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Three Critical Ingredients for AI, Machine Learning & Cognitive Computing Success

Ramon Chen, Chief Product Officer, Reltio

You may already know a lot about Artificial Intelligence (AI), Machine Learning (ML), Deep Learning or even what some vendors call Cognitive Computing. Or maybe you are still trying to understand the nuances and differences between each term, and how they relate to each other.

Either way, it’s easy to be seduced by the “black magic” of technology that can solve a variety of your business challenges by just asking Watson, Einstein, Siri, Alexa, Hal (click for the iconic HAL 9000 scene in Space Odyssey) or other “humanizing” names.

In fact Gartner’s 2016 Emerging Technologies Hype Cycle has Machine Learning at the very “Peak of Inflated Expectations.” Those who are familiar with the cycle, know what is likely to come next <Trough of Disillusionment cough cough>. 


If you want to protect yourself from the hype, here are 3 critical ingredients for your consideration:

At Reltio we’ve been articulating a vision, which includes a pragmatic perspective of machine learning (ML) for over 3 years now. Realizing that not only does ML not offer a silver bullet, but there is still much to learn (pun intended) as to how such technologies can ultimately benefit both IT and business. Noted Big Data expert Bernard Marr provides a nice list of use cases that might be applied to your specific business and industry. The key is that a focused set of benefits for each users’ role, must be defined in order for it to be accurately measured so it doesn’t get labelled yet another (data) science project with limited value.

#1 Create a Reliable Data Foundation

Most companies are NOT ready for any form of AI, ML, or Cognitive Computing to help their business user, because their data is such poor shape to even attempt such an endeavor. Ironically, a great IT use case is to use ML to first help improve the consistency, accuracy and manageability for better data quality (DQ), uncovering patterns, anomaly detection and assisting humans, such as data stewards, to make their job more focused and efficient.

#2 Bring Analytics and Machine Learning to the Data

Just as the process of aggregating data to perform historical or predictive analytics is a cumbersome and expensive process, gathering and blending all of the right data that will guarantee machine learning is effective must be the in the DNA of any Modern Data Management Platform as a Service (PaaS).  

Bolting on AI or ML into legacy master data management (MDM) systems, or using such MDM tools to feed downstream disparate ML tools is putting lipstick on hosted managed services disguised as cloud. Reliable data, relevant insights and recommended actions via machine learning needs to be seamlessly combined into one, single multi-tenant cloud platform, architected from the ground up, for both analytical intelligence and operational execution, through data-driven applications.

Successful execution requires a closed-loop of all data, insights and actions, to ensure accurate metrication for continuously improved outcomes. Further, a multi-tenant cloud environment is the only way sufficient storage and processing capacity can be elastically accessed on demand to meet any business need.

Another benefit of a multi-tenant cloud PaaS is the potential to use a wide variety of anonymized data to help with machine learning across all industries. Having a large enough set of data is a critical factor for smaller companies to benefit from the right recommended actions, for common industry use cases.

#3 Don’t Go all in on one vendor

In a rush to market that “our tools do it too,” large vendors will unfortunately, over promise, and under deliver. It’s not their fault, as they must respond to the market, but many face an unenviable task of achieving ingredient #2 above, let alone attempting to now also execute on a plan to deliver their own AI technologies. 

An open ecosystem that allows you to choose and partner with the technologies, and domain experts of your choice is critical to getting the most out of a still young and evolving landscape. Most companies are already trying to evolve out of their legacy MDM platforms. Getting further locked into a single vendor, delivering both MDM and ML, through siloed disparate tools will not provide “clarity,” and may further complicate an already fragmented data management strategy.

At Reltio, we formed strategic partnerships with companies like QuintilesIMS to combine our strengths to jointly deliver on a vision for the next generation MDM and analytics capabilities for life sciences.

In summary, look to master your data in #1 for a reliable data foundation. #2 ensure that it covers all data types, sources and modes of consumption in a seamless feedback loop on a Modern Data Management platform architected from the ground up to avoid further siloing your data. Finally, #3 give yourself the openness and flexibility of your partners of choice to meet your business needs. 

You don’t want a HAL-like failure that prevents you from realizing your true goal of improving your business.

How Reliable Data Helps You Personalize Engagement with Your Customers

Today we issued a joint announcement with ThinkMojo an innovative video production company, as we unveiled our 99 second product video about Reltio, and our Modern Data Management Platform as a Service.

