Alveo Blog Data Management

Total cost of ownership for data management

New technologies can significantly reduce the TCO for financial data management platforms – what are the key aspects to consider when selecting one?

When exploring potential technology solutions for your financial data management challenges, it’s crucial to recognize that the vendor license fee is just one component of the total cost of ownership (TCO). To gain a comprehensive understanding of your data management TCO, it is important to consider the following factors when evaluating solution providers:

  • What is the underlying technology stack and is it best in class?
  • What is the disaster recovery process and how much does it cost?
  • What does a change management process look like and how many Business Analysts and IT personnel will I need?
  • Is the vendor strategy to offer a self-serve model to the underlying business or will I need specialized resources to help the business get what they need?
  • What are the distribution capabilities of the product?

The specifics of the technology stack impact both a deployed (internal or external cloud – for example, AWS, GCP or Azure) or managed service where a vendor is managing the instance and operations on your behalf. This is typically wrapped up in additional service charges.

You may be asking yourself, why does it make any difference to me what the technology is if I use the vendor’s managed service?

A good question, but consider the situation where a vendor’s underlying technology stack is based on a client-server architecture. Essentially, in a monolithic deployment, the vendor must resource the platform’s resources based on the point of maximum load or utilization. For example, loading back-office files at the start of the day, but during the rest of the day, the resources are underutilized.

The customer will be paying for those larger resources that are not needed 95% of the time. If you are managing the cloud, this means higher running costs. If a vendor is managing the platform these higher running costs are passed on to the client.

Sadly, this is not the only additional cost you will incur.

Impact of architecture on hardware cost

In the case of a client-server architecture you cannot simply add more hardware resources to better run certain services. Instead, you must replace your current platform with another virtual server which can again significantly increase infrastructure costs.

A microservices architecture is a step forward in solving these challenges by enabling you to scale the solution at peak times and shut down resources when not needed. Furthermore, it enables you to scale services so that the overall cost of this solution is less expensive in terms of cloud and compute costs for the vendor, which should make their solution more cost-effective.

Microservices as piecemeal solution components

Another advantage of a microservices architecture is that specific microservices from a vendor can be blended with inhouse developed applications. This means that institutions don’t need to replace complete inhouse built solutions with a vendor’s client server monolithic platform. Instead, they can choose to enhance the functionality of an inhouse application by using vendor components to solve a particular problem.

For example, you want a component to acquire data. This may be available already from a vendor as a separate microservice and offered as a managed service with regular updates as data sources make changes to their feeds. Equally, you can use a vendor’s microservices components to address requirements including distribution, cross-referencing, quality reporting and consumption monitoring and benefit from best in class capabilities without requiring a wholesale overhaul of your existing data management infrastructure. This way, you can augment your data management setup piecemeal to address specific pain points.

My point is that a stateless microservices architecture enables customers to use vendor specific functionality within your own in-house build applications which is more cost effective, enables you to use best in class components and delivers faster time to market and ROI.

Operational resilience

Disaster recovery is important for any regulated financial service organization. However, this requirement can lead to very high cloud infrastructure costs or internal resources as typical client-server architectures require a HOT-HOT or HOT-COLD standby.

Not ideal for those looking for a lower TCO. Microservices architectures enable customers to deploy a single instance but through well-defined DevOps deployments with the use of multiple zones and cloud providers, institutions can ensure a fault-tolerant solution for less cost than a typical standby routine required by a client-server architecture.

Another factor that is generally overlooked is the use of open-source technology and the impact it can have on financial data management. This is an important part of any TCO decision. The use of open-source technology, specifically data storage can impact the TCO of financial data management platforms dramatically.

Using open-source data storage is significantly cheaper and can still offer the same performance. However, one important factor that is overlooked is whether the open source technology is a tier-one support application in your chosen cloud providers. Cloud providers such as GCP, AWS or Azure offer services to help support clients with running these technologies. For example, AWS Keyspaces for Cassandra or AWS RDS for PostgreSQL.

These services can significantly reduce TCO as you will not need to hire internal staff to run them. Check with your data management vendor that they offer tier-two support for any underlying open source technology specifically around their product.

Lowering the cost of change

Data management processes in financial organizations are always in flight and you cannot fight the tide of change coming from the business. So it is important to consider that the change processes within an organization can not only impact the effectiveness of the business to deliver but also its costs.

In most instances a change process can be the largest component of TCO if you make the wrong vendor decision. To ensure you do not fall into this trap ask the following questions to any perspective data management solution provider:

  • What are the underlying technology and data storage methods and how easy it is to update the schema in the underlying database?
    Where possible avoid a normalized data structure as it adds significant overhead when changing the schema. Furthermore, it requires the vendor to be involved when extending custom relationships and they will typically charge you for this.
  • Do you manage the data model centrally and can you extend the data model without asking the vendor?
    By not controlling your own data model this could significantly impact your ability to deliver to the business in a timely manner.
  • Can the vendor extend the data model with new attributes but not impact the customizations?
    Typically you may need to add some fields of your own such as internal IDs or taxonomies or other fields used for downstream processing. It is important to select a vendor who can deliver maintenance updates without impacting customizations otherwise you will incur a high testing overhead within your organization.
  • Does the vendor offer standard support for the integration of data sources and managed feeds whereby the delivery of regular updates is included out of the box?
    A managed service with respect to data feeds can significantly reduce your TCO. Market data vendors regularly make changes to their feeds and this can be a full time job so put the burden on the vendor it will reduce your ongoing TCO and ensure better accuracy as the vendor specializes in doing this task.
  • Does the vendor offer an out of the box data model for normalization and golden copy creation?
    An out of the box model for golden copy creation can significantly reduce the implementation lead time and overall cost.
  • When extending our data model for the business do data attribute changes automatically become available for distribution?
    When selecting a solution it is important to get them to show you how easily data model changes pass through to data distribution. In my view it should be automated and be available in technologies like Kafka automatically or easy to extend through the user experience for bulk delivery files.

