All posts by Katarina Jensterle

Xmon is the leading solution for monitoring reference data usage and spend. While Xmon operates seamlessly as a cloud service, some clients opt to integrate their data requests flow with Xmon using an on-premise Xmon agent. Such hybrid mode is required when using a dedicated private line with their data vendors or when credentials need to stay within the client environment. Ensuring the continuous uptime and optimal performance of this on-premise agent is where Xprocess steps in.

Why Monitor The Xmon Agent?

It is important to monitor any component for uptime, availability and reliability. In fact, our team uses Xprocess to monitor our backend cloud infrastructure. For clients using an on-premise agent, it is important to monitor the service for:

Service Continuity: Monitoring the Xmon agent is crucial for uninterrupted service. Any downtime can impact the flow of critical data and processes.

Quick Incident Reaction: Timely identification of errors or performance issues is essential. With Xprocess, you can swiftly react to incidents and address them before they escalate.

Proactive Notification: Xmon support can be notified promptly about any issues with the agent. This proactive approach enables the support team to take immediate action and resolve potential problems efficiently.

What Does Xprocess Monitor?

The Xmon agent has a seamless interface to Xprocess and this allows for monitoring of:

Xmon Agent Uptime: Track the continuous operation of the Xmon agent to ensure it is always available, when needed.

Realtime Error Monitoring: Identify and address any errors generated by the Xmon agent promptly to maintain a smooth operation.

Performance Evaluation: Keep an eye on the performance of the Xmon agent to guarantee optimal functionality.

The Power of Xmon and Xprocess Integration:

Although Xmon and Xprocess are distinct products, this use case illustrates their seamless integration. Together, they provide a comprehensive solution to ensure proactive monitoring and maximum reliability of components.

In conclusion, the combination of Xmon and Xprocess is a powerful duo for clients looking to bolster their monitoring capabilities. By keeping a watchful eye on the Xmon agent’s uptime, errors and performance, businesses can ensure a continuous and efficient operation. It’s all about staying ahead of potential issues and maintaining a proactive approach to service reliability. If you’d like to learn more about Xprocess or Xmon, please reach out to us here.

The Challenges

The data management issues that firms, across industries, face because of the abundance of data they generate and consume and its dispersion across a plethora of internal and external systems that are complexly intertwined cannot be understated.

Time Lost Looking for Data

When data is not properly catalogued and is often spread out across the different departments of the firm, employees waste a lot of their time looking for the data they need. Instead, they could be using this time to analyse the data, draw insights from it and drive their business forward.

Duplicated Data

Without a data inventory, it is also difficult for data consumers to find and leverage existing data sets. As a result, they end up duplicating data by recreating it or purchasing it from vendors multiple times.

Siloed Data Impedes Collaboration

Data is typically held in silos and not easily or entirely available to other departments or teams. This makes data sharing and collaboration difficult across the firm.

Limited Visibility of the Data Landscape

Such a data culture also limits the visibility of the data landscape, and, as a result, data managers have a poor overview of what data exists and where it is.

Poor Data Quality

Siloed data, together with the data duplication that usually results from it, give rise to inconsistencies in the data which compromise its quality, integrity and reliability.

Non-compliant Data Access

Non-compliant data access is yet another problem. Even with the best intentions to do the right thing, firms face challenges when trying to comply with, and report on, all the regulations around data access and usage entitlements.

Xplore: Data Discovery, Data Classification and Data Management

Xplore is Xpansion’s Data Discovery, Data Classification and Metadata Management solution, which tries to mitigate these challenges.

Data Discovery

First and foremost, Xplore is a data discovery platform that allows firms to connect to their various data sources and launch crawlers, which discover the datasets on the sources and pull their metadata into Xplore.

Data Classification

Moreover, Xplore allows the firm to build a data classification that is customised to the specific business context and needs. This classification can then be used to place the datasets represented by their metadata into a meaningful organisation which facilitates navigating through, and searching for, the data.

Metadata Management

Equally important, Xplore is a metadata management system that is used to manage the technical metadata obtained by the crawlers as well as add a layer of user or business metadata such as descriptions, reviews, additional notes, and custom metadata fields depending on the use case.

Access Rights Management

Finally, Xplore can be used to control the permissions that users have over the different datasets and allows the firm to enforce the regulations around access and entitlements. It also allows and tracking, and reporting on, data usage.

