4 Key Takeaways from “A Guide to Centralized Intelligence”

Discover why centralized intelligence is the future of big data, and learn how your organization can make the transformational leap from centralizing data to centralizing intelligence.

Managing data, and making the most of what it can provide for us in the way of actionable insights, is an evolutionary process. As data volumes increase, and the types of data we collect become increasingly complex, our ongoing challenge is to develop the techniques, technologies, and tools that allow us to continuously leverage this data for maximum beneficial impact.

When we succeed, we significantly enhance our ability to make better and more proactive business decisions. When we fail, we leave behind us vast and untapped data lakes that represent missed opportunities, lost intelligence, and the perpetuation of unnecessary friction.

Centralized intelligence is the future of big data.

This is the subject of DataVisor’s new eBook: A Guide to Centralized Intelligence. In the book, we describe the evolutionary path from centralizing data to centralizing intelligence, and we detail the importance of executing this transformation. We present feature engineering as an expression of centralized intelligence in action, and make clear how advanced feature engineering capabilities can modernize, accelerate, and optimize the centralization of intelligence.

We use DataVisor’s new Feature Platform as an example of how centralized intelligence can be productized, and illustrate how advanced technologies and deep domain expertise can be combined to build intelligent systems that can solve a wide array of business use cases. We take readers through several of these use cases, before concluding with a look at how a solution like this can be successfully integrated.

What follows is a summary of some of the key takeaways from “A Guide to Centralized Intelligence.”

4 Key Takeaways from “A Guide to Centralized Intelligence”

Centralized intelligence is about the predictive power to create actionable insights

One of the main differences between centralizing data and centralizing intelligence comes down to this: centralizing data is fundamentally about interpreting, visualizing, spotting trends, and trying to make sense of them. With centralized intelligence, we have the power to go beyond mere interpretation by visualization. Advanced machine power bridges the gap between analysis and action, and actionable intelligence resides within the system itself. Centralized intelligence is about the predictive power to create actionable insights, through unleashing the full power of big data. When it comes to actually delivering on the promise of data, it’s all about speed, scale, production-readiness, and more. Traditional approaches are hampered by a host of challenges that hinder real progress. As we show in our eBook, a solution like DataVisor’s new Feature Platform offers a better way.

Advanced feature engineering is critical for building an intelligent system

Actionable intelligence derived from data is a supremely valuable commodity, and feature engineering is the process by which teams can produce these actionable insights and transform them for use by powerful algorithms. In our book, we state that features “unleash the full power of big data.” To do this, however, requires overcoming key challenges that organizations face today, including the lack of domain expertise, heavy reliance on advanced engineering, and the requirement of significant computation power. Flexible solutions such as DataVisor’s Feature Platform offer a sophisticated balance of automation and customization to overcome these challenges.

Centralized intelligence promotes and enables empowerment through entire organizations

DataVisor’s Feature Platform is designed to give organizations the power of centralized intelligence, and a central advantage of the platform is the flexibility it offers organizations. Not only does it deliver automated feature engineering capabilities, but it also offers the ability to custom-engineer features that are specific to organizational data and needs. Teams can engineer any features using the comprehensive functions and operators built into the Feature Platform. Among other benefits, this enables re-use and easy maintenance, and allows for multiple different teams to share features instead of having to start from scratch with every new scenario.

Centralizing intelligence is both a vertical and horizontal process

The evolution from centralizing data to centralizing intelligence requires genuine organizational transformation, and achieving transformation at this level means addressing an organization both vertically and horizontally. The vertical challenge is to enable every team within an organization to both contribute to — and derive actionable insights from — vast stores of data. The horizontal challenge is to make this possible across all use cases and scenarios.

Integration of a fully intelligent system powered by AI and machine learning, that provides advanced feature engineering capabilities, can empower organizations to modernize, accelerate, and optimize the centralization of intelligence across teams and use cases. Flexibility of the kind afforded by DataVisor’s Feature Platform is uniquely valuable, as it offers the ability to custom-engineer unique features tailored to specific organizational needs. Teams can infuse the feature creation process with their own internal expertise, ensuring powerful degrees of customization and optimization. They can engineer any features using comprehensive functions and operators, and these features can be at the event level, user level, or cluster level.

The Call Study Use Case

One of the case studies we cover in our eBook has to do with call center scams. Call centers are ubiquitous across industries, and as such, they present an excellent opportunity to highlight the agnostic value of an intelligent solution such as DataVisor’s Feature Platform. As but one timely example of the burgeoning new importance of call centers, the following news was announced on Monday in Fortune:

“Investors from venture capital firms like Andreessen Horowitz and Greylock Partners said on Monday that they had invested $21 million in Cresta, a call center technology startup co-founded by Sebastian Thrun, a high-profile Silicon Valley executive who founded Google’s so-called X moonshot factory and online education company Udacity.”

Call centers that draw on the power of AI and natural language processing represent a potentially valuable solution, but as we note in our eBook, call center scams are also a growing, industry-wide problem that cost financial institutions hundreds of millions of dollars each year:

• Contact center loss is expected to increase from $393M in 2015 to $775M in 2020.
• 61% of fraud in the U.S. can be traced back to call centers.
• Call center fraud increased from 1 in every 2000 calls in 2016 to 1 in every 937 calls in 2017- a 113% fraud rate increase.

In “A Guide to Centralized Intelligence,” we show you how, through using unsupervised machine learning, financial institutions can analyze their unstructured data to expose fraudulent behavior patterns early — before any scams succeed, and before any damage is caused. We make clear that this shift to proactive detention can make the difference between success and failure. Finally, we describe how this approach is an example of the value of centralized intelligence.

DataVisor’s embrace of a centralized intelligence approach is the foundation of our success as we work to protect enterprise clients across the globe from sophisticated threat attacks. Our product suite, which includes our new Feature Platform, represents the coming together of advanced technologies and super domain expertise, and our solutions enable organizations to derive the absolute maximum benefits from their raw data. To learn how your organization can make the leap to centralized intelligence, download “A Guide to Centralized Intelligence” today.