That report outlined key 3 characteristics of Machine Learning Data Catalogs (MLDC):
1. Interpret, define, classify, link, and optimize the use of disparate data sources
Reltio is used by companies globally to define logical business schemas, capture and discover relationships through the Reltio Self-Learning Graph, suggest ongoing improvements, and to organize and bring siloed data together across the enterprise to meet their business objectives. This continuous reliable data foundation feeds better operational execution, predictive analytics, and sets them up to evolve towards a self-learning enterprise.
2. Reconcile policies across data use
Reltio Cloud’s built-in data security and privacy, regulatory, life-cycle, and data quality policies coexist to adapt data for multiple uses through a powerful audit trail, and role based access to data. This is critical in the face of evolving compliance measures such as GDPR. Flexibility and agility to ensure that you can track not just where the data originated, but how it’s being used and the outcomes it generates, is a critical component of any forward looking ML strategy.
3. Democratize data to the edge of business
Reltio is particularly well suited to meet this requirement through frontline business user facing data-driven applications and workflow and collaboration capabilities that come OOTB with Reltio Cloud. It allows teams to submit comments, suggestions, filter and easily segment information through a UI that’s as easy to use as Facebook and LinkedIn.
Data science teams are then able to use Reltio IQ, with Apache Spark to run their algorithms without the pain associated with cleaning and onboarding data in separate environments. This is increasingly important as enterprises deploy machine learning systems, with data scientists requiring relevant, curated data sources to train algorithms to improve results.
As this video illustrates, the true value comes from being able to synchronize ML-algorithm derived IQ scores back into master profiles as aggregate attributes. Making them available for segmentation by marketing, sales, and even data stewards and compliance teams. Teams can then continuously reconcile results to recommendations in a closed loop to self-learn and improve outcomes.
We are tremendously proud and honored to have been included in the MLDC Wave as it reflects our core belief that machine learning cannot be used in isolation from the overall data organization and management needs of the business.
Whatever your desired outcome, MDM forms the backbone of high quality, reliable data which allows ML to thrive.
ML in turn provides unique capabilities to improve and increase the efficiency of data quality, and enterprise data organization operations. Like the graphic I selected for this post, they go hand in hand, and are interconnected across all points of the data continuum and life cycle.
Here are a few predictions and perspectives from industry experts to learn from and be smarter this year:
The Promise of Artificial Intelligence (AI) and Machine Learning (ML) Continues on
There have been repeated predictions over the last couple of years touting a potential breakthrough in enterprise use of AI and ML. This year is no different as the potential benefits from adding some kind of intelligent AI/ML layer to software emboldens more organizations across industries to adopt these technologies.
ML and predictive analytics are leveraged to suggest next-best-actions for sending relevant and timely information to customers and finding opportunities for up-sell and cross-sell. Insights like churn propensity, life-time-value, preferences and abandonment rates can be delivered to relevant teams, along with recommended actions that allow them to capitalize on this information.
Effective May 25, 2018, the European General Data Protection Regulation (GDPR) will force organizations to meet a standard of managing data that many won’t be able to fulfill. They must evaluate how they’re collecting, storing, updating, and purging customer data across all functional areas and operational applications, to support “the right to be forgotten.” And they must make sure they continue to have valid consent to engage with the customer and capture their data.
Meeting regulations such as GDPR often comes at a high price of doing business, not just for European companies, but multinational corporations in an increasingly global landscape. Companies seeking quick fixes often end up licensing specialized technology to meet such regulations, while others resign themselves to paying fines that may be levied, as they determine that the cost to fix their data outweighs the penalties that might be incurred.
With security and data breaches also making high-profile headlines in 2017, it’s become an increasingly tough environment in which to do business, as the very data that companies have collected in the hopes of executing offensive data-driven strategies, weighs on them heavily, crushing their ability to be agile.
Customer-obsessed, Data-driven Retailers will Thrive
With the Cloud Infrastructure as a Service (IaaS) wars heating up, players such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure continue to attempt to outdo each other on all vectors including capabilities, price, and service.
To avoid being “Amazoned,” some retailers have even adopted a non-AWS Cloud policy. For most, however, it’s about efficiency and cost. Multi-cloud means choice and the opportunity to leverage the best technology for the business challenges they face.
