Best Machine Learning Tools for Automated Insights

Companies are investing millions into machine learning (ML) and artificial intelligence (AI) tools that can give them data insights that translate into real business value.

Data insights refer to the understanding of business phenomena by analyzing a dataset using ML and AI technology. For example, an ML model that estimates customer churn rate will reveal the factors that cause churn, and with this information, business managers can change their processes and strategies.

Insight generation through analytics and business intelligence (BI) has been around for more than five decades, but trained analysts usually did the task. Many analysts of the past relied on experience and intuition as opposed to the data. In addition, the technologies available were difficult to use, which made it hard for most people to find and analyze data.

Today, several machine learning tools offer users automated insights. These tools overlap three broad software categories, business intelligence, predictive analytics, and insight engines.

Also read: Gaining a Competitive Edge Using BI and Data Management

Features of ML Tools for Automated Insights

Before we get into the tools we found useful, below are some features to look for when investing in an ML tool for automated insights.

Self Service Analytics: Self-service analytics with regard to data preparation, visualization and creation, and collaboration and sharing is a must-have feature. The tool should help users to tap into insights without waiting for IT to provision resources. Users can combine several data sources for visual analysis and create reports and dashboards through self-service analytics capabilities.
Analytics Apps and Dashboards: Large groups of less-skilled users require more than basic reports. They want a mechanism to search and examine data, discovering patterns, connections, and insights that lead to sound decisions. Interactive dashboards and guided analytics applications allow you to do so, benefiting a wide range of business users, executives, and managers.
Custom and Embedded Analytics: With open and standard APIs, you may construct completely customized applications for almost any scenario or purpose. Embedded analytics also allows you to embed dashboards and visualizations into operational apps, allowing even more people to discover insights into your data. Software and application developers will develop custom and embedded analytics, but everyone in your company will benefit from them.
Mobile Analytics: Mobile analytics provide insight value that extends beyond the workplace walls, allowing any employee in your company to access data on almost any device. Salespeople, field business users, and executives benefit from these features the most.
Reporting and Alerting: The ability to record and analyze data is a fundamental business need, so any platform you pick should make these activities intuitive and straightforward. Operational teams and executives are more passive users, making reporting and notification essential.
Platform-Wide Analytics Capabilities: Consider the basic set of capabilities that will make your job easier. The best modern analytics platforms require a robust underpinning in easy data access, widespread deployment choices, and centralized administration.
Governance and Deployment: New machine learning tools and applications can create a new set of challenges with data security, privacy, and governance. A platform that enables centralized administration and deployment helps you gain control over your machine learning initiatives.

5 Best Machine Learning Tools for Automated Insights

Here is how our top 5 machine learning platforms compare.

1. Qlik Sense

Qlik Sense is a self-service analytics platform that enables users of all kinds across an organization to discover and understand data. It supports the full spectrum of analytics applications, including self-service visualization and exploration, guided analytics applications, custom, and embedded analytics, mobile analytics, and reporting. And it does this with a governed, multi-cloud architecture that gives organizations maximum trust, scale, and flexibility. Qlik Sense offers an enterprise analytics solution that empowers everyone in your organization.

For businesses, Qlik Sense comes with open and standard APIs for customization and expansion, significant data integration, extensive data connectivity, and data-as-a-service (DaaS) while also supporting centralized management and governance.

The platform that can be used across hybrid, multi-cloud, and on-premises environments. Qlik Sense Cloud, the SaaS-based solution for organizations and teams to create and share analytics online, is available both on-premises and across multiple clouds.

There are two pricing tiers Qlik Sense Business that starts at $30 per user per month (billed annually), and Qlik Sense Enterprise. In addition, the company offers a free trial.

2. Looker

Looker for Google Cloud Platform is changing the way businesses analyze their data. Looker functions like the internet, being browser-based and allowing any employee to utilize your best data analysts’ work.

Looker helps organizations build a discovery-driven culture from the ground up. Its web-based data discovery platform provides the horsepower and finesse needed by data analysts while also empowering everyone to discover their own solutions.

Looker breaks the mold by reinventing business intelligence (BI) with a new data discovery solution that transforms BI into three key areas.

Users can now operate on big data and discover the last mile of value in the new era of fast analytic databases, thanks to a simplified web-based stack that utilizes Google’s 100 percent in-database architecture.
Data users can model data and produce end-user experiences that make sense for each particular company, rather than transforming the data on the way in.
Looker’s self-service data discovery leverages the browser. The self-service data-discovery experience works like the web, allowing business users to delve into and examine massive datasets without ever leaving the browser. As a consequence, Looker customers enjoy the advantages of conventional business intelligence at internet speeds.

