What common data modeling mistakes should you avoid in financial services?

Based on my experience, I would strongly recommend that in financial data science, aligning data modeling with business needs is crucial. Over-focusing on technical aspects like database choice or model types can lead to irrelevant or overly complex models. Begin with a solid understanding of the business problem, data sources, stakeholders, and expected outcomes. Domain knowledge is key; it helps you pick the best model that fits your business requirements. Continuously communicate with business users, integrating their feedback. This approach ensures your technical expertise is effectively utilized to meet real-world business objectives.

Understanding the business problem and exact requirements is key at the initial stage when you are figuring out what exact solution to build. Often simpler solutions have a higher impact, but you can only figure them out once you understand the business context. Imagine a shop trying to sell more items together when you buy something. First, they use a very smart computer program that looks at everything you bought before, what you looked at online, and other details to suggest more things to buy. But this was too complex and did not work well. So, they tried something simpler. They just displayed things that people usually buy together. This small change made more people buy extra stuff.

My tips summarized from experience; - Engage business leaders to define clear objectives for data models. - Foster ongoing dialogue with stakeholders for real-time refinement. - Aim for simplicity to ensure models are intuitive and adaptable. - Test models against real scenarios to capture business nuances. - Educate users to demystify models, encouraging ownership and ease of use.

In the realm of data science, according to me a business growth depends on the collected data and how the data is being used. On empathizing one can define the problems in the service or process of business, one cannot implement the processes if the basic requirements are ignored therefore proper ideation is required before implementation. Thus basic requirements like undersatnding the interest and need of the stakeholders, financial status of business, forecasting upcoming problems etc should be kept in mind before data modelling.

I have seen data scientists getting carried away as soon as they get hands on data they spent least amount of time in understanding the context or domain. As data scientist we need to avoid misinterpretation of data at early stages of project. This will ensure right data models are created to develop data science/analytical model.

common pitfall in data modeling is fixating on technical details like database choice or format, neglecting business needs. This can result in overly complex or irrelevant models. To sidestep this, begin with a solid grasp of the business problem, engage with stakeholders, and validate models against user feedback, ensuring alignment with objectives.

It's blunder to ignore business requirements. The foundation should be business requirements in each domains. Especially in financial domains, we choose some attributes just on the basis of business requirements over it's lower statistical significance. Same use case if observed through business perspective will have different requirements and if observed through customer or user perspective will have different perspective. Business is the key

By working closely with stakeholders to develop a thorough understanding of the underlying business requirements, you often can substantially simplify the data model structure, as well as reduce the dataset itself to only the fields and records needed to address those requirements. Such reductions make the model more transparent, faster, and easier to implement , document, and maintain.

Focusing solely on technical aspects may result in data models that are either overly complex, overly simplistic, or simply irrelevant for the business context. Lack of alignment with business users and stakeholders can lead to data models that don't resonate with their needs. An isolated focus on technical aspects may overlook critical aspects within the business context. A lack of ongoing collaboration with business users can result in data models drifting away from evolving business needs.

One thing to look out for is to be sure that your data modeling approach is not CAUSING data quality issues. For example, the use of bidirectional relationships in your data model, if not fully understood and extremely carefully tested, can introduce ambiguous paths and completely undermine the accuracy of your calculations. In almost all cases, bidirectional relationships can be eliminated through the use of alternative modeling approaches, such as bridge tables.

Implement comprehensive data quality checks and assessments at various stages—before, during, and after the data modeling process. Make data quality assurance a proactive, ongoing practice rather than a reactive task. Apply data cleansing, transformation, and integration techniques to rectify identified issues, such as missing values, outliers, inconsistencies, duplicates, errors, or biases. Develop a set of remediation strategies that can be deployed whenever data quality issues are identified. Document any assumptions, limitations, or changes made to the data during the modeling process. This documentation provides transparency and context to the data quality journey.

In this regard, it's very important to understand that a simple box plot is not sufficient to call out the outliers and excluding them from the modelling process. The outliers might not be random but could come because of very specific reasons and they could recur or they might influence something else etc. Always remember to call these datapoints as potential outliers. This nomenclature helps psychologically as you then don't overlook these datapoints just as outliers or discard them from your analysis...

