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 10 – Move to Maturity

March 25, 2013

This is the last post in a series of blogs on setting up a governance program for the effective management of investment product data, in this blog I will wrap up the series with a discussion on how to move your governance program towards a mature model.

If you have followed the earlier 9 posts you will probably recognize many of the steps in the data governance evolution scale  I referred to in one of my previous blogs on maturity models for governance of investment product data (see image below).

Data Governance Evolution - from Chaotic to Predictive

The Data Governance Evolution Scale

As you embark on the instantiation of your program you will more than likely be working within a quite chaotic environment – one lacking in clear, top down driven policies and standards, and very much reactive.

As you start to take action and follow some of the steps that I have outlined in earlier posts you will start the journey towards maturity – initially you will need to go through the process of securing C-Level buy-in, and formulating terms of reference for the program, before embarking on the broader strategy definition.

Do not neglect the importance of the culture and organizational structure that will be needed to support and enable the governance program to succeed – in particular you have to think about the target operating model of stewardship that will be most effective for how your business is structured both now and into the future.

In order to move from the “chaotic” and “reactive” levels in the maturity model you must focus heavily on the following:

  • Ensure your strategy is clearly defined and communicated to all actors within the domain you are trying to apply governance too
  • Set out the policies and standards you want to promulgate, with clear alignment and reference to the strategic goals
  • Once you have the policies and standards down pat you will need to focus on ensuring you have a set of applicable processes and procedures that are aligned with the overall strategy – each process should be directly traceable back to a specific standard/policy
  • In parallel to the process of defining the policies, standards, processes and procedures (the how) you need to be working on building out your master data plan (the what)

If you have worked through the process above, you will have moved from a position of total vacuum, to a chaotic, to a reactive program of governance – in fact you have probably got further than many firms who claim to have governance will ever move past.

To move from “reactive” to “defined” you need to make sure that there is a clear understanding of the data within the terms of reference of the program – all participants have to use the same nomenclature when talking about data, as inappropriate usage, miscommunication and misunderstanding of what is being discussed is the most common root cause of data quality issues I see. To this end, the construction of  a data dictionary is a fundamental step in moving your program to state of ”defined”.

Other areas you will need to focus on before you can consider your program to have achieved the level of “defined” in our maturity model are as follows:

  • You need to start the process of measuring your program – don’t fall into the data quality denial trap – identify the key areas (KPI’s) where useful measurement can contribute to a balanced score card that reflects how well the program is being executed. Think about KPIs that can point to the quality of the data – focus on the standard facets of data quality – timeliness, consistency, completeness and accuracy
  • Examine the operating model you have in place for stewardship - to what extent can it be made more effective and efficient by driving the rights issues to the right experts – in my opinion the stewardship model needs to be n-tiered in line with the back to front alignment of traditionally asset management businesses
  • As you develop and hone the standards and policies, the underlying process and procedure will start to snow-ball and it will become increasingly difficult to achieve the oversight and accountability that all governance programs require to be successful – to that end you will need to consider how technology can help support and frame your program, but remember technology is not a panacea to the ills of data governance and quality
  • For every issue, exception and concern raised through the program start tracking the root cause – this really does require a good underlying collaboration tool

Moving from the level of “defined” and onto “pro-active” and “predicative” will seem at face value to be relatively easy, but is very rarely achieved, in my opinion this is generally culture related – some of the key elements in moving onwards and upwards is buying into the continuous improvement principle – if this is not a core value in your firm you will find it difficult to progress in any meaningful way

So what are the key elements moving your program to the highest levels of maturity?

  • You need complete (firm level) buy-in to the concept of continuous improvement – with the appropriate feedback loops designed into your processes to ensure this becomes a core tenet in your program’s modus operandi
  • You need a specific set of activity centred on reviewing the root cause of issues being surfaced, such that they are being feed into the continuous improvement cycles for the relevant processes
  • Your master data plan and data dictionary will be seen as living breathing entities, that are constantly being updated and reviewed in line with the changes in your BAU operating models – these artefacts must never become stale. I often find this is the easiest way to demonstrate to a firm that their program is still in the “defined” stages of maturity, when they might argue otherwise
  • Your program will be making correct and efficient use of technology to empower and enable the data quality management process, supporting the people (stewards) to apply the governance
  • Your data architecture will be coherent and well designed for your organization and how it does business – I find discussion on architecture and the correct use (or not) of MDM, warehouses, marts, hubs, silos etc can be fraught with generalisms and so I will avoid pontificating on what I consider good architecture

When you are able to demonstrate that your program is constantly being tuned and refined, that you have demonstrable audit-trails, and your people, processes and technology are working in harmony, you are well on the way to achieving a highly predictive and mature governance program.

