The simple answer is that the Wave’s qualification criteria includes several key areas in which Reltio is naturally strong. As the only Master Data Management platform recognized in the Wave we believe our core MDM capabilities contributed to our strong showing. In fact, Reltio had already been included, together with 23 other excellent companies, in Forrester’s Now Tech: Machine Learning Data Catalogs, Q1 2018 report preceding the Wave.
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.
Post May 25, 2018, per the General Data Protection Regulation (GDPR), companies with business ties to the European Union need to comply to GDPR standards. The cost of non-compliance is huge, but the regulation is meant to benefit individuals as well as businesses. Let’s look at what it can unlock for you and your brand if you approach it in the right way. What about being able to say that you are the safest enterprise in your marketplace when it comes to data? How about if you can not only reduce operational cost but can also create new revenue streams by being compliant to GDPR and other upcoming regulations?
1. Replace Legacy Systems by Future Proof Cloud-based Applications
When companies are taking steps to comply with GDPR, they are required to perform a ‘spring clean’ of their data, which can in turn lead to multiple efficiency gains. Organizing data improves the way firms carry out analytics and take business decisions. To comply with the regulation, companies must be able to illustrate the entire data flow – how data comes into the company; how they store and manage it; and how they treat it at end of life. This will encourage businesses to replace legacy systems by flexible cloud services to be more nimble and transparent especially when regulatory regime keeps evolving. In addition, most large enterprises have grown through M&A. Thus, they can look at GDPR as an opportunity to get rid of obsolete software and accelerate application retirement.
2. Gain Brand Loyalty and Attract New Customers
Companies can leverage GDPR to change the landscape from risk mitigation to improving their long-term competitive advantage. They can see early GDPR compliance as a competitive differentiator and position themselves as leaders of an emerging new normal. We trust those businesses who values our privacy beyond mere legal compliance. Thus, GDPR is an opportunity for businesses to get their data in order, get compliant and become consistently transparent with their customers. In a post-GDPR world, data sharing would be seen in the context of mutual respect and value exchange. It is an opportunity to re-connect your business with your current and potential customer base and start a new relationship based on mutual trust and responsible personalization.
3. Invest for the Future
The criticism that GDPR compliance might restrict innovations in AI ignores a subject’s right to privacy and consent. In fact, not being GDPR compliant would impose far more constraints on data collection and processing, slow down the ability to leverage innovations in AI and pay an opportunity cost such as market share losses in the future. Read this article for more details – Understanding GDPR and Its Impact on the Development of AI. In addition, in an era of data-driven innovation, business partners need to work together across the value chain. Data-driven innovation requires a clear understanding of the data to be collected and the reasons for collecting it. There are double opt-ins in such value chain: both partners need to be clear about what data they have about each other, and why. It’s very important that their data sharing practices are compliant with GDPR and other upcoming data regulations. As a first step to GDPR compliance, companies must define the scope of GDPR-relevant personal data that is collected, processed, and shared. Once a company identifies the scope of GDPR-relevant personal data, it should catalog all internal and external data sources that fall within this scope.
4. Execute A Delicate Interplay of Offense & Defense Data Strategies
In the post-GDPR era, personal data protection will become a data strategy issue. To comply, businesses need to have solid data organization and data governance in place. The GDPR gives companies the opportunity to holistically re-assess all their data, not just personal data. Data defense is about minimizing regulatory risk and preventing theft. Data offense focuses on increasing revenue, profitability, and customer satisfaction. Strong regulation in an industry (e.g. financial services or health care) would move the company toward defense; strong competition for customers would shift it toward offense. The CDOs and the rest of the leadership should see GDPR as an opportunity to establish the appropriate trade-offs between defense and offense to support company’s overall strategy. Read this blog post for more details – Is Your Data Strategy Defensive or Offensive? Why Not Both?.
Data is a company’s most important asset, and it’s constantly growing. Taking mandated compliance and turning it into an opportunity to personalize, delight and exceed customer expectations would fuel innovation reliably and responsibly.
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.
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.
Evolving from traditional to next-gen data management would mean taking these four key steps.