Today’s Modern Data Management PaaS are naturally multi-cloud, seamlessly keeping up with the best components and services that solve business problems. Acting as technology portfolio managers for large and small companies who want to focus on nimble and agile business execution, these platforms are democratizing the notion of multi-cloud for everyone’s benefit.
The Deck will be Cleared for Accelerated Enterprise Digital Transformation
The business landscape is changing like never before. New revenue models, new competition, newer regulations and exceeding customer expectations are forcing organizations to rethink how they do business.
Digital transformation is one of the key initiatives for many organizations looking for ways to leverage digital technologies, become agile, more productive and above all, provide a connected digital experience for their customers. For digital transformation to succeed, a solid data management foundation is a must.
Today’s Modern Data Management Platforms as a Service (PaaS) seamlessly powers data-driven applications, which are both analytical and operational, delivering contextual, goal-based insights and actions, which are specific and measurable, allowing outcomes to be correlated, leading to that Return on Investment (ROI) Holy Grail, and forming a foundation for machine learning to drive continuous improvement. As an added bonus, multi-tenant Modern Data Management PaaS in the Cloud, will also begin to provide industry comparables, so companies can finally understand how they rank relative to their peers.
With the emergence of IDNs, ACOs and MCOs, the approach to healthcare is evolving. The focus is on overall well-being and quality of life, rather than a one-time treatment. This requires a new patient-centric approach, complete understanding of the patient’s needs, behaviors and preferences, and focus on building long-term relationships.
In this changing healthcare environment, a modern approach to data management that enables complete understanding of patients, physicians and other partners across all clinics and facilities, while guaranteeing HIPAA compliance is necessary.
Whatever the industry or business need, most enterprises will need to first focus on IA (Information Augmentation): getting their data organized in a manner that ensures it can be reconciled, refined and related, to uncover relevant insights that support efficient business execution across all departments, while addressing the burden of regulatory compliance.
Given the vast volume and variety of data that CPG companies manage, ensuring the accuracy and reliability of data is critical. All digital transformation and personalization efforts would fail if data underneath is of poor quality, siloed and delayed. Using machine learning within modern data management platform not only helps determine and improve data quality but also enriches the data with relevant insights and provides intelligent recommended actions for data quality and operational improvements. For example, if you are running a campaign for a major product launch, you can eliminate consumer profiles with low data quality (DQ) scores.
2. Be Agile with Multi-model Data Management
Using legacy tools built on relational databases are too rigid and inflexible, making it difficult to support the dynamic needs of a modern business. For example, adding new data sources or attributes to the customer profiles can result in costly data migration projects. Another challenge is the inability to manage the relationships between various data entities, such as people, products, organizations and places. Modern data-driven CPG brands prevent big data indigestion by using a multi-model, polyglot storage strategy to store and efficiently manage the right data in the right storage. It helps them deliver faster and higher business value from their varied data assets.
3. Leverage the Power of Multi-domain
With “single domain” Master Data Management (MDM), each data entity type has its own unique data store and business logic. On the other hand, a Modern Data Management Platform manages multi-domain (customer, products, stores, suppliers) master data along with transaction and interaction data, third-party, public and social data. Its graph technology makes it easy to describe and visualize complex, many-to-many relationships among customers, products, stores and locations for faster and reliable decision-making. For example, with the help of a graph, CPG brands can rapidly traverse links between consumers, products, purchases, and ratings to make personalized recommendations. They can also tell if the visitors and shoppers browsing their website are from the same household or not.
4. Uncover New Business Models
“Servitization” of products is commonly seen in consumer categories such as music (iTunes and Spotify) and books (Amazon Kindle) but also in business services such as Xerox moving from photocopiers to document services. Historically, CPG companies have been resistant to the move from products to services. Their relationship with their consumers has often been mediated via retailers. Modern data-driven CPG brands often bypass retailers and sell directly to customers (DTC). For example, Dollar Shave Club is offering a monthly subscription to deliver razors and other personal grooming products by mail. This gives them the opportunity to engage directly with their customers, to collect interaction data, and to expand their digital footprint.
