Survey results 2013: Regulation and client servicing are driving the demand for better data management

May 23, 2013

According to the recent MoneyMate Data Management 2013 survey, regulation is the top driver of new data management projects for asset managers in 2013, with 68% of respondents flagging it as a key driver.

This was closely followed by client servicing, with 60% of respondents indicating that demand for better client service was driving demand for new data management initiatives.

Interestingly, driving efficiency was flagged by only 30% of respondents. This tallies with the view that strategic spend is outweighing tactical spend in 2013.

I was encouraged to see client service polling so high in this survey.  I believe it validates my long-held view that the reason we focus so heavily on applying good governance to client facing investment product data is because data is the oil in the distribution engine for many investment managers – feed the engine with poor quality oil (data) and within a short space of time that engine will seize up.

The indication that 68% of firms’ new initiatives are driven by regulation correlates tightly with what I hear on the ground – many firms are cognizant of the fact that transparency is something that is going to have to be embraced. Those that see this as a strategic opportunity are positioning themselves now for even greater demands for data in the to-be regulatory landscape that is developing in front of us.

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Posted by Ronan Brennan

From TabbForum: Shifts in Mandate Selection Process Leave RFP Teams High & Dry

April 22, 2013

As institutional investors re-evaluate their investment mandates, there has been a distinct move away from risk-adjusted performance as the be-all and end-all selection criteria.

The new post-2008 normal is well and truly upon us, and the changes we see in the asset management landscape will continue at pace for some time to come. In particular, the institutional investment landscape has changed and will continue to evolve.

One of the key changes is the not-so-subtle shift in the mandate selection process. There has been a distinct move away from risk-adjusted performance as the be-all and end-all selection criteria. Of course, the investment management process was always a critical selection criteria, as was structure and size of the investment research team – but these tended to act as exclusionary factors.

What we see today is performance per unit risk being used as a low hurdle that all potential providers need to pass – it probably still is the most important of the hurdles, but by no means does it hold the importance it once held.

Some new (or old, but previously not-so-important) selection criteria we see entering the fray are:

  • Willingness of the asset manager to share holding data in a much timelier manner — i.e., without the typical 30-day embargoes many active managers like to impose – and its ability to do this on a consistent basis.
  • Ability of the asset manager to deliver data on underlying holdings such that there are no black-box investments in the picture — i.e., full portfolio look-through. This is becoming increasingly important for fund-of-fund, multi-manager, sub-advised and fund-of-hedge fund offerings.
  • Capital efficiency of the portfolio from a regulatory perspective. In certain segments of the market, specifically the insurance and pension industry, there is a growing use of performance, per unit risk, per unit capital as a key selection criterion. This issue becomes very visible when fund-of-fund type structures are in play – two funds with equal risk adjusted returns could have very large differences in performance per unit risk, per unit capital – specifically where one fund is transparent and provides full look-through, thus allowing the investor to apply a granular capital charging model, as opposed to the other fund, which could be non-transparent, thus forcing the investor to apply punitive capital charging to account for the lack of detail available to feed into a risk model. In a Solvency II environment the relative difference in adjusted returns could be double-digit in size.

All of the above criteria have a direct correlation to the firm’s willingness to be transparent and, ultimately, this is what the institutional investor is asking for. Institutional investors are frustrated with the receipt of embargoed data that is so out of date that it is useless in practical terms when it comes to running an efficient and effective risk management process.

The same goes for black-box investments — institutional investors now want their investments reported with full look-through to the underlying securities so that they can feed this data into their own risk models and reporting platforms.

Consultants are particularly tuned into the problems at hand, and they, along with the institutional investors, are leading the changes we see in the landscape in front of us. The regulators are also getting in on the act – in Europe you have the push for look-through from EIOPA through the Solvency II Directive, as well as the demands for transparency and custody look-through with the AIFM Directive. This is just the thin end of the wedge, though; the FSB, through the FSOC (in the US) and ESRB (in Europe), has a clear mandate to drive greater transparency in the financial markets, strengthening prudent oversight of risk, capital and liquidity, and ultimately trying to ensure the next crisis is not as severe.

