Who should be accountable for data quality?

The cardinality of a countable set - ex-mathematicians are allowed the occasional pun

linkedin CIO Magazine CIO Magazine forum

Asking the wrong question

Once more this post is inspired by a conversation on LinkedIn.com, this time the CIO Magazine forum and a thread entitled BI tool[s] can not deliver the expected results unless the company focuses on quality of data posted by Caroline Smith (normal caveat: you must be a member of LinkedIn.com and the group to view the actual thread).

The discussion included the predictable references to GIGO, but conversation then moved on to who has responsibility for data quality, IT or the business.

My view on how IT and The Business should be aligned

As regular readers of this column will know, I view this as an unhelpful distinction. My belief is that IT is a type of business department, with specific skills, but engaged in business work and, in this, essentially no different to say the sales department or the strategy department. Looking at the question through this prism, it becomes tautological. However, if we ignore my peccadillo about this issue, we could instead ask whether responsibility for data quality should reside in IT or not-IT (I will manfully resist the temptation to write ~IT or indeed IT’); with such a change, I accept that this is now a reasonable question.
 
 
Answering a modified version of the question

In information technology, telecommunications, and related fields, handshaking is an automated process of negotiation that dynamically sets parameters of a communications channel established between two entities before normal communication over the channel begins. It follows the physical establishment of the channel and precedes normal information transfer.

My basic answer is that both groups will bring specific skills to the party and a partnership approach is the one that is most likely to end in success. There are however some strong arguments for IT playing a pivotal role and my aim is to expand on these in the rest of this article.

The four pillars of improved data quality

Before I enumerate these, one thing that I think is very important is that data quality is seen as a broad issue that requires a broad approach to remedy it. I laid out what I see as the four pillars of improving data quality in an earlier post: Using BI to drive improvements in data quality. This previous article goes into much more detail about the elements of a successful data quality improvement programme and its title provides a big clue as to what I see as the fourth pillar. More on this later.
 
 
1. The change management angle

Again, as with virtually all IT projects, the aim of a data quality initiative is to drive different behaviours. This means that change management skills are just as important in these types projects as in the business intelligence work that they complement. This is a factor to consider when taking decisions about who takes the lead in looking to improve data quality; who amongst the available resources have established and honed change management skills? The best IT departments will have a number of individuals who fit this bill, if not-IT has them as well, then the organisation is spoilt for choice.
 
 
2. The pan-organisational angle

Elsewhere I have argued that BI adds greatest value when it is all-pervasive. The same observations apply to data quality. If we assume that an organisation has a number of divisions, each with their own systems (due to the nature of their business and maybe also history), but also maybe sharing some enterprise applications. While it would undeniably be beneficial for Division A to get their customer files in order, it would be of even greater value if all divisions did this at the same time and with a consistent purpose. This would allow the dealings of Customer X across all parts of the business to be calculated and analysed. It could also drive cross-selling opportunities in particular market segments.

While it is likely that a number of corporate staff of different sorts will have a very good understanding about the high-level operations of each of the divisions, it is at least probable that only IT staff (specifically those engaged in collating detailed data from each division for BI purposes) will have an in-depth understanding of how transactions and master data are stored in different ways across the enterprise. This knowledge is a by-product of running a best practice BI project and the collateral intellectual property built up can be of substantial business value.
 
 
3. The BI angle

It was this area that formed the backbone of the earlier data quality article that I referenced above. My thesis was that you could turn the good data quality => good BI relationship on its head and use the BI tool to drive data quality improvements. The key here was not to sanitise data problems, but instead to expose them, also leveraging standard BI functionality like drill through to allow people to identify what was causing an issue.

One of the most pernicious data quality issues is of the valid, but wrong entry. For example a transaction is allocated a category code of X, which is valid, but the business event demands the value Y. Sometimes it is possible to guard against this eventuality by business rules, e.g. Product A can only be sold by Business Unit W, but this will not be possible for all such data. A variant of this issue is data being entered in the wrong field. Having spent a while in the Insurance industry, it was not atypical for a policy number to be entered as a claim value for example. Sometimes there is no easy systematic way to detect this type of occurrence, but exposing issues in a well-designed BI system is one way of noticing odd figures and then – crucially – being able to determine what is causing them.
 