Pricing information is only available by requesting a quote on the company website. However, on the upside, you can try out the tool for free for a limited period.

3. Sisense

The Sisense data and analytics platform makes it simple to connect disparate data sources to create robust, actionable analytics applications that can be embedded anywhere. As a result, thousands of businesses worldwide, including innovative startups and global brands such as General Electric, Wix, Nasdaq, and Philips, use Sisense to accelerate innovation and drive digital transformation by embedding Sisense into their operations.

Sisense can help you meet your analytical application strategy’s needs regardless of where your data is kept: in the cloud, on-premises, or a mix of both. It gives you everything you need to successfully deliver today on your analytics application strategy, allowing you to do the following:

Create a single store for your data, securely connect and ingest data into cloud data warehouses.
Get a comprehensive report by combining data from several sources.
Manage resource usage and optimize performance by combining live or cached data models.
With the comprehensive library of integrated widgets and certified add-ons, you can construct stunning visualizations and operational application components.
Use a complete suite of APIs and developer toolkits to develop and white-label more quickly.
For customers on the move, provide fully integrated, white-label analytics in the office or on their mobile devices to help them differentiate products and services.

Whether you’re trying to raise an organization’s analytical maturity or boost its products and services by incorporating analytics, the Sisense Single-Stack approach has everything you need to transform complex data into branded, embedded analytics that your users will appreciate. Sisense lets you go from web portals to bespoke apps — even chatbots and Alexa — without sacrificing the quality of service or lowering the total cost of ownership at scale.

Sisense provides pricing information on a quotation basis and has a free trial.

4. Exago

Exago BI is a web-based, entirely embedded business analytics package that IT firms may use to offer ad hoc and operational reporting, interactive dashboards, and scheduling to their customers.

It’s an entirely white-labeled, end-to-end business intelligence solution that connects with your web-based SaaS or on-premise application and allows non-technical individuals to build reports and dashboards without the help of IT.

Exago allows you to create interactive data visualizations, linked dashboards, drill-downs, user-defined formulas, and geo-maps. You can also merge data pre-defined pixel-perfect RTF, PDF, and Excel templates.

Exago’s open API, extensibility features, and completely customizable user interface give technical staff the freedom and control to mix Exago into their existing infrastructure and across several applications and data sources without the need for plug-ins.

The only drawback is that it lacks advanced analytics capabilities. However, it makes up for this by having strong data updates through real-time updating and email reports.

It also works with Chrome, Internet Explorer, Firefox, and Safari on PCs and is available for mobile devices.

The price is available on request at the company website.

5. Dundas BI

Dundas BI is a highly adaptable, end-to-end business intelligence platform that streamlines the entire analytics cycle and allows everyone to see and analyze data. As a result, users may build their dashboards, produce their reports, perform ad hoc queries, discover visual data, and communicate with others.

Users can connect and integrate with any data source in real-time and in-memory on any device, using the Dundas BI platform’s immense out-of-the-box capabilities and open APIs. In addition, users may create and view dashboards and reports from desktop to mobile devices thanks to the touch-based user interface and adaptive design.

Dundas BI also works with your existing systems, administration, and web portals to allow white-label access to data from within your apps. Dundas BI was created to be embedded and offers a degree of freedom unrivaled by other vendors.

One drawback is that Dundas BI lacks advanced data modeling and blending features.

The price is available upon request at the company website.

Considerations for ML Tools for Automated Insights

Businesses everywhere are using machine learning tools to reinvent customer experiences, reimagine processes, discover new revenue streams, and find new ways to gain a competitive edge. But, choosing the right platform is key to getting insights with the power to drive real change.

We have provided you with a comparison of five of the best tools we could find. But, to settle on the right platform, there are three additional critical considerations you must make:

Where and how will you derive value? What are your long and short-term goals? How will the platform drive value? How do you expect the insights you get to impact your business? For example, are you looking to understand your KPIs better, solve a specific problem, empower your teams to make better decisions, democratize analytics, uncover hidden insights, provide partners and customers with analytics, or support strategic decisions with advanced analytics?
Who are your stakeholders? Tools that help everyone become data-literate have more impact. Knowing who your stakeholders are will help you select the right tool.
Total Cost of Ownership (TCO). The TCO can include many things in addition to the initial licensing. As you seek competitive pricing, ensure you are comparing apples to apples. Also, watch out for hidden costs.

By integrating machine learning capabilities into self-service BI platforms, advanced analytics can be brought to the masses in a way that is intuitive and easy to use without expertise in machine learning or coding. These machine learning tools have become imperative today because they allow companies to predict future events such as market behavior, making better decisions faster than ever before.