So the asset manager needs to carefully balance the need to prevent its special sauce being divulged and therefore exposing its investment strategies to free-riding and front-running predators, at the same time it has to become more transparent in an attempt to grab the opportunities that come up via RFP processes – this is the mainstay of any institutional business.

Asset managers also need to invest in the data management and reporting infrastructure to ensure they can meet not just today’s demands for transparency, but those of tomorrow as well.

Finally, data management – and in particular a firm’s ability to deliver the depth and breadth of information needed to support a demanding investor, and to gain trust in the investment management process – are becoming critical selection elements of the process. This is being exposed by questions such as:

  • Do you have a data governance program in place that has specific terms of reference that covers client-facing data?
  • Does your data governance program have specific data quality management processes that allow for timely, complete, accurate and consistent reporting of data to investors?

Clearly, if you cannot demonstrate you are in control of your product data, then how can you claim you are in control of your investment management process?

Is it any wonder some RFP teams are being left high and dry with dwindling win rates, while others are mopping the floor…

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Posted by Ronan Brennan

Setting up a governance program for effective management of investment product master data – Part 8 – Data Dictionary

March 6, 2013

This is part 8 of a series of blogs on setting up a governance program for the effective management of investment product data - in this blog I will explain why building and maintaining a data dictionary is probably one of the most important factors in the success of your program.

Like many business buzzwords, data dictionary means different things to different people. The common thread is that the dictionary is an inventory of the data items being consumed or produced within a specific defined business unit or process.

Why do we create them? Again, there are many reasons – but the most prevalent one is to bring a common understanding to play within a specific environment such that everyone is speaking the same language when it comes to data. Data management projects live and die by the quality of their data dictionaries because even within small teams you can have wildly different nomenclatures in existence for what seems at face value very simple, easily understood data items.

Before I get onto what makes up a data dictionary I would like to clear up a couple of misnomers I often come across:

- A data dictionary is not a document. Documents are two-dimensional, while data dictionaries work across many planes. They are best represented in a relational database, or if needs must, a set of interrelated Excel worksheets.

- A data dictionary is not a project resource – yes, every data management project needs a dictionary, but as a resource it has a life outside of the project. You do not create a dictionary to serve the needs of a project only – the dictionary is also required within the business-as-usual activities that come into play post a project delivery i.e. it is a resource that requires and demands constant attention, updating and refinement.

So what is commonly found in a data dictionary? As I mentioned earlier it is a centralised inventory of information on data items/fields that describes in detail the data items semantics, how the data relates to other data, where the data is consumed, where it is processed and from where it is sourced. The dictionary should also describe the correct format and syntax for each field.

So for each entry in the dictionary I would expect to find the following

- A specific unique name for the item

- A clear definition of the data items meaning, including references to other common/aka names for the item

- A list of all “consumer” entities and processes that consume/use this data item

- A list of all the “suppliers” or source systems that produce this data and deliver to processes downstream

- Specific mention of any master rules for choosing correct source system for specific situations

- A list of all business rules applied to the data item as part of any data quality management process that touches the data

- Reference to stewards or stewardship teams that are responsible for the management of the data

- Reference to subject matter expert(s) who can deal with questions about the data item

- Detailed syntax specification for the data item – including type, structure, format and example values

- Good dictionaries allow users enter and update specific notes and references à la a wiki

If you have constructed your data dictionary using a database then you can easily provide very helpful alternate views of the dictionary for example:

- Show all data items consumed by process X

- Show all business rules

- Show the data items touched by Rule Y

- For data item Z show all sources

- For data item R show all consumers

- and so on…

More advanced dictionary implementation have an integrated audit trail with the live system that can instantly show as-of  transactional views i.e. the dictionary and the real-world systems it relates to are integrated.

So how does one build the dictionary? In MoneyMate we build them out using a SIPOC process in reverse [COPIS]

- So we start off identifying all of the consumers of information

- From here working out what outputs are consumed by each consumer#

- From here working out which processes deliver the outputs

- From here working out which inputs are used in each of the processes

- Before finally identifying the source/supplier systems producing the inputs

A critical element of the COPIS/SIPOC analysis is identifying where certain data items have multiple source systems – in these cases we need to carefully specify the master data rules that indicate which source is correct for the variety of situations that dictate different usage of the data.

