As we start a new decade, the top trends for business analytics platforms are cloud, artificial intelligence, automation, on-device (edge) analytics, and augmentation.
Cloud ecosystems empowered with AI have matured greatly in recent years. Smart, augmented prediction and decision-making tools are at a stage where they are ready to be deployed across organizations, from the boardroom to the shop floor. The challenge is making sure your business is ready to use them.
Though cloud will continue to encroach on all areas of business computing – including analytics – the ability of platforms to integrate edge applications into their ecosystem is also increasing. This means that greater volumes and variety of data can be stored and analyzed locally, with no need for it to be sent backwards and forwards between the user and the cloud, for increased efficiency. In some cases, this is helping to alleviate security and privacy concerns, too.
This means the biggest barriers to benefiting from advanced analytics are certainly now organizational rather than technological. The barriers to entry are falling constantly, but not every business is yet ready to dive in head-first. However, the unique business challenges brought about by this year’s unprecedented global events mean that leveraging data-driven efficiency and growth strategies is more important than ever.
So, here’s an overview of the best and most popular analytics and business intelligence platforms at this time. They range from free, open-source solutions to fully serviced commercial packages – although most offer a free trial to give you a flavor.
Microsoft Power BI
Power BI is an end-to-end analytics solution that has the huge advantage of being immediately familiar to most information workers, due to the way it slots into the Office 365 ecosystem. However, it only comes as standard with enterprise licenses. Power BI has been around for nearly ten years now and has become the analytics workhorse of choice for thousands of organizations, but its most recent editions have placed it firmly at the head of the pack thanks to comprehensive and constantly evolving automation and augmentation capabilities.
Oracle Analytics Cloud
Oracle – the original king of the hill when it comes to databases – has in recent years revamped and relaunched its product and service offerings to fit with the cloud-and-AI era. Its natural language capabilities are among the most sophisticated in the field, accepting queries in more than 28 languages – more than any other platform.
Oracle is also pushing hard on the concept of an autonomous database. This means using machine learning algorithms to carry out many of the functions that would previously have required organizations to employ an expensive human database administrator to carry out. This includes data management, security updates, and performance tuning.
IBM Cognos Analytics
Cognos puts AI at the forefront of IBM’s own end-to-end analytics solution, enabling users to ask and receive answers to their queries in natural language. This means that rather than simply giving you graphs and charts to look at, it can explain what each one means, and point you towards the insights it thinks you should be getting. It also uses a high level of automation for its data cleansing and preparation functions, meaning the AI will automatically spot and clean up bad data, remove duplicate information, or highlight areas where something is missing. As with Microsoft’s solution, it can be run entirely in the cloud or installed locally on-premises, depending on your needs and the requirements of the data you’re working with.
ThoughtSpot is another comprehensive analytics suite that lets you query datasets in natural language and emphasizes a friendly, pick-up-and-play approach to analytics. It incorporates UI features that will be familiar to anyone who is used to social media – such as autonomously curated feeds providing real-time insights into what is going on with your data. This was a great move as social networks have evolved to become extremely efficient at generating user engagement, and adapting their innovation to fit an analytics platform has proven to be a winning formula. Its AI-powered assistant, SpotIQ, uses machine learning to understand what a user is thinking and make suggestions, pointing out insights that may have been overlooked or suggesting alternative methods.
Qlik is another key player that has made confident moves into machine learning-driven automation, most apparent in its Associative Engine that lets users see connections between important datapoints before they make a single query. Another advantage is that Qlik’s Data Literacy Project initiative is baked into the platform, which aims to ease the pain of introducing analytics tools across traditionally non-technical or non-data-savvy workforces.
Spark is a mature open-source platform that has been around for six years and has become incredibly popular during that time. That means there is a rich ecosystem of extensions and plugins, making it up to just about any enterprise analytics task, such as its MLib machine learning library. It also has a huge community of users and vendors offering support and assistance, so its applications are adaptable to workforces of differing levels of IT skill, and as you would expect it integrates easily with other Apache projects such as Hadoop
Sisense is another solution that has grown in popularity and developed a reputation as a market leader. It excels at allowing users to create collaborative working environments where they can slice, dice, and analyze data as a team, using its Crowd Accelerated BI features. Data can be lifted in from just about any source due to its “API-first” ethos, and its powerful but user-friendly web browser interface simplifies the process of getting started.
Talend is another very popular platform that has increased its automation capabilities in line with current trends towards machine learning and smart computing. It carries out automated data quality and compliance operations in the background to provide its users with faster access to better quality insights. It is also open source, meaning there’s a strong community of users to learn from and work with, and it’s simple to find example tools and templates for just about any job you might need to do.
Salesforce Einstein Analytics
Gartner’s analysts this year rank Salesforce’s Einstein engine as having the strongest capabilities when it comes to automated analytics. There are questions about how the company will integrate Tableau’s technology, which it acquired last year, with its existing and hugely popular cloud analytics tools. Salesforce practically invented data-driven marketing, and its customer relationship management (CRM) tools have been industry-standard for years. Today, it is continuing to live up to its reputation as a leader of innovation, enabling a level of automation that competitors are struggling to match.
SAS continues to offer one of the world’s most popular BI platforms. Trusted by thousands of companies worldwide, SAS has built out its visualization capabilities with the release of the Visual Analytics component and worked to enhance its automation capabilities. It’s designed to let users keep their entire analytics workflow on one unified platform.
By Bernard Marr