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🎓️ Scoring Academy Pilot - Meet Johnathan, a recent graduate! 🎓️
We recently shared with you the experience of one of the first students who recently graduated in the new Quantexa Scoring Academy pilot, aimed at data engineers and data scientists to get a broad overview of our Assess scoring framework. The Scoring Academy will see the light in April 2023, delivered by our LMS and providing virtual desktops to get hands on experience. When enrolling the upcoming Scoring Academy, students will learn how to set up scoring pipelines, write different types of scores, develop unit tests & interrogate generated scoring data. While we wait for the launch of the exciting Scoring Academy, we have had the opportunity to talk to another of the students who participated in the pilot and successfully graduated. Today, we want to share with you the conversation we had with @JohnathanJacobs , Data Engineer at NextWave Infinium. Keep reading below to find more about Johnathan's experience and what to expect in the future Scoring Academy. 1. Please let us know a bit about yourself and your professional background. Any fun fact you would like to share? 😊 I have over 6 years of experience in bank consulting in the data space. I have worked in Regulatory Reporting, Anti-Money Laundering, Client Due Diligence, and Data Warehousing. Professionally, I have primarily worked with SAS, PL/SQL, and Scala – though most of my personal projects are in C++. My passion for programming started with developing mods for games I liked when I was about 13, which accounts for the fact that I do hobby game development in my free time. 2. What do you enjoy most in your role? As a consultant I get to be involved in a wide variety of clients and projects, which means that I’m always learning. It’s exciting to always be in the position to have the opportunity to learn, and to share my own knowledge. 3. Tell us about your Quantexa journey so far. I have completed both the Quantexa Data Engineer academy, as well as the Scoring Engineer Academy pilot; in about 7 and 3 weeks (respectively). 4. You recently graduated on the Quantexa’s Scoring Academy pilot. Congratulations! Could you describe your experience on the Quantexa Academy in three words? - Exciting - Tough - Rewarding 5. How did you find the Scoring Academy pilot? What did you enjoy most during your time as a learner? I found it interesting. It was an exciting mix of practical and theoretical knowledge that allowed me to “stretch my legs” mentally. 6. Is there anything you would like to have seen more of within the Scoring Academy pilot or any improvements that could be made? - I feel that the theory test could be refined. Questions are in general vague in comparison to the specific answers required. - There were a few pieces of esoteric configuration strings that were not present in the learning material. It would be greatly advantageous if they were mentioned, as they were needed. 7. How do you envision putting your Scoring knowledge into practice? Any projects that come to mind? Currently, my main aim is to share the scoring knowledge that I have gained. I have colleagues who are implementing scores on various projects who don’t currently have the time to go through the Scoring Academy pilot. With the knowledge from the academy, and their experience, we can work together to gain a deeper understanding and improve implementations. 8. Do you have any advice for future Quantexa Academy learners? Quantexa is a complex platform that takes time to learn. Don’t get hung up on trying to understand everything all at once, the key is to incrementally learn and understand. 9. Is there anything in particular you would like to see on the Quantexa Academy that’s not available at the moment? There is a module that goes over Event Scores, though only in theory. While I can understand the content might be too similar, I believe that the additional practice of implementing it would be worthwhile. 10. Have you heard about the new Quantexa Community and do you think it will become a valuable resource in your day to day? Yes, the new Quantexa Community is quite the step up from the Slack support channel that we relied on. Although the feedback might not feel as immediate, the additional search and discovery features over slack make it a much better platform for learning and sharing knowledge. Do you have any questions or comments around the future Scoring Academy? Would you like to enroll when available? Just let us know in the comments below 😊🎓️ Scoring Academy Pilot - Meet George, a recent graduate! 🎓️
Quantexa's Academy team has been hard at work with our recent Scoring Academy Pilot, aimed at data engineers and data scientists to get a broad overview of our Assess scoring framework. The three months Scoring Academy pilot has seen over 10 students and has been an invaluable opportunity for our Academy team to test out this brand new program, gain useful feedback and make small adjustments where necessary before full launch. The Scoring Academy will see the light in April 2023, delivered by our LMS and providing virtual desktops to get hands on experience. When enrolling the upcoming Scoring Academy, students will learn how to set up scoring pipelines, write different types of scores, develop unit tests & interrogate generated scoring data. While we wait for the launch of the exciting Scoring Academy, we have had the opportunity to talk to a couple of the students who participated in the pilot and successfully graduated. Today, we want to share with you the conversation we had with @George_van_Rensburg, Data Engineer at NextWave Infinium. Keep reading below to find more about George's experience and what to expect in the future Scoring Academy. 