How Are Automated Data Analytics Tools Becoming More User Friendly?

How Are Automated Data Analytics Tools Becoming More User Friendly?

Big data projects often fail due to a disconnect between business executives and the data science process. Because data analytics projects are complex, it takes a significant amount of time, as well as deep expertise and domain knowledge, to benefit from them.


The adoption of data science and artificial intelligence (AI) is growing exponentially, and they work well together to make the job easier for business users like marketers and sales executives.


IDC projects worldwide revenue of big data and business analytics solutions to hit $189.1 billion in 2019, a 12 percent increase from 2018. But despite the growth in data science and AI, there's a rate of failure that comes along with many of these initiatives and projects. In fact, Gartner reported that 85 percent of AI projects will fail through 2022 due to bias in data or algorithms or because of failure of the teams managing the data. That's because of the difficulty inherent in implementing data analytics solutions.


Companies need to address a gap between business executives and their understanding of AI and data science projects. Business users sometimes lack knowledge or understanding of data analytics tools.


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Making Data Science Digestible

Only 36 percent of business professionals in a recent PwC survey described themselves as “highly data-driven." This stat underscores the need to make analytics tools easier to use for business professionals. Meanwhile, only 4 percent of companies successfully use business intelligence and AI tools, says Ryohei Fujimaki, founder and CEO of dotData, a data science automation company.


Gartner refers to everyday business users of analytics software as “citizen data scientists." Moreover, Deloitte notes that data science is becoming democratized" through automation, which enables a technology process to operate without human intervention.


Analytics has traditionally been handled mostly by highly trained data scientists. But now, automation enables business professionals to perform analytics. Automated data analysis platforms help business users with everything from database aggregation to choosing and evaluating algorithms.


Now, there are powerful software solutions that can help marketers develop customized data-driven analytics. To help ensure the success of your AI projects, you should consider the benefits of automated data analytics, in which software does much of the heavy-lifting. Moreover, automated data analysis can bridge the gap in understanding between C-suite executives and data scientists by making data analysis easier to implement. For example, business users can analyze data without the need for an understanding of algorithms or complex data models.


Data analytics tools, such as Ki, draw on information from data sets to help analysts and marketers come up with successful business strategies. By using automation, companies can train machine learning models to help business professionals uncover data patterns without the skills of a data scientist and just a click of a button. In fact, Gartner predicts that businesses will automate more than 40 percent of data science tasks by 2020.


They can also automatically generate insights related to common business questions, like how to reduce customer churn. These types of analysis need AI to process vast amounts of information that could not be handled manually.

AI as a Game-Changer

To overcome that skills gap between business users and data science projects, companies should keep innovating and turn to AI to improve their key performance indicators (KPIs). It can help marketers make better decisions to improve their business by helping to gain insights on groups of customers. Automated data analytics applications can also pull data from your customer relationship management (CRM), enterprise resource planning (ERP), and accounting software.


What's more, a tool like Ki gives business professionals an easy-to-navigate user experience — without having to worry about coding. Ki automates that functionality, and even ranks the business insights it collects.


Still, the use of AI in marketing is still in its infancy. The new software solutions can level the playing field between the business professionals and trained data scientists.


An automated data analytics tool that uses AI is worth pursuing because it ultimately puts the power of analytics into the hands of front line marketing teams. Thanks to a combination of time savings, customer segmentation benefits, and actionable insights, a data-driven analytics platform that features automation can be a game-changer for your business.


If you'd like to learn more about how an automated analytics tool can help your business, download the 4 Step Automated Process brochure.



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