This range of functionality allows data managers and data stewards to get a better overview of their data and to put it at the disposal of all the data consumers across the firm. The datasets are discovered, classified and described through their metadata in Xplore. Data consumers can then consult this catalogue or inventory of datasets, to search for the data they need as well as to browse the data assets available in their firm. By looking at the metadata, users can evaluate the usefulness and relevance of datasets before accessing the data itself and thus find what they need more quickly and more efficiently.

To find out more about Xplore or request a product demo, please contact us. We would love to connect with you!

Xmon is vendor agnostic and as such it is able to track usage and control spend over a multitude of reference data vendors. One example of these is ICE. Xmon is able to ingest and monitor ICE EOD price requests and track usage in real-time. Using Xmon you can configure your ICE commercial models and start tracking usage and spend in real-time as the month progresses. Using our advanced analytics, reports and dashboards, you’ll be able to view your organisation’s spend as it is progressing and meeting your estimated budget: No more surprises at the end of the month!

Using the pay-as-you-go rate card, a charge is incurred every time a data request is made. Many data vendors follow this model for their products, including ICE. Xmon is able to monitor ICE EOD Prices usage intra-month, and helps avoid any unwanted spikes in usage and cost. In addition to tracking usage in real-time, with Xmon you can configure alerts that are triggered on abnormal requests or consumption and you can allocate spend fairly to data consumers within your organisation.

Integration of ICE EOD prices is supported in all integration modes of Xmon. For more information on tracking ICE reference data usage and other vendors, reach out to our team here!

As presented in one of our previous articles “Xmon: Managing Reference Data Flowing Within The Organisation”, Xmon has the capability to monitor reference data requests flowing between internal systems. This is especially useful to do in organisations that leverage capabilities of an Enterprise Data Management (EDM) platform, as it enables deeper tracking of data flows and a more complete understanding of usage patterns. In this article we illustrate the benefits of such an integration and highlight how market data officers are able to fully view and manage the flow of market data throughout the whole organisation.

Enabling Internal Cost Allocation

The above diagram illustrates how Xmon can be integrated to track data flowing into the organisation, i.e. from external data vendors, as well data flowing within the organisation, i.e. from internal data distribution systems, such as EDM platforms to internal consumers.

By defining the same commercial model for the ‘Internal Requests Contract’ as the vendor’s rate card, Xmon is able to reconstruct a complete cost profile for each internal consumer, and produce a true cost allocation by consumer or cost centre. Consequently, this allows market data officers to allocate spend fairly to each business line/consumer, based on their actual consumption.

 Monthly cost allocation by internal system/consumers

In addition to providing cost transparency to end data consumers, Xmon can also deliver a Return On Investment (ROI) dashboard, which measures the efficiency of the internal data distribution platform. It calculates the savings generated by consolidating the requests for market data internally before acquiring the actual data from the vendors.

Return on Investment dashboard measuring the efficiency of clients’ internal data distribution platform

Enabling Data Lineage

Maintaining data lineage is crucial for ensuring data integrity, compliance and cost efficiency. Xmon takes data lineage to the next level by offering field-level visibility and look-through capabilities, by tracing data flow from data acquired from the vendors to its final consuming applications. This data lineage is made possible thanks to the definition of field dictionary synonyms, which maps equivalent fields between vendors’ mnemonic and internal data distribution layer dictionary.

In addition to this field-level lineage, Xmon can report on the list of security ID’s requested by each consumer, which among other things, serves as a base for calculating a fair cost allocation based on actual consumption.

Enabling Cost Optimisation

With its advanced reporting capabilities, Xmon’s transparency empowers organisations to identify opportunities to mitigate their market data spend and ensure they only pay for the data they actually consume. Thanks to the field-level data lineage we can easily trim unnecessary costs by identifying fields which are collected from the vendors but not used by any internal system. In addition, reporting on the internal consumer usage enables market data officers to highlight to the business some expensive categories triggered by just a few fields and discuss alternative solutions to source such fields using a different commercial model from the same vendors for optimisation. Alternatively, the universe of securities requesting those fields can be trimmed down.

The analysis described above is able to generate additional savings between 10% to 15% of total spend, and importantly, enables fair spend allocation and powerful usage metering for data flowing within the organisation.

If this article is interesting to you and you would like to find more about this topic, please get in touch, we would love to help you!

The effective management of reference data has emerged as a pivotal element in sustaining operational efficiency, optimising spend and fostering a transparent, repeatable, and informed decision-making process. Reference Data Management (RDM) entails the organisation, categorization, and oversight of reference data – the foundational data used for classifying and contextualising other datasets. However, distinguishing what strategies yield success and what pitfalls to avoid is integral to building a solid RDM strategy.

What is Reference Data Management?

