3 Data Analytics Trends to Keep Track of in 2019

2018 was an exciting year for data analytics. At various conferences and events on the subject, it was clear to see how rapidly the technology in this area is developing and how fast new application fields are growing. The year has just begun, so I would like to take this opportunity to give you a brief outlook on what we can expect in 2019.

1. Artificial Intelligence will optimize analytics.

There are concerns about the increasing use of Artificial Intelligence (AI) and its possible negative impact on jobs. But in the near future, AI is expected to create more jobs than it replaces. Consulting company Gartner forecasts that AI will create 2.3 million jobs worldwide by 2020, while eliminating 1.8 million jobs at the same time. This positive trend suggests that there will be fewer concerns in the future and that the importance of AI will steadily increase.

This also applies in particular to the use of AI in data analytics, especially because of two developments. Firstly, there is a huge gap between the data produced and the human capacity to process and respond to it. Secondly, there is a gap between the high availability of today's analytics tools and their limited use in enterprises. Both gaps can and should be closed. AI can help to eliminate bottlenecks along the entire information value chain, from data acquisition to preparation, critically analyzing with less distortion and presenting contextual results. Because with AI, people have more time for what they do best - that is, for considering complex issues and problems in context, and connecting nonlinear points through intuition and empathy.

2. Data visualization, presentation technologies and conversational analytics merge.

Those of us who love data are convinced and compelled by it. But when we try to share our passion with others, we often disclose data without providing any context or background story, thereby losing our audience. Most people are more responsive to stories than facts. Studies have shown that people remember stories up to 22 times more than facts alone.

Today, most stories about specific data are created using presentation software. However, most software does not yet include user-friendly methods for telling these "data stories", in which visual elements can complement the data results. For example, conversational analytics can support this approach and make data analytics and presentation much more interactive. Over time, data visualization, conversational analytics, and presentation technologies will gradually merge. A combination of all three could increase data adoption, help more users to express data and analytics in a more compelling way, and more people who don't work with data on a daily basis will become more involved in analytics. There may also be an overlap in the skills of data analysts and graphic designers.

3. Data literacy becomes a KPI.

In addition, data literacy will continue to gain importance in 2019. Data literacy was not really measurable for a long time. However, new methods for measuring data literacy are emerging that will allow companies to better target employee skills and score data literacy. If you want to improve data literacy in the enterprise, you need to first identify your current position in comparison to other companies.

This is particularly exciting because I think it will show that there is a strong correlation between a company's data literacy and its performance in metrics, such as gross margin, return on investment, return on equity and return on sales.

Closing Thoughts

The data analytics domain is constantly changing. Accordingly, the systems, key figures and skills of employees also have to continually face up to new challenges and develop further. The distribution of AI plays an important role here, as does the competence of the employees to present data in an appealing way for a growing audience and to use the company data successfully. It's an exciting area, and I'm excited to learn which trends will come to fruition this year and which will reemerge.

What trends do you expect in data analytics for 2019? Which trends do you find particularly exciting?

 

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