Aspects to include when selecting a data management solution

The trend we are seeing in buy and sell-side organizations has been the rise in adoption of technologies such as Kafka to distribute market data. Adopting technologies like this enables customers to focus on adopting a self-serve model. Enabling consumers of data to request their universe of interest and attributes revolutionizes an organization’s ability to adapt to the needs of their internal customers.

However, this concept, though revolutionary for most, creates challenges in some instances in data management platforms. In my experience, you need to focus on these important questions:

  1. How seamless and timely is it for a data management platform to distribute datasets to new users and feeds?
  2. What is the timeliness of adding new attributes to a feed?

The answer to these questions may surprise you. However, it is important to understand distribution whether through technologies like Kafka or fixed schema flat files can be a significant proportion of the total cost of ownership and more importantly a self-serve platform can really transform your business users’ use of financial data.

When selecting a financial data management vendor it is important to get answers to these questions. These in my view are the hidden cost of any TCO and you only find out about these costs once you are managing your platform. These costs impact you whether you are hosting or using the vendor’s managed service.

In conclusion, choosing a financial data management platform involves more than assessing license fees. Delving into the total cost of ownership (TCO) requires scrutiny of factors like technology stack, disaster recovery, open-source usage, and change management processes. It is important to note the impact of underlying technology on costs, particularly in a hosted but also managed service scenario, as this will simply be a cost passed on to you.

The benefits of a microservices architecture can be overlooked aspects of a decision-making process but this can save you significantly in the long run through running costs and testing, ensure better business user satisfaction and – simply put – get the most out of your data.

Alveo Blog Data Management

Towards a Data Bill of Rights

The solutions and tooling available for data management have developed rapidly. Driven by the advent of public cloud ecosystems and the continuously increasing data intensity of the financial services industry, every employee of a financial services firm has had to work on their data literacy.

The self-service data access and analytics firepower available to staff today comes with increased responsibility. Sometimes firms have learned the hard way that data – as any other asset – needs to be maintained and cared for. Not properly looking after data can lead to huge backlashes from legislators, regulators, customers and investors. The concept of data citizens as employees who use data in an informed and responsible manner has rightfully found fruitful ground.

However, with increasing automation, business process integration and a move in prescriptive analytics from decision support to decision automation, the role of the human is sometimes turning into that of overall process design and the supervisory and control function. The advent of generative AI is driving further automation. It may therefore be helpful to look at the rights and responsibilities from the perspective of the data that is entering organizations, flowing through business processes, created from scratch and that is part of the interface of a firm to its customers, investors and regulators.

To what extent does the data look after itself? What would be the fundamental principles of data management looking at it from the point of view of a data element and not a human running a business process? What could a bit of data reasonably expect from a data management function to maximize its value to a firm. What are its rights?

If I were to sum up the fundamental principles of sound data management through this lens of a Bill of Rights for data we could come to something as follows:

  1. The right to be easily accessible. Data needs to be seen and to be discoverable to play its part. Within the constraints of regulatory and content licensing, anyone that benefits from knowing this data exists should be able to access it via different methods. Needless to say, data has freedom of movement, within the constraints mentioned above.
  2. The right to lineage. Data needs to know where it came from including its ancestry. This can either be the internal or external data provider, a human who entered it or an analytical model that produced it.
  3. The right to be properly administered. Data needs to be looked after and cared for, this includes keeping track of its date of birth and demise in case its use is discontinued or archived. Caring for data should include tracking any changes it has undergone in the form of an audit trail. This should also include any changes in ownership and access permissions. Metadata is data too.
  4. The right to shelter. Data needs to be housed properly in an environment where the principles above can be guaranteed and where its value to the organization can be maximized. The data management equivalent of habeas corpus means that data should not be lying around somewhere where the principles of easy access and clarity on metadata are compromised.
  5. The right to assemble. Data often becomes more valuable when linked and combined with other data sets coming from other external sources or other parts of an organization. This way it can contribute to new insights and produce new, often highly proprietary, information. Cross-referencing symbologies and identifiers is a necessary precondition for this.
  6. The right to proper care. Data should not be abused or used inappropriately where duties of care, content licensing, permission or access constraints or indeed legal principles are violated. The accuracy of data values should be checked via proper data cleansing procedures and values kept up to date and periodically ascertained.
  7. The right to not be overlooked. Data has the right to be seen and to play its part in adding value to a firm’s operations. It has the right to be used to produce new information, again within the constraints mentioned above and provided it does not infringe the rights of other data.

 

To sum it up, keep the points above in mind to make the most of your data assets. If you are kind to your data, the data will reciprocate.

Alveo Blog Data Management

#TopicalTimeSeries: a closer look at some of the key time series in financial markets (part 2)

Financial markets are dynamic and changes in the value of financial products are driven by many factors including economic growth, geopolitical news, scarcity, corporate earnings, demographic trends as well as different perspectives on future risk and uncertainty and, at the most basic level, the trade-off between fear and greed.

There are millions of financial products including many thousands of publicly listed companies, hundreds of thousands of ETFs, mutual funds and other investment products and millions of bonds and derivatives such as options, futures and warrants. Combined with that there are hundreds of thousands of macro-economic statistics time series including GDP, unemployment and inflation that can feed into research and policy.

