streamline data management. We see statistical augmentation being leveraged as part of data quality programs to perform tasks such as entity matching and merging, data munging, and data enrichment and inference to fill the holes that exist in the data. Also, methodologies are being used to create statistically consistent synthetic data sets whose purpose is to enhance the training of valuable machine learning algorithms. We are also seeing major DBMS providers looking to artificial intelligence as a basis for auto-tuning their performance, augmenting the database management team so it can focus on more strategic projects.

Augmentation and Data Discovery

In parallel to data preparation, we will continue to see increased utilization of automated data discovery tools to isolate anomalies in the data that need further research. By using clusters that are constantly learning from the data and adjusting their boundaries, identifying outliers to these clusters becomes a more effective process. This automated data discovery step allows data science teams to focus more on explaining the reasons for current outcomes and creating algorithms that better predict and anticipate future outcomes. Augmenting their capabilities allows businesses to shift their efforts from the routine descriptive analytics to higher orders of analytics, including diagnostic, predictive, and prescriptive analytics.

Augmentation and Data Visualization

Today's analytics tools are becoming smarter and more capable of automating the process of creating data visualizations. With the use of augmented analytics, users can now start to point their visualization tools at a set of data and have the tools present them with intelligently chosen visualization options that eliminate much of the up-front preparatory work. This augmentation allows users to spend more time interpreting the results rather than building visualizations around the data.

Also, with the increased capabilities of natural language search and natural language answer generation, tool providers can become much more user-friendly in the way that they present data options to end users. These natural language technologies will fundamentally change how self-service analytics are structured and executed and can lower the barrier to entry so companies can take on initiatives to create a more data-literate user base. With these increases in capabilities, data storytelling will become a more sought-after skill than simple data interpretation. This move to data storytelling will move data scientists further towards alignment with the business than with the IT department.

Changes Ahead

With these advances in augmented analytics, machines will not be taking over jobs in 2020 but instead will focus on increasing the capabilities of users and removing the need to spend time and energy on repetitive tasks that can be automated. These advances directly translate into a more capable and efficient workforce that can focus on activities that directly correlate to corporate objectives.

The decade ahead is destined to be an exciting one with great advances in analytics, and 2020 will be a stepping stone to that future. Many of the highlights that we will remember as we look back at the end of next year will be focused on how augmented analytics has made businesses more effective, especially in data preparation, data discovery, and data visualization.

About the Author

Troy Hiltbrand is the senior vice president of digital product management and analytics at where he is responsible for its enterprise analytics and digital product strategy. You can reach the author via email.

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