Before signing off of this 10 blog series, remember to keep your eye on the ball – think about the green grass on the other side of the river – the reason we focus so heavily on applying good governance to client facing investment product data is because data is the oil in the sales engine! Feed it with bad data and the engine will start to seize up…..and a business without sales has no future!

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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….

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Posted by Ronan Brennan

Setting up a governance program for effective management of investment product master data – Part 1 – Organization

January 15, 2013

It is not without reason that I chose “organization” as the first theme in this series of blogs on a blueprint for setting up a program of governance for effective management of Investment Product Master (IPM) data in an asset management firm – every firm is different. Each firm has its own unique blend of culture, history and esoteric business practices that mean that there is no cookie cutter type solution to implementing a program of governance for IPM data. This is why having a clear understanding of the mechanics that make your organization tick is a key element in kick-starting the activities needed.

Some key questions that need to be asked:

- Who in the firm cares about the quality of the product data being pushed into the market about your funds? I would expect the answer here to include at least someone at C-Level, probably the COO and potentially the CFO. I expect the Head of Distribution will care passionately about the quality and timeliness of information being pushed to prospective and actual clients. I expect the Risk/Compliance department to care – after all this is their job! I would expect various operational heads will have a keen interest – such as, Head of Product, Head of Performance/Attribution and finally, I expect that someone in the Treasury function whose role is oversight of back-office accounting data flows i.e. price and income would have a vested interest. For sure, there are others that belong in this group but I am trying to keep this at a high level.

- Is there one person or team in the firm who is directly accountable and /or responsible for the quality of information published about the firm’s funds and investment accounts? I often get a variety of answers here – sometimes you will find there is a data governance team and/or Chief Data Officer / Czar in place, but more often than not you will find their effective remit is actually quite limited in scope and the breadth of the data they are responsible for does not extend to your own Investment Product Master data – why is this? I think it is because firms believe this data, being their own, must be of good quality. The reality is that this data is often cobbled together across a disparate range of in-house silos, many of which are collections of Excel macros and Access databases, mixed in with some actual systematic feeds of data from systems that have not been manually altered. Sometimes, you will find that in fact there is a person or team with direct responsibility for the product data – quite often the Head of Marketing, or the Head of Performance.

What is the goal here? Well if you have found through your questioning that there is neither a person nor a team who is directly accountable, nor any team that is responsible – you need to work through the list of people who ‘care’ to create a initial committee or steering group to run your governance program. This is the most basic step – the formation of groups within your firm who will take the initiative and drive this forward.

Whether the program is managed by committee or by a specific person depends on the organizational culture – do whatever works in your firm. But, without a doubt,  this initiative will fail within weeks if you do not have executive sponsorship and full engagement from at least one C-level executive.

One thing I would stress – particularly if your firm already has a data governance function – if this existing function does not have a specific and obviously executed mandate to take and apply control to your investment product data, then set up a new team/program to take on this very specific mandate. Too often you will find that there is a broad program of activity in place – but that it is highly focussed on the investment books and records and the data/systems that are feeding into investment decisions made within the trading and portfolio management teams. There is often little or no focus on the product data that the firm is sending out to the public domain and on which the firm’s clients are making (or not making) decisions to invest in the firm’s own funds (or accounts).

Certainly if I were the Head of Distribution, Client Services, Marketing or Compliance/Regulation - this would be very high on my agenda. The sales and distribution engines need good quality and timely data to compete for new assets and retain existing clients. Remember my earlier post, data is the oil in the sales engine!

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Posted by Ronan Brennan

Setting up a governance program for effective management of investment product master data – Overview

January 11, 2013

I am planning a series of blog entries on a blueprint for setting up an effective program of governance for investment product data – this will be of interest to companies who might be considering implementing a solution for investment product data management – or – who might be supplementing an existing EDM data governance program with investment product information – or – who are looking on building out a program for the first time.