When it comes to modelling financial risk or conveying a summary view of financial markets, there is a much smaller number of time series that is frequently used as a model input or simply as an overall bellwether. Alveo’s data management solution handle time series of every asset class or macro-economic category, in every frequency and for every length. Alveo’s Ops360 user interface includes data derivation to construct new time series or proxy missing values, data quality management, workflow configuration to onboard and distribute new data sets, extensive search capabilities and integration with external libraries.   

In the first episode of this blog series, we looked at some of these risk factors covering gold, foreign exchange, volatility and inflation. In this second episode we take a closer look at some other key time series including interest rates, the property market, the equity market and crude oil.

1.Crude Oil

The future will be different with the electrification of transport and the adoption of non-fossil power sources but oil has ruled the planet for the last century and still rules large chunks of the world. Key benchmarks include Brent Crude and WTI Crude with many different variations and spreads based on grade.

The history of the crude oil price is also the history of geopolitical developments. In the chart below we show the crude oil price showing upheavals around the first and second Gulf Wars and deep drops during recessions.

In the other half of the chart we highlight the evolution of the oil price during a turbulent period in 2008: oil prices went up during the first half of 2008 to come spectacularly down when the financial crisis hit later that year.

2.Interest rates

Interest rates convey the price of money and can apply to periods ranging from the overnight rate to the 30-year rate – and sometimes beyond that – together making up an interest rate curve.

Looking at the chart below, we plot ~60 years of history of the 10-year yield. We see the long boom in the bond market due to steadily falling interest rate following the inflation upheavals of the 1970s. We see that coming to an end with rates hitting zero in 2021 – when at one point the entire German bund curve was below zero – following the massive stimulus programs during corona. We find ourselves now in the – for many – unfamiliar territory of steadily rising interest rates.

Grouping interest rates of different tenors for the same asset type gives us an interest rate curve. There are many different interest curves, ranging from curves tracking the yield on government bonds to different credit curves reflecting the borrowing costs for firms with a specific credit rating or in a specific industry sector. Below we plot a yield curve for set of consecutive days.

Other than government bond yields, there are different interest rate benchmarks. Paraphrasing a popular saying, it can be said that the hand that sets the benchmark, is the hand that rules the world.

Another way to look at it is to track the history of individual tenor points side by side which can give another view of whether curves are steepening or flattening.

3.Property

Like many other asset prices, that of property is linked to the interest rate climate. If we look at the time series of the residential property index of the Euro19 countries (prior to the accession of Croatia) we see a steady rise followed by a prolonged slump for 8 years after 2008 digesting the crisis. We then see strong growth from 2016 onwards culminating in 2021. This has recently reversed due to a fast-changing financing climate.

This is an aggregate index and there will be many variations in development between countries and cities. Other property indices focus on residential or on commercial real estate.

4.Equity

For our equity example we show a subset of the long history of one of the most well-known indices of all time: the Dow Jones Industrial Average. Somewhat unusually, the Dow Jones is a price-weighted equity index, meaning its value is derived from the price per share for each stock divided by a common divisor. This simply means that stocks with the highest share price receive the greatest weighting. More common in equity indices is to weigh the different constituents differently as determined by their market capitalization or the free-float part thereof.

The history of the DJIA goes back to 1884 when it largely covered railway companies. The first published value of the index was 40,94 points – since then it has multiplied about 800 times. We show the evolution of the index in the early part of the 20th century up to the strong post-war growth in the 1950s and 1960s and the slump in the 1970s – before the long boom in equity prices starting in the 1980s. You can see the strong growth in the 1920s followed by a deep decline after the 1929 market crash.

Alveo Blog Data Management

#TopicalTimeSeries: a closer look at some of the key time series in financial markets (part 1)

Financial markets are dynamic and changes in value of financial products are driven by many factors including economic growth, geopolitical news, scarcity, corporate earnings, demographic trends and, most basic of all, fear and greed.

There are millions of financial products including many thousands of publicly listed companies, hundreds of thousands of ETFs, mutual funds and other investment products and millions of bonds and derivatives such as options, futures and warrants.

However, when it comes to modelling financial risk or conveying a summary view of financial markets, there is a much smaller number of time series that is frequently used as a model input or simply as an overall bellwether. Alveo’s data management solution handle time series of every asset class or macro-economic category, in every frequency and for every length. Alveo’s Ops360 user interface includes data derivation to construct new time series or proxy missing values, data quality management, workflow configuration to onboard and distribute new data sets, extensive search capabilities and integration with external libraries.

In this series we examine some of them and their characteristics.

1. Gold

The traditional inflation hedge or safe harbor in times of market turmoil and the traditional basis for currency (alongside silver). The price of gold has seen a few big upswings followed by long periods of sideways movement. Below we show 50+ years of gold prices in our Ops360 data management user interface. We can see the big uptick during the global financial crisis followed by a drop and then another uptick during the Corona pandemic.

2. Foreign Exchange (FX)

There are about 180 currencies in the world circulating 197 countries. (source : Wikipedia). However, a smaller number is used in international trade and used as reserve currencies, topped by the USD and followed by the EUR and the Chinese Yuan.

Some time series data comes from a specific supplier, for example indices such as the Dow Jones Industrial Average which are created by data companies. Central bank lending rates too are controlled by a single institution. Other time series data can be pieced together from different brokers or exchanges. Examples are currency data or interest rate swaps information.

To help collate a complete picture, or also to compare different sources about the terms and conditions of financial products, Alveo’s data management solutions can help.