 
4. The IT character angle

I was searching round for a way to put this nicely and then realised that Jim Harris had done the job for me in naming his excellent Obsessive-Compulsive Data Quality blog (OCDQ Blog). I’m an IT person, I may have general management experience and a reasonable understanding of many parts of business, but I remain essentially an IT person. Before that, I was a Mathematician. People in both of those lines of work tend to have a certain reputation; to put it positively, the ability to focus extremely hard on something for long periods is a common characteristic.

  Aside: for the avoidance of doubt, as I pointed out in Pigeonholing – A tragedy, the fact that someone is good at the details does not necessarily preclude them from also excelling at seeing the big picture – in fact without a grasp on the details the danger of painting a Daliesque big picture is perhaps all too real!  

Improving data quality is one of the areas where this personality trait pays dividends. I’m sure that there are some marketing people out there who have relentless attention to detail and whose middle name is “thoroughness”, however I suspect there are rather less of them than among the ranks of my IT colleagues. While leadership from the pertinent parts of not-IT is very important, a lot of the hard yards are going to be done by IT people; therefore it makes sense if they have a degree of accountability in this area.
 
 
In closing

Much like most business projects, improving data quality is going to require a cross-functional approach to achieve its goals. While you often hear the platitudinous statement that “the business must be responsible for the quality of its own data”, this ostensible truism hides the fact that one of the best ways for not-IT to improve the quality of an organisation’s data is to get IT heavily involved in all aspects of this work.

IT for its part can leverage both its role as one of the supra-business unit departments and its knowledge of how business transactions are recorded and move from one system to another to become an effective champion of data quality.
 

Is outsourcing business intelligence a good idea?

Outsourcing
 
Introduction

The phrase IT outsourcing tends to provoke strong reactions. People either embrace it as a universal panacea capable of addressing any business problem, or recoil in horror at the very sound of it. Just for a change, I am somewhere in the middle; to me it is another tool at the disposal of businesses which can either be used wisely or poorly (much like IT itself you might say). As always the difference between the two extremes comes down to how well the project is led. Regardless of this, there are some benefits and some disbenefits associated with IT outsourcing and this article will explore the case for applying outsourcing to business intelligence.
 
 
Benefits of general IT outsourcing

Before I plunge into the world of BI, it is perhaps worth revisiting the general reasons for IT outsourcing, some of the most regularly quoted are as follows:

1. Reduction in costs

The provider of outsourcing (I’m just going to say “the provider” from now on to save typing) can carry out the same tasks at a cheaper cost to the client organisation (while still presumably turning a profit). There can be a number of bases for this; the one that generally comes to mind is wage arbitrage between different economies. However, it could also be that the provider has economies of scale; for instance, less people being required to run the consolidated data centres of several companies, than is required to run each separately. Also the provider may have staff who are more productive than at the client.

2. Ability to scale-up and scale down resource

The nature of business is such that sometimes all hands are required on the IT deck and at others there is spare capacity (this is something I address in my two articles on Problems associated with the IT cycle and Mitigating problems with the IT cycle). Now IT departments are normally quite good at finding (hopefully) useful things for people to do, but the issue remains. The promise of an outsourcing arrangement is that the tap of resource can be adjusted to meet demand without having to either fire and rehire staff, or rely on bringing in expensive contract resource. It is often hoped that this feature of outsourcing will also help to speed IT products to market.

3. Making IT provision a contractual relationship

An arrangement with a provider, depending on how the contract is drafted, can make the provision of IT services subject to penalties and claw-backs when service levels drop below those that have been agreed. While there are clearly some sanctions that can be applied to underperformance by internal IT departments, the financial benefit to the organisation is likely to be less (unless your CIO is a multi-billionaire of course). Companies are used to these contractual relationships, they are often the lifeblood of business, and it is a more familiar way of dealing with issues for them.

