Survey results 2013: Performance aside, client service and reputation deliver mandate success

May 30, 2013

According to the recent MoneyMate Data Management 2013 survey when you take away the impact of strong past performance, success in winning institutional mandates is driven by client service and the reputation of your firm!

The respondents to the survey very clearly identified client service and reputation as being critical drivers in winning new mandates – 78% of respondents indicated reputation as something that is important, while 71% indicated that client service was also important.

Interestingly, fees registered very low at 74%, in terms of respondents view on how important fees were with respect to winning more mandates. Sales teams will not be happy to hear that the sales process and presentation registered poorly, with 59% indicating this is not an important factor in winning new business.

It was interesting to see client service poll so highly – it correlates well with what I see on the ground, with many firms investing heavily in their after sales client service and client reporting functions. It is no surprise either that reputation was ranked so highly – the asset management world is a small pond and reputation can make and break a business very quickly, hence firms are always working on two fronts – carefully and strategically managing their brand perception and awareness, while operationally working to minimize the firms exposure to reputational risk – which often leads to great investment in client facing data initiatives.

Leave a Comment » | asset management, client reporting, Data management, Data Quality, Survey | Tagged: , , , , | Permalink
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!

1 Comment | Data Quality | 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

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

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.

Leave a Comment » | asset management, Data Quality, performance measurement | Tagged: , , , | Permalink
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

Buy or Build?

August 22, 2011

In the world where asset management technology and data quality management departments intersect, a perennial question is raised vis-a-vis implementing technology frameworks that support the data quality management process, build and manage various master data systems (e.g. security master or product master) – should we partner with a technology vendor with a best of breed solution, or should we just build it ourselves?

Like many such perennials there is no right or wrong answer. As a technology vendor, I often argue that something like data quality management is not actually the core competency of an asset manager and rather than figuring out how to manage their data, they should focus on their investment product strategies, growing their customers etc. I do sometimes wonder though if some of the asset managers out there are financial technology companies with an asset management firm bolted on or just plain vanilla asset managers. There are some managers that have actually spun off technology companies themselves based on internal developments.

My own experience is that there really are just three camps:

 1. Build it ourselves unless there is an ultra compelling reason not to;

 2. Apply a balanced decision-making process to weigh up the pros/cons of doing an internal build versus finding a vendor to work with;

 3. Use a vendor unless there is an ultra compelling reason not to.

Are any of the camps more correct than the other? Not really – they have their reasons for the strategies they employ. There are ultra successful examples of all 3 company types – so adopting one or the other strategy does not seem to have held anyone back, but that all being said – you would have to perceive that those in camp #2 have a more pragmatic view on life.

Camp #1 companies tend to be IT-led organizations, where technology is a key driver in all aspects of what the company does and so is at the forefront of all strategic decisions – hence the need to retain internal (and full) control of all technology in use. They would normally be fundamentally opposed to outsourcing any aspect of their business.

Camp #3 companies tend to be “IT-deniers” – they are obviously the complete polar opposite of camp #1 companies and tend to be 100% led by business. The IT department is there to support and maintain systems and does not form part of the strategic fabric of the organization. One of the goals will be to maintain a low IT footprint and outsource wherever possible.

Camp #2 is the hybrid – they recognize that technology is important, but are not beholden to their own IT department. They are of the view that if there is a specialist vendor out there that has specific domain expertise and has built the same solution/product over and over again for many of their competitors, then this company will deliver a best in class solution – they retain their own IT resources for delivery of standard solutions for which an external vendor adds no specific value, or  for areas where they believe they have unique USP.

In my opinion, Camp #1 is made up of about 30% of the market, Camp #2 would account for 50% and Camp #3 would account for 20%.

The pragmatists amongst us recognize that camp #2 are probably the most balanced of organizations, but these companies really do struggle with the challenge of identifying what they should and should not outsource… it may depend on the size of the potential project or the expertise required.. or the business may influence a final decision.

Of course once a decision is made to use an external vendor, next choice is “local-install or cloud”?

1 Comment | Cloud computing, Data management, Data Quality, Outsource, service provider, Technology | Tagged: , , , , , , , | Permalink
Posted by Ronan Brennan

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.

 

Leave a Comment » | Cloud computing, Data management, Data Quality, Event, FINRA, Outsource, Regulation, Technology | Tagged: , , , , , , , , , , , , , , , , , | Permalink
Posted by Ronan Brennan

Data Management & Client Service

June 23, 2011

There has been a building murmur of conversation of late in the asset management community about client service, specifically with regard to the impact of data management of all things on this. It is fair to say that given the regulators’ continued defence of the investor and their insistence on the fair treatment of customers that the necessity to communicate timely, accurate, and consistent information to existing and prospective clients is growing by the day. This combined with the increasing demands of the end investor for a more up-to-date and frequently updated, broader range of data means that today’s asset managers need to sort out their information “plumbing” or face being left behind by their competitors (and their customers).

 Three years ago, buy-side firms were looking to embark on data management projects to improve efficiency and remove silos and manual processes. While these drivers are still valid, more and more data management projects today are driven by a desire to improve the quality of information delivered to the front-office. In fact, a recent asset management survey confirmed this as the number one operational focus for most buy-side firms. In addition to this, asset managers looking to achieve best in class client service or break into new customer segments and/or markets commonly recognise the value of a solid product master as the base platform that can be leveraged in order to achieve all of these strategic goals.

