Client Reporting – Data Management: The Critical Issues

November 29, 2010

I recently  participated on a panel discussion at the Osney Media’s Client Reporting Conference in London that was chaired by Peter Bambrough a management consultant at Citisoft.  The topic for the panel was “Data Management: The Critical Issues”,  and on the panel I was joined by:

  • Philip Keeler, Head of Operations IT, Hermes Fund Investors Ltd
  • Bob Simon, Senior Director of Business Development, CorrectNet

The first question that was presented to the panel was “Why do data management projects go wrong?”

My own view point here is that projects I have seen fail were nearly all down to a lack of clear data governance, stewardship and generally poor communication.  In order for a data management project to work there has to be a common understanding of the issues at play. Communication is key here, especially between the middle and back office –  all teams have to be speaking the same language.  Another issue that the data management projects face is the lack of understanding from senior management. As Philip Keeler of Hermes said, “there needs to be a holistic view within the company, senior managers need to be aware of the issues and the implementation processes involved”.

Also when implementing a data management project is it important to break down the project into manageable chunks, make realistic deadlines and achievable goals, this in turn will reduce the risk and make the project less likely to fail.

Some of the interesting points that came out of the discussion with respect to running successful data management projects were:

  • Silo approach is only helpful if you have complete view of the landscape
  • Warehouse approach to everything is always going to lead to failure as they take too long to implement and the landscape invariably changes before the project finishes
  • Better model may to  have data warehouses feeding data hubs, from which business unit ‘fit-for-purpose’ data marts are published
  • Communication both top-down and bottom-up is critical
  • Senior management buy-in to project is essential
  • Multi-tiered stewardship
  • Governance and stewardship operating hand-in-hand
  • Clear understanding of current cost exposure versus the new target state

Another question put to the panel was in relation to getting the right people to work with the data – who are the right people?  We know that it is not a job for marketing departments or indeed asset managers. Organizations need to avoid the “Just in Time” data management operating model where a team of client reporting or marketing execs scrub and cleanse the data just prior to publication.  This is a critical job and the right people need to be there to ensure that it is being carried out correctly.   So who are the right people for the role? It was agreed unanimously that you need to adopt a multi-tiered approach to stewardship – you need stewards operating at the data source level – data analysts – that are comfortable dealing with the low level source oriented quality issues, you need product specialists that are comfortable looking at the data from a product/strategy perspective and you need business analysts working in the front-line teams (client reporting / sales / marketing) that are comfortable looking at the data from a reporting / presentation perspective.

The general consensus among the panel was that communication, understanding the data issues and ensuring the correct people are managing the data are all important elements in the fight against combating data management issues.

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Posted by Ronan Brennan

Data Damnation – how do I get message across that there is a problem?

May 4, 2010

I spoke to a really frustrated “Client Reporting Data Manager” at the FSO “Investment Management Industry Transformation and Outsourcing Strategies Forum” in London on April 20th last.

Their issue was that their institutional client reporting team spent more time fixing up masses of data prior to publication than they do actually on reporting to clients.

I have referred to this concept on many occasions as “just-in-time” data management – the just-in-time data management operating model can be a disaster and I would not recommend it as a modus operandi.

So how do you go about getting out of the state of “data damnation”?

First of all you need to drop the operations hat and don the sales hat – because you clearly have an issue and you are going to have get buy-in from top-down and bottom-up that the issue should be addressed.

Next question – how do I go about getting buy-in that there is a problem that needs to be solved? Well before you start talking about your problem you need to build a business case – don’t waste valuable C-level time bringing a problem to the table without bringing the solution. Remember at C-level many of the actors are not aware there is an issue – using the duck pond analogy – what they see is a duck swimming across the pond gracefully i.e. they believe that the company’s client-facing data is of good quality and is timely, accurate and consistent – what they do not realize or see is that beneath the surface the duck’s legs are paddling furiously i.e. the process of producing high quality data is enormously manual, non-systematic, high-risk and resource intensive.

So…

1. Build a solid business case that highlights the upsides that will be delivered by moving away from the ‘just-in-time’ model to a model that is structured around governance, de-centralized ownership, accountability, oversight and transparency. Examples of upside sells are:

  • Better client facing data will mean you have happier, “stickier” clients. Your sales/distribution network will place greater trust in your data and you will ensure that there are no outflows, loss of mandates etc due to poor quality data being received by your clients. Identify clients / mandates you have lost due to poor service or bad quality data – identify the exact financial costs to your company.
  • Identify the potential upside in new mandates and inflows as a result of brand recognition in the market for having excellent high quality data
  • Identify how your own team’s ‘output’ will improve – get specific on the activities you will be able to devote more time to as a result of not having to chase your tail, fixing data at the last minute.

