20 Risks that Beset Data Programmes

Data Programme Risks

This article draws extensively on elements of the framework I use to both highlight and manage risks on data programmes. It has its genesis in work that I did early in 2012 (but draws on experience from the years before this). I have tried to refresh the content since then to reflect new thinking and new developments in the data arena.
 
 
Introduction

What are my motivations in publishing this article? Well I have both designed and implemented data and information programmes for over 17 years. In the majority of cases my programme work has been a case of executing a data strategy that I had developed myself [1]. While I have generally been able to steer these programmes to a successful outcome [2], there have been both bumps in the road and the occasional blind alley, requiring a U-turn and another direction to be selected. I have also been able to observe data programmes that ran in parallel to mine in different parts of various organisations. Finally, I have often been asked to come in and address issues with an existing data programme; something that appears to happens all too often. In short I have seen a lot of what works and what does not work. Having also run other types of programmes [3], I can also attest to data programmes being different. Failure to recognise this difference and thus approaching a data programme just like any other piece of work is one major cause of issues [4].

Before I get into my list proper, I wanted to pause to highlight a further couple of mistakes that I have seen made more than once; ones that are more generic in nature and thus don’t appear on my list of 20 risks. The first is to assume that the way that an organisation’s data is controlled and leveraged can be improved in a sustainable way by just kicking off a programme. What is more important in my experience is to establish a data function, which will then help with both the governance and exploitation of data. This data function, ideally sitting under a CDO, will of course want to initiate a range of projects, from improving data quality, to sprucing up reporting, to establishing better analytical capabilities. Best practice is to gather these activities into a programme, but things work best if the data function is established first, owns such a programme and actively partakes in its execution.

Data is for life...

As well as the issue of ongoing versus transitory accountability for data and the undoubted damage that poorly coordinated change programmes can inflict on data assets, another driver for first establishing a data function is that data needs will always be there. On the governance side, new systems will be built, bought and integrated, bringing new data challenges. On the analytical side, there will always be new questions to be answered, or old ones to be reevaluated. While data-centric efforts will generate many projects with start and end dates, the broad stream of data work continues on in a way that, for example, the implementation of a new B2C capability does not.

The second is to believe that you will add lasting value by outsourcing anything but targeted elements of your data programme. This is not to say that there is no place for such arrangements, which I have used myself many times, just that one of the lasting benefits of gimlet-like focus on data is the IP that is built up in the data team; IP that in my experience can be leveraged in many different and beneficial ways, becoming a major asset to the organisation [5].

Having made these introductory comments, let’s get on to the main list, which is divided into broadly chronological sections, relating to stages of the programme. The 10 risks which I believe are either most likely to materialise, or which will probably have the greatest impact are highlighted in pale yellow.
 
 
Up-front Risks

In the beginning

Risk Potential Impact
1. Not appreciating the size of work for both business and technology resources. Team is set up to fail – it is neither responsive enough to business needs (resulting in yet more “unofficial” repositories and additional fragmentation), nor is appropriate progress is made on its central objective.
2. Not establishing a dedicated team. The team never escapes from “the day job” or legacy / BAU issues; the past prevents the future from being built.
3. Not establishing a unified and collaborative team. Team is plagued by people pursuing their own agendas and trashing other people’s approaches, this consumes management time on non-value-added activities, leads to infighting and dissipates energy.
4. Staff lack skills and prior experience of data programmes. Time spent educating people rather than getting on with work. Sub-optimal functionality, slippages, later performance problems, higher ongoing support costs.
5. Not establishing an appropriate management / governance structure. Programme is not aligned with business needs, is not able to get necessary time with business users and cannot negotiate the inevitable obstacles that block its way. As a result, the programme gets “stuck in the mud”.
6. Failing to recognise ongoing local needs when centralising. Local business units do not have their pressing needs attended to and so lose confidence in the programme and instead go their own way. This leads to duplication of effort, increased costs and likely programme failure.

With risk 2 an analogy is trying to build a house in your spare time. If work can only be done in evenings or at the weekend, then this is going to take a long time. Nevertheless organisations too frequently expect data programmes to be absorbed in existing headcount and fitted in between people’s day jobs.