1. Tear Down the Silos Between IT and Business
As per the “State of the CIO 2017 Survey”, 59 percent of CIOs said IT collaborates with business units to build business cases for new MDM (and technology) initiatives, while only 24 percent of business leaders said that was the case. This indicates clear departmental silos between IT and Business. To bridge this gap, you must stop seeing your MDM as a separate siloed discipline, requiring complex IT infrastructure, processes, leading to months of design and implementation. Modern data management encourages an alignment and partnership between business and IT through an extremely user-friendly data governance interface, thus maximizing the ROI of your MDM investment. It has inbuilt collaboration and workflow capabilities to meet your enterprise’s governance framework and way of doing business. In addition, it allows IT and business users to provide feedback in a collaborative and controlled manner thus preserving valuable intelligence and competitive advantage.
2. Close the Loop Between Operations and Analytics
According to a recent McKinsey survey, 86% executives reported that their organizations were only somewhat effective at meeting the goals they set out for their data and analytics initiatives. The biggest culprit is a gap between analytics and embedding these insights into the operating model of the larger organization. Turning data into real value requires a profound reshaping of your day-to-day workflow and digitization of transactions and processes to generate and collect all useful data. A modern data management solution helps consolidate and cleanse data from all sources, transform it into reliable data, and provides relevant insights and recommended actions in the context of your operational applications using predictive analytics and machine learning. It allows you to apply analytics to improve the performance of your core operations. It doesn’t stop there and further correlates downstream business actions and results back in an integrated closed-loop, thus converting big data into smart data, providing faster Time To Analytics (TTA), measurable ROI, and better outcomes.
Modern Data Management Brings Together Best of Breed Technology in a Unified Platform
3. Take Polyglot Data Storage Approach to Achieve Big Data Scale as well as Performance
Different databases are designed to solve different business problems. Using a single database for all the requirements usually leads to non-performant solutions. A data-driven application should be able to bring together data from different database types to achieve the business objective. Most of the operational business applications run on relational, columnar databases, but they do not manage relationships well. Graph databases, while suited for uncovering and handling relationships, don’t have the horizontal scalability and agility to meet enterprise needs. This limitation is leading to the emergence of modern data management platforms built on columnar-graph hybrid stores. Once you create data-driven applications on a reliable data foundation of a Commercial Graph, you can visualize all relevant information and relationships as well as quickly pivot from one application to the other. For example, you can see all the purchased products and stores visited in a consumer’s profile, and with a simple click, you can drill into the product profile, roll-up dynamic hierarchical information (revenue, value, product usage) or find key influencers in customer networks.
4. Strike the Balance Between Offensive and Defensive Data Strategy
Retailers who fail to comply with data security can be fined up to 4% of their revenue and lose the confidence of their customers forever. On the other hand, personalization can deliver five to eight times the ROI on marketing spend, and can lift sales by 10% or more. Thus, the need of the hour is to strike a balance between your defensive and offensive data strategies and make considered trade-offs between “defensive” (e.g. security, governance, and compliance) and “offensive” (e.g. revenue growth, profitability, and customer satisfaction) uses of data as illustrated in this HBR article. A modern data management platform offers flexible data and information architectures that involve both a single source of truth (SSOT) and multiple versions of the truth (MVOTs). It focuses on reliable data at scale for “defense” and delivers relevant insights for “offense” from complete contextual 360-degree views, for personalized engagement.
Can you prove the ROI of your data management efforts? Are you able to conquer the gap between your analytical insights and operational execution? Can your business teams leverage reliable data and relevant insights to solve their day-to-day challenges without compromising on scalability and performance? Are you able to implement a mix of defensive as well as offensive data strategies to meet your data security as well as personalization goals? If the answer is no to one or more of these questions, you must rethink (and reinvent) your data management philosophy.
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.
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.
Extract More Business Value & Insights with Reliable Data
This year’s Big Data Innovation Summit 2017 in San Francisco included leading data experts offering use cases, best practices, challenges they faced and the solutions they established in response.
With the tagline “Cultivate the Data, Yield the Profit,” the summit tackled weighty themes to help attendees avoid costly mistakes from inaccurate data, attain best practices for harvesting data with high potential and futureproof their current models, tools and predictive capabilities to name a few. Top discussion areas included:
Data Science: Market research and advisory firm, Ovum estimates the big data market will grow from $1.7 billion in 2016 to $9.4 billion by 2020. As the market grows, enterprise challenges will shift, skills requirements will change and the vendor landscape will morph. As the biggest disrupter for big data analytics, the use of machine learning is growing to create a true 360° view of anything (customers, employees, products and suppliers). However, it requires a reliable date foundation, bringing together data from all internal, external and third-party sources. This blending requires careful matching and merging of the data. Machine learning within modern data management platforms can help derive the matching rules automatically from data and active learning training by data stewards. With a single click, they can show the machine learning system how to treat the data and determine new match rules. The system adapts to the customer data and user behavior.
