Superior decision making is an essential aspect of life, whether it be in business, national security, health, environment, and every other aspect of human existence. Look no further than geopolitical affairs, such as North Korean or Iranian relations, to understand the importance and impact of decisions on human life, indeed the entire planet. This is not an exaggeration. From an Information Technology point of view, the goal is to provide complete and accurate information on demand to support decision making.
At the Gartner Data & Analytics Summit 2017, Cambridge Semantics' very own Barry Zane, Vice President of Engineering, and Ben Szekely, Vice President of Solutions, discussed how the Anzo Smart Data Lake® (ASDL) solution empowers business users with on-demand analytics of their rich data during their session entitled “Accelerating Insight with High Octane, Graph Fueled Data.”
Today marks the 100th day of the New Year, and we’d like to take a moment to reflect on a few of the more interesting developments we’ve seen thus far in the fast-moving big data analytics arena.
At Gartner's Data & Analytics Summit 2017, Alok Prasad, President of Cambridge Semantics, was joined by Peter Horowitz of PricewaterhouseCoopers for their session entitled “Accelerating Insight: Smart Data Lake Customer Success Stories”. During this presentation, they discussed how Cambridge Semantics’ in-memory, massively parallel, semantic graph-based platform, Anzo Smart Data Lake®, delivers an accelerating edge to data-driven organizations, while maintaining trust with data security and governance.
Real-world events demonstrate our inability to understand rapidly and accurately what we already know. In other words, we cannot answer questions completely, despite the fact that we may hold the requisite data. For example, if someone attempted to enter the United States (US) at an airport, and US officials initiated a query to the “system” and found nothing, that person may enter the US erroneously. This might occur because US officials asking a question such as “What do we know about this person?” cannot assuredly answer it – and not in a timely fashion.
"The legal and ethical collection and analysis of information regarding the capabilities, vulnerabilities, and intentions of business competitors" - Strategic and Competitive Intelligence Professionals
This is how Strategic and Competitive Intelligence Professionals (SCIP), the most well-known global body on Competitive Intelligence (CI), defines the concept of CI. For the uninitiated, the page on the organization's Code of Ethics for CI professionals sheds lights on the innards of the practice. One does not have to belabor the importance of the practice in the context of successfully operating a business. It is in the operational details, as is the case with most details, that the devil lies.
Earlier this month, the Cambridge Semantics team set off for Grapevine, Texas, for the Gartner Data & Analytics Summit 2017 to join more than 3,000 big data industry customers, Gartner analysts and solution providers in the continuing discussion on driving business intelligence and analytics forward.
“If you know the enemy and know yourself, you need not fear the result of a hundred battles. If you know yourself but not the enemy, for every victory gained you will also suffer a defeat. If you know neither the enemy nor yourself, you will succumb in every battle.” - Sun Tzu
Most Competitive Intelligence (CI) and Business Strategy practitioners are likely to have come across this immortal advice from Sun Tzu. One does not cease to be amazed at how this simple fact underlies the multi-million dollar intelligence gathering industry.
We’d like to introduce you to a new member of our industry advisory council: Carl Reed, formerly managing director at global financial services company Goldman Sachs and Credit Suisse. Carl is an expert in data discovery and analytics for financial institutions, and recently participated in our Tweet Chat earlier this month on “An Insider’s View: Finding Value with Data Engineering & Semantic Standards in Finance.”
The financial industry is facing a perfect storm of disruptive drivers for data management. While regulators seek accuracy and transparency, institutions are struggling with fragmented data and IT infrastructures. The path forward is “data engineering” – applying consistent semantics with scalable infrastructure to harmonize data and enable traceable and dynamic analytics.