 A product master puts an asset manager in control of the information about their funds and accounts. Once all of the data controls are in place to centralise and clean the product information, the product master can be leveraged across the enterprise to ensure that all consumers of the information (internal and external) can have full confidence in the timeliness, accuracy, and consistency of the data they are viewing. It also provides auditors and regulators with the evidence that the asset manager has recognised the importance of this data and has put systematic controls in place to address it.

While the concept of a product master may be relatively new… it is gaining momentum and we’re hearing more and more about it in the press, at events, and directly from the industry. Watch this space!

Leave a Comment » | Data management, Data Quality, product master, Regulation | Tagged: , , , , , , , , , , , , , , , , | Permalink
Posted by Conor Smyth

Regulation Survey Results: Part 2

May 10, 2011

This piece is a follow-up to my most recent blog posting on the results of our recent “data management and regulation” survey. I think the survey gave us a good chance to get a snapshot of what people are concerned about in terms of upcoming regulation and how it will impact their business processes.

The last posting was getting very long so I only posted some of the results. I finished up talking about the regulations that people were most concerned about in North America. The third question in our survey concerned European regulation and the topical issues there.

The question was: “in Europe, which of the following regulatory discussions concerns your organization most: RDR in the UK, UCITS IV & KIID for cross border funds, AIFM for hedge funds, the rise of Newcits, the upcoming UCITS V directive or the changeover from CESR to ESMA?”

 

Not surprisingly nearly two-thirds of all respondents indicated the big discussion point in their firm was UCITS IV and KIID – if anything the surprise is that it did not have a higher response. Maybe this is because many firms have focussed so heavily on preparation that they are very confident they are well placed to deal with the upcoming requirements that UCITS IV brings as well as the ability and readiness to initiate publication of KIID documents.

Nearly 30% of respondents indicated the AIFM directive, and an additional 17% indicated Newcits were items of discussion and concern in their firm – a clear indication that alternative strategies and the hedge fund industry are key industry focus points in the years ahead.

Somewhat surprising though is the fact that UCITS V is already a discussion point for 17% of respondents – the belief here is that the depositary structures that facilitated Madoff and manager remuneration are going to be addressed – these topics will ensure this is a hot topic of conversation for years to come.

Finally 17% of respondents indicated Solvency II was a key discussion point – this will become a key topic of conversation for any asset manager that has mandates emanating from the Life and Pension sectors – the demands on risk control, asset liability, ability to be transparent and report accordingly are all hot topics in the Sol 2 world.

Next up was the question: “How prepared is your organization for dealing with upcoming changes in regulation - are you totally prepared, somewhat prepared or not at all prepared?”

 

Thankfully the vast majority of respondents are at least somewhat prepared, but surprisingly only 15% or so indicated they believed their firms were totally prepared.

So it seems like the adage – a lot done, but more to do – seems prevalent here. Most firms are aware of what needs to be done, they have plans in place, but in some cases these plans have not been fully executed.

More reassuringly, we see that only 3% of respondents indicated that their firm is not prepared at all for the changes in regulation that are coming down the tracks. Overall, the responses to this question indicate that firms are struggling to prepare for the enforcement of regulatory reform, particularly in terms of their product data.

The next question in the survey was: “Do you think that new regulatory reporting requirements will change your organization’s attitude towards data management?”

The responses to this question were most interesting…… 

Nearly one-quarter (23%) of all respondents indicated that recent regulatory changes will force their firm to totally change their processes for getting their product data into the market, while only 12% of the respondents indicated that their existing processes were fully supportable, automated and left a full statement of record to facilitate audit.

The greatest number of respondents (56%) indicated that their firms only needed to make some amendments to the existing processes in their firm, for the management of product data.

The response to this question aligns with previous responses – where it would appear that in most cases firms know what they need to do, but have not fully executed their plans.

Overall, the responses to the survey questions indicate that firms are struggling to prepare for the enforcement of regulatory reform, particularly in terms of their product data.

The final question in the survey was: “What are the biggest challenges in getting your product data to market – are they manual processes, timeliness, cost, accuracy or something else?”

 The big shock here is that just over half (53%) of respondents indicated that the biggest obstacle to getting their product data to market is manual processes, with a similar number indicating that timeliness was an impediment. It is not surprising that 40% indicated cost was a problem – this most likely results from having issues with manual processes.

Of most concern though should be the more than one-third of respondents that indicated accuracy was a problem for them. This is worrying indeed when you consider the considerable focus that has been placed on data management in recent years and the vast amount of IT dollars that have been spent trying to address the problem.

Finally the 6% other – commented that ability to maintain a statement of record or auditable trail of ownership was their greatest challenge – which is interesting – the inference we derive here is that these firms have automated processes and reasonable levels of accuracy, but proving this and showing a demonstrable audit trail to auditors and regulators alike is a particular concern.

So that’s it on our survey results – I thought they were worth sharing with a wider audience and it might give you an insight into how people are preparing for upcoming regulation.

Leave a Comment » | Data management, Data Quality, Dodd Frank, KIID, Regulation, Survey | Tagged: , , , , , , , , , , , , , , , , | Permalink
Posted by Ronan Brennan

« Previous Entries
Follow

Get every new post delivered to your Inbox.

Join 26 other followers