5. Explore New Data Partnerships
Data is an enabler of innovation. To keep up with the rapid pace of digital transformation, CPG brands need to develop a culture of collaboration and pursue intra and extra-industry partnerships. They need to recognize that many new entrants are not simply additional competitors. Instead, they represent possibilities for completely new types of business models that over time will blur traditional distinctions between retailers and manufacturers.
6. Augment Decision Management with Artificial Intelligence (AI)
Data-driven CPG companies look at AI through the lens of three business capabilities: automating business processes, gaining insight through data analysis, and engaging with customers and employees. They constantly innovate and disrupt by embracing new technologies to meet the high expectations of consumers. A Modern Data Management Platform coupled with Machine Learning enables contextual information and helps consumer brands answer high-impact business questions such as – Will my customer buy this product or not? Is this review written by a customer or a robot? Which category of products is most interesting to this customer? And so on.
7. See GDPR Compliance as an Opportunity to Improve Customer Experience
CPG brands will be required to be more transparent about how they use consumer data. New regulations like GDPR and increased oversight has important implications in terms of regulatory compliance, product development and marketing messages. Moreover, there are increasing consumer demands for transparency on how companies perform when it comes to sustainability and corporate social responsibility as well as where products are made. A Modern Data Management Platform as a Service (PaaS) helps you create a complete consumer profile with full data lineage, governance, and workflows to continuously manage consumer rights and consents.
Consumer brands are facing unsteady growth, tightening profit margins, complex regulations, and growing competition from lower cost private label brands. Adopting these seven habits would help them reverse the digital curse, achieve hyper-personalized customer engagement, and stay ahead of competition.
And that’s just the perception and impact of basic data quality (DQ). Even more critical, business decisions are made every day on uncorrelated data which may not be “dirty,” but missing key information that might have resulted in a better decision and outcome.
What can companies do to not just minimize the impact of dirty data, but thrive by using data as a strategic asset? Here’s a quick checklist that can be used to achieve the best outcomes.
1. Use basic cleansing and Data Quality (DQ) not in isolation, but as a precursor to the correlation of data across different siloed sources
2. Leverage core master data management capabilities to match and merge entities to do the correlation of master profiles of any entity (people, products, organizations, places) in a single multi-domain platform
3. Once a continuous process is in place for reliable data, use graph technology to capture relationships of any type between such entities (e.g. people-to-people, people-to-products, people-to-orgs, products-to-suppliers, locations-to-people-to-orgs-to-suppliers)
4. With this reliable data foundation in place, bring in transaction, interaction and social data related to each entity to get a true 360-degree understanding of the behaviors and pattern that can provide the relevant insight that can help improve operating efficiency and execution
8. Repeat the process in a efficient seamless manner while adding new data sources across the enterprise to tie in more siloes to solve more business problems
Modern Data Management Platforms as a Service today do all of the above and more. They help both IT and business work together to prevent dirty data from flushing “3 Trillion dollars” worth of wasted effort and lost opportunities down the drain.
“Fake news is a type of yellow journalism or propaganda that consists of deliberate misinformation or hoaxes spread via traditional print and broadcast news media or online social media. Fake news is written and published with the intent to mislead in order to damage an agency, entity, or person, and/or gain financially or politically, often with sensationalist, exaggerated, or patently false headlines that grab attention.”
“Dirty data, also known as rogue data, is inaccurate, incomplete or erroneous data, especially in a computer system or database. Dirty data can contain such mistakes as spelling or punctuation errors, incorrect data associated with a field, incomplete or outdated data, or even data that has been duplicated in the database. It can be cleaned through a process known as data cleansing.”
And that’s just the perception and impact of basic data quality (DQ). Even more critical, business decisions are made every day on uncorrelated data which may not be “dirty,” but missing key information that might have resulted in a better decision and outcome.
I was honored to be invited to participate on a panel discussing the evolution of Machine Learning (ML) at the Thomson Reuters Emerging Tech Conference. The panel consisted of luminaries such as moderator Jonathan Weber, Global Tech Editor, Reuters News, Asif Alam Global Business Director, Thomson Reuters, and Vikram Madan Senior Product Manager, AWS, Machine Learning.