Examples of this problem would be:

- You could have multiple back-office providers which means your daily NAV could be flowing from multiple parties/systems

- You could also have different legal structures in play that have different statements of record for different data types e.g. for holdings you maybe using the accounting book of record for your mutual funds but for managed accounts you are taking data from your investment records.

- You could have standard source of performance for all in-house funds, but for sub-advised you take data from the sub-advisor

Clearly the dictionary needs to capture all of this information in a well structured manner and allow for specific notation of the master rules for each item which has more than one source.

So hopefully you have a better understanding of what a data dictionary is, what it contains and why it is needed.  If you have anything to add yourself – send me a PM or comment below.

Next up in the series is a review of the role technology should play….

4 Comments | asset management, client reporting, Data Quality, Technology | Tagged: , , , , , , , , , , , , , , , | Permalink
Posted by Ronan Brennan

10 Things worth remembering about Solvency II – Part 1

February 21, 2013

JD has some great insight here on lesser known salient points many S2 practioneers should take note of 10 Things worth remembering about Solvency II – Part 1.

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Posted by Ronan Brennan

Making the most of your data

April 10, 2012

We all talk about how important data quality has become, how important it is to deliver transparent, high quality information to our customers, and how that’s been driven by regulation and by changing investors. However, I’ve been at a number of events recently, and talking to customers and prospects about data management and I think that the stakeholders in data management projects have changed – it used to be technology, now it’s predominantly the business.

The drivers for these initiatives have moved beyond improving operational efficiencies – now it’s about improving your client service and your customer experience by sending out high quality data, and it’s about how you use that data to promote your messages as well.

It used to be all about getting the data in one place and it was all manual processes – in many organizations the processes are now automated, they can get the data faster and they have time to analyze it and use it for marketing. Wouldn’t it be great to link your sales data to your fund data so when you have news about one of your funds, you can push it out to the sales force so they have immediate access to that information for their customers … or you can push it out to your marketing department so they can immediately execute a targeted campaign to a particular group of prospects. You could really add value to your organization’s sales processes by leveraging the information in your product data…and connecting it to your advisor and customer data …and then tying it all back together with your books and records data flowing from TA.

Many asset managers have empowered their sales teams with iPads so that they have access to all the latest product information … anywhere, anytime. At NICSA’s recent conference in Miami, it was revealed that 76% of advisers share content online (up from 67% in 2010). This includes performance information, white papers, commentaries etc…. it underlines the importance of being able to provide that information, ensuring that it is always accurate.

There is no point having all these silos of business intelligence in the distribution front-office if you cannot leverage it – make the most of your data!

1 Comment | client reporting, Data Quality, distribution | Tagged: , , , , , , | Permalink
Posted by Ronan Brennan

Data is the oil in the sales engine

April 3, 2012

I love using analogies and metaphors… I used to talk a lot about C level’s perception of data management…and equating it to a duck paddling across a pond… what C level doesn’t see is the duck’s legs paddling furiously to get from one side to another… C level doesn’t see the immense and often chaotic manual processes and time spent on getting fact sheets, client reports and sales decks out to market.

Everyone is talking about data … and everyone has a different perspective. Who really cares about data? Well, today it is a hot topic in the front-office, specifically with the distribution team.

It’s really important for the sales and distribution teams to have timely, accurate, consistent data appearing in fact sheets, RFPs, presentations, client reports, sales decks and on their websites.

Ultimately, data is the oil in the sales and distribution engine … good data helps them to sell their products… and enables the process of communication with clients and prospective investors to run smoothly. 

Good data will help them deliver excellent client service, retain their customers and gain new clients… and of course, good data will ensure that the company reputation is upheld.

Even more important is ‘agile data’ -> the pipeline feeding distribution with product data and market intelligence has to be adaptable and scalable and capable of reacting to new product launches, changes in distribution channels and market regulations. The demand for more strategies is leading to more products being added to the arsenal, and the demand for greater transparency in reporting is leading to more data points per product -> this 2 dimensional demand on breadth and depth of information means the data quality management processes, compliance and governance functions all have to be agile enough to meet the demands of distribution.