Reference Data Management (RDM) is a set of processes, people and technologies that allows the organisation, maintenance, and optimisation of reference data within a financial institution. At a high level, RDM can be divided into three functional areas:

– Managing Reference Data usage and spend
– Managing Reference Data quality, availability and distribution
– Managing Reference Data governance and policies

Managing Usage and Spend management, as well as Managing Data Governance are cross-business functions and generally benefit from being managed by central teams within organisations. Managing data quality, availability and distribution are entity specific and therefore their functions should typically be siloed within organisations, as different business units will have differing requirements for data and how it is sourced and distributed.

At Xpansion we provide solutions for all three functions above and we believe it is important to distinguish these, as each will bring its own challenges and requirements. In this article we provide some best practices and common pitfalls when dealing with Reference Data Usage and Spend.

Reference Data Management, best practices, and common pitfalls

Best practices

Monitoring usage 

Gaining insights into data consumption behaviour is a cornerstone of a robust RDM strategy. Metering data usage allows you to ascertain data consumption patterns, understand data flows, and paves the way for better decision making through precise actions and predictable outcomes. Data usage metering should focus both on inbound and outbound data for a more comprehensive view and deeper analytics.

Actively Controlling Non-production data usage

By limiting access to data without genuine business value, you curtail wasteful expenditure on irrelevant datasets and avoid unnecessary spend and mitigate cost spikes. Actively creating data access control rules in the organisation also avoids compliance pitfalls and licensing issues, so that it is not just applicable to non-production data.

Checking and reconciling invoices

Reconciling invoices maintains accountability and reduces spend leakage. It is imperative to have mechanisms to check that your organisation is being charged correctly, in line with current agreements in place. Mistakes happen, however having tools in place to validate invoices removes doubt and maintains accountability and transparency.

Know what data you have and what data you have access to

Cataloguing data isn’t limited to merely identifying what data your organisation possesses. It extends to understanding the spectrum of available data, including both external vendor sources and internal sources, as well as using metadata attributes to enhance the meaning of the data. By assessing the data landscape comprehensively, you minimise redundancy, understand user requirements and curb inefficient expenditure.

Establishing cross-organisational team units for RDM

To be most effective, Reference data management usage and spend function should be incorporated within a cross-business team. Optimal results arise from globalising RDM operations within expansive organisations, facilitating insights-driven decisions that benefit the entire enterprise in an efficiently orchestrated manner. This is especially true for usage and cost optimisation, as well as data governance.

Leveraging External Expertise

Recognising the intricacies of RDM and how complex reference data commercials can be, seeking guidance from experts augments your data management strategy. Collaborating with knowledgeable professionals lends insights into best practices, innovative tools, and strategic methodologies. Seek experts that continuously add value to your team rather than performing one-off exercises.

Common pitfalls

Introducing complexity through data caching 

Data caching, when not offered as part of a more complete Enterprise Data Management (EDM) or Master Data Management (MDM) solution, adds both technical and operational complexity which, in most cases, yields disappointing returns, masks data quality issues and creates confusion with data consumers.

When properly implemented, a solid EDM/MDM solution will provide the ability to remove duplicated requests for data, store bulk file information and provide a golden data copy to users within the organisation, as well as the possibility to benefit from multiple data vendors at the same time.

A solid data strategy in an organisation should include one or more EDM systems which are properly configured, and which will also negate the need for a separate and complex data caching and distribution system.

Using multiple vendor accounts

Relying on multiple accounts for cost allocation and usage reporting fragments the organisation and leads to inefficient processes, duplicated usage and increased spend. Streamlining cost allocation through a unified reference data hierarchy enhances financial tracking and reporting accuracy and is in the spirit of keeping things simple. Rather than duplicating accounts, it would be more advisable to reduce the number of accounts and apply usage monitoring to allocate spend internally, in a fair way and combined with a full usage report.

Using a One-off approach

RDM is a continuous process, not a sporadic effort. Treating RDM as an isolated task might deliver some value in the short term, however it is a false economy as over time, inefficiencies will arise again and will be more costly to address. A more effective strategy would be to introduce continuous controls and monitoring to enable a proactive management strategy that is able to respond in real-time to business requirements and changes.

Conclusion

We hope this article has helped shed some light on some best practices and common pitfalls in the area of reference data management. RDM can be a daunting task with clients not quite sure where to start and how to deliver value quickly with little impact to the business. At Xpansion we advocate a bottom up approach: start with small projects that deliver and gradually expand best practices throughout. We have been helping organisations of all sizes for more than 10 years and are here to help. Get in touch with us for a demo of our products or to discuss your needs further!