While the blog will be primarily focused on investment product data, it will be possible to derive valuable insight for other data types within asset management or in alternative verticals.

The following are the 10 themes I will cover over the coming weeks….

  1. Organization
  2. Terms of Reference
  3. Defining the Strategy
  4. Model for Stewardship
  5. Standards and Policies
  6. Process and Procedures
  7. Master Data Plan
  8. Data Dictionary
  9. Technology Frameworks
  10. Move to Maturity

7 Comments | asset management, Data Governance, Data management, Data Quality, distribution, product master, Regulation | Tagged: , , , , , | Permalink
Posted by Ronan Brennan

What type of performance reports do shareholders expect?

December 19, 2012

This is a piece that was published in Ignites earlier in the year. I was asked the question: “What type of performance analysis on individual securities and sectors within a fund do shareholders expect?” .. I thought it was an interesting question and in case you didn’t see the original article, here’s my answer:

Shareholders in mutual funds are driving many changes in the performance-reporting arena. This trend, in conjunction with demands from regulators, is leading to fund firms delivering greater depth and breadth of performance analysis via the Web and retail factsheets.

Shareholder expectations are breaking down along the following lines: Many shareholders are demanding deeper comparisons with benchmarks and peers, and greater depth of analysis at unit value level, portfolio sector levels and holding levels.

When presenting a fund’s performance to advisors and end-investors, the asset manager also is now expected to deliver all of the standard views and values, including gross and net returns, and performance figures that factor into the sales load’s impact. There are also expectations that the fund firm will deliver:

  • Comparison with the stated benchmark and peer averages based on Lipper and Morningstar data
  • Cumulative values up to the latest reporting date, including year-to-date, quarter-to-date, daily, weekly, monthly, three-month and other metrics

  •  Discrete period performance for the last five calendar years

  •  Charts tracking growth and a comparison with the benchmark and peer averages.

All investors expect a basic level of risk-adjusted performance reporting, so ratios such as Sharpe, Sortino, M-squared and information ratio (IR) are now required on many factsheets and client reports. Portfolios that have non-normal return distributions (such as hedge funds) are expected to include measures, such as omega, which account for the upper and lower partial moments in a distribution. However, mutual funds usually do not include omega data in their reporting.

The discrete performance returns of the fund are used to generate and report on many risk measures. The following are now the most common and expected: standard deviation, downside deviation, alpha, beta, covariance, correlation, R-squared, tracking error, value at risk (VaR), skewness and kurtosis.

In addition, there is increasing demand for performance attribution and contribution at sector and holding levels so that a shareholder can gain greater insight into the source of performance in the fund. This is nearly always calculated side by side with the benchmark – including to the benchmark constituent level, where funds are reporting analyses of holdings-level returns.

Where a portfolio is balanced across multiple asset types, one would expect to see attribution and contribution analyses performed on the discrete sections of the portfolio. For example, shareholders expect to see specific attribution of the fixed-income portion of the portfolio, which will be very different compared to the views expected of the equity portion.

In terms of the performance measurement methodology considered the best, Global Investment Performance Standards (GIPS) is the base requirement that regulators and investors alike expect.

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Posted by Ronan Brennan

Webcast available for on-demand playback…

August 10, 2012

For those of you who missed the recent webcast on regulation and its impact on data management strategies in the investment fund world, it is available for playback here…

4 Comments | Data management, Data Quality, Dodd Frank, Event, FATCA, KIID, RDR, Regulation, RFP, RFP, Survey, UCITS, Volcker Rule | Tagged: , , , , , , , , , , , , , , , , , , , , , | Permalink
Posted by Ronan Brennan

Simplicity, Trust, Opportunity, Low Cost Air Travel and Data Quality – what’s this got to do with the future of fund management?

May 25, 2012

This is the first blog published by guest contributor, Jason Cooke – VP Product at MoneyMate

In a previous blog Making the most of your data, Ronan wrote about how he was finding that the stakeholders in data management projects have changed from technology to predominantly the business.

When I attended the IEA’s 13th Annual Conference on The Future of Fund Management recently this viewpoint was shared, with many of the speakers talking about how the industry needed to focus on the end customer and work with the current and pending regulations to re-establish trust with those customers, especially after the fallout of 2008 which saw the reputation of the industry being badly damaged. This focus on the business of servicing the end customer led to some interesting thinking around how funds need to be presented.