4. Access to skills

The nature of IT is that it does tend to evolve, sometimes quickly, sometimes slowly. For organisations this means keeping their IT people’s skills up to date though courses, or continually looking to bring people with new skills into an organisation (such people generally not being the cheapest). The idea with an outsourcing arrangement is that these issues become the headache of the provider, not the client. This area can be particularly pertinent when there is a technology change or a significant upgrade; these are times at which the prospect of being shot of IT worries may seem very attractive. The effort and cost of, as it were, upgrading your in-house IT staff may seem prohibitive in these circumstances.

5. Focus on core competencies

This has been a business mantra for many years, why should a company engaged in a wholly separate area of human endeavour want to become experts in building and supporting complex IT systems, when they can get a specialist organisation to do this for them? This moves towards the idea of a lean, or even virtual, organisation.

6. Failure of in-house IT

It is sad to have to add this item, but it is often the implicit (and sometimes even the explicit) driver of a desire to outsource. CEOs, COOs or CFOs may be so fed up with the performance of their IT people that they feel that surely someone else could not be worse. There is an adage that you don’t outsource a problem, but this is often honoured more in breech than observance.

I am sure that there are other advantages, claimed or real, for IT outsourcing, but the above list at least covers many of the normal arguments. At this stage a fully-balanced article would probably present arguments against IT outsourcing. However, my objective here is not to provide a critique of IT outsourcing in general, but to see whether the above benefits apply to business intelligence. Because of this, and I should stress purely for the purposes of this article, I am going to accept that all of the above gains are both realisable and desirable for general IT. There will therefore you will find no comments here about arbitrage (of its very nature) resulting in differentials of pricing closing over time.

The only benefit that I am going to rule out is the final one; addressing failed IT departments. Applying outsourcing in these cases is only likely to make things worse, and probably more expensive. Far better in my opinion to work out why IT is failing (most typically due to poor leadership it has to be said, see also my article: Some reasons why IT projects fail) and draw up plans for addressing this. If outsourcing is a strong element of this, then so be it, but thinking that it will resolve this type of issue is probably naive in most circumstances.

So, as always seems to be the case in these types of articles, we have five potential benefits against which to assess outsourcing BI. Before I look at each in turn, I wanted to make some general observations.
 
 
Things that are different about BI

The main fly in the ointment with respect to outsourcing business intelligence is the fact that good BI is reliant upon four things (see also BI implementations are like icebergs):

A. An in-depth understanding of business requirements, developed by close collaboration with a wide range of business managers. In particular, what is necessary is understanding what questions the business wants to ask and why (see Scaling-up Performance Management and Developing an international BI strategy)
B. An extensive appreciation of the data available in different business systems, its accuracy and how data in different places is related to each other.
C. Developing creative ways of transforming the available data into the required information and presenting this in an easy-to-understand and use manner.
D. A focus on change management that includes business-focussed marketing, training and follow-up to ensure that the work carried out in the first three areas results in actual business adoption and thereby the creation of value (see my collection of articles focussed on cultural transformation).

With the possible exception of item C., which is more technical, the above are best carried out in a symbiotic relationship with the business. Ideally what develops is a true IT / business hybrid team, where, though people have clear roles, the differences between these blur into each other. In turn, building thus type of team is predicated on developing strong relationships between the IT and business members and establishing high levels of trust and respect.

Also with item C., this is not precisely a stand-alone activity. It is one best carried out collaboratively by technically-aware business analysts and business-aware data analysts, ETL programmers and OLAP designers. Once again, distinctions blur somewhat during this work and a different type of hybrid team appears.

I have tried to illustrate the way that these tasks and teams should overlap in the following diagram.

bi-venn-w300

Clearly it is not impossible to achieve what I have described above in an outsourced environment, but it seems that it might be rather tougher to do this. One key point is that the type of skills that are necessary for success in BI are cross-over business / IT skills and these are generally less easy to buy off the shelf. Another is that the type of intellectual property that a BI team will build up (basically extensive knowledge of what makes the organisation tick) is precisely the sort that you would want to retain within an organisation.