But, on the other hand, bad data will muddy the waters…  pour dirty data into your sales engine will over time lead to it seizing up completely! The consequences of getting it wrong are that you’ll have inaccurate, inconsistent data in the public domain … you’ll be at risk of getting in trouble with the regulator, potentially being exposed to fines and worse still,  bad press – your reputation will suffer and you’ll likely lose business as a result. That really doesn’t help the sales engine run smoothly at all … outflows, loss of business, poor client service…. that could all make the sales engine seize up.

5 Comments | client reporting, Data management, Data Quality, distribution | Tagged: , , , , , | Permalink
Posted by Ronan Brennan

Data at your Service

February 2, 2012

Ever wondered how you can improve client services? I would argue that easy access to timely, accurate and ultimately reliable information about your products i.e. their investments, being delivered through an effective data governance programme, is a key enabler to service excellence. Arguably, the main differentiators for investors in terms of client services are the timeliness and quality of Investment Reporting coupled with a responsive and assured service that they can rely on if they wish to enquire about their investments. In the age of transparency, there is no room left for complacency in these areas.

Timely and Reliable Investment Reports

Strong data governance coupled with effective stewardship enables shorter reporting cycles therefore providing your clients with their investment reports earlier and exceeding their expectations for up-to-date information on their investment portfolio. However, timeliness of delivery will not do it alone. It has to go in pair with reliable data. The validation process in a data governance programme ensures that you get it right the first time; which in turn will save time by removing the iterative process of checking, correcting and rechecking reports. Clients do not only expect to receive reporting on time. The content has to be complete, accurate and consistent for it to deliver value.

Finalising reports earlier also provides your client service team with more time to focus on adding value when delivering the information by analysing the data and preparing to review the investment report with or pre-empt questions from their client.

Assured and Responsive Client Service

In the current environment and under increased regulatory scrutiny, asset management firms are adopting a fiduciary mind-set and strive to be as transparent to their clients as possible. Therefore, your customer service team’s ability to navigate your product data and have timely and accurate information at their finger tips is critical to your success. The changing regulatory landscape requires customer service staff to ensure they are prepared to address any question or concern that an investor may raise in a responsive and knowledgeable manner. Therefore, conveying confidence, building trust and making the investor feel that they are in good hands.

A strong data governance system will empower client services to achieve these high standards by building their own confidence in the information that they source internally, by providing them with the most up-to-date data and by allowing them to quickly identify the owner of specific data points to route investor enquiries to the right source of expertise within your organisation.  Therefore, helping them to get the answer right the first time.

1 Comment | client reporting, Data management, Data Quality | Tagged: , , , , , , | Permalink
Posted by Jeff Willems

Improving reliability and trustworthiness of investment product data will deliver returns

November 1, 2011
More than ever before, investors are demanding that asset managers up their game with regard to the quality of information they are presented with at point-of-sale or indeed in post-sale statements and reports.

This in turn is leading the heads of distributions and sales in asset management firms to demand more reliable and trustworthy data from operations. They have recognised that high quality information about their products can be used as a differentiator in winning new business, and that it positions them to deliver best in class client service which leads to higher levels of customer retention.

This data is directly used in the monthly and quarterly production cycles that serve their clients with regular updates on investments and power up the sales engines and related materials in the go-to-market side of the business.

But, surely investors are only interested in the risk-adjusted performance? Why would the quality of information in point-of-sale documentation or reporting influence an investor?

The reality is that investors do not, and should not, use past performance as the sole criteria in their decision process any more  – so many other factors are important. The same applies to distribution channels for funds -  fund providers need to differentiate themselves from the pack.

So clearly the distribution channels want good products to sell, but they need good materials (and good information) to help them make their products stand out from the crowd.

They not only want good sales support materials though, they want them on time, and ideally, they want them before their competitors have theirs. They want to wow the investor with the breadth, depth, and timeliness of the information. They want to ensure that whatever they present matches 100% what the investor will find on the web.They want to use the latest technologies to deliver the information to the client – support for a touch screen tablet is the new must-have request from  the field sales teams.

So, having a good product is a given. Having smart and exciting ways of delivering point-of-sale information to the potential investor is a given. The best product in the world, and the sexiest of sexiest tablets will be useless if the content you are delivering is late, limited or just plain bad.