In this article we show how Xmon, through the use of Rules and a new Standard Report, can help flag users or systems that have exceeded their usual or allowed data consumption and, therefore, mitigate potential spikes in multi-hit cost. As a reminder, multi-hit cost is a feature of certain reference data pricing models and is incurred when an organisation requests the same billable category multiple times on the same day for the same securities. Click here to recall how Xmon can be used to understand and mitigate multi-hit cost.

Standard Report 32: Average Daily Usage by System

Xmon has benefited from the recent addition of a Standard Report which computes the average daily usage per system over a selected period of time. By default, the time period is the latest quarter.

New Standard Report 32 in Xmon computes the average daily usage by system

This report is available under REPORTS > Standard Reports > Standard Ratecard.

Example output of the Standard Report 32 in Xmon

The example above shows that, during the given period of time, System 1 has requested on average 68 securities per day. The maximum number of securities it requested in a single day was 151. System 2 averaged a daily multi-hit cost of $168.54 over that same timeframe and its highest multi-hit cost in one day was $575.60.

Setting up Rules to Monitor Daily Consumption

These average and maximum figures can be used to draw limits on the daily consumption of the different data users and systems. And these limits can, in turn, be used to define rules that would issue warnings when exceeded. As cost could vary between the first day of the month and the subsequent days of the month, we suggest defining these limits on daily volume. For instance, based on the maximum daily number of securities requested by System 2 over the last quarter (11,756 securities), we can setup a limit of 12,000 securities on the daily volume of this data consumer. If this limit is breached on a certain day, a warning e-mail is sent to the “Market Data 1” mail group.

Example rule setup in Xmon to raise an alert when System 2 requests data for more than 12,000 securities on a day

When rules are created on the daily consumption, they can be monitored in real-time from the dashboard. We can see below an example showing the Dashboard when System 2 has already requested 10,273 (amounting to 86% of its “allowed” daily volume) on the day.

The Dashboard in Xmon showing the actual consumption of System2 compared to its daily limit

If this article is interesting to you and you would like to find more about this topic, please get in touch. We would love to help you!

In our first article in the series of Xmon Insights use cases, we take a look at how Xmon’s security level feature helps you optimise pricing data acquisitions. To refresh your memory on Xmon Insights service, follow this link to one of our previous articles on this topic.

As is the case for some data vendors, when pricing data is requested at different times of the day it triggers different snapshots. Each different snapshot has a significant cost impact. As a consequence, if price is required only for the end of day P&L or Books and records generation, then a single snapshot should be downloaded per day for each security, as multiple price updates throughout the day are not necessary.

In order to review the pricing snapshot usage and optimise the cost according to the vendor pricing rate card, the organisation needs a report providing security level details. Xmon is able to address this need and provides a report available to be generated and downloaded on demand. This report will display security ID details with the list of all the pricing snapshots used within the organisation, and users can review and confirm with business if some snapshots can be turned off for those securities. Once security ID’s with redundant snapshots have been identified, then Xmon is able to provide the list of requests that contain those securities to be amended and optimised. 

With Xmon’s Insights guidance, many of our clients, such as asset managers and hedge funds, achieved 10-15% savings of their total reference data consumption. If all of the above sounds of interest to you, then get in touch, we would love to hear from you!

Managing reference data with confidence has become increasingly important in today’s data-driven business environment. Effective reference data management ensures that businesses can make informed decisions, comply with regulatory requirements, and manage risk. It is important to track reference data coming from external sources, i.e. data vendors, into the organisation, as well as tracking data moving between systems within the organisation. Tracking reference data inside an organisation can be a daunting task, especially when it involves multiple systems and data sources formats and mappings. This is where Xmon can help!

How Xmon Can Help

1. Customizable Mappings for Securities and Data Attributes

One of the key features of Xmon is its customizable mappings for securities and data attributes. This feature enables businesses to map their own internal security references and data attributes, ensuring that their data is accurately represented, classified, and organised. This feature allows tracking reference data points within the organisation, even if they are modified or mapped on their journey from source to consumer.

2. Customizable Cost Models

Xmon also includes customizable cost models. This feature enables businesses that wish to do so, to track the costs associated with their reference data as it flows within the organisation, allowing them to monetize and allocate spend internally in a fair and transparent way.

3. Customizable Data Formats

Whether data is distributed on queues, in files or other formats such as JSON or XML, Xmon provides integration adapters that allows the ingestion of data requests in their native formats for tracking within the organisation.