I would suggest that if an organisation wants to outsource BI, then they should start that way. Once a BI team has gone through tasks A. to D. above then I can’t see how it would be cost-effective to subsequently outsource. The transfer of knowledge would take too long and be too costly.

To provide some context to this let me share some non-confidential details of a study I performed recently comparing the efficiency of a well-established BI team in a developed country with a less mature BI team in a lower-cost location. Rather than considering relative costs, I looked at relative productivity. A simple way to do this is to get quotes for carrying out a certain type of work from both teams (though I also applied some other techniques, which I won’t go into here). My main finding was that the ostensibly high cost team was more than twice as productive as the allegedly low-cost team. Just to be clear, if the “high-cost” team quoted $X for a piece of work, the “low-cost” team quoted over $2X,because they required much more resource and/or time to carry out the same work.

So, in what follows, I will assume that a decision is taken to outsource at the inception of a project. With this assumption and the previous background, let’s go back and look at the five benefits of outsourcing from the beginning.
 
 
Matching the benefits to BI

1. Reduction in costs

It will take external BI resource at least as long as internal BI resource to understand business requirements and available data. In fact internal staff probably have something of an advantage as they should already have an appreciation of what the organisation does and how IT systems support this. The external resource also has the disadvantage of it probably being more difficult for them to build business relationships, this can be exacerbated if there are personnel changes during the project; something that is perhaps more likely to happen with an external provider. If the provider is located in another country, then this raises even more challenges and inefficiencies (and leads to travel expense).

It will take an external BI team at least as long as an internal one to dig into the available data and how the various systems inter-relate. Again, having some familiarity with the existing systems’ landscape would be an advantage for an in-house team.

If an external team can get to the position where they understand the business needs and the available data really well in a reasonable period of time, then they could possibly have an advantage in the arena of transforming data into information. Something that may mitigate this however is that fact that most BI development is iterative and that a rolling set of prototypes needs to be reviewed closely with the business. This element introduces the same challenges as were apparent with defining business requirements above.

Similar arguments as were made about the business requirements phase apply to deployment and follow-up.

2. Ability to scale-up and scale down resource

While it may be possible (subject to contract) to scale-down resource with a provider (though perhaps tougher to get them back when you need them), scaling-up is just as hard as it is in-house at it means more staff at the provider going through the learning curve about the organisations business needs and data.

4. Access to skills

This is the crux of the matter. The skills in question are not Java programming (or even Cobol), they are business knowledge. ETL and OLAP skills are important, but only if they are applied by people who understand what they are doing and to what purpose. These skills are not just lying around in the market place; they are acquired through hard work and dedication.

3. Making IT provision a contractual relationship

Clearly this is a benefit of outsourcing. However, given that the contract is there for when things go awry, it is worth asking the question “are things more or less likely to go wrong with a provider?”

5. Focus on core competencies

While it is quite easy to argue that building e-commerce systems is not necessarily a core competency, good BI is about understanding what is necessary to best run the business. If that is not a core competency of any organisation, then I struggle to think of what would be.
 
 
Summary

My main argument is that BI is different to general IT projects (an assertion to which I will return in a forthcoming article). Having successfully run both, I am confident in this statement. I also think that you need different types of people with different skills in BI projects. These facts, plus the closeness of business / IT relationships which are necessary in the area mean that outsourcing is less likely to be effective. I am sure that an outsourcing arrangement can work well for some organisations in some circumstances, but I would argue strongly against it being best practise for most organisations most of the time.
 


 
After penning this article, a further problem with outsourcing business intelligence came to my mind; security. On part of most BI systems is a facility to analyse the organisation’s results. Ideally the BI system will have these figures in place very soon after the end of a financial closing. Such data is market sensitive and there may be concerns with trusting an external provider with both producing this and ensuring that it remains confidential until market announcements are made. I am not suggesting that providers are unethical, just that companies may not wish to take a chance in this area.
 
I should also credit a thread on the LinkedIn.com EPM – Business Intelligence group, which got me thinking about this area (as ever, you need to be a member of LinkedIn.com and the group to view this)