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.


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.


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!


Model for Stewardship – Part 4 of “Setting up a governance program for effective management of investment product master data”

February 6, 2013

The stewardship model for managing client-facing product data in an investment management business is a critical factor for success in your governance program.

If we consider governance the ‘strategy’, then stewardship is the tactical execution of that strategy i.e. the walk to the talk!

Traditionally, the mantra was to drive accountability and stewardship as close to source as possible, and while this is a good general principle to follow, you need to be careful not to apply it in a dogmatic fashion.

Asset management businesses have many layers of complexity, and the challenges experienced in the back-office can be very different to those in the middle-office, and often a million miles away from the challenges in the front-office. It is because of this diversity that Enterprise Data Management systems within an asset management business are often supplemented with niche solutions that tackle domain specific challenges – witness the change in strategy from the singular data warehouse approach for the whole business to a multi-layered EDM strategy with best-of-breed providers interacting across many interfaces.

There is also a shift in stewardship models towards a multi-tiered approach. Traditionally the best practice was to drive accountability and ownership as close to the source as possible, but this has now evolved to a multi-tiered approach. The multi-tiered approach recognizes that as information flows through a publication process that starts in the back-office and manifests itself in a client communication in the front-office, the data undergoes a metamorphosis along the way. At each stage in the process, the data is manipulated and aggregated with other streams of data into “new data” with new meaning, and the ability to handle data quality exceptions becomes more specialized.

The following (simplistic) view of the various tiers illustrates how they are often implemented.

Tier Business   / Department Example of interaction with the Data Quality Management (DQM) Process  
1 IT This steward is typically responsible for exceptions relating to timely delivery, file format and syntactical issues with the content.
2 Functional Team e.g. performance This steward is typically responsible for issues relating to accuracy, completeness, consistency and staleness of data. They will be a subject matter expert for the data sub-domain in question. Note: there will be scenarios where the rule set/type is of a nature where it is not domain specific and is best handled by a product and/or portfolio specialist.
3-a Product Management Within your DQM you need to be able to direct product specific exceptions to an expert that is familiar with the product/range in question.
3-b Portfolio Manager As per the product management requirement – you will also need access to a portfolio specialist to deal with issues directly relating to the strategy and potentially specific intelligence on holdings / securities at the root of an exception.
4-a Marketing Operations Once data is brought together for publication you will need expertise to deal with layout / presentation exceptions.
4-b Compliance Finally, you may require input from a stewardship perspective for certain exceptions – for example, if you had a mandate breach, where specific knowledge is needed to move forward.

The next blog in this series will focus on the importance of establishing standards and policies.


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!


Technology in good hands

June 14, 2011

No matter how sophisticated the plane is, we trust the pilot to bring us safely to our destination. Don’t we? The same principle should apply to the management of your Product Master. No matter how good the technology, it is the people who will make data governance a success.

When selecting a partner in data management, do not underestimate the service element of their offering. Effective data governance and stewardship requires a cultural shift in the organisation that can only be nurtured through people. Technology has a key role to play but it is human interactions that will win the hearts and minds of the stakeholders and secure their buy-in. This is particularly true when data is coming from a wide range of sources with different attitudes towards data quality.

Your data quality management service provider should be focused solely on your industry. The better your service team understands your business and the business of your data sources, the sooner they will seamlessly integrate with your data supply chain and become part of the fabric of your organisation. This will generate trust and goodwill on the part of the data suppliers as they will see the data management service team as a partner that can help them improve the quality of their reporting. Knowing that the service team has an intimate understanding of their data will also generate respect and promote accountability on the provider side therefore driving them to achieve the data quality standards required by your organisation.

 Managing processes and data provider relationships is only part of the value that you should seek from your service team. Management reporting is another area that will benefit from a strong service provider with a deep understanding of your industry. The technology will generate all kinds of statistics on the reporting cycle such as data timeliness achievement rate, number of validation rules applied to the data, number of exceptions raised by such rules, number of data points resubmitted, etc. These are of little value unless analysed by a team of experts that can deliver to you meaningful content and recommendations that will empower your business to improve data quality on an ongoing basis. Your service team should report on the performance of your data providers in all four dimensions of data quality: timeliness, completeness, consistency and accuracy. You should be provided with trends for each of these Key Performance Indicators, benchmarks that you can measure against and clear recommendations on how you can exploit further the technology to drive data quality.

Technology combined with Service Excellence that is focused on your industry is the right combination to bring your data governance programme safely to where you want it to be.


Data Management in the Cloud..

May 27, 2011

To date there has been some reluctance amongst asset managers with respect to managing their security and product master data in the cloud, yet the same organizations are actively pushing their CRM data into the cloud. Why is this? Why does the sales side of the organization readily embrace such change when the investment operations teams are more cautious?

Security concerns cannot be the reason, even if they are the reason most often cited by investment operations teams who are not willing to embrace the cloud. For a financial services organization, there is nothing more sensitive than their clients’ personal details – so if you consider the number of firms actively using cloud-based CRM systems like Salesforce.com – this negates the security argument.