We can we extend the building metaphor to cover risk 4. If you are going to build your own house, it would help that you understand carpentry, plumbing, electricals and brick-laying and also have a grasp on the design fundamentals of how to create a structure that will withstand wind rain and snow. Too often companies embark on data programmes with staff who have a bit of a background in reporting or some related area and with managers who have never been involved in a data programme before. This is clearly a recipe for disaster.

Risk 5 reminds us that governance is also important – both to ensure that the programme stays focussed on business needs and also to help the team to negotiate the inevitable obstacles. This comes back to a successful data programme needing to be more than just a technology project.
 
 
Programme Execution Risks

Programme execution

Risk Potential Impact
7. Poor programme management. The programme loses direction. Time is expended on non-core issues. Milestones are missed. Expenditure escalates beyond budget.
8. Poor programme communication. Stakeholders have no idea what is happening [6]. The programme is viewed as out of touch / not pertinent to business issues. Steering does not understand what is being done or why. Prospective users have no interest in the programme.
9. Big Bang approach. Too much time goes by without any value being created. The eventual Big Bang is instead a damp squib. Large sums of money are spent without any benefits.
10. Endless search for the perfect solution / adherence to overly theoretical approaches. Programme constantly polishes rocks rather than delivering. Data models reflect academic purity rather than real-world performance and maintenance needs.
11. Lack of focus on interim deliverables. Business units become frustrated and seek alternative ways to meet their pressing needs. This leads to greater fragmentation and reputational damage to programme.
12. Insufficient time spent understanding source system data and how data is transformed as it flows between systems. Data capabilities that do not reflect business transactions with fidelity. There is inconsistency with reports directly drawn from source systems. Reconciliation issues arise (see next point).
13. Poor reconciliation. If analytical capabilities do not tell a consistent story, they will not be credible and will not be used.
14. Strong approach to data quality. Data facilities are seen as inaccurate because of poor data going into them. Data facilities do not match actual business events due to either massaging of data or exclusion of transactions with invalid attributes.

Probably the single most common cause of failure with data programmes – and indeed or ERP projects and acquisitions and any other type of complex endeavour – is risk 7, poor programme management. Not only do programme managers have to be competent, they should also be steeped in data matters and have a good grasp of the factors that differentiate data programmes from more general work.

Relating to the other highlighted risks in this section, the programme could spend two years doing work without surfacing anything much and then, when they do make their first delivery, this is a dismal failure. In the same vein, exclusive focus on strategic capabilities could prevent attention being paid to pressing business needs. At the other end of the spectrum, interim deliveries could spiral out of control, consuming all of the data team’s time and meaning that the strategic objective is never reached. A better approach is that targeted and prioritised interims help to address pressing business needs, but also inform more strategic work. From the other perspective, progress on strategic work-streams should be leveraged whenever it can be, perhaps in less functional manners that the eventual solution, but good enough and also helping to make sure that the final deliveries are spot on [7].
 
 
User Requirement Risks

Dear Santa

Risk Potential Impact
15. Not enough up-front focus on understanding key business decisions and the information necessary to take them. Analytic capabilities do not focus on what people want or need, leading to poor adoption and benefits not being achieved.
16. In the absence of the above, the programme becoming a technology-driven one. The business gets what IT or Change think that they need, not what is actually needed. There is more focus on shiny toys than on actionable information. The programme forgets the needs of its customers.
17. A focus on replicating what the organisation already has but in better tools, rather than creating what it wants. Beautiful data visualisations that tell you close to nothing. Long lists of existing reports with their fields cross-referenced to each other and a new solution that is essentially the lowest common denominator of what is already in place; a step backwards.

The other most common reasons for data programme failure is a lack of focus on user needs and insufficient time spent with business people to ensure that systems reflect their requirements [8].
 
 
Integration Risk

Lego

Risk Potential Impact
18. Lack of leverage of new data capabilities in front-end / digital systems. These systems are less effective. The data team is jealous about its capabilities being the only way that users should get information, rather than adopting a more pragmatic and value-added approach.