The EUR / USD, note the spike in the first half of 2008 leading up to the stock market crash in the autumn of that year.

3. Volatility

The volatility index from CBOE is also known as the fear index. Looking at the VIX index over a longer time period provides a snapshot of major geopolitical and economic uncertainty including the Iraq War in 2003 and the height of the European Sovereign Debt crisis in the early 2010s. Zooming in on specific periods shows some gaps in the time series for this index.

4. Inflation

Few economic time series are as closely monitored by central banks, governments and the general public as inflation. Below we show some time series with data from the Research Division at the Federal Reserve Bank of St. Louis. The first shows the annual US CPI number over the last 60 years. The second shows month on month numbers including the recent sharp growth in inflation.

Alveo Blog ESG Data Management

The case for SBOR – does the buy-side need (yet) another “book of record”?

In the investment management industry, the acronyms ABOR, IBOR and PBOR are used to designate accounting, investment and performance book of records, respectively.

The need for these terms arose because of segregated front- and back-office systems as well as systems often siloed by asset class: fixed income, equity and so on. This complicated consolidated views across asset classes and funds and made it more difficult to have accurate intraday position overviews to guide investment decisions. Combine this with the growth in volume and diversity of data sets that can be used in investment management and the strain on legacy systems is high.

The accounting book of record (“ABOR”) is a centralized set of accounts to support different investment functions and measurements.  It is the basis for daily NAV and fund admin activities and external reporting – to clients and regulators. The more commonly used term ‘investment book of record (“IBOR”) goes a step further in terms of granularity and real-time views of performance and risk data. An investment book of record looks at market prices, intraday positions and performance at the position level. The need for IBOR arose because of a need for better informed decision-making, compliance and operational efficiency – with a focus on near-real time portfolio information as opposed to a typically batch oriented ABOR. Given an often siloed application landscape and diversity of data sources, putting an IBOR in place that services the front, middle and back-office is a nontrivial task though.  Lastly, the performance book of record (“PBOR”) includes more investment information, performance information and risk factors enabling more transparency and drill-down into investments and their performance drivers.

When it comes to ESG investing, the asset management industry faces a similar aggregation challenge and in addition must add new data sets and reporting requirements to the inputs for ABOR, IBOR and PBOR.

ESG data comes from different suppliers and includes corporate disclosures, expert opinion, different third-party rating providers as well as intraday news or sentiment data. Despite the range of sources, there are significant gaps in the record as well as a range of external reporting frameworks. Establishing linkage and hierarchy across corporates and financial instruments is needed as ESG data is typically disclosed at the corporate level. There is often a lack of historical data for indexing/benchmarking and ESG data needs to be integrated with asset allocation, portfolio management, client and regulatory reporting. The use cases range from simple exclusion lists to proprietary analytics to create custom investment factors.  These data management challenges come on top of the problem of disjointed systems between front, middle and back office, frequently siloed across fund families or asset classes.

Given the diversity of data sources and applications, what is needed is an SBOR or “sustainability book of record” which aggregates ESG data with internal positions, trades and pricing data. SBOR provides stakeholders across departments an accurate, intraday view of the ESG characteristics and exposures of the overall portfolio. An ability to have comprehensive, portfolio-wide end-of-day ESG information is a prerequisite to client and regulatory reporting. The ability to easily access aggregated data will enable asset managers to respond quickly and appropriately to market changes and new reporting challenges. Whereas corporates may have a quarterly reporting cycle, rating and third party export opinions can change far more frequently and ESG news and sentiment data comes in intraday. An accurate, intraday view of ESG, with any gaps highlighted or transparently proxied can be the basis for asset allocation, portfolio management and regulatory reporting. Institutional clients would likely also require frequent reporting on (changing) ESG characteristics of their assets. The SBOR could be an add-on to PBOR if we interpret ESG factors as just additional investment factors but given the regulatory scrutiny, backlash against greenwashing and investor appetite for socially responsible investing, it looks as if it deserves its own label.

Given the range of different ESG data sources and gaps in ESG data disclosures, there are material data availability, comparability and quality challenges. Data management capabilities to aggregate, cross-reference, verify and, where needed, derive ESG data is indispensable to coming to accurate portfolio level ESG metrics for portfolio management, investment operations, client reporting and regulatory reporting.

Alveo Blog Data Management

Alveo’s Data Integration Services: the case of WM Daten’s transition to EDDy_neu

Financial services firms require accurate, timely, and complete information regarding securities identification, terms and conditions, corporate actions, and pricing for their pre-and post-trade processes. With Alveo’s Security Master solutions, firms can quickly onboard new data or consuming applications, track and improve data quality, and even view data consumption and distribution in real-time.

Alveo provides its customers with a single user experience and dashboard to interact, intervene and visualize the market and reference data, data models, business rules, and workflows we process on their behalf. It allows our clients to seamlessly access and self-serve data without the need for IT support.

The world of financial information products can sometimes be as dynamic as the financial markets themselves. New information products regularly come up to address new content and new use cases – for example in ESG data management or alternative data sets using a credit card, POS, geospatial or other information to provide additional color on projections/financial products.

Existing feeds undergo regular change as well – in particular, the enterprise data products that (by definition) cater to a range of use cases and cover comprehensive information on financial instruments, corporate actions, and issuers. This could include information on new trading venues, new types of financial products, regulatory information, going back a bit further these were tax information fields such as EUSD or FATCA indicators.

Part of Alveo’s standard service is maintained integration with data providers. We sync with the release schedule of the data providers – we have arrangements with them for support, documentation, and the use of their data in development/test. Alveo’s industry-standard data model follows suit as it reflects all content covered in our integration and is regularly expanded.

