Advanced Analytics Poised For Disruption
Advanced analytics is set to cause major disruption in the coming years. The fastest-growing segment in the business intelligence (BI) and analytics market, Advanced analytics is forecast to grow almost 14 percent to reach $1.5 billion in 2016, according to analyst firm Gartner.
Gartner also predicts that by 2018, more than half of large organizations globally will compete using advanced analytics and proprietary algorithms, causing the disruption of entire industries.
“Advanced analytics has already been changing entire industries for over a decade and is a key factor for how most new entrants disrupt established markets and beat their incumbents — whether selling books, renting movies, borrowing money or even building a professional sports team,” said Jim Hare, research director at Gartner.
“Today, with fewer regulated monopolies and the Internet eliminating geographical boundaries, more companies are starting to use statistical analysis, predictive modeling and decision optimization to compete, instead of using traditional approaches.”
Hare believes that to survive in the new digital economy, end-user organizations and vendors will both need to accelerate the shift in focus of their investments from measurement to advanced analysis or risk being left behind. Leading organizations are developing proprietary algorithms that can lead to faster, more insightful analysis and are moving away from “gut feel” decision making.
Gartner however elieves the trust factors influencing the ethical use of analytics are identifiable — transparent, accountable, understandable, mindful, palatable and mutually beneficial. Unfortunately, these underlying factors of fostering trusted business relationships based on data are seldom given much, if any, consideration. As a result of this by the end of 2018, a minority of organizations will have a rigorous approach to demonstrating the trustworthiness of their analytics algorithms.
“The resulting business, social and ethical impacts arising from the use of data and analytics are understood by few, ignored by many and tracked by virtually no one,” said Alan Duncan, research director at Gartner. “The resulting impacts are tangible — unrealized business opportunities, additional inefficiencies, increased brand risk and even criminal proceedings.”
According to him, leading data-driven organizations will increasingly recognize the causal relationships between data, analytics, trust and business outcomes. Those organizations that choose proactively to govern these ethical impacts will be able to foster more productive and trusted relationships with their customers, suppliers and employees; drive increased competitive advantage and brand loyalty; and maximize their market share in comparison with competitors that do not address these issues.
Say, in the next two years, algorithm marketplaces will be combined with Platform as a Service (PaaS) to boost advanced analytics and enable secure sharing and monetization of raw data.
Gartner believes that advanced analytics could provide significantly more benefits if there was more sharing of detailed, event-level data. However, this so far is hindered by significant licensing, trust and data integration issues. The solution will be the combination of algorithm marketplaces and PaaS-runtime environments, where only specifically certified functions are allowed to process the secured data.
“Today’s situation of sharing data is problematic,” said Alexander Linden, research director at Gartner. “Data providers don’t typically trust end users with detailed, event-level data. On the other hand, data consumers do not like the involved complexities of data licensing and data integration. As a result, there is a significant impediment to sharing and monetizing data.”
Within three years, Gartner expects technology to be available that can radically simplify the trust, licensing and data integration challenges, by placing controls on the algorithmic data processing. Only certified components will be able to run sensitive data and transform it into scoring and optimization models. In essence, the data processing will be constrained to ensure that the underlying detailed data cannot be copied, saved or reverse-engineered, said the analyst firm.
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