Rupert Todd (President – Investment Services: T. Rowe Price International Ltd) spoke about the proliferation of investment products that has sprung up in Europe and Asia and how this added to the air of complexity about funds to the end investor. One of the key messages from this opening address was that funds were ‘not simple enough yet’.

Throughout the day this continued to be a key theme where various speakers spoke about the iPad generation which expected all the complexity to be delivered in a simple and easy to understand package.

But bringing in simplicity is only part of the story – another key element was building trust through transparency. Making things simple does help bring transparency, but can it bring about trust?

Yes there is a need for fund managers to know their customers and be able to engage with them in such a way that they are seen as trustworthy. A strong element of this is focussing on the end user and ensuring that the data being given to the end user is of sufficient quality and accuracy to help the fund manager connect with the end user.

So where do regulations come into play? Does the fund management industry see these as a burden or an opportunity? Karen Hamilton of Northern Trust gave a clear picture of how the industry should see this as an opportunity to reassess tactical approaches and put in place good governance practices to ensure asset safety, transparency and ultimately investor protection.

When trying to look at how this focus on simplicity, trust and opportunity was going to affect the future of fund management, parallels were drawn on how the airline industry changed with the introduction of low cost carriers that not only made air travel cheaper but also reduced the complexity of buying a ticket and gave greater transparency on how charges are broken down. This has changed the perception of how people view air travel and now air travel is easy to understand and is accessible to all…and perhaps more importantly, it helped break the perception the large established carriers had of air travel and they have had to change to survive. The point was well made and understood on what the funds industry needs to do.

To return to Ronan’s earlier view that the stakeholders are changing to the business, he also highlighted that access to and usage of high quality data was necessary to improve client service and customer experience. Given that a direct movement to promote simplicity, transparency and a regaining of trust was being suggested as compulsory to the future of fund management by the speakers at the IEA conference, it’s clear to me that there also needs to be a renewed focus on addressing data quality to help simplify information, regain investor confidence, restore transparency and ultimately underpin the success of the fund management industry.

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Posted by Jason

Recent Panel Discussion at TSAM USA

July 29, 2011

July has been a busy month with client engagements and travel but I wanted to add a blog about the event I attended in New York in mid July.

Many people will be familiar with TSAM, the annual buy-side technology and operations event, which is usually attended by senior operations, marketing and IT executives. I always enjoy these industry events as they offer a great opportunity to network and catch up with people in the industry as well as finding out about the latest trends and developments.

I had the pleasure of participating in a panel discussion on “Critical issues in data management” together with industry veterans: Regina Trach, VP Marketing Services at J.P. Morgan Asset Management, Gerard Walsh, Head of Delivery, Global Strategic Solutions at Schroder Investment Management, and David Bates, Principal at Citisoft. The discussion was moderated by Uday Singh, CEO of Osney Media. It was only supposed to go on for 30 minutes but ended up stretching into an hour as there were so many questions and such a lot to talk about.

Initially, we focused on what the key issues were in the data management area, with most of the panel agreeing that drivers for data management projects centred around managing risk, complying with regulation and also managing the data “overload” – what to push out, when, and to whom. Gerard from Schroders said that as clients became ever more demanding, they needed to get timely and accurate data as fast as possible in whatever way they wanted it whether in person, in a report, on a web page or as an app on an iPad. J.P. Morgan recently launched an iPad app for advisers and feedback has been phenomenal. But, getting information to devices is a major data and integration challenge.

In terms of regulation, one of the concerns is that asset managers know there will be demands for transparency but don’t know what they will be. They are wary of the SEC and FINRA and what they will actually be looking for. The SEC is likely to take information and fact sheets from an organization’s website and compare it – and will want to ensure it’s all accurate. They will also want to know historical information e.g.”can you show me what your website looked like on April 11th, 2009″? Asset managers still have a business to run and the wall of regulation can be a challenge – but they must be compliant.