The number of changes in the vendor data models runs in the 1000s per year. The bulk of these are static attribute changes and mappings but new pricing fields –any data that is changing frequently – come up regularly as well. This includes for example new ratings, macro-economic indicators, news sentiment, or climate information.

Alveo Industry Data model changes include fields in enumerations, e.g. industry sectors, rating scales, and currency codes. Static means any referential or master data, terms and conditions of products, information on legal entities, tax, and listings. And then there are mapping changes. Representing new domain values accurately in our standard model for example. In some cases, these are fairly straightforward 1-1 mappings where we standardize terminology and naming conventions. In other cases, there will be interdependencies between different fields and it is not a straightforward mapping.

During the mapping, we represent data and cross-ref to our standard form. In this process, we also make links between object types (ultimate parent – issuer – issues – listings – corporate actions) explicit. The standard model as mentioned is extensible by clients.

One of the many data providers supported in Alveo’s data management solution is Alveo’s standard integration with WM Daten. On occasion, there is a very large-scale upgrade or entire feed replacement in the financial data industry. One example of that is the upcoming change of the data management infrastructure of WM Daten, i.e. the upcoming transition from EDDy (“enhanced data delivery”) to EDDy_neu. See https://eic.wmdaten.com/index.php/home for more information from WM Daten on this project.

Essentially, WM Daten is overhauling its data delivery infrastructure and moving from EDDy to EDDY_neu. From March 2022 there is a parallel run and from April 2023 the old platform will be shut down.

The change is material and brings numerous changes to the feed format – as well as increased flexibility in data consumption to users of WM’s data.

Reference data changes include:

  • Replacement of primary key (issuer identification number)
  • Newmarket identifier (enabling multicurrency listings)
  • New number ranges, new variables, and obsolete existing fields
  • Corporate actions changes
  • New fields/identifiers
  • New order for sorting data records

Included in Alveo’s data management solution is full maintenance of any changes data providers make to their data products. On top of that, Alveo abstracts from specific data formats in a standard business domain model which helps our clients combine different sources.

To discover how Alveo can help you with the WM Daten transition or any other pricing and reference data management challenges, please click here.

Alveo Blog ESG Data Management

Tackling growing pains: ESG Data Management is coming of age

Until recently, investing according to ESG criteria was the remit of specialist companies known as green or impact investors. These investors would have their own in-house data collection processes and their proprietary screening or selection criteria to assess potential investments. Although there were different reporting frameworks in places such as the PRI and GRI standards, the absence of standard data collection, integration, and reporting solutions required them to create their own “ESG data hub” to provision their own analysts, front office, and client reporting teams. As ESG investing has become mainstream due to both a regulatory push as well as an investor pull, ESG information management is fast becoming mainstream for research, asset allocation, performance measurement, operations, client reporting, and regulatory reporting.

With the deadline for key ESG regulations like SFDR fast approaching, asset managers and asset owners must do more to anchor ESG data into their end to end workflow processes. Simply having a source of ESG data to feed to the front office is not sufficient as businesses need this data from across the organisation to integrate into the whole investment management process – from research to client and regulatory reporting.

ESG integration is needed across buy-side and sell-side business processes

Any firm that sells or distributes investment products into the European Union will have to follow the SFDR regulation. SFDR requires firms to report on 18 mandatory Principal Adverse Impact (PAI) Indicators as well as some optional ones. Paradoxically, the reporting requirements for publicly listed companies that asset managers invest in lag behind the SFDR timetable. This causes an information gap and the need to supplement corporate disclosures with third party ESG scores, expert opinion as well as internal models to come to an overall assessment of ESG criteria.

There is also a need for ESG-data on the sell-side of financial services. For instance, in corporate banking, ESG data is increasingly crucial to support customer onboarding and, in particular, Know Your Client (KYC) processes. Banks will have to report their ‘green asset ratio’ – in essence, the make-up of their loan book in terms of business activities of the companies they lend to according to the EU Taxonomy.

In the future, if a company signs up to get a loan from a bank as part of the screening criteria, it will be asked to disclose what kinds of business activities it is involved in and what kinds of sustainability benchmarks it has in place.

Banks and other sell-side financial services firms will also frequently screen their suppliers, as part of a process called Know Your Third Party (KY3P). They will want to know who they are doing business with, so they can then report this in their own Annual Report. Banks will also want to climate stress test the products they hold in their trading book for their own investment against certain climate scenarios. The ECB, MAS as well as the Bank of England have all incorporated climate stress test scenarios in their overall stress testing programmes to gauge the solvency and resilience of banks.

ESG data also has a role to play in the way banks manage their mortgage book as they are increasingly looking for geospatial data, for example to work out the flood risk of the properties they finance.

Both sell-side and buy-side financial services companies will also need to integrate ESG data with data from the more traditional pricing and reference providers to give a composite view, incorporating not just the prices of instruments and the terms and conditions but also the ESG characteristics.

ESG data now needs to spread across the whole of the organisation, integrating with all the different data sets to provide a composite picture, becoming a key source of intelligence, not just for the front office but also for multiple business functions.

ESG data challenges

Common ESG data challenges firms encounter as they develop their ESG capabilities include data availability, usability, comparability and workflow integration. Many corporates do not report the information investment managers require for their decision making or indeed their regulatory reporting. This leads to the need to combine corporate disclosures with third-party estimates and scores, as well as internal assessments.