The Importance of Tracking Reference Data Within The Organisation

1. Transparency of Usage and Accountability

Tracking reference data inside an organisation is important for several reasons. Firstly, it promotes transparency of usage and accountability. By tracking reference data, businesses can have a better understanding of who is using what type of data in the organisation and enable transparency of spend and reporting. This promotes accountability and ensures that reference data is being used effectively and in the most cost efficient way possible.

2. Data Lineage

Secondly, tracking reference data enables data lineage. Data lineage is important because it enables businesses to trace the origin and transformation of their reference data. With data lineage, businesses can ensure that their reference data is accurate and reliable, and that it meets the specific needs of their business.

3. Internal Cost Allocation

Finally, tracking reference data enables internal cost allocation. By tracking reference data, businesses can ensure that their data management costs are allocated appropriately across their organisation. This ensures that data management costs are optimised, and that reference data is managed in a cost-effective manner.

Client Use Case: A Large Asset Management Firm

A global asset management firm had been using Xmon to track data requests from their central Enterprise Data Management (EDM) system to external vendors for several months, reducing costs and improving transparency. Following the successful deployment on the external side, the client then moved on to leverage Xmon internally to track reference data between the EDM system and ultimate internal consumers.

By using Xmon’s flexible data mapping and realtime tracking functionality, the client optimised spend by identifying data points that were acquired but never used, removing them from vendor calls and started allocated costs fairly to internal data consumers based on actual usage. This resulted in improved transparency, better usage reporting for compliance and audit purposes and a further cost reduction of 8% on overall data spend.

Xmon empowered this client to track reference data effectively both internally and externally, leading to decreased costs, enhanced transparency, and fairer cost allocation.

Conclusion: Managing Internal Reference Data flows with Xmon

Managing reference data with Xmon enables businesses to track their reference data with ease, ensuring that data it is accurate, consistent, and reliable. With its customizable mappings for securities and data attributes and customizable cost models, Xmon promotes transparency of usage and accountability, enables data lineage, and facilitates internal cost allocation. Ultimately, Xmon helps businesses improve their operational efficiency, enables transparency and accountability in the organisation and allows stakeholders to make better decisions based on reliable and consistent data.

If you’d like to know more about how Xmon can help you track reference data inside your organisation, don’t hesitate to reach out by clicking here.

In this fourth issue of our series of articles about Xplore, our Data Discovery and Metadata Management platform, we zoom in on searching for datasets. In our first article, we looked at Data Discovery in Xplore and its instrumental role in uncovering the different data assets in the firm and making existing datasets available to as many data consumers as possible. In the second article, we shed light on Data Classification in Xplore and explained how fundamental it is in organising the firm’s data, making it more efficient to search and navigate and reducing redundancies and inconsistencies which would otherwise compromise data quality. And in the third article, we went on to discuss Metadata Management in Xplore and how it makes datasets more discoverable and allows users to assess if the dataset is relevant for their needs before they access the data.

Searching for your data does not have to be like looking for a needle in a haystack.

Employees spend valuable time looking for the data they need. When this data is scattered in different departments and segregated in silos, searching for it is very inefficient and sometimes ineffective. It is almost like looking for a needle in haystack! With Xplore – through data discovery, data classification and metadata management – all your datasets (actually the metadata describing them rather than their underlying data) are organised in a central inventory. Searching this data catalogue is guaranteed to help you locate that dataset you are after!

How does Xplore facilitate searching for datasets?

When data consumers log in to Xplore, they are presented with a customisable homepage called MyDataSpace. The most prominent feature of this page is the search box, where users can search for datasets. They may type one or more keywords, which is particularly useful when users are searching for specific dataset names or column names. This lexical search is enhanced by its ability to cater for misspellings and to support matching wildcard patterns and regular expressions. Xplore’s search engine also supports full-text searches which allow users to find matches within dataset descriptions, notes and other free-text metadata fields.

Consider a specific example to illustrate the effectiveness of Xplore’s search functionality. In financial services, reference data is critical for financial operations and a common reference dataset is the security master, which contains information on securities such as bonds, equities and derivatives. With Xplore’s data search functionality, a user can simply type “country of risk” into the search box and locate the dataset containing the reference data for this field.

Searching for Data in Xplore

Searching for data is an interesting and constantly evolving area and we are super excited about continuously enhancing our search engine in Xplore. Stay tuned to read about our filters and facets for searching datasets as well as the other functionalities and features of Xplore in subsequent articles. In the meantime, if you would like to enquire about Xplore or request a product demo, please contact us. We would love to hear from you!