How to Centralize Intelligence, Lead Organizational Transformation, and Prepare for the Future of Big Data
Advanced feature engineering is an example of how new technologies can unleash the power of big data in ways that power growth, prioritize privacy, and promote transformational integrity.
The story of our modern digital economy is, in many ways, a story about our evolving efforts to manage the increasing volume of data we produce and to derive value from those efforts. We have understood from the start the potential value of data, and when we look back, we can see that, as our volumes of acquired data increased in size and complexity, we responded by developing more scalable means to process and analyze that data. But are we getting the most value and intelligence possible from this most important resource?
For many years now, we have been in what’s commonly referred to as the “big data age,” and with transformational technologies such as artificial intelligence and machine learning becoming increasingly commonplace, our ability to harness the power of data has taken several significant leaps forward.
And yet, there is still an acute imbalance between the amount of data we collect, and the value we derive from it. We may have begun the process of centralizing this data, but oftentimes we overly focus on the quantity, and we begin to question whether we’ve collected the right — or the most relevant — data, and whether we’re deriving its maximal value, despite our massive efforts towards data collection and storage. Far too often, we are left with vast lakes of decaying data that continue to sit siloed and untapped all across the digital landscape, symbols of excess and failed promise.
Today, consumers and regulators alike are challenging prevailing patterns of aggressive data acquisition and a critical lack of transparency as to its use. The rapid and ongoing rise of dramatically more sophisticated digital fraud threats has left the digital economy in a perilous state of conflict, with risk management and customer experience seemingly at odds in one arena, and privacy and intelligence facing off in another.
Fortunately, the data evolution has not stalled out. In fact, we stand at the cusp of a new era, one that will see us move beyond our current (and ultimately limited) data centralization processes, to an advanced mode of data management sophistication defined by centralized intelligence.
In DataVisor’s new “A Guide to Centralized Intelligence,” we discuss the difference between centralizing data and centralizing intelligence:
“With centralized data, we’re still interpreting; we’re still visualizing; we’re still spotting trends and trying to make sense of them. With centralized intelligence, we have the power to go beyond interpretation by visualization. We can harness machine power to go straight to meaningful action derived from data. Most importantly, the actionable intelligence resides within the system itself — it is centralized.”
Another way to understand the difference is to think of it in terms of empowerment. While centralizing data enables equal shared access, centralized intelligence promotes and enables empowerment through the whole of an organization.
At the tactical level, “A Guide to Centralized Intelligence” focuses on the importance of feature engineering. As we write in the book, “feature engineering is critical for building any intelligent system. Features can uncover actionable insights from big data, and transform them for use by machine learning algorithms and rules-based systems. In short, features unleash the full power of big data.”
As crucial as feature engineering is, we recognize that the use of advanced feature engineering is not widespread, and that, historically, the process of building advanced features has been tedious and time-consuming. We note that extensive domain expertise, heavy engineering, and a great deal of computation power are required.
“A Guide to Centralized Intelligence” is precisely that — a guide that will help organizations achieve centralized intelligence through a new approach to advanced feature engineering, one that reduces complex processes from months to literally minutes.
Later sections of the book specifically discuss DataVisor’s new Feature Platform, and present it as an expression of centralized intelligence in action. As we state in the book, “in building Feature Platform, our broad goal was to modernize, accelerate, and optimize the centralization of intelligence.” Using Feature Platform gives organizations access to automated feature engineering across multiple data sources, making it possible for data scientists, business analysts, and fraud and risk teams to build powerful features in minutes, instead of weeks or even months.
The eBook provides details on specific client use cases, including large-scale insurance fraud and the unique challenges presented by call center scams. Additional insights are provided around the topic of real-time responsiveness, and how automated feature engineering enables it at scale:
“The very concept of centralized intelligence is predicated on the idea that only when the best intelligence is centrally and directly available can a system reach optimum performance. Feature Platform’s power emerges from years of experience deriving actionable intelligence from vast stores of enterprise client data. This experience and expertise are why Feature Platform can reduce inefficient and time-consuming processes from months to minutes across a wide array of case studies.”
Whether at the conceptual or tangible level, what we are ultimately talking about is organizational transformation. Centralized intelligence is the next evolution in the big data era, and it is the ideal approach for a world facing a daunting array of challenges, the solutions to which will define and shape the digital economy for years to come.
Today’s enterprises must successfully achieve a balance of risk and experience and find ways to power growth without subverting privacy. Real-time threats must be dealt with in real time, and authentication practices must consistently reward good users and block malicious ones. Radical transparency must be the new normal. Through the use of advanced solutions like DataVisor’s Feature Platform, and the comprehensive embrace of centralized intelligence approaches to data acquisition and management, we can march into the new decade confident in our abilities to do more with less. This is real progress.
To learn more about centralized intelligence, advanced feature engineering, and DataVisor’s new Feature Platform, please download “A Guide to Centralized Intelligence” today.