Usability issues include the disparity in methodologies third-party firms use to estimate or score firms on ESG criteria. Rating firms have their own input sets, models and weights and often come to different conclusions. Compared to credit ratings, the correlation between the scores given to a firm by different rating agencies is lower. However, credit analysis is as old as the banking industry and the metric gauged (probability of default) is clear. It could be that, with increased global disclosure standards under IFRS, ESG scores will converge.

Comparability issues in ESG are exacerbated by different standards, different reporting frequencies or calendars and also the lack of historical data to track progress and benchmark performance over a longer time period.

The biggest issue however is how to anchor the ESG data in a range of different business processes to put users on a common footing – which requires the capability to quickly onboard users, reports and business applications onto a common set of quality-vetted ESG data.

Looking ahead

Accessing ESG data and ensuring it is of good quality, comparable with other ESG data sets and well-integrated within existing workflows can be difficult.

Organisations will need to cross-reference, match and combine the data, as well as assimilate it with traditional data on companies and their financial products. Traditional prices and security terms and conditions of financial services providers will help build a composite picture from those different sources.

However, data management solutions and Data-as-a-Service offerings are now available to help firms get the ESG information they need, the capabilities to quality-check, supplement and enrich it with their own proprietary data or methods and the integration functionality to put users and applications on a common footing. This will enable firms to have an ESG data foundation for their end-to-end investment management processes on which they can build – for asset allocation, operations, client reporting and regulatory reporting alike.

Click HERE to find out more about Alveo’s ESG solution.

Alveo Blog Data Management

Data. Delivered.

A SIX and Alveo video series talking about current topics and trends for data supply and data management.

Data. Delivered.

Optimizing the data supply chain to empower business users.
Watch the video below to see and hear Martijn Groot, VP Strategy & Marketing at Alveo speak to Roy Kirby, Head of Core Products, Financial Information at SIX about how the data supply chain can be optimized to empower business users.

Data. Delivered.

Better understand and validate ESG data with time series data from social media.
Alveo’s Boyke Babeolal and Tanya Seajay from Orenda, a SIX company, had an exciting chat about how social media offers real-time data to give more insight into ESG data. It can help investors gain a deeper understanding of traditional news, assist in validating ESG ratings, and add emotions as an influential factor to financial moulders multi-factor models. Watch the video now to find out more.

Data. Delivered.

Get more from your data with the right processes, tools and controls.
SIX’s Sam Sundera and Mark Hermeling from Alveo talk about the need for traceability, data lineage, usage monitoring and other audit metrics and how those have shaped data management solutions until now. In particular, cloud adoption, the continued increase of volume and the range of data, for example, through ESG data, and increased self-sufficiency of business users, look to be continued drivers for change and improvement in data management. Watch the video now to find out more.

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Around the World in 30 Days – Data Management Insights from Around the Globe

Different regions have different financial priorities and initiatives. During our Summer Series, we’re stopping in 6 countries to discuss the top issues they’re facing when it comes to financial services and new regulations.

Scratch your travel itch and come along with us over the next 30 days to gain a new perspective on your approach to data management.

Putting ESG data to work: overcoming data management and data quality challenges

Environmental, Social and Governance (ESG) based investing is growing rapidly. The data landscape to support ESG use cases includes screening indicators such as board composition and energy use, third-party ratings as well as primary data such as waste and emissions. There is a wide range of primary data sources, aggregators and reporting standards. ESG ratings in particular are very dispersed reflecting different methodologies, input data and weights – which means investors need to go to the underlying data for their decision making.

Role of ESG in investment operations

Depending on the investment style, ESG information plays a key role in research, fund product development, external manager selection, asset selection, performance tracking, client reporting, regulatory reporting, as well as voting. In short, ESG data is needed through the entire chain and must be made available to different stakeholders across the investment process.

Increasingly, ESG is becoming an investment factor in its own right. This means ESG indicators and ESG-based selection criteria need to be distilled from a broader set of primary data points, self-declarations in the annual report and third-party assessments. Additionally, ESG information needs to be standardized, to roll up company-based information to portfolio-level information, track ESG criteria against third-party indices or external reporting requirements. However, a lot of corporates do not (yet) report sufficient information causing a need to proxy or estimate missing data points or leaving them outside investment consideration altogether.

Data management challenges

Legislatures are promoting sustainable investment by creating taxonomies that specify which economic activities can be viewed as environmentally sustainable. From a data management perspective, this classification refines and is an additional lens on the traditional industry sector classifications.

Other ingredients are hard numbers such as carbon footprinting (detailing scope 1, 2 and 3 emissions, clarifying whether scope 3 is upstream or downstream and so on), gender diversity, water usage and board composition. More qualitative data elements include sustainability scores, ratings and other third-party assessments that use some condensed statistics. A key requirement is the accurate linking of financial instruments to entities.

As ESG investment criteria become operationalized, ESG data management is rapidly evolving. Whenever new data categories or metrics are introduced, data management practices typically start with improvisation through desk level tools including spreadsheets, local databases and other workarounds. This is gradually streamlined, centralized, operationalized and ultimately embedded into core processes to become BAU. Currently, the investment management industry is somewhere halfway in that process.

ESG data quality issues

Given the diversity in ESG data sources  and the corresponding variety in data structures, as well as different external reporting requirements, ESG data quality issues prevent effective integration into the end-to-end investment operation.

In the table below, we highlight some of the more common data quality and metadata considerations with typical examples of those in financial services and how they surface in the ESG data space.

Table 2: example ESG data management challenges

What is required to fully embed ESG data into investment operations?

To overcome these data quality issues, firms need a process that seamlessly acquires, integrates and verifies ESG information. The data management function should facilitate the discoverability of information and effective integration into business user workflows. In short, data management should service users from the use case down, not from the technology and data sets up.