We covered a lot of topics including deep learning, neural networks, image recognition, reliable data foundation as an ML imperative, digital personal identities, the increasing value of enterprise data, how you should safeguard your private data, GDPR, closed-loop as the last mile in ML, how LinkedIn is an example of the next generation data-driven application, autonomous data management and machine learning for data matching and correlation, classification of different types of data, Gluon and the Microsoft – AWS partnership, how elastic cloud computing with unlimited processing power makes ML a reality, and more.
Here are some key takeaways from the panel:
Machine Learning requires a foundation of continuous reliable data to ensure that algorithms are acting on the right information. Generating reliable data is usually the task of master data management (MDM) tools, that blend and correlate profile attributes across disparate siloed sources and applications. However MDM itself cannot deliver the complete picture as it’s missing the critical set of interactions and transactions that complete the 360-degree view. Today’s modern data management platforms go beyond MDM, and beyond data lakes that have been largely ineffective by providing a seamless feed of reliable data to maximize the potential of machine learning.
One challenge for, not just machine learning, but advanced analytics in general has been the friction of synchronizing data models between operational applications and data sources, and downstream data warehouses and lakes that are being used as the data pool for analysis and ML. Today it is possible to eliminate that friction by seamlessly transitioning information into Spark on demand, so that machine learning can operate on the latest, most up-to-date data, without the need to wait for data model updates and changes which have traditionally hindered business agility.
Another critical element of making sense of the output from machine learning and advanced analytics is closing the loop and bridging the gap between insight, action and outcomes. Today’s insights are still siloed from the actual actions that business teams will eventually take based upon the data. Further the outcomes of any actions take are rarely correlated back to the originating insights. The added value of a continuous feed of reliable data, relevant insights and recommended actions generated from ML is to have a closed loop where users can contribute to data reliability, and provide data on the outcomes, implicitly through their actions, or explicitly through feedback responses, so that ROI can be tracked, and ML has the historical data to learn and improve
It was a fun night with the audience contributing to the discussion. The future for AI and ML is a bright one. Everyone agreed that in order for such initiatives to deliver true value, a reliable foundation of data must be established, in order to ensure success.
After all these years, master data management (MDM) has finally emerged from its awkward teenage years as a pimply-faced young adult, not quite sure if it’s ready to take on the world. A few industry analysts have even said that MDM is officially in the “trough of disillusionment,” confirming that while MDM is no longer in diapers, it is not quite mature enough to get a real job or get married.
Having worked in data management for the past 23 years, with most of that time in MDM, I thought I had seen it all.
Traditional 20th century MDM has certainly seen its ups and downs throughout its short history, but what excited me about joining Reltio was the idea of starting with a clean slate and building a 21st century Modern Data Management solution from the ground up. A solution that not only revolutionizes MDM, but goes beyond the basic single version of the truth.
Fortunately, Reltio doesn’t have any legacy 20th century pieces and parts to “Frankenstein” the next generation MDM offering. A luxury that legacy MDM vendors typically cannot afford.
As part of redefining not just the MDM market, but data management in general, Reltio decided to focus on refining one of the key capabilities of MDM–data matching. Although matching algorithms and techniques haven’t changed much over the years, the way these algorithms and techniques are applied could certainly be improved.
By applying a modern approach, with techniques including an ongoing emphasis towards leveraging machine learning to improve how matching is done, allows companies to be flexible in the early phases of development.
At Reltio, we are about being right faster. Therefore, our ability to tune and re-match all of your company’s key business entities faster, enables your organization to be more agile and accurate in a way that’s a clear departure from today’s MDM norm.
Being able to fire off all match rules at once, versus the traditional way of traversing match rules one at a time, and stopping once a match is found is one example.
In another example, a life sciences customer of ours defined over 100 match rules with a non-Reltio MDM solution. When they deployed Reltio Cloud, they were able to reduce the number of match rules to just 16. Reltio Cloud is a clear departure from the norm that provides key stakeholders with a modern, agile and simplified approach to data matching.
When you distill all of this information down, you’ll find that today’s traditional MDM solutions suffer from the same fatal flaw–a relational database that is used to manage and store data used in the match process.