Recognizing and addressing data quality issues is not just a technical necessity but a strategic imperative for any organization. The integrity of data serves as the foundation for informed decision-making and strategic planning. Overlooking data quality could undermine the trust in organizational insights, impacting the overall effectiveness of business strategies. Therefore, embracing a proactive approach to data quality management becomes crucial in fostering a culture of data-driven excellence and ensuring the resilience of analytical outcomes.

As I have mentioned in one of the previous collaboration data quality = data maturity. Data quality can affect the data model in many ways: 1. Biasing the data 2. Missing values 3. Deviation in objective 4. Wrong interpretation (at EDA) 5. Quality or results

This post is apt and on point. Prioritize data quality by implementing data cleansing processes, validation checks, and regular audits.

Absolutely, especially in places like finance where every decision is based on data and can have big impacts. If the data's not right from the start, it's like a domino effect – everything that comes after can go wrong. It's more than just cleaning up data; it's about setting up solid, ongoing ways to make sure the data stays good. Putting these quality checks right into the workflow of how we model things is key. And it's not just a job for the tech team. Good data management should start the moment we collect data and continue all the way through how we store and use it. It's about making everyone, from the IT crew to the business analysts, feel like keeping data quality high is part of their job.

While learning data science, I always try to think first of a process, that is descriptive statistics. Believe me or not, this is the best way to check the data quality and identify the underlying problems of it. We have to fix the data problems can ensure us a better model in future and it also increases scalability and flexibility.

Overlooking data quality issues is a perilous pitfall in financial data mining. Inaccurate or incomplete data can distort analyses, leading to flawed predictions and unreliable insights. Financial services rely heavily on precise information for risk assessment and decision-making. Failing to address data quality compromises the integrity of models, potentially causing severe financial repercussions. Vigilant data cleansing and validation are paramount to ensure the reliability and accuracy of mined information. Prioritizing data quality safeguards against misleading conclusions, fostering trust in the analytical processes essential for sound financial decision support.

Dirty data leads to bad decisions. Scrub your data for accuracy, consistency, and completeness. Regularly check for anomalies and keep it squeaky clean.

Data privacy and security should be built into the initial requirements development process. I like to segment user personas into four distinct groups: 🔸 Those who need access to all records (rows) in the data model, but a limited set of objects (fields) – example: Human Resources 🔸 Those who need access to all objects (fields) in the data model, but a limited set of records (rows) – example: Divisional Manager 🔸 Those who need access to only a limited set of objects and records - example: staff-level employees 🔸 Those who need access to all objects and records - example: Senior Decision-makers Generally, access should be granted on the principle of "least privileges" (i.e., the lowest level access needed to perform a given role)

Another oversight in data modeling is neglecting data security and privacy requirements. In industries like finance, mishandling data can lead to severe consequences. To prevent this, adhere to best practices like encryption, authentication, and comply with data protection laws such as GDPR or HIPAA. Obtain necessary consent and permissions from data owners to ensure robust security measures.

Treating data security and privacy as an afterthought is not the right approach instead, embed these considerations into your data modeling process from the beginning. Utilize robust encryption techniques for sensitive data at rest and in transit, and implement stringent access controls to limit who can access what. Remember, building a secure foundation early on helps prevent costly breaches and builds trust with customers. The financial services industry has a lot of data protection regulations like GDPR, CCPA, and HIPAA. Ensure your data model adheres to these by understanding the specific requirements for your region and data type.

Data models can be at risk if the regulatory norms are not adhered during collection, storage or modeling data. This can put the project at risk and open to legal risks. Especially in financial domain it posses a grave risk if not being vigilant can lead to financial losses for individuals or firms. Hence, Adherence to data norms is very important.

Financial data is sensitive. Lock it down with access controls, encryption, and clear audit trails. Keep an eye on data privacy regulations and adjust your model as needed.

Another oversight in data modeling is neglecting data security and privacy requirements. In industries like finance, mishandling data can lead to severe consequences. To prevent this, adhere to best practices like encryption, authentication, and comply with data protection laws such as GDPR or HIPAA. Obtain necessary consent and permissions from data owners to ensure robust security measures.