1. Please let us know a bit about yourself and your professional background. Any fun fact you would like to share? 😊 Professionally I would classify myself as a Software Developer / Data Engineer. I started my career as a developer at a bank in South Africa. It mostly consisted of work on a legacy system, but I did get some exposure to Big Data while I was there. After that I moved into the consulting space working for a client in the UK. The role involved a lot of Data Engineering and working with Big Data while utilising PySpark. I decided to emigrate to the Netherlands and that’s how I joined Infinium. 2. What do you enjoy most in your role? Being challenged daily and the problem-solving that’s required. 3. Tell us about your Quantexa journey so far. I haven’t been on a Quantexa project yet, but I’ve completed both the Data Engineer Academy and Scoring pilot since I joined Infinium in July 2022. 4. You recently graduated on the Quantexa’s Scoring Academy pilot. Congratulations! Could you describe your experience on the Quantexa Academy in three words? - Challenging - Fulfilling - Interesting 5. How did you find the Scoring Academy pilot? What did you enjoy most during your time as a learner? I enjoyed it. It was nice to delve deep into scoring as I felt it was more of a quick overview in the Data Engineer academy. 6. Is there anything you would like to have seen more of within the Scoring Academy pilot or any improvements that could be made? - Additional mentions of the configuration settings - General revision of the test questions, sometimes ambiguous due to the language used - An event score task included 7. How do you envision putting your Scoring knowledge into practice? Any projects that come to mind? My colleagues have been tasked with creating several scores on a project. Thus, it will be extremely beneficial to share what we’ve learned on the Scoring Academy pilot with them and implement it in future projects. 8. Do you have any advice for future Quantexa Academy learners? Don’t expect everything to make sense at the start. Some things will become a lot clearer when you can see the whole picture. 9. Have you heard about the new Quantexa Community and do you think it will become a valuable resource in your day to day? Yes. Slack was a great resource, but limited due to 6 months of history and a not-so-great search function. Questions and answers are also better organised, which is very helpful. Do you have any questions or comments around the future Scoring Academy? Would you like to enroll when available? Just let us know in the comments below 😊The Inherent Problems of MDM
The aim to have one master record for each real world entity has always been hard to achieve—and it’s becoming even harder. Companies are contending with: Multiple internal applications—many of which will contain different versions of the same master data record Numerous external data sources that provide additional—sometimes contradicting—information about companies and individuals Bringing together all these views of master data is incredibly difficult, because of: The data quality problem Traditional MDM has an inherent data quality problem. And it affects your decision making, regulatory compliance, business effectiveness and efficiency. Traditional MDM solutions don’t focus on solving data quality issues. In fact, they tend to fail at the first hurdle of matching data—because they struggle to join data from across disparate data sources such as multiple internal applications. When you add the volume and variety of data from external sources it’s even further beyond their capability. Instead, they use roughly the same matching algorithms they’ve used for the past 20+ years, which relies on record-to-record comparison that is very fragile when key attributes are missing or different. And that’s a problem. Because crucial information goes unreported when your MDM solution can’t catch essential links between data, and obscure relationships and connections are often overlooked. It also makes your MDM implementation very high risk. Bad data quality means: Data remains trapped in silos and is duplicated across channels Master records aren’t accurate and true-to-life Bad decisions are made, due to the fact they’re based off incorrect or delayed data Key information and critical links go unnoticed Opportunities are missed The transformation challenge MDM is both a business and a technology transformation challenge. You start out with multiple users updating the master records in separate applications, all with their own ways of working. Inevitably, it’s messy, it’s haphazard and it results in duplicate records, inconsistencies and confusion. So, to combat this, you decide to standardize things. Everyone is to use just one application, with a standardized set of rules on how to input data, how data should be formatted, what records look like and more. It’s a good idea—in theory. But the problem is that the real world rarely plays out quite so neatly. So what you end up with is this: A logistical nightmare, as you try to migrate decades of data and make it conform to your ideal record format. Confused and frustrated users who need to be transitioned to the new service—with all the training and business change support that entails. Backwards-compatibility issues, as information moves to new places and takes on different—unrecognizable—formats, meaning users and business applications can no longer find information. Challenges updating future records. If your records only track A, B and C, what happens when users later need to add D and E? Is it updated across the entire data store? And what happens to data that is initially discarded for non-conformity, but is later needed? The varying needs of different data consumers For an MDM initiative to be considered successful it needs to be able to serve data to consumers