Survey results 2013: Surprise, surprise, gaining new clients is important!

May 28, 2013

Gaining new clients was indicated as the key priority in 2013 for asset management firms, with 46% of respondents flagging new client acquisition as an important factor in their 2013 plans, according to the recent MoneyMate Data Management 2013 survey.

The rest of the factors had a mixed response when it came to flagging their importance:

- reducing costs did not feature highly at all with most respondents ranking it on the lower end of the importance scale

- increasing efficiency had a similar result, not surprising when you consider the relationship between cost and efficiency

- complying with new regulation polled lower on the priority rankings than I expected with a 53:47 split between those that ranked it as a high, rather than low, priority across the result spectrum

- there was no surprise that launching new products polled as the lowest priority in 2013 – 46% ranked it low, in fact I suspect that had we asked if product consolidation was a high priority, it would have figured prominently

- improving client service had a balanced view with the results spread across the range of priorities indicating that new client acquisition was higher priority than servicing existing clients – this is the type of result you tend to expect in a strongly bullish market – so maybe good times are ahead.

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Posted by Ronan Brennan

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.

Leave a Comment » | asset management, client reporting, Data Governance, Data management, Data Quality, distribution, product master, Regulation, Survey | Tagged: , , , , , , , , | Permalink
Posted by Ronan Brennan

Survey results 2013: firms are upping the spend on data management!

May 21, 2013

According to the recent MoneyMate Data Management 2013 survey - firms are increasing their spend in data management in 2013.

This shouldn’t be news to anyone – 85% of respondents in the survey said their firms plan to spend more on data management in 2013, with 12% indicating the budget will be on a par with 2012.  One could argue that the biggest surprise was that 3% of respondents indicated their spend in 2013 would be less than the previous year! Maybe they have it all sorted and are sitting back and taking a breather….

From what I can see on the ground, just about every firm out there has some new initiative under way at the moment – be that looking at an IBOR solution, a security master, a product master, client reporting or a broader EDM program.

I see lots of strategic projects getting budget, which is a good sign for the industry, as the previous few years saw a tactical spend far outstripping any strategic view points.  This was to be expected with the bearish sentiments on the global picture sapping many firms’ will to embrace large strategic spend when AUM and fee income was under such pressure.

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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

Data governance is not data management

April 17, 2013

I was on a panel at the recent TSAM UK 2013 conference in London – where the topic was “Good data governance – the challenges for the business“.

On the panel I was joined by Phil Tattersall (Simitas), Steve Clark (KPMG) and Andrew Barnett (Friends Life Investments), our moderator was Chris Johnson of HSBC Security Services.

One of the topics we discussed on the day was around governance frameworks, what had we seen in practice that worked and to what extent was governance being driven by business (as opposed to IT).

One of Phil’s comments still resonates with me today “Data governance is not data management” – why so? …unfortunately the perception that governance = management is something I come across too often. Data governance is what ensures that data management happens properly i.e. in a way that is aligned with the original goals and terms of reference for the initiative under way. Data management professionals need to clearly understand the difference between governance, stewardship and architecture!

Another of Phil’s points was also very salient  ”treat data as an asset”  any of you that read the recent Citisoft white paper “Data is the new oil” will see the connection – data is something we need to value, it can often be presented in a very raw form, and like oil, it requires careful refinement to extract maximum value. I am fan of the data and oil metaphor myself – from a slightly different angle though, I often refer to data being the oil in the distribution engine for investment managers – feed the engine with poor quality oil (data) and within a short space of time that engine will seize up.

Steve also had some good points in the above discussion, he stressed the importance of setting out the “policy and procedures in governance” and the criticality in “defining good data quality” and the measurement and feedback that should exist in your process management framework to drive improvement.

With respect to whether governance should be driven by business or IT, Andrew indicated that in his business “data teams are made up of business people” – myself and the rest of the panellists agreed that data governance initiatives should be driven by business, but that clearly IT have a role to play and their involvement will always help.

My own view was that frameworks for governance are important, in the sense a framework is anything that providers structure and guidance to the application of governance to any data management initiative. This ‘structure and guidance’ can take the form of technology that assists the empowering of the stewards to manage data in line with the stated strategies and principles as laid out by governance.  Technology can also assist in  driving ownership, accountability and transparency into the process. The key point though is not to overly rely on technology. Technology does not fix bad quality data – that is what good data management professionals do – we just need to enable them to do their job more effectively.

1 Comment | asset management, Data Governance, Data management, Data Quality, distribution, Event, Technology, transparency | Tagged: , , , | Permalink
Posted by Ronan Brennan

Setting up a governance program for effective management of investment product master data – Part 9 – Technology Frameworks

March 12, 2013

This is the penultimate 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 explore the importance of technology frameworks.

Investment product data quality is determined by: Completeness, Consistency, Timeliness and Accuracy – but to solve data quality  you need to consider: People, Process and Technology!

People Process Technology

Implementing a formal program of data governance and effective stewardship requires investment in supporting frameworks that empower people to apply the process.  While technology is not the solution to data quality– it has a really important role, and that is to provide a structured framework that empowers the stewards (people!) to apply the process!

Remember technology and your IT department cannot and will not solve your data quality problems – it’s role is to support and frame the process such that the people can do their job effectively.