It's not just about tough encryption and making sure only the right people can get to the data. We also need to think about how the data models themselves can keep sensitive info under wraps. Techniques like making the data anonymous or using fake (but still useful) data can really help protect privacy, while still letting us dig into the data for insights. And then there's the whole legal side, like GDPR and CCPA. Following these rules isn't just about not getting into trouble; it's about building trust with our customers. They need to know we're serious about keeping their info safe. So, when we're working on data models, thinking about security and privacy isn't just a "tick the box" kind of thing.

Failing to account for data quality and accuracy is a critical misstep in any data-driven endeavor, especially in financial services. Inaccurate or unreliable data can lead to flawed analyses, jeopardizing the credibility of insights and decision-making processes. In the financial sector, where precision is paramount, neglecting data quality introduces significant risks. It's imperative to implement robust data validation, cleansing, and verification mechanisms to ensure the integrity of the information used in models. By prioritizing data quality, financial institutions can fortify the foundation of their analytical efforts, fostering more dependable and trustworthy outcomes.

A critical question with enormous implications for data model scale and performance is "at what granularity does your data need to be modeled in order to answer the key business questions?" Modeling at a higher granularity than necessary (e.g., example incorporating a time dimension when all key questions could be answered at a daily granularity) can increase cardinality within the model by orders of magnitude, leading to reduced performance, and increased costs of implementation and maintainence.

I think THE most common mistake hampering performance is to load more data into the model than you actually report on. Get rid of columns AND rows you do not need. And then group the data to reduce the granularity!

In the financial world, data isn't just valuable, failing to prepare your data model for scalability and performance is going to be a huge risk factor. Consider transaction surges, market fluctuations, and new data sources. Opt for horizontal scalability, like cloud-based solutions, where adding resources seamlessly keeps pace with demand. Choose data structures and access methods that prioritize fast retrieval and analysis. Using - indexing, partitioning, and caching techniques to ensure queries zip through even the most massive datasets. Remember, slow data is worse than no data. Don't let data get bogged down in inefficient processing. Utilize parallel processing technologies to distribute heavy workloads across multiple servers.

Neglecting data scalability and performance is a critical mistake while designing data models in any industry. While working with finance, the data escalates fast and comes from multiple sources, causing a big variety, so overlooking scalability issues can cause misleading analysis or even block it completely. Scalability involves handling large and complex datasets efficiently, so the way to address this would be improving your pipeline and database design to ensure a smooth querying process for analytics and a good relationship among sources. A good place to start is by asking yourself "how can I combine data from sales, invoices, contracts and payroll?", this will trigger the thought process for you, and it might help on development!

During my work with a finance client as a data scientist, we faced a critical issue. The client insisted on storing 200+ data points per customer in a single table. While it met immediate needs, as data grew, efficiency suffered. This experience emphasized the importance of scalability from the outset to handle growth and ensure timely insights.

In finance, dealing with ever-growing and complex data means scalability and performance in data modeling are crucial. Neglecting these leads to inefficiencies, impacting timely insights. Models must handle current and future data loads, possibly using distributed databases or cloud tech for scalability. Performance optimization is ongoing, requiring regular system monitoring and tuning to avoid slowdowns. This involves query optimization, indexing, and possibly in-memory processing. Seeing scalable, high-performance data infrastructure as a strategic, not just technical, investment is key. It keeps pace with industry demands, enabling effective data-driven decisions and innovation.

A perfect service model is made, when it uses latest data, which can match the velocity of this fast paced transforming of data, with the changing attitudes of clients, and also with additions of new clients or stakeholders there comes increase in volume and variety of the data, all these challanges the data handling ability of the company and can also act as a boundary between the latest and efficient services and services that are still following the old traditional ways which will go dead with time.

Neglecting data scalability and performance considerations in financial data mining can impede the efficiency and responsiveness of analytical processes. As financial datasets grow, failure to plan for scalability may result in sluggish performance and delayed insights. This oversight hampers timely decision-making in a sector where real-time responsiveness is often crucial. Implementing scalable architectures and optimizing performance are pivotal. By anticipating and addressing scalability challenges, financial institutions can ensure that their data mining operations remain agile and capable of handling increasing volumes of data, thereby enhancing the effectiveness of their analytical endeavors.

