AI, machine learning, and predictive analytics are used synonymously by even the most data-intensive organizations, but there are subtle, yet important, differences between them. Machine learning is a type of AI that enables machines to process data and learn on their own, without constant human supervision. Predictive analytics uses collected data to predict future outcomes based on historical data.
Regardless of the application, these forms of advanced analytics have common threads, and can ultimately determine the success of an organization’s digital transformation initiatives. Here are some real-world examples:
Boost and sustain revenue
Perhaps the most talked about use case, big data analytics has become a technology imperative to augmenting the top line. Emerging streaming technologies dramatically increase overall data volume, particularly sensor data from the Internet of Things (IoT). New ways to bridge formerly distinct data silos now enable organizations to finally bring analytics to the data. As a result, organizations are more successful in deriving accurate and actionable insights to outpace competitors by acting on unmet customer needs, under-funded parts of the business, emerging business models, and more. In fact, 61% of organizations are already realizing higher revenue growth than competition with an effective digital strategy (1), and this is largely attributable to personalized customer behavior analytics.
Drive customer engagement
Organizations are constantly looking for new and better ways to engage customers at a reasonable cost, and analytics play a critical role in this endeavor. Organizations often eliminate intermediaries and employ digital platforms to reach and serve customers directly, closing the loop between data and action and truly understand their customers and better satisfy their needs. Around 70% of customer engagements will be driven by intelligent systems by 2022 (2), which will largely be driven by cognitive search and knowledge discovery and ChatBot technology.
Streamline and enhance processes
Today, IoT is creating massive volumes of sensor data with untapped value. By applying IoT analytics at scale, organizations can reduce service costs, improve customer satisfaction, and create entirely new business models. For instance, IoT analytics delivers on the promise of predictive maintenance, smart metering, intelligent manufacturing, and more. Operations analytics ensures automated IT monitoring and remediation to reduce MTTR and operations costs. Legal departments use predictive coding, or technology-assisted review, to improve and streamline the process of reviewing billions of data objects for legal matters instead of sending each data object to an attorney to review individually. Analytics even drive better collaboration and productivity across geographies and departments. This might be why more than half of organizations are planning to leverage AI and machine learning in the next year (3).
Protect customer privacy
One of the hot-button topics in boardrooms around the world right now is protecting customer privacy. While there were earlier, smaller-scale privacy regulations in place, this topic went mainstream once the General Data Protection Regulation (GDPR) became effective in May 2018. This action to protect EU citizen data has since mushroomed globally, as other jurisdictions move to protect their citizens’ data as well. Analytics has yet again come into play, as the sheer volume of information to protect requires a new level of intelligent classification. Organizations do not have to protect all their data—nor do they have the budget or infrastructure to do so—but instead just the right information. File analytics and structured data management technologies play a critical role in protecting organizations from fines, sanctions, lawsuits, and erosion of market credibility.
Detect and prevent risk
Enterprise risk comes in many forms, and analytics are critical to address virtually all of them. Security Operations (SecOps) and Intelligent GSOC (Global Security Operations Center) can benefit by automating the analysis across vast amounts of data—a task that would take SOC analysts months to complete on their own. With proven and targeted analytics, security teams can investigate real threats instead of testing hypotheses or chasing false alerts. When looking for insider threats, for example, user and entity behavioral analytics (UEBA) centers on user information — abnormal logins, time of work, processes, etc.—to identify these difficult-to-find threats. Analytics even delivers real-time threat intelligence, or physical security, by scrutinizing video, text, and audio from CCTV, social media and sensors.
In summary, digital transformation is upon us—worldwide spending is expected to reach almost $2 trillion in 2022 (4). What may be less obvious is the critical importance of analytics—across many departments such as marketing, operations, legal, compliance, privacy, and security—to support this transition. Organizations that expand their vision of digital transformation, pursue holistic technology solutions spanning the above use cases, and ensure these solutions are underpinned by advanced analytics, will more accurately predict and influence outcomes and attain long-term fiscal health and commercial success.
Attribution:
(1) Harvey Nash / KPMG CIO Survey 2018, 5 June 2018
(2) Forrester Research, Feb 26, 2018, Digital Rewrites The Rules Of Business
(3) IDG, State of Digital Business Transformation, 2018
(4) IDC, Worldwide Semiannual Digital Transformation Spending Guide, Nov 2018