ESG data management capabilities should facilitate the easy roll-up of information from instrument to portfolio and blend ESG with pricing and reference data sets, so it becomes an integral part of the end-to-end investment management process.

Data derivation capabilities and business rules can spot gaps and highlight outliers, whether it concerns historical patterns or outliers within a peer group, industry or portfolio. Additionally, historical data to run scenarios can help with adequate risk and performance assessment of ESG factors. Having these capabilities in-house is good news for all users across the investment management process.

Risk Mitigation: Maximising Market Data ROI

Watch the video below to hear our CEO Mark Hepsworth, sit down for a discussion with 3di CEO John White, as they discuss risk mitigation and how institutions can truly ensure max ROI.

Interview Questions:

  1. What are some of the major issues you are seeing from clients around market data and have these issues changed over the past few years?
  2. Most institutions are increasing their spending on market data, but how do they ensure they maximize the ROI on this spend?
  3. How important is data lineage in allowing clients to use market data efficiently?
  4. As clients are moving more market data infrastructure and services to the cloud, how is this impacting their use of market data?
  5. Are you seeing organizations looking at both market data licensing and data management together and if so why?

Post-Brexit, post-pandemic London

For the City of London, the last few years have been eventful, to say the least. Midway through the worldwide Covid pandemic, Brexit finally landed with a free trade agreement agreed on Christmas eve 2020. A Memorandum of Understanding on Financial Services was agreed upon at the end of March. However, this remains to be signed and is entirely separate from any decisions on regulatory equivalence.

Large international banks prepared for the worst and the possibility of a hard Brexit by strengthening their European operations in the years leading up to Brexit. However, the discussion on the materiality of EU-based operations will continue to rage for some. ESMA adopted decisions to recognize the three UK CCPs under EMIR. These recognition decisions took effect the day following the end of the transition period and continue to apply while the equivalence decision remains in force until 30 June 2022. One immediate effect of Brexit was a sharp drop in share trading volumes in January, with volume moving to continental Europe. For other sectors, Singapore and New York are well-positioned to nibble at the City’s business.

Financial services, together with industries such as fisheries, remain one of the most politicized of topics in the EU – UK relationship. The U.K. government must consider to what extent it should diverge from the EU’s system of financial services regulation. It is unlikely that any announcement on equivalence decisions will be forthcoming in the short term. A decision to grant full regulatory equivalence would depend upon UK alignment to EU regulation on a forward-looking basis – which would defeat the whole point of Brexit. Equivalence may not be worth the loss of rulemaking autonomy that is likely to be a condition of any EU determination. The longer equivalence decisions are delayed, the less valuable they are as firms adapt to the post-Brexit landscape.

As the financial services sector is coming to terms with the post-Brexit reality, it must prepare for regulatory divergence with the level of dispersion still an open question. Differences can emerge in clearing relationships, pre-and post-trade transparency, investor protection, requirements on (managed services) providers, derivatives reporting, solvency rules, and future ESG disclosure requirements. Having a flexible yet rigorous data management infrastructure in place and using suppliers with operations in the UK and the EU will mitigate this divergence and prepare firms for the future.

FRTB: the need to integrate data management and analytics

After some delays, the deadline for FRTB implementation is now approaching fast. As of January 1, 2023, banks are expected to have implemented newly required processes and begin reporting based on the new Fundamental Review of the Trading Book (FRTB) standards. With Libor’s transition taking place over the next years, it is a busy market data world.

FRTB poses material new demands on the depth and breadth of market data, risk calculations, and data governance. A successful FRTB implementation will need to address new requirements in market data, analytical capabilities, organizational alignment, supporting technology and overall governance. In this blog, I focus on the need for integrated data management and analytics.

FRTB requires additional market data history and sufficient observations for internal model banks to ascertain whether risk factors are modellable. These observations can be committed quotes or transactions and sourced from a bank’s internal trading system and supplemented with external sources. Apart from trade-level data, additional referential information is needed for liquidity horizon and whether risk factors are in the reduced set or not.

The market data landscape continues to broaden. Apart from the traditional enterprise data providers, many firms that collect market data and trade level information as part of their business now offer this data directly. This includes brokerages, clearinghouses and central securities depositories. Different data marketplaces have been developed, providing further sourcing options for market data procurement. Effectively sourcing the required additional data and monitoring its usage to get the most out of its market data spend is becoming a key capability.

Organizational alignment between front office, risk and finance is required as well. Many firms still run different processes to acquire, quality-proof and derive market data. This often leads to failures in backtesting and in comparing front-office and mid-office data. FRTB causes the cost of inconsistency to go up. Regulatory considerations aside, clearly documenting and using the same curve definitions, cut-off times to snap market data prices and models to calculate risk factors can reduce operational cost as well. Clean and consistent market data makes for more effective decision-making and risk and regulatory reporting.

FRTB accelerates the need for market data and analytics to be more closely integrated. Advanced analytics is no longer mostly used at the end-point of data flows (e.g. by quants and data scientists using desk-level tools); it is now increasingly used in intermediate steps in day-to-day business processes, including risk management.

Data quality management, too, is increasingly getting automated. Algorithms can deal with many exceptions (e.g. automatically triggering requests to additional data sources). Using a feedback loop as pictured above, the proportion of them requiring human eyes can go down. To successfully prepare data for machine learning, data management is a foundational capability. Regulators take a much closer look at data quality and the processes that operate on the data before it is fed into a model, scrutinizing provenance, audit and quality controls.