Scale for Growth: Data explodes like popcorn. Choose flexible storage solutions like cloud platforms and optimize queries for speed. Monitor performance and keep your model nimble.

Adopt a "need-to-know" approach, loading only essential data for maximum value. Users often desire complete visibility, but it's not always entirely accurate.

Always keep in mind that it’s VERY rare for one person to be responsible for a model from cradle to grave. Thus, for every model you create, you should always be thinking “how can I make the transition easier for the person who will take this over from me?” Some areas to focus on: 🔸 Always develop a comprehensive data dictionary with the definition, type, and source of each field 🔸 Focus on writing clear code, and always format it and comment thoroughly 🔸 Remove unneeded data fields, rows and measures from your data model 🔸 Always maintain a copy of your raw data 🔸 Document all changes made to the data in cleaning and transform it 🔸 Take advantage of automated model documentation tools and rerun them as needed

Some tips to ensure the maintenance of predictive models is to automate the update process as much as possible with MLOps. Establish alerts to notify users when models show any deviation in adherence or performance and create a documentation process to track every model update. Some tools like MlFlow and Deepchecks can help in this aspect.

Model Evolution: Adapt! Update data sources and parameters regularly to stay relevant. Document your model clearly and embrace version control for smooth upgrades.

Indeed, in the ever-changing world of finance, treating data models as if they never change is a big mistake. These models need to keep up with shifts in business and data trends. Constantly checking how well they work in the real world is key. This helps spot when a model is getting stale or isn't predicting things well, showing when it's time for an update or a complete do-over. From where I stand, keeping our data models up-to-date is all about making sure they stay relevant and reliable. It's about not falling behind in a world that's always moving. By regularly refining models based on new info and feedback, we ensure they stay solid, precise, and in line with the latest goals and rules of the game.

Simple way is to have a training loop that restrains the existing model with new data and shows the predictions If the model trained on latest data (trained on more data than the deployed one) is performing better, then this is done to investigate

Forgetting to update and maintain data models poses a substantial risk in financial data mining. Over time, market conditions, regulations, and business dynamics evolve. Failing to adapt models accordingly can lead to outdated predictions and suboptimal decision support. Regular model updates, incorporating new data and adjusting algorithms, are essential for relevance and accuracy. In the fast-paced realm of financial services, the persistence of effective models hinges on proactive maintenance. Neglecting this aspect undermines the long-term viability of data-driven insights, potentially exposing institutions to avoidable risks and missed opportunities.

Don't shy away from de-normalizing your data model where it makes sense to do so. Strict normal form arose from an era when storage was extremely expensive. De-normalizing your dimensions from a snowflake schema to a star schema will often greatly simplify your model and associated code, and increase performance. And due to more efficient compression algorithms, the resulting duplication may only result in a minimal increase in file size.

Get the owner right- everything else usually will fall into place. Most orgs miss this and make this bureaucratic hurdle . Clarify decision making - voting is a poor choice usually..good to get opinions but have clear accountable decision makers.

One more addition to this post would be - Ignoring Model accuracy. Always verify and ensure that the model used is correct for the type of data and insights that we want to derive.

In finance, If you're dealing with customer data rather than transactions, risk models, etc., one common pitfall is to pipeline all your data into a seemingly flexible model, treating the population like a unified group of subjects. However, in a real-world scenario, you should seriously ponder over behavioral patterns that might not be visible at first look. Especially studying segmented data requires building hierarchical models and the ability to interpret them. Each level of group has unique distributions, expectations, and behaviors to be analyzed. In short, just avoid modeling the data without conducting the needed brainstorm and remember that a hierarchical look is rarely unnecessary when it comes to financial customer data.

Understand the domain in which you apply your science. Many financial domains are heavily regulated. Therefore, before running any model in production, it should be well understood whether it complies with the rules and regulations in that domain.

Always try to follow best practices as star scheme etc Most of the time (not always) thee is way to get to that and it will help you thousands time more Only try 😇