The Advanced Game of Data Analytics & AI

We have started to just scratch the surface. We are far from implementing and witnessing AI’s potential to its full extent. AI is refining business processes, rediscovering interactions, and redefining markets. Above all, AI is self-learning and getting smarter and faster.

During the current economic crisis, the adoption rate of AI and Data Analytics has skyrocketed. This is no surprise, as digitally laggard enterprises have witnessed how their digitally transformed business peers are excelling in business continuity and have an upper hand in market precedence using AI tools.

Enterprises are shifting towards few specific trends in Artificial Intelligence & Data Analytics paradigm to create new opportunities and solve business challenges:

• Robot Economy: Covid-19 has instilled the idea of touchless services and companies are fast-tracking to place physical robots taking over requisite tasks. Supply chain operations are at a standstill with lockdowns, and companies that earlier started to experiment with robot workforce are now rolling them out a scale.
• AI complementing humans: AI will not supersede humans. First, in the wake of the Covid-19 crisis, it's a new opportunity to reskill the workforce and utilize its capabilities to strategize and streamline operations using data insights. Human intervention is the ultimate decision taker of the context and quality of AI analysis. It's time to invest and upskill workforces in various sectors to work using data-driven decision-making.
• Augmented analytics and data management: Automated Deep learning techniques will enhance business optimization through real-time decision making. This is powered by data analytics through real-time data. Data management will be boosted by deep automated learning, which will selfly scrub data through multiple data sources, organize them in a structured manner for use in augmented data analytics platforms.
• Multiple Data sources to perfect AI: A data-driven enterprise is genuinely one when they create a data backbone, that incorporates and siphons in all data from all the bases in an enterprise and create a shared data fabric for AI to perfect its skills and utilize the data fabric to shape data for usage in various AI models within an organization.
• Commercialization of Machine Learning: Now that AI is the most important tool for fast digital transformation of businesses inside and out, AI boutiques will build specific machine learning algorithm platforms and leverage the API market for ready integration of these platforms in various legacy software applications.
• Intelligent Cybersecurity: With more devices and apps, will come continuous cyber intrusions. Advanced predictive algorithms will play a smart shield against incessant attacks made against these digital products. It can detect nefarious acts by identifying patterns and signatures within ongoing transactions.

Current Market Scenario - Consolidation of AI and Data Science Ecosystem

Since mid-2019, the AI market has been ripe with significant acquisitions and partnerships in the AI and Data Analytics market. Salesforce acquired Tableau, and Google brought in Looker. Smaller firms like DataRobot acquired three companies (ParallelM, Cursor, and Paxata), and Appen acquired Figure Eight. Whereas simulation software giant Altair partnered with Datawatch Corp and Ayasdi sold its majority stake to SymphonyAI. Most of these acquisitions and partnerships have happened to attain the market readiness of AI-centric consumer business. The disruption in business services and offerings is where these organizations want to be among the first movers.

Infallible Principle: If AI is the engine, data is the fuel to propel it forward

The organization's AI strategy is successful when the foundation of AI's structural components is strong. One of those components is insights from data analytics. If organizations are interested in building an AI-driven organization, they should start with identifying all possible data sources within and outside the organization. The leadership should create a blueprint around data architecture, integrate multiple data sources, and create a data backbone that can facilitate proper data management. They should initiate their AI journey by propelling data analytics as a first step.

Think through the steps of implementing AI 

• Define and standardize goals that AI can achieve being a tool
• Look into the AI capability portfolio along the line of appropriate use cases.
• Create a data infrastructure integrated with multiple data sources, clean and structure complete data for use.
• Strategize proper business rules and models to get contextual information.
• Embed analytics in every part of your business operations and offerings/customer lifecycle.
• Establish AI capabilities at the top of everything, which is fed data analytics-based outcomes to provide information that can directly imply business outcomes.

Digital transformation accelerates through business focussed and data-intensive AI platforms. It should be a strategic priority for all organizations that want to challenge and lead ahead during any market shift scenario. For enterprises to achieve AI-driven business goals faster, they must consider investing in AI specializing firms and partner with them. We expect the continuation of leading mid-market tech firms to make huge strides in the AI-as-a-service market.