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.

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.

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.

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.

First up was a question on regulation - What regulations are impacting your firm’s operations the most in 2013?

Dodd-Frank topped the poll at 51%, which is high when you consider the respondents in the survey came from both sides of the Atlantic. Heretofore, many firms had indicated that Dodd-Frank was their chief concern – this was from a future impact perspective. The 2013 survey is indicating that this is now actually hitting home when it comes to day-to-day operations in 2013. If anything, I expect the impact of Dodd-Frank to grow and would expect next years survey to reflect that.

Another interesting finding in the survey is that 41% of the survey respondents have had an operational impact in 2013 with respect to preparing for FATCA, one can imagine what the impact will be once FATCA hits home fully. RDR was another notable hot spot  with 30% of respondents highlighting it as something that was having a real impact in 2013. UCITS IV and Solvency II polled 36% and 21% respectively indicating European regulation is still a hot topic – even if EIOPA and the EU council have delayed the full deployment of the Solvency II framework. Interestingly, I have heard on the ground that RMORSA in the US is starting to surface as an issue for institutional managers with demands for more holding level transparency, including demands for look-through in multilevel portfolios e.g. fund-of-fund like structures. It will be interesting to see if RMORSA surfaces as a key trend in 2014 and it is something I will be keeping an eye on as 2013 rolls on.

Steve Clark had some interesting take away’s, he stressed how it is important to have “different owners for different (data) types” or domains of responsibility. Specifically he referenced the need for IT ownership and involvement of technology teams in taking ownership for the movement and delivery of data from source to consumer, while at the same time requiring ownership from business for the semantics of the data. This ties in well with my own well published views on ownership and stewardship models. In my opinion it is not just about driving ownership to data source. Today in asset management firms I am firmly of the opinion that the ownership and stewardship models need to be multi-tiered. As data passes through the firm from back-, to middle- to front- office, it starts to snowball in terms of added meaning, enrichment, added value  and increased importance. Typical operating models I see today have owners and stewards ranging from; IT for data delivery to schedule and agreed formats, to data domain specialists for specific data domains across many products, to product specialists that work across multiple data domains for single products, on to front-office specialists such as portfolio managers, or indeed distribution IT specialists involved in delivering data to market. So in effect ownership and accountability needs to follow the nested layers within the back to front publication cycles that so often permeate asset management firms.

Phil Tattersall had some interesting points too – he stressed the importance of “establishing the concept of data ownership” early, and how important it is in the overall scheme of setting up an effective governance structure. Another interesting anecdote from Phil was “ownership helps shift the attitude towards data management” – this was specifically with reference to getting C-Level engagement. My own view here is that ownership top-down is as important as bottom-up, i.e. you cannot neglect one to the detriment of the other. You cannot gain any traction in driving ownership if you are not working it top-down and gaining C-level buy-in and engagement, at the same time you need to be working the process bottom-up to reach the parts of the organization that are handling and managing data as their one and only focus.

I think it is fair to say all the  panellists agreed that getting ownership and accountability is one of the toughest tasks in any program of governance you are trying to gain traction with. The key problem being finding people who WANT to own data. A couple of pointers came up – it is your job to sell the reasons why ownership will help drive the program forward, you need to find those people in your organization that eat, breath and live data and do not fear ownership – but most importantly you need to break down the perceived fears people associate with data ownership!

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.

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).

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!

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!