The personalized video has been delivered to tens of thousands of individuals globally demonstrates the power of reliable, accurate data, combined with relevant insights that can help sales and marketing teams across all industries improve engagement and customer service. This video was launched to help promote Reltio Cloud Release 2016.1 which features Reltio Insights, the first and only Modern data management platform, with built-in master data management (MDM), to Apache Spark connectivity.

The foundation of reliable data using cloud MDM is essential to even attempting such personalization programs, since the accuracy of personal details can make or break such a program. Reltio was recently recognized by Forrester as a leader in the Forrester Wave: Master Data Management, Q1 2016 report (free copy available for download). In the report, Forrester recognized that the goal of MDM was to deliver higher business value and personalized context. This video scratches the surface of what can be done.

In a future post, I’ll discuss how the technologies combined together to produce such targeted messaging and provide more details around the results. But for now, please take a look at the videos and let me know what you think.

Oh and if you are at Strata Hadoop World, stop by the Reltio booth #1144 to see a demo of Reltio Cloud in person!

Click here for an example of the personalized version with Reltio as the company name and Ajay Khanna VP of Product Marketing as the video viewer (and a cameo in the video)

Don’t Gamble with Your Data. How to Stack the Cards in Your Favor

Blackjack is a game that many people believe offers the best chances to win in a casino. Because it is a game where the odds of any particular outcome can be determined somewhat accurately … if you have the right data. The best players in Blackjack do not make winning any particular hand a priority, but strictly make the best moves based on the probable outcomes, and the data they have through card counting.

If you played “basic strategy” in blackjack, you would be executing based on odds and probable outcomes, not gut feel. If you are familiar with the rules of blackjack you’ll likely make the correct decision in this following scenario:

Let’s say you are one-on-one against the dealer at the table and have been dealt these two cards: 10 and 6 totaling 16.

YOUR HAND TOTALS 10 + 6 = 16

YOUR HAND TOTALS 10 + 6 = 16



And the dealers up card is a Jack, value of 10. Do you Hit? (take another card) or do you Stand? (stop and take your chances and hopes the dealer busts)

The odds favor you taking a card.


Why? Because there are 5 cards that can improve your hand. Ace, 2, 3, 4, 5 (giving you 17, 18, 19, 20, 21 respectively) and only one card, a 6 that can really hurt you. That is because the cards 7, 8, 9, 10, J, Q, K are irrelevant because if you take them you bust (go over 21), but if the dealer gets them he/she gets 17 or better and you lose anyway.

So the odds are theoretically 5 cards to 1 in your favor. Even though the dealer’s advantage is that you are going first, you must hit 16 against a dealer 10 or face card, otherwise you are destined to lose.



Under what circumstances might you decide to go against these odds? If you happen to be a card counter and you’ve kept track of all the cards that have come out of the deck thus far, and know exactly what’s left in the deck, allowing you to “predict” at a higher-level of certainty to go against the odds and conventional wisdom of the hand.

Ironically, this simplified blackjack strategy highlights how you might be gambling with your data.

Firstly, if you don’t have reliable data , i.e. you don’t really have a good handle on exactly what cards have gone before, or are still remaining in the deck, you can’t be sure any decision you make will yield your preferred outcome.

Second, you can collect as much data as you want, such as the history of the dealer, the skill of your fellow players, the way the table has been paying out tonight, and even extrapolate that across all the tables in the casino, but that doesn’t offer the relevant insight at this specific moment in time, which is what you need if your objective is to beat the dealer.

Finally, why does the Casino offer Blackjack as a game, if it’s possible that if you play according to the odds and execute basic strategy correctly, you stand a chance of winning? Well, apart from the fact that it still retains an edge because the dealer gets to go last, and there are other esoteric rules that tilt the odds back in the house’s favor, they know that most people don’t have the knowledge, or the recommended action to take based on the situation.

Just like playing blackjack without reliable data, your business decisions are a gamble, regardless of the analytics and visualization tools you’ve invested in. Most applications and data warehouses end up operating on inaccurate data. BI and analytic tools still require you to sift through mounds of irrelevant data to find what you need. And process-driven apps like CRM, don’t encapsulate the best practices or offer recommended actions of what to do given even the most common scenarios.

Reltio has helped leading companies stack the cards in their favor by showing them the Reltio Modern Data Management PaaS, with Data-driven applications to tilt the odds in their favor way. If you’d like to know more, contact us, and we’ll send you an actual professional tournament deck of cards that could save and make you and your business a fortune.