Today’s MDM requirements go beyond yesterday’s repository of simple “common” master data in the thousands of records, and necessitates a modern solution that is able to integrate millions of transaction and interaction data across multiple systems.
Trying to manage relational database cross-reference tables, joins, intersection tables and more across newly mastered entities, including millions of transaction and interaction relationships creates a relational “spaghetti” mess that just won’t scale.
In the end, what business users need today is a single place where they can find reliable data and relevant insights that drive recommended actions across their entire enterprise.
DISCLAIMER: I have no inside knowledge into what may or already have been discussed, or data analyzed by either AMC Theaters or MoviePass. This article is purely based on my thoughts as a movie-goer, marketer, and product manager.
“AMC Theatres has threatened MoviePass with a lawsuit, less than a day after the subscription cinema service dropped its subscription fees to $9.95 a month, reports Variety. That means subscribers are able to watch one movie every day for a month for only $9.95. MoviePass would still have to pay AMC full ticket prices each time someone uses the subscription, though. An average ticket is priced at $9.33, so a subscriber would only need to attend two movies a month to put MoviePass at a loss.
In 2016, the service started at $15 per month and ran up to $50 per month for unlimited movies in bigger cities. AMC, which is the largest theater chain in the US said in a statement that MoviePass’ model is unsustainable. The company argued that ticket prices below $10 a month over time wouldn’t be able to generate enough cash to operate quality theaters, nor produce enough income that would allow film makers to make movies of value.” – Source The Verge
I never knew about MoviePass as a subscription for unlimited movies. As a father of twins my wife and I barely get to go to the movies but once every 2 months, and it costs us an extra $100 in babysitting to go, and there’s nothing really compelling as far as “good” movies in our opinion, but I digress! Moviepass’ offer of $9.95 a month does seem to be very compelling, and ultimately very disruptive.
My first reaction in seeing that AMC is suing MoviePass for this action is to wonder out loud whether AMC had gone to MoviePass and offered to jointly analyze their respective datasets in order to see if there might be synergies in such an action.
An Outsiders Product Manager’s Perspective:
Showtimes of movies (beyond opening week of new blockbusters) are rarely full, meaning there is unused inventory in every single time slot
Pricing strategies to try and fill these slots don’t appear to have changed much beyond off peak time discounted ticket offers
Loyalty and rewards programs have now started to become more prevalent so efforts are ongoing to capture consumer profiles
Concession sales per customer are lucrative with a large popcorn and drink often costing more than the standard ticket (letting MoviePass fill shows to capacity could yield more in concession revenues than tickets itself)
Clearly I would need more data to find patterns and analyze this information to form the right conclusions. The steps would be to:
Form a Reliable Data foundation – leading to a 360-degree view of the consumer/movie-goer profile, with demographics, attribution, captured in part through AMC’s loyalty programs, but also could then be cross-referenced (Matched and Merged) with MoviePass’ subscribers to enrich both data sets.
Benefit from Commercial graph technology to find friends and family affiliations to drive offers (see marketing perspective later) to make it more of a social/group movie-going experience
Generate Relevant Insights – by bringing together the transactions processed via the tickets bought through MoviePass vs. walk-ins, and other avenues such as Fandango, promotions etc. Stanalone Master Data Management profiles are insufficient as the real valuable insights are in the transactions/behaviors exhibited by those movie-goers, and they need to be analyzed and seamlessly aggregated back into the master profiles for marketing segmentation
Deliver Recommended Actions – So marketing teams can jointly highlight how AMC and MoviePass could gain synergies from the increased traffic to theaters. Applying machine learning and data science to the reliable data foundation, not just at a macro-level, but to generate the right programs that can take advantage of the identified profiles, to drive more personalized experiences, and revenue-generating concession sales
Leverage Data as a Service – to securely share insights between AMC and MoviePass, preserve consumer privacy, and to bring in more data from suppliers of concessions to negotiate discounts and for synergies such as just-in-time ordering to improve margins
An Outsiders Marketing Perspective:
Once all this data is aggregated, made reliable, and analyzed, the joint market teams of AMC and MoviePass could work on promotions and programs using data-driven applications. With a Modern Data Management foundation they would be able to correlate Recommended Actions back to actual outcomes. Personalizing and improving customer experiences are just the cusp of benefits that can be realized. New business models that could easily be supported might include:
Making it more of a social experience –convert real-estate into Starbucks-like hangouts, with good coffee, wireless, and a place to meet. Offer better higher-end desserts so people come 30 mins before the movie with family and friends after dinner, or stay afterwards to chat about the movie and what they thought about it
Increased kids focus – more tie-ins and kids activities, pre- and post-movie with merchandise sales in a movie “store” with branded items tied-ins. Sales immediately after the event for instant gratification is the a way to command a premium over online sales and their lower prices
Given the fact that VOD, Netflix, Virtual, and Augmented Reality are literally right in the face of and challenging the movie theater going experience, AMC and other theater operators face being disrupted. A Modern Data Management Platform as a Service is essential to not only improve revenue, margins and partner better, but possibly survive.
