Putting AI and Machine Learning to Work in Cloud-Based BI and Analytics – AiThority

Artificial intelligence (AI) and machine learning (ML) are powering a whole new generation of business intelligence (BI) solutions. And these mission-critical software packages are in turn one of the primary drivers behind the migration of enterprise big data to the cloud.

BI tools are designed to collect and analyze current and actionable data delivering insights into processes and workflows that can impact business operations in the near term. But what if you need those insights immediately, and you need them in the hands of employees and experts who are working simultaneously across the globe? IT stakeholders are turning to the cloud for faster, more accurate and timelier BI insights especially in the face of Covid-19 where companies are looking to operate as economically possible and millions are forced into remote working locations. Even before the pandemic, a 2019 survey by TechTarget found that 27% of respondents plan to deploy BI in the cloud in the coming year.

That same study points to an increase in cloud technology as the number two activity that companies are employing to improve employee experience and productivity, and notes that 38% of companies plan to bolster their cloud technology within the next year.

There are multiple reasons that organizations are moving their BI and analytics to the cloud.

First among them is cost: The move streamlines a workforce, so even though there are start-up costs involved in the migration process, the long-term cost-benefit analysis plays out in their favor. Companies are also able to run faster and lighter with cloud-based BI, with no need to run dedicated client-side applications and IT teams freed of the necessity of coordinating upgrades across an entire infrastructure.

Then theres security: Companies tap into a whole extra layer of security and protection for their data as there is only one point of access, and data cant accidentally be merged with another companys, or worse, intentionally and maliciously accessed by someone who does not have access.

Accessibility will also improve, as companies will be no longer tethered to one distinct physical location to store data. When their BI systems are migrated to the cloud, it offers real-time access to critical data and analyses from any laptops, tablets and smartphones, meaning that access to the information required to make better business decisions is constantly within reach.

Scalability will also jump dramatically, as the cloud offers an elastic infrastructure that provides a simple platform for scaling up as a company grows.

And, performance is enhanced since cloud infrastructure is customizable to each companys specific needs. An added benefit is centralized collaboration, allowing entire teams to work within the same framework with the same tools, no matter how scattered or far-flung they might be.

TDWIs recent report on BI and analytics notes that demand is rising for systems that can provide views, analytics, and prescriptive recommendations based on data generated by events happening now and predictive insights into what could happen in the future.

A vivid example of clouds analytics advantages is the use of Spark, with its extremely high memory demands. The elasticity of the cloud enables Spark to perform orders of magnitude faster than Hadoop/Hive on-prem. The differences can be dramatic: a 10- to 12-hour Hive query can literally take only 15 minutes with Spark in the cloud.

Increasingly, cloud big data vendors and their customers have rich AI-driven BI ecosystems at their disposal, like Snowflake and Tableau (which was acquired by Salesforce). For those using Apache Spark, Databricks provides a unified analytics platform that accelerates innovation by unifying data science, engineering and business with an extensive library of machine learning algorithms, interactive notebooks to build and train models, and cluster management capabilities that enable the provisioning of highly-tuned Spark clusters on-demand.

Businesses of every size are learning that leveraging AI technology can improve business processes and significantly enhance the customer experience. This is happening across several industries healthcare, finance, and life sciences (despite heavy regulation) are quickly adopting AI-driven business models, and AI is transforming medicine in how and when treatments are discovered and tested.

Cloud computing has completely transformed entire industries, computing paradigms and enterprises, and has become the ideal for storing and accessing big data.

The COVID-19 pandemic has only accelerated this move given the need to operate as economically as possible with more employees working remotely. Cloud computing saves both money and time, which makes it immediately attractive to businesses, while also increasing access for global companies, providing a synergic platform for coordination and cooperation between far-flung employees, and it creates an impressive security buffer through a single point of access that ensures companies data its most precious asset and its most critical investment is protected from malicious actors. AI-powered business intelligence and analytics are driving the migration of enterprise big data to the cloud.

Choosing the right BI platform can dramatically enhance productivity with unprecedented business insights, and a more intimate knowledge of customers and trends.

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Putting AI and Machine Learning to Work in Cloud-Based BI and Analytics - AiThority