2. Outline the risks that will be mitigated by moving to the new target model – you need to don the insurance sales person’s hat here. You should talk about the following:

  • Identify the cost of the accident which is waiting to happen
  • Identify the probability of the accident happening if no action is taken
  • Put an actual value on the following: the damage to your brand and reputation – what cost would be involved from a marketing perspective to dampen negative PR as a result of the accident happening? Some would argue your brand and reputation are priceless – that is because the PR cost to put it right runs into millions and tens of millions od dollars in many cases. What impact would it have on your AUM base – note the 400m USD outflows from AXA Rosenberg recently due to negative news – this was reported on FUNDfire on April 29th 2010 – “AXA Rosenberg has been fired from a $400 million enhanced large-cap equity mandate by theFlorida State Board of Administration...“
  • Put a value on the cost of a fine from the regulator – remember the fines are now commonly a 7  figure value
  • What impact would a regulator fine have on your brand?

3. Outline the costs that will be saved and include:

  • How many FTEs will be reduced / re-allocated as a result of your new operating model?
  • How will your vendor relationships change? – outline how it will be simpler to move particular vendors once you have a clean data interface – typically vendors who supply services such as client reporting, automated fact sheets, micro-sites and compliance have deeply-embedded, difficult to shift relationships – they know this and charge a premium as a result.

If you do not have a strong data governance organization permeating your company, set about introducing one – this really does require strong “C-level” leadership and drive – many companies adopt the ‘Chief Data Officer’ role, or Data Tzar, while others employ a broader steering committee approach where senior data stewards oversee the data governance at a company level. Each approach has its own merits and typically the organization’s culture will determine the best fit.

Identify data stewards who will take ownership of data at the ‘origination’ of that data i.e. at the earliest point in your structure – i.e. where the data enters your structure or is created within your structure. This is the aspect of the ‘sales process’ that is bottom-up. This will be a thankless, fruitless task if you have not executed the top-down sales process.

I will follow up soon with a post that deals with what the target operating model for client-facing data should look like…

As an aside, at the same FSO event, I was the moderator on the “Thought Leadership: Best Practices for Data Management, Performance Measurement and Client Reporting” panel.

The background theme to the panel discussion centered on the rapid technological advancements and evolving operational initiatives that have brought into focus the importance of centralized data management. These changes also highlight the need to translate mundane data into meaningful strategies and analysis to enhance client reporting. The panelists’ goal was to debate the pressures of effective data management and the role of shared industry data utilities in the financial services sector. The discussion was also to focus on the latest technological advancements that support valuable data management, improved client reporting and servicing and a sound performance measurement framework.

The specific topics discussed were:

  1. Drivers for re-architecting data management post the financial crisis Read the rest of this entry »

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Posted by Ronan Brennan

New axiom for investment data management: “Information Firewall”

April 13, 2010

For too long asset managers have been focusing on their data warehouse initiatives, often spending many years building them out, by which time the company in question has moved on and acquired other businesses with new data silos, warehouses, databases, data marts – you get the message – just as one data warehouse project finishes a new ‘enterprise data warehouse’ project is needed to bring the latest silos under the same umbrella. So the “information stores” which a business uses to deliver data to the external world are constantly changing – which leads to a lot of client and vendor related unhappiness.

At the same time the client reporting teams have been working away to build a client reporting infrastructure to deliver bespoke glossy reports, often without taking into account that a client report that is generated with poor quality data, is still a poor report, even if it is completely tailored to the client needs and is very flashy and glossy to the eye of the (be)holder. Client reporting vendors are left to carry the can to a large extent – the asset manager expects the client reporting vendor to clean the data up, while the client reporting vendor expects the asset manager to deliver clean data !

The marketing teams are busy to trying to automate the production of the product fact sheets – again the fact sheet automation vendor community place a strong reliance on good quality data being submitted to the process, while the asset managers to a large extent assume the data is good quality, why else would they allow it enter an automation process?

Micro-site content publication suffers from the same ills as the fact sheet automation process, except it is even more acute as the data is expected to be updated daily, and not monthly or quarterly.

The legal and compliance teams are at the same time busy working with their financial printers getting the simplified (or summary) prospectus, KID (if you’re in UCIT IV prep mode), annual/semi-annual reports and any other regulatory product documentation automated. Again though, the financial printers expect that they receive clean and timely data.

So what we have is an industry that unwittingly expects it’s vendors to work with poor quality data to generate high quality output – i.e. it is set up to fail miserably when it comes to getting good quality (= timely, consistent and accurate) data to the end investor, be that via a custom client report, a product fact sheet update, a web page view or a regulatory document.

If asset managers applied the same principles that network engineers apply to physical networks to protect the integrity of their inner network, by applying an “information firewall” that presented all client facing data to each of the vendors that required access to that data they would have; (a) a much happier vendor community and (b) superior content being presented to their end clients.

So an information firewall for investment product data should do some or all of the following; Read the rest of this entry »

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Posted by Ronan Brennan

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