Data driving decisions, actions and outcomes is no more a moot point. Rather, organizations are more focused on using the right techniques, methods, processes, and tools to streamline the data-to-decision journey. Organizations that understand and overcome data and analytics challenges are the ones that will reap profitable rewards from their analytics initiatives, enable revenue and business growth.

With analytics promising rewards out of data, here are 9 analytics trends for 2020 that can help address organizational challenges, accelerate analytics journey, bolster efforts, and make the most of data-driven insights.

AI and ML push to big data

Big data is getting bigger. Organizations now pair up AI & ML with big data to gain more room to manoeuvre big data, extract more value from cloud-based data lakes and build more use cases using this combination. The enterprise world is making progress in terms of using video, audio, pictures and images to gain insignificant insights, as in the case of using image classification (machine learning classifiers) to detect cancer types.

Setting machine learning algorithms to work on big data will yield business value via trend spotting, anomaly detection, pattern recognition, price optimization models, customer churn modelling, video analytics, demand forecasting and sentiment analysis and more.

Staring with big data and ending with AL&ML, organizations are opening up more of the predictive and prescriptive analytics frontiers. For businesses, AI is now a key force to leverage cloud-based data lakes and make predictive analytics work (predicting customer churn and predicting sales) and prescriptive analytics provide value by way of forward-looking strategic insights. This big data-AI medley is also evolving in the areas of reasoning, machine learning, robotics, natural language processing and automated learning.

The enterprise-future points to ‘intelligent systems’ that will take advantage of the Big data/AI & ML medley to use data for solving business problems – in terms of seeking contextual intelligence and answers rather than relying on historical reporting.

Prevalence of Augmented analytics

A more pronounced and advanced way of consuming analytics has taken hold over the enterprise landscape. Leveraging the combination of AI and ML, organizations seek to automate the analytics life-cycle, from data preparation to insight discovery and sharing, to ensure users are able to consume easy-to-use analytics. Increasing in popularity, augmented analytics is now being relied upon by organizations to tap into smart data discovery, augmented data preparation and analytics, ML model development as well as deployment to enable seamless analytics process and maximize value from this self-service paradigm.

Pay-for-service AI

AI is furthering its reach across businesses, sectors and industries. With AI empowering businesses to promote enriched customer experiences and optimize business operations, more and more organizations will tap into AI opportunities to enable transformational changes and get future-ready. With critical business operations, needs, and challenges in mind, organization will go in for use cost-effective AI-based systems to extract value from artificial intelligence.

This will prompt the emergence of Pay-for-service AI, wherein service platforms made available through providers will allow organizations to use only the ML algorithms that matter to them in solving business problems and pay for just the services that are being used by the organizations. The Pay-for-service AI will rise into an ideal alternate for organizations to do away with the costly initiative of deploying their own systems built on AI.

Get the ‘Insight’ picture with deep learning-driven computer vision

Today, a picture is worth more than a thousand words. For businesses, it is a treasure trove of insights that can drive better and enhanced business value. Where organizations across healthcare, retail, banking, automotive and manufacturing sectors are now relying on computer vision more than ever to identify, classify and categorize image files and video files to seek intelligence in real time.

From using statistical methods, businesses have moved on to leverage deep learning-driven computer vision, which is now being used prevalently to solve complex problems across industries. Organizations make the most of this deep learning-led Computer vision combination to witness profitable results from image classification, face recognition, semantic segmentation and object detection.

Intelligent automation

With automation gripping the enterprise world, RPA is turning intelligent and paving the way for intelligent automation – made possible by the combination of RPA, AI and ML. Infusing ML and AI into RPA has allowed RPA bots to learn on their own, adapt, and make decisions without relying on human intervention.

Moving towards intelligent automation leveraging the powerful tech combination (RPA, AI and ML) and cognitive technologies, organizations are supporting more and more use cases (organizations across industries), automation of end-to-end processes as well as workflows. Intelligent automation, powered by cognitive technologies, is now being applied across different business problems and is proving to be the organizational ally in terms of enhancing employee experience and customer experience, saving time and cost, reducing errors, and improving efficiency.

Continuous intelligence empowered by real-time data

Every organization wants contextual intelligence to bolster decisions and actions at the point of every operational micro-moment. In short, businesses are implementing systems that offer continuous intelligence leveraging real-time contextual data for making the best business decisions. Using robust mechanisms, processes and tools, organizations combine transaction data such as product and customer data with other data streams like web interactions, social media data and device data to gain contextual insights at every operational micro-moment.

To reap rewards from continuous intelligence, organizations make the most of optimization, augmented analytics, ML, and event stream processing to integrate real-time analytics into business operations and prescribe actions based on events.

Decision automation

Decision automation has come of age. As organizations want to use data to take better decisions, automated decision-making capabilities are being infused into workflows to facilitate automated decisions. The proliferation of data has not only given a pause for thought to use data in building predictive models but also leverage AI systems in looking beyond data-driven predictions and enabling decision automation. Data-intensive environments across industries including retail, insurance, banks, travel & transportation, and healthcare are creating possibilities to embed decision automation, giving room for enterprises to go beyond marketing automation as well as credit decision automation and make use of decision automation possibilities across operations.

Broadened use of DataOps for data governance

Every organization now wants to make the most of the overwhelming flow of data collated from various data sources like IoT devices, social media, different channels and other sources. And with AI and ML raising the organizational hope of using data to augment KPIs, extracting insights to address business problems, predicting outcomes and gaining the power to make the right decision in future, what matters most are the ability to assure data quality, nurture robust data management practices and processes to democratize data in driving successful outcomes from data-driven insights. Organizations are now resting their faith on DataOps to facilitate agile data management, manage data effectively and reduce cycle time of analytics.

Strengthening AI security

Though rich transformational potential of AI is yet to be realized, opportunities abound with fair taste of success experienced across industries. That said, as more and more AI systems find their way into the enterprise world, malicious ways to exploit the security vulnerabilities can lead to lethal attacks. Organizations leveraging AI systems must also anticipate attacks such as the injection of corrupted data and data poisoning to detect and prevent such security attacks. Using machine learning is also giving the lead for organizations to augment cybersecurity in the way ML is leveraged to detect patterns and predict possible attacks.