We then went on to talk about the amount of data that is available and how accessible it needs to be… With large global asset managers averaging 4.5 million items of data each month, it’s hard to answer the question “Do you know how good the quality of your data is?”  You really need to work out what to push out to your various audiences… this is where using segmentation/ audience management is very powerful. If you have a contact strategy where you test email open and click thru rates, track website visitors and monitor Twitter, you will know who is listening to you and find out what they want to hear. 

We then went on to talk about what is the right material to push out? Should we be reviewing what we need to report on. What do customers need?  We also need to focus on the consistency of information across the organisation e.g. surveys, web presentations. Separate areas of the business are generating data and enabling it to get out. I talked here about how marketing ops have not been well served by IT and there are lots of manual processes involved in getting data to market. If data points are managed on spreadsheets, you have to have proof readers coming in to get material out to market and you have a much higher risk of error. Setting up a data governance process and ensuring that data is corrected at source will help greatly and you won’t end up with marketing teams chasing, checking and keying data at the last minute.  Also, if you automate the process, you will significantly reduce your fact sheet production time.

Then we talked about actually getting data management projects off the ground. It can be quite difficult as often times C level doesn’t realise there is anything wrong with the data. It might be easier to focus on a smaller project first and try building it out from there. For example, for Schroders, the web was a big driver and they wanted to provide their sales force with tools that can help people make investment decisions – having timely, accurate and consistent data available on the web was a key influencer.

The other key influencer will be cloud computing– not just on the entire IT area but on other areas within the organisation e.g. Salesforce.com.  Asset managers are more likely to outsource if it’s not a strategic advantage to do it themselves.

 

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Posted by Ronan Brennan

Who do you think you are KIIDing?

January 14, 2011

Warning: this is a rant….

I am totally fed up with getting emails from vendors claiming to have the “be-all-and-end-all” solution for supporting the UCITS IV Key Investor Information Document – unfortunately all of the solutions I have seen are focusing on a narrow aspect of the problem space…

I see the following problem spaces being targeted: distribution, production, comparison, work-flow, narrative management, and data management / data quality management.

Some vendors see KIID as a distribution problem – which let’s agree is part of the issue. Asset management companies will have a challenge in getting their KIID documentation out to market, but this will be no different to the problems they have today with getting their other compliance (e.g. simplified prospectus) and marketing documents (fact sheets) to market. The emergence of global and local document stores from e.g. Morningstar, FundsLibrary and FundInfo will facilitate and make this process more straightforward. The platforms and open-architecture distribution houses will also have problems sourcing the latest KIID, again though the document store vendors will solve this problem.

Some vendors see the issue purely as a document production exercise – they do not care where the data is coming from, what quality it is, nor do they have any interest in the data – other than to collate it all together into a nice glossy document. They seem to have lost sight of the fact that this is a legal document, albeit it in the guise of a marketing document that should be understandable by the average person on the street. KIID is not a marketing document – the gloss factor of the document is a very low priority for asset managers who are concerned about KIID - their issue is getting the document to market with consistent, accurate, timely data – with narrative that is clear and understandable. Some of these vendors are even offering to create the KIID directly from the simplified prospectus, even though the KIID guidelines very clearly and without any ambiguity state that the content for the document should not be extracted from the prospectus documents.

Other vendors see the problem space as one of workflow – so they have spotted that this document is not your average marketing output and does have some requirement for approval across many departments. I think these vendors are starting to touch on the aspects of KIID that are of true concern to compliance and marketing officers who are actively engaged in KIID projects today. You see the document is definitely a legal document and so compliance want to have a defined role in the document sign-off, but marketing also have a role to play in ensuring the document is drafted in language that can be understood by the average investor of the fund – marketing may even be the main sponsor of the project since the document is ultimately required at point-of-sale.

My own discussions lead me to the conclusion that what is worrying asset managers most is how they will manage the narrative texts within the document, ensuring that compliance, marketing and the investment manager all get to review and comment before publication. But implementing a review cycle with multiple interested parties for what could be hundreds of documents for a medium-sized asset manager and potentially thousands for a larger manager is a daunting task. When you add to this the task of managing the quality of the data flowing into the document production process you have a problem of truly epic scale.

To have a scalable and efficient KIID process, you do need all the wheels and cogs in your machine working in tandem – so do not lose sight of the broader problem spaces – ensure your project has in-scope: distribution, production, comparability, work-flow, narrative management and data quality management.

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Posted by Ronan Brennan

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