Data Governance: Organizations that have decided to build data lakes are advised to pay attention to data governance, quality and security to keep it from becoming a data swamp. Even if enterprises use sophisticated tools to examine and interpret patterns for predictive analytics and machine learning from their structured and unstructured data, without proper metadata and quality assurance of data, the data in lakes becomes unusable over time. The lack of correlation back to accurate master profiles and operations means there are no guarantees that the answers are either relevant or reliable. With existing big data projects recognizing the need for a reliable data foundation, and new projects being combined into a holistic data management strategy, data lakes may finally fulfill their promise.
Predictive Analytics: The use of predictive models and big data is transforming how we reach complex decisions such as consumer credit risk, personalized retail marketing and insurance pricing. Effective predictive modeling helps organizations figure out where to look for problems, how best to invest scarce resources and how to anticipate needs, instead of constantly playing catch-up. The consumer intelligence from a predictive model is only as good as the quality of the data collected for analytical customer relationship management. Starting with a reliable data foundation, business teams can ultimately benefit from recommended actions that in turn will allow them to confidently leverage the information for personalized customer engagement.
Data Strategy: Successful businesses know that data is the new currency and the lifeblood of the entire organization. It should enlighten every function of the business, including customer experience, operations, marketing, sales, service and finance. In the age of the customer, everyone within the organization should be using a personalized contextual source of truth (not just a single/golden source) of information across all of the operational applications and channels needed to support a customer’s journey to deliver great customer experiences. Therefore, a data management strategy is critical for providing business functions with quick and complete access to the data and analytics that they need, both now and in the future.
As someone who has been in the master data management (MDM) space since 2002, I thought I had seen it all, understood all the customer requirements that could or could not be met with an MDM solution.
But I was wrong. Reltio’s Modern Data Management Platform solves an entirely new class of operational problems, as well as solves the existing operational challenges in a faster, flexible and more scalable manner.
I spent a number of years implementing operational MDM solutions. There were several areas of the implementation that slowed everything down. Fast time to value? Only if you didn’t have to meet any business requirements as different Lines of Business (LOBs) had different requirements about how to curate and use the data. But traditional MDM solutions force you to have one common view. What happens if your LOB needs different – a more personalized and contextual view? Get in a room and hash it out? Good luck. And while the project snowballs, you can kiss your twelve week implementation goodbye!
Another area that slowed the implementation down was the data load. This was caused by two problems. First, you had to map the data to the data model. Even if you had an ‘industry best practice’ data model, this was a difficult task. No one knew the data model well enough. Oh! Don’t forget to map the foreign keys and intersection tables! The data validation rules that were not built into the database, remember the ones at the application layer? I’m sure you know all those by heart. Because they will cause your records to fail, too. Second was the quality of the incoming data. Data quality tools can help you, particularly profilers. But you still need an analyst to sort through the problem, determine solution and implement it. And you might find thousands of types of data issues that affect millions of records. Good luck. And again, your time to value becomes years, not weeks!
A Modern Data Management platform handles relationships in a modern way. Relational databases handle relationships by way of foreign keys, joins and intersection tables. Hopefully, the application server logic correctly commits the data so that referential integrity of the database does not get compromised. That can be pretty tricky if you try that across applications (which is why most companies don’t). This approach is fine until something changes. Oh, your business requirements changed? You need to relate entities in ways that had not been thought of in the initial implementation? Sure. Step right up to consulting hell as someone tries to figure out how to undo what has been done, get the new relationships in place and put together a plan to get from the existing state to the new one. Oh, you wanted that next week? Or yesterday? Hmmmm…. We’ll get back to you on that.
