This TDWI report, Driving Digital Transformation Using AI and Machine Learning, reveals the latest research on the business intelligence and analytics market, as well as best practices for a successful transformation.

TDWI specializes in educating business and IT professionals about the strategies and tools necessary to create, maintain, and enhance data and analytics across an organization.

Designed for business leaders, this report explores AI and machine learning as they’re currently implemented and where these technologies will go in the future.×

Click here to download “Driving Digital Transformation Using AI and Machine Learning.”

This TDWI report covers topics like:

How AI is Used in Digital Transformation Right Now

Why do 90% of respondents surveyed in this report think AI is a competitive advantage?

From automating work to augmenting intelligence, the use cases for AI are growing, as are the investments companies are putting toward deployments.

AI has already arrived. What do businesses need to know to capitalize on this technology before they get left behind?

The State of AI and Augmented Intelligence

Half of respondents believe automating the generation of insights with augmented intelligence tools is a dominant use case for AI.

This report dives further into the ways humans can augment their own intelligence and workflows with AI to make smarter decisions.

Plus, it discusses the nuances of machine learning and why it dominates AI technology. As stated in the report:

“BI and analytics solutions can also employ machine learning to explore data automatically and spot trends and patterns that users working with standard querying and reporting capabilities may not have seen.”

The Case for Open Source AI

Concern over the “black box” and biased insights aren’t unfounded, which is why transparent, open source AI technology is critical.

Survey respondents are “big believers” in tools such as R and Python. Open source AI vendors recognize the value in allowing IT departments to deploy their own models within a proprietary solution.

Success Factors for Digital Transformation with AI and Machine Learning

Do you know what data infrastructure you need to support AI? Quality data and streamlined pipelines are critical. Having the right team is paramount, too.

The best practices outlined in this report explain which users can leverage augmented intelligence analytics and how to determine the skill sets needed for successful AI deployment.

Get the report recommendations for a successful digital transformation with AI and machine learning.

The post How to Drive Digital Transformation With AI and Machine Learning first appeared on AnswerRocket.

Yet, not all solutions are designed to meet the unique needs of large-scale enterprises. Here, we cover 6 key enterprise-grade capabilities to look for in an augmented analytics platform.

But first, let’s define advanced self-service analytics.

What is Advanced Self-Service Analytics?

First, self-service analytics refers to business intelligence (BI) platforms that allow business users to access and interact with their data directly, instead of relying on a technical team member like a data analyst to compile data for them. 

Self-service analytics enable business people to get the insights they need to act, all while streamlining data access and combating bottlenecks caused by routine reports.

While self-service analytics have grown in popularity, advanced self-service analytics bear distinctive traits that elevate them above their more routine counterparts.

According to Advanced Analytics: The In-Depth Guide:

“Advanced analytics leverages AI-based technologies in business intelligence tools to produce deep insights that help business people uncover and understand the stories hidden in their data. Advanced analytics combines technologies like machine learning, semantic analysis, and visualization to automate analysis.”

This automation of data analysis is key. Next-level analytics should provide deep, contextual, and user-friendly insights for business users without technical expertise– and theses solutions should provide these insights in seconds.

Now, let’s discuss the features needed to scale advanced self-service analytics solutions to the enterprise level.

Enterprise Capabilities for Advanced Self-Service Analytics

1. Open Data Platform

An analytics platform is only as valuable as the data that it’s connected to. As an open solution, AnswerRocket can flexibly connect to your existing data platform, whether it’s on-prem, in the cloud, or a hybrid solution.

We currently support over 25 different data platform providers and are continually adding new ones.

We can also host your data for you.

2. Security & User Administration

Security is a priority for AnswerRocket. Admins can customize functional permissions and set row-, column-, and table-level security to ensure that access to sensitive data— such as employee performance— is carefully controlled and governed.

Admins can also define entitlements guiding access rights at the user-, role-, and group-level. Authentication is handled with Active Directory and single sign-on integration.

3. Metadata Management

What good is self-service analytics if you can’t trust the answers?

AnswerRocket enables users to leverage a centralized semantic model and metadata.

This management feature helps establish a single source of truth capable of generating consistent, accurate answers you can have confidence in.×

See AnswerRocket’s enterprise capabilities with a custom demo of our advanced self-service analytics solution.

4. Data Storage and Loading

One of the biggest benefits of a solution like AnswerRocket is being able to access all of your important data in one place.

ETL tools allow you to extract, transform, and load data into a self-contained storage layer. Easily index data, manage data loads, and refresh scheduling.

5. Support for AI & Machine Learning Libraries

If you have existing AI and machine learning algorithms in use, then having an extensible analytics platform that can leverage those assets is key.

In addition to the AI automation and ML algorithms included out-of-the-box, AnswerRocket also makes it easy for you to build and operationalize your own machine learning algorithms.

Use any open source machine learning library, such as TensorFlow or scikit-learn.

6. Branding and Personalization

Need to provide a seamless experience for your team members?

The AnswerRocket platform can be white-labeled to reflect your company’s logo and branding. Customize colors to complement your style guide. Define your preferred format and default colors for visualizations to ensure they work harmoniously within your company’s templates.

Each of these capabilities is important for an enterprise-grade analytics solution.

Ready to see an example in action?

Get a custom demo of AnswerRocket.

The post Advanced Self-Service Analytics: 6 Must-Haves for Enterprise first appeared on AnswerRocket.