The aspects of data governance, and of the data quality management process, where technology plays a key role are as follows:

  • Automation of data quality checks, ideally with a business intelligent rules engine. There are many generic DQM/EDM solutions on the market – think about a best of breed for the niche you are in though – they will deliver a greater ROI. Ensure your business rules engine is capable of schedule management, workflow structures, validation, reconciliation, transformation and derivation – ideally choose one with a Domain Specific Language that allows custom rule engineering
  • Effectively measurement of the process and reporting meaningful and actionable information – be that in the form of traditional MIS, KPI’s, Balanced Scorecards or bespoke dashboards. Operational oversight, trend monitoring and feedback loops are key elements in driving  a process to maturity (see next post in the series)
  • Assignment and delegation of ownership and accountability
  • Exception management, alerting, reminding and escalating data quality problems
  • Data mining and reporting
  • Critical to all processes under the remit of the governance program is that they are repeatable, automated and systematic – with a clear audit trail that ties stewards, to data exceptions, to historical temporal views of the platform

Remember though, the key role that technology plays is in providing a framework that empowers the stewards to apply the governance strategy, while allowing the governance function to oversee the application of the strategy.

In the last post of this series I will look at how to drive your governance program from day-care to maturity….

2 Comments | Data Governance, Data management, Data Quality | Tagged: , , , , , , , , , | Permalink
Posted by Ronan Brennan

Data governance – Part 7: Master Data Plan

February 26, 2013

Setting up a program for effective management of investment product master data This is part 7 of a series of blogs on setting up a governance program for the effective management of investment product data - in this blog I will briefly consider the importance of developing a master data plan (MDP).

There are lots of definitions for ‘master data plan’ in use today,  what I am referring to specifically is the intersection of the data governance strategy, standards and policies with the physical data. The International Foundation for Information Technology have defined ‘Master Data Plan’ as “An ordered or sequenced set of clearly defined and governed Tasks, Activities or Work that are often date and/or time bounded and which exist to facilitate in the strategy, delivery or operations of one or more specific Master Data Items or Entities.“  This is a reasonable definition – it is the overall plan that brings the strategy, standards and policies together to form a coherent plan for managing the data in scope , with specific execution detail specified in the process and procedures. If we consider the previous posts in this series of blogs, what we doing at each stage is effectively building up the layers of the MDP.

Why is an MDP important though? Because without it you can lose that centralized holistic view of the moving parts – which needs to exist to ensure all the various actors within the program are operating coherently and toward a common outcome. It is the constantly evolving project plan that supports and feeds back into the overall program, driving the correct set of activities to achieve the strategic goals for the program.

What is the data dictionary and how does it come into play with the MDP? Earlier, I referred to the MDP as relating to “the intersection of the data governance strategy, standards and policies with the physical data” – well the data dictionary is what defines the scope and breadth of the physical data on a continual/moving basis – in fact we will dig into the specifics of the dictionary in the next instalment of this series of blog posts…

2 Comments | asset management, Data Governance, Data Quality | 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.

Leave a Comment » | asset management, client reporting, Data Governance, Data Quality, Solvency II | Tagged: , , , , , | Permalink
Posted by Ronan Brennan

Data Governance: Part 6 – Processes & Procedures

February 20, 2013

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

This is part 6 of a series of blogs on setting up a governance program for the effective management of investment product data - in this blog I will briefly consider the importance of process and procedure documentation.

In my previous blog in this series, I highlighted why standards and policies are important to the task at hand. Similarly, process and procedure are also key elements of an overall program of governance for your investment product data.

If you consider the statement of your policies and standards as the clear communication of the strategy for your program, the explicit documentation of the processes and associated procedures speaks directly to the execution of the strategy.

A process is an operational workflow description that takes into account starting states, end states, resources, participant teams and individuals who interact at each stage or state in the process – a procedure on the other hand is a simply detailed set of instructions for carrying out a specific task, or part of an overall process.

If you consider the example I used in the standards and policies post:

- Policy: ”the policy in our firm is that all client requests for data must be signed off by compliance“

- Standard: “all requests from clients for bespoke client reports should be logged in the ‘reports-requested’ database by the client service team. Only users with a security role of ‘compliance authority’ will have the capability to approve a request in this database and no report shall flow to a third-party without reference to an approval record in the ‘reports-requested’ database”. 

In this scenario we clearly need a detailed description of the process which starts with the event of a client request being received, ending with the delivery of the report to the client. In between the start and end points we need to clearly document the various valid paths or stages the process would need to pass through – for example, showing the logging of a request for compliance approval – this process document should call out the specific valid (and invalid) paths, stages, resources, systems and people interacting at each stage in the process. Typically, the document will contain a process flow diagram (there are tens of different diagram types) and related verbiage that explains in detail each step.

Within this process we will need a set of detailed procedure documents that explicitly call out the instructions for carrying out specific tasks – for example, you may decide you need to have a specific procedure for calling out how to log a client request into the compliance database.

From my own perspective, the definition of the processes that come under the remit of your program is a far more important task than the documentation of the procedures - get your processes down pat first, and only then start to consider the documentation of your procedures.

Next up I will explore the creation of a master data plan…

2 Comments | asset management, Data Governance, Data Quality | Tagged: , , | Permalink
Posted by Ronan Brennan

Data stewardship program: Quality booster, but a hard step for many

February 14, 2013

There is a really great article on the TechTarget SearchDataManagement site on data stewardship - Data stewardship program: Quality booster, but a hard step for many.

Leave a Comment » | Data Governance, Data management, Data Quality | Permalink
Posted by Ronan Brennan

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