Important to improve any process is to have a feedback loop that provides built-in learning to change the mix of data sources and business rules. In data quality management, this learning has to be both:

  • Continuous and bottom-up. Persistent quality issues should lead to a review of data sources. For example, using false positives or information from subsequent manual intervention to tune the screening rules. Rules that look for deviations against market levels taking into account prevailing volatility, will naturally self-adjust.
  • Periodic and top-down. This could, for example, include looking at trends in data quality numbers, the relative quality of different data feeds and demands of different users downstream. It also includes a review of the SLA and KPIs of managed data services providers.

If you cannot assess the accuracy, correctness and timeliness of your data sets or access it, slice and dice it and cut them up as granular as you need for risk and control purposes, then how can you do what matters: make the correct business calls based on that same data?

Data management and analytics are both key foundational capabilities for any business process in banks but most definitely for risk management and finance, which are all the functions where all data streams come together to enable enterprise-level reporting.

The Importance of Data as an Asset

Watch the video below to hear our Sales Director of the APAC region, Daniel Kennedy, discuss why the way in which we look at data is changing. Data is universally seen as an asset, but as is the case with other assets, they can depreciate quickly if you don’t manage them. So what does it take to keep your data value?

Interview Questions:

  1. Why is data considered a new asset class today?
  2. In your experience, what are the critical elements of data life cycle management?
  3. What else do firms need to consider when dealing with this highly valuable asset?

Engineering Trends in Financial Data Management

Martijn Groot is speaking from Berlin with Mark Hermeling about how data management technology advances rapidly to help financial services firms onboard, process and propagate data effectively so firms get the most out of their content. Would you know which are the best open sources, standards, or could strategies for you?

2021 Summer Series eBook
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FRTB and optimal market data management Whitepaper

Discusses the challenges of FRTB as well as their overlap with other risk and valuation needs and business user enablement.
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Achieving Data Alpha: Top FAQ’s in financial data management

Financial services has always been a data-driven business. Achieving accurate and timely data and achieving an information advantage over the competition has long driven the industry. From carrier pigeons to early automation and from the low-latency race to using modern-day data integration, data governance, and data accessibility technologies to fuel user productivity and informed decision-making.

With an explosion of data sources (the alt data boom), the opportunity and the challenges to achieve and maintain an information advantage are immense. We call this challenge achieving data alpha.

In this blog series, we list some common questions we are often asked to help firms on their way to improving their data management and improve data alpha.

Q: What does a financial data management solution do?

A: A financial data management solution helps financial services firms effectively source, onboard, cross-reference, quality-check, and distribute financial data such as prices for valuation, historical price data for risk and scenario management, and master or reference data such as legal entity data, index data, ESG data, calendar data, financial product terms and conditions and corporate actions including changes in company structures as well as income events such as dividends. Simply put, a data management solution should make sure users and applications are effectively supplied with the data they need to do their jobs. See our Solutions Guide for more information.

Q: How do I improve the data quality?

A:  To an extent, data quality depends on the use case. There are different aspects of quality that can be measured, including timeliness, accuracy, and completeness, and often there are trade-offs between them. For use in the front-office, speed is paramount. In risk and financial reporting, the turnaround time for decision making is longer, and a different trade-off will be made. Generally put, a data management system normally keeps track of gaps or delays in incoming data feeds and any manual interventions that occur. It should differentiate between false positives and overlooked mistakes and feedback into the configuration of screening rules in such a system. Reporting on Data Quality Intelligence will help optimize the mix of data sources, business rules, and operations specialists. See our Data Quality Intelligence Brochure for more information.

Q: How do I reduce my data cost?

A: Financial data costs have been sky-rocketing and has reached 32B$ in 2019 (see https://www.tradersmagazine.com/am/global-spend-on-market-data-totals-record-32b-in-2019/ ). Data management solutions can help keep tabs on costs simply by streamlining data sourcing and supply – preventing multiple independent entry points. Also, they can warehouse data to prevent unnecessary repeat requests. Due to the quality metrics mentioned above, these solutions can help make more informed data sources. Another aspect of data cost control is that data management solutions can also track usage permissions to ensure firms do not breach content license agreements. Lastly, through tracking consumption and other data flows, firms can better match and map costs to individual users and departments. See our Smart Sourcing and Smart Data whitepaper, for more information.

Q: What is data governance?

A: Data governance is a rapidly developing concept that speaks to organizational capabilities to ensure high-quality data and appropriate controls on that data. It covers a range of topics, including the accessibility of data, clarity on the data assets a firm has through a proper inventory, and documentation on metadata aspects leading to transparency on where those data sets can be used. For instance, it can include documentation and monitoring of quality metrics, content licensing restrictions, and the sensitivity or regulatory constraints. Data governance counters poor quality and improves the awareness of available data to improve business operations and Data ROI. See our Data Quality Intelligence Brochure for more information.

Q: What is data lineage?

A: Data lineage refers to the ability to track and trace data flows, not just from source to destination but also from end result back upstream. Concretely put: data lineage should explain the values of verified data points in terms of identifying and exposing the process that led to these values, including which sources played a role, which business rules were enacted, and any user interventions that happened on the way. Data lineage is a tool for diagnostics on data errors and helps field any questions from customers, internal audit, risk, regulators, or other users. Increasingly it is a regulatory requirement and a common practice in supplying analytical models as firms realize that the best models in the world will fall flat when fed with poor data. See our Data Lineage fact sheet for more information.

We hope you find this blog insightful and helpful in your journey towards achieving data alpha. Let us know of any other data management questions you have via [email protected], and stay tuned for another post soon!