across the organization. For instance: Analytics teams who need to link data sources for decision intelligence Fraud monitoring applications Customer services or relationship managers who need rich views of their customers Finance teams who need to aggregate risk reporting Different business units often have different views on the data they require. So, when an MDM initiative attempts to standardize master data attributes across the organization, it may mean dropping attributes that these data consumers rely on—which, naturally, can result in tension and impact business performance. MDM needs to be able to present a rich and deep view of data to areas of the organization that consume it, while on the journey to standardizing key attributes. But that often runs counter to the way a lot of existing MDM software works, which relies on a fixed view of data that needs to be adhered to from day one. The need for governance and control In large organizations there are often many applications that hold customer data—each controlled by different business units. And that means a wide range of stakeholders with different priorities. Implementing traditional MDM often requires each business unit to give up control of their applications and data to a central initiative—which can result in a great deal of pushback and agitation. To successfully implement an MDM initiative, you’ll need to be ready to address the political challenges that come with it. A lot of that relies on bringing people together around a vision of a service that will benefit them, within a transformation program that can actually deliver. Building a Single, Golden Point of Truth The key to resolving the traditional MDM data quality issue lies in powerful entity resolution that retains context and doesn’t force data into a standardized format. We call this contextual MDM. How contextual MDM stands apart Originally built to tackle financial crime, contextual MDM (cMDM) ingests data from both internal and external sources to build an accurate, connected and enriched single-entity view using entity resolution and network generation technology. This is different from traditional MDM solutions, which rely on record-to-record matching—a method that does not work well on disparate records, as it relies on many attributes matching. At Quantexa, we use an expanded range of data—including address, phone, email, country, and third party data—to make further connections and enrich your data. Which is why our solution can make connections between records even when data quality is poor. Play video Traditional MDM Traditional matching does not work well on sparsely populated records—because it relies on many attributes matching. Records can only be accurately linked if a number of fields match (for example, if two records have the same name, date of birth, and address on file). The lack of additional contextual data makes deduplication difficult and leaves questions unanswered. Traditional MDM also relies on you to set rules. If the rules are too rigid, records will be under-linked (meaning duplication is more likely to go uncaught). If the rules are too loose, records will be over-linked (meaning different records are more likely to be mistakenly deduplicated, even when the entities in question are different). Quantexa cMDM With our entity resolution software, connections can be made intelligently across records. Using additional fields and an expanded range of records from any number of internal and/or external sources, our software makes it possible to accurately determine when multiple records exist of a single entity—and to turn duplicated records into a single, enriched entity view. Resolved entities can also be seen in context with their networks—so you can see how different entities relate to each other. With cMDM, data across different records is iteratively updated to enrich all sources, leading to better match rates and higher quality records data. The Benefits of Contextual Master Data Management With contextual MDM, you gain: A single, complete view of connected data A foundation for trusted data Flexible and open architecture Low risk implementation The power to make better decisions Consumer oriented views of master data So you can Create and update accurate master records, in real time Spot hidden risks and identify high-value growth opportunities Share essential data among your teams Offer frictionless digital-first experiences for your customers Develop good data practices and upkeep organizational data hygiene Scale your business easilyHow to raise a good support request 🆘
In order to request support we highly recommend reviewing our guide on Maximizing Value from Community Support (login required), which outlines: How to Maximize Value from Community Support General Troubleshooting Tips How to Search the Community Effectively Where to Raise Support Requests How to Write a Good Support Request Tips & Tricks to Ensure you get the Answer you're Looking for ASAP How to Contribute to the Community (and Earn Rewards) You will then need a concise description of your problem with enough detail for someone providing support to get back to you with suggestions without lots of back and forth. Additionally, you should always be careful to avoid violating any data security policies. For more details on what items should a good request include, visit our article below: How to raise a good support request - Quantexa Community Preparing to Request Support In order to request support you should have already been through the steps mentioned in “Troubleshooting Guide for Quantexa Developers”. You will then need a concise description of your problem with enough detail for someone providing support to get back to you with suggestions without lots of…
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