 But positioning of security and product master data in the cloud is just as sensitive. Of course, the security and product masters contain commercially sensitive data, but no more sensitive than client data found in many CRM cloud implementations. So we should agree that security concerns, while valid, are not the core reason we do not see the same level of enthusiasm.

Some would argue that the sales side of the organization are by their very nature risk takers, but the technology side of the business might argue that it is the quality of the offerings that is the only impediment to such decisions.

Cloud service providers, whether they are CRM or data management vendors, are all massively aware of the security risks posed by hosting sensitive third-party data – the fact is your data is probably more secure in a cloud provider’s environment than in your own, such is the focus on security.

Some believe that it is the complex relationship that often exists between IT and the sales and marketing units that is the root cause for so many sales units engaging the cloud. In the asset management world, the sales and marketing teams are often at the end of a long line of business units looking for strategic IT initiatives to be acted upon. To this end, sales and marketing teams have learned to become self-sufficient, which as an aside is probably also the root cause for the creation of the myriad of manual processes and Excel / Access-based data management initiatives found in the marketing and sales departments.

Since the investment operations units in asset management organizations have traditionally had a much closer relationship with the IT department, they have never felt the same need to explore alternative solutions. This is not to say that investment managers are not exploring data management in the cloud, just that they require a greater level of understanding of the advantages and disadvantages of such a venture. The providers of cloud-based technology and services themselves  have also had to up their game to sell the benefits.

So what merits does the cloud bring to an investment operations team? First of all, let’s debunk a myth – putting your data management solution in the cloud is not outsourcing, nor is it off-shoring. Cloud data management service providers generally engage in partnership-led operating models where they work hand-in-hand with the client towards a common goal, or they simply use the cloud provider as a technology platform in the same way they would engage with their own internal IT department.

 Working in the cloud means:

 1. Not having to worry about where you currently fit into your IT department’s strategic roadmap

 2.Your environment is managed by a team of professionals whose only goal is to ensure that  your environment is working and secure

 3. You are always on your vendor’s latest released platform version

 4. You have one less system to worry about in your BCP plans

 So what about the disadvantages? And how do you mitigate against any risks? What  should you be worried about?

1. What happens if you want to disengage from your cloud provider and take your process and data back in-house, or indeed have it managed by a different provider?

  •  This is something that needs to be considered carefully before engaging with any solution in the cloud. Before you engage, ensure that your contract and your SLA are watertight and replicate data back to your own data center so you always have a local copy at arms reach.

 2. How do you integrate the solution into your organization’s broader BCP plans?

  •  Ensure the vendor you choose to partner with has a fully documented and regularly tested business continuity plan that ensures your data is available according to your own stated ‘Recovery Time Objective’ and ‘Recovery Point Objective’ – then ensure your vendor runs the BCP tests with your involvement.

 3. How do you know your data is secure?

  •  You absolutely must do your full security due diligence – including externally-commissioned penetration testing.

 4. What about latency between your site and the cloud?

  •  Run full latency checks before the engagement and ensure latency is captured as a KPI for SLA measurement. Cloud providers are generally located at key Internet hub data centers to reduce latency concerns

5. How do you know the vendor will provide a good service?

  •  If you go down the partnership route, ensure you have an SLA that is considered an evolving document which is regularly reviewed and enhanced as your relationship develops. The SLA should set out the expected minimum service levels and the target service levels – with appropriate KPI measures identified for regular reporting.

 6. What if your vendor goes bust?

  •  Before any engagement, ensure your due diligence process includes a full financial review. In addition, insist on an escrow agreement to ensure you have access to the technology software in the event that the vendor is no longer financially viable.

White paper on Mastering Investment Product Data

May 26, 2011

Last October I posted a blog on “Mastering Investment Product Data” . At that point I was gathering my thoughts on the subject, but I have just published a white paper which explores the concept of master data management as it applies to investment products and why asset management organizations should have a product data management strategy. It also specifically introduces the concept of an investment product master and sets out to explain its value in the context of a data management strategy for an asset management firm. You can download the white paper here – or drop me an email and I will send you on a copy – do drop me a line with comments or feedback on it.


Video: my top tips on data governance

November 12, 2010

Information firewall and the product master

October 27, 2010

I had an interesting comment from an industry insider which referred back to earlier posts on Investment Product Master and Information Firewall – the question was “in an earlier post you raised the concept of the information firewall within an asset management firm, while more recently you raised a discussion on product master – where do the two intersect, or are they the same

In essence both are one and the same concept – when I was discussing information firewalls in the context of the marketing and client reporting departments within investment management companies I was describing a top level strategy that should  be applied to ensure consistent, accurate and timely product data  is communicated externally – a product master on the other hand is a concrete technology / process that is used to deliver on many of the strategic goals as laid out in my post on the information firewall.

While the deployment of an “information firewall” / “product master” alone should not in itself be seen as the defacto solution to solving client facing data quality problems, it is a key element of what is needed to setup a successful investment product data quality management process – remember technology is not a panacea to all data quality ills!


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