Investment decisions are built on trust, trust in the advisor, trust in the brand of the provider, and trust in the material being presented.

Trust in the product is built by providing clear, deep, transparent information on the product at point of sale – so one or two page fact sheets that are two months old do not cut the mustard.

Trust in the information being communicated is the foundation on which the investor will build their impressions – it is their window on to the organisations they are doing business with (or considering doing business with).

The investor wants an appropriate mix of qualitative and quantitative information – too much text and not enough stats make it look like you’re hiding something, too much stats and not enough text make it look like you have a lightweight analysis team.

The investor wants first-class, qualitative analysis of the market segment / strategy that the fund is targeting – they want to understand the product and market risks at play. They want quantitative and technical analysis that open the lid on where the performance and risk of the fund is being generated, and they want to understand how this breaks down when compared to peer-groups, external category averages and the stated benchmark.

Something which very few asset managers have embarked upon is providing advice on which products from the same provider (currently) have a correlation co-efficient that would lower the overall risk of a portfolio while maintaining overall target performance – think about how Amazon.com markets books that are related to each other.

Finally, clear unambiguous presentation of the fees/charges for the product, build confidence and support the trustworthiness of the advisor, provider and product alike.

To summarise, by sorting out the “plumbing” i.e. the flows and quality controls around product information from various internal and external sources, sales and distribution can leverage this reliable and trusted data to accelerate new customer acquisition and increase customer retention rates.

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Posted by Ronan Brennan

Convergence of retail and institutional

October 20, 2011

I have noticed a definite trend over the last number of years with respect to the convergence of the retail and institutional worlds within asset management firms.

It is not simply just a convergence of the product and service offerings, but also the internal alignment of the teams responsible for each business line.

The operating models that were at play 2-3 years ago had these teams run on separate lines, now firms are aligning their internal structures along functional roles as opposed to business lines, in turn blurring the line between retail and institutional.

 So what is happening out there? What are the drivers? What is causal? What are the symptoms?

There are several key drivers that I see in play:

1. There is board and shareholder pressure to build leaner operating models that scale better and deal with financial market changes in a more flexible and predictable manner. This is borne out of the major flux we have seen in the financial markets since the end of 2008 and the renewed focus on operating costs.

2. There is a growing level of investment savviness amongst retail investors, in particular with the key market segment that has a high level of disposable income. These investors are demanding greater depth and breadth of information on their portfolios, thus driving the retail (product- focussed) reporting model ever closer to the client-focussed reporting model of the institutional market.

3. Institutional clients are demanding glossier client reporting artefacts – something which the retail side of the business are generally more adept at producing. This combined with the demands from the institutional sales teams and channels for product-like factsheet documentation for the various strategies and composites being marketed, is a key driver in getting the output production teams internally more closely aligned.

The results of these drivers are that internally the business lines are being remodelled and combined such that the retail (product) reporting structures are a by-product of the more bespoke client-focussed institutional lines.

The retail investor is also being offered increasingly complex products; synthetic ETFs, Absolute Return funds, Long/Short strategies and SMA/WRAPs.

In turn, retail investors are demanding increasingly complex statements and monthly factsheets – note the increase in retail asset managers offering detailed equity and fixed income attribution reports, both at product and account level.

Asset management firms have been quick to grasp the obvious efficiencies available by viewing the product side of the company as just another institutional client – thus enabling them to unleash the power of their considerable investments in client reporting solutions to tailor them for the retail line of business.

Another driver in the area which is driving consolidation of the systems that service both lines of business is the focus on building an investment product master to deliver a formal data quality management framework to support the considerable desire to produce better quality data and content in a more timely and efficient manner.

So in the future, we should expect to see more, not less, convergence of the business lines. Clearly, the two lines of business will always have clear demarcation lines in terms of level of service, reporting, fee structures and distribution, but the back- and middle- office teams and services that serve the business lines will see continued consolidation to leverage the obvious efficiencies and quality improvements being demanded by investors and shareholders alike.

Leave a Comment » | client reporting, Data Quality, product master | Tagged: , , , , , , , , , , , , | Permalink
Posted by Ronan Brennan

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