One of the key advantages of the Reltio platform is that instead of using one or two technologies to solve the operational master data management problem, it uses a number of them. Highly scalable database? Check. Search available immediately when data is loaded? Check. Reltio Graph to manage all kinds of relationships dynamically? Check. Data Quality and Survivorship? Check. Am I forgetting something? It’s all there. Without the need to put together the pieces yourself and worrying about messy installs or upgrades, all running on the scalable and security of the AWS platform. In addition, Reltio platform leverages machine learning to improve data quality and enrich data with relevant insights.
Hadoop, HBase, Cassandra, Graph Databases, MapReduce, Spark and the continuous stream of new technologies have changed the way data can and should be managed. However, stitching together all of the pieces required to have a complete end-to-end offering and support a wide variety of business needs across an enterprise is a complex undertaking. A Modern Data Management Platform helps you keep up with this evolving technology landscape and allow you to solve all your operational (as well as analytical) challenges faster and in a more agile manner.
Why Cognitive Computing Solutions, Driven by Advanced Analytics will Replace Traditional Applications
Guest Blog post by: Judith Hurwitz, President & CEO Hurwitz & Associates
In this guest blog post, renowned industry analyst and author, Judith Hurwitz provides her POV on Cognitive computing, predictive analytics, reliable data and data-driven applications.
Like this post or add a comment to enter into a drawing for free copies of Judith’s latest book “Cognitive Computing and Big Data Analytics”
Cognitive computing is not a single technology or a single market. Rather it is an approach to solving problems by leading with data. This means that rather than creating all the logic first and flowing data into a solution, you begin by analyzing the data to determine the patterns in that data. As more data is added, the cognitive system gets smarter and adapts to this new data. Therefore, it is no surprise that one of the most important aspects of cognitive computing is advanced analytics. Advanced analytics is defined as the collection of algorithms and techniques that leverages both structured and unstructured data sets to identify patterns. There isn’t a single approach to analytics that is useful in a cognitive system. For example, information from neural networks, text analytics, sophisticated statistical models, predictive analytics and machine learning is all core to creating and managing a cognitive system.
By building the right predictive models that are able to react to changing business environments, companies will be able to prevent problems from happening before they occur. Analytics models must be able to take into account current information from customer interactions and respond quickly. The systems incorporate large sets of structured, unstructured and streaming data to improve the predictive capabilities. The sources of this data needed to make the models as effective as possible come from social media, customer relationship systems, web logs, sensors and video.
How are organizations beginning to use a cognitive computing approach? Given that cognitive computing is a young field, organizations are beginning with areas such as healthcare where there are huge volumes of unstructured data that cannot easily be analyzed or managed. Fraud detection is another important area where advanced analytics and the cognitive computing system can have a significant impact. An insurance company may be faced with thousands of fraudulent damage claims. Using advanced analytics it is possible to create a model that can detect even subtle indications of fraud. The resulting cognitive system can detect new threats by learning from data even before they are even noticed by management.
While it is always possible to use analytics on a set of structured or unstructured data, a cognitive system can advantages. A traditional application is based on an organization’s understanding of a process or business problem. By the time that application has been written, debugged and implemented, the business may have changed significantly. Contrast this to an approach the developer begins with a hypothesis and then trains available data. If that hypothesis is not born out by he data then either the team will need to change the hypothesis or to bring in new data. The analytics process is allows you to understand relationships that already exist but have not previously been identified. Using machine learning greatly improves how effective predictive models are can improve accuracy especially when companies need to analyze large amounts of data sources that are primarily unstructured.
It is clear to me that a cognitive approach to advanced analytics will have a dramatic impact on hundreds of different market segments. When we have the ability to gain insights that is hidden and then apply learning to that data there is a potential to transform industries ranging from healthcare, to financial services, metropolitan area planning, security, and IT itself. At the heart of business transformation is the ability to make sense of massive amounts of data. To be successful, this data has been refined in a way that creates trusted systems that have the potential to learn the secrets buried inside that data.
Judith Hurwitz is the President of Hurwitz & Associates, a consulting and research firm focused on important emerging technologies. Hurwitz has co-authored eight books including Cognitive Computing and Big Data Analytics (John Wiley & Sons, 2015).
Strictly Necessary Cookie should be enabled at all times so that we can save your preferences for cookie settings. These are cookies that are required for the operation of our Site and under our terms with you. They include, for example, cookies that make use of certain Services offered through the Site.
If you disable this cookie, we will not be able to save your preferences. This means that every time you visit this website you will need to enable or disable cookies again.