How do you think the experience could be improved as a movie-goer?
As a Product Manager, how would you use data to gain better insights and possibly partner better. Have you used shared data and insights in similar situations between partners, or perhaps in M&A scenarios? Please share.
When was the last time you tried to access information and ended up spending an hour looking through multiple systems to find it? Even worse, when was the last time your data failed to give you the right information, at the right time to help you do your job?
In observance of Friendship Day, we would like to point out how the qualities of a lasting friendship have much in common with the effects of well-managed data upon everyday life:
A friend is someone who is there for you when they rather be anywhere else
Just as you expect your good friends to be dependable, reliable data is non-negotiable. It’s table stakes for any company who is looking to be data-driven.
How about a healthcare organization with siloed, duplicated hospital and patient data? You could probably use some support for improving data management efficiencies, and omnichannel engagement with your patients.
These sound like problems only a real friend could understand.
I get by with a little help from my friends
A real friend is always willing to lend a helping hand. Good data behaves similarly by providing you with consistent and contextual information that helps you with your daily activities.
Reltio’s data-driven applications combines reliable data, relevant insights and recommended actions into cloud applications. These enterprise data-driven applications give business users a consumer-grade experience, like LinkedIn, Google and Facebook.
Tailored to your industry and functional role, these data-driven applications can help you identify and reveal relationships among the people, products, places and activities you care about. They can also guide you with powerful visuals and intelligent recommendations that help you make better decisions as you complete tasks.
Workflow and collaboration capabilities also allow you to manage, curate and provide real-time feedback on data quality. These out-of-the-box workflow processes accept input and requests from business users, without using email. They also improve the accuracy and effectiveness of information through social collaborative voting, ranking and rating.
Everyone is a friend, until they prove otherwise
Your true friends have likely proven themselves to be trustworthy. Putting your data in the cloud involves a high level of trust in your data management provider.
Companies, like Reltio have gained the trust of their customers, through earning HITRUST CSF Certification status, for information security by the Health Information Trust Alliance (HITRUST).
A friend is someone who knows the song in your heart, and can sing it back to you when you have forgotten the words
Good friends always provide you with insightful information that you may not have thought of. Reltio’s Commercial Graph is like that.
Architected for today’s agile enterprise, it is built with a columnar and graph hybrid store to combine, relate and store an infinite number of attributes and relationships. Created for the purpose of delivering any type of data-driven application, for any business need.
It also handles operational and analytical functions within the same data-driven application. Covering every possible scenario, while giving you the ability to imagine your data from any perspective.
True friendship is like sound health; the value of it is seldom known until it be lost
The irreplaceable value of a true friend is not always apparent. True friendships, like sound health, need maintenance and continuous improvement to endure.
Data quality works similarly. One cannot tell whether data is valuable until it is analyzed. Oftentimes, the more maintenance and improvements that are made to the data, the more valuable the insights become.
Rather than mourn over lost opportunities from poor data, make sure your data scientists and analytical platforms have access to reliable data. Reltio Insights enables advanced analytics with machine learning, so that you can access accurate information for faster results.
Unlike analytics-only tools, Reltio’s bidirectional connectors bring aggregated insights back to master data profiles. Offering valuable recommendations for business improvement to the users of data-driven applications. Thereby making your data as reliable, consistent, trustworthy, insightful and valuable as your best friend.
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