It is important for the data team to realise that their work, however important, is just one part of driving a business forward. Opportunities to improve other system facilities by the leverage of new data structures should be taken wherever possible.
 
 
Deployment Risks

Education

Risk Potential Impact
19. Education is an afterthought, training is technology- rather than business-focused. People neither understand the capabilities of new analytical tools, nor how to use them to derive business value. Again this leads to poor adoption and little return on investment.
20. Declaring success after initial implementation and training. Without continuing to water the immature roots, the plant withers. Early adoption rates fall and people return to how they were getting information pre-launch. This means that the benefits of the programme not realised.

Finally excellent technical work needs to be complemented with equal attention to business-focussed education, training using real-life scenarios and assiduous follow up. These things will make or break the programme [9].
 
 
Summary.

Of course I don’t claim that the above list is exhaustive. You could successfully mitigate all of the above risks on your data programme, but still get sunk by some other unforeseen problem arising. There is a need to be flexible and to adapt to both events and how your organisation operates; there are no guarantees and no foolproof recipes for success [10].

My recommendation to data professionals is to develop your own approach to risk management based on your own experience, your own style and the culture within which you are operating. If just a few of the items on my list of risks can be usefully amalgamated into this, then I will feel that this article has served its purpose. If you are embarking on a data programme, maybe your first one, then be warned that these are hard and your reserves of perseverance will be tested. I’d suggest leveraging whatever tools you can find in trying to forge ahead.

It is also maybe worth noting that, somewhat contrary to my point that data programmes are different, a few of the risks that I highlight above could be tweaked to apply to more general programmes as well. Hopefully the things that I have learnt over the last couple of decades of running data programmes will be something that can be of assistance to you in your own work.
 


 
Notes

 
[1]
 
For my thoughts on developing data (or interchangeably) information strategies see:

  1. Forming an Information Strategy: Part I – General Strategy
  2. Forming an Information Strategy: Part II – Situational Analysis and
  3. Forming an Information Strategy: Part III – Completing the Strategy

or the CliffsNotes versions of these on LinkedIn:

  1. Information Strategy: 1) General Strategy
  2. Information Strategy: 2) Situational Analysis and
  3. Information Strategy: 3) Completing the Strategy
 
[2]
 
Indeed sometimes an award-winning one.
 
[3]
 
An abridged list would include:

  • ERP design, development and implementation
  • ERP selection and implementation
  • CRM design, development and implementation
  • CRM selection and implementation
  • Integration of acquired companies
  • Outsourcing of systems maintenance and support
 
[4]
 
For an examination of this area you can start with A more appropriate metaphor for Business Intelligence projects. While written back in 2008-9 the content of this article is as pertinent today as it was back then.
 
[5]
 
I cover this area in greater detail in Is outsourcing business intelligence a good idea?
 
[6]
 
Stakeholder

Probably a bad idea to make this stakeholder unhappy (see also Themes from a Chief Data Officer Forum – the 180 day perspective, note [3]).

 
[7]
 
See Vision vs Pragmatism, Holistic vs Incremental approaches to BI and Tactical Meandering for further background on this area.
 
[8]
 
This area is treated in the strategy articles appearing in note [1] above. In addition, some potential approaches to elements of effective requirements gathering are presented in Scaling-up Performance Management and Developing an international BI strategy.
 
[9]
 
Of pertinence here is my trilogy on the cultural transformation aspects of information programmes:

  1. Marketing Change
  2. Education and cultural transformation
  3. Sustaining Cultural Change
 
[10]
 
Something I stress forcibly in Recipes for Success?

 

 

Themes from a Chief Data Officer Forum – the 180 day perspective

Tempus fugit

The author would like to acknowledge the input and assistance of his fellow delegates, both initially at the IRM(UK) CDO Executive Forum itself and later in reviewing earlier drafts of this article. As ever, responsibility for any errors or omissions remains mine alone.
 
 
Introduction

Time flies as Virgil observed some 2,045 years ago. A rather shorter six months back I attended the inaugural IRM(UK) Chief Data Officer Executive Forum and recently I returned for the second of what looks like becoming biannual meetings. Last time the umbrella event was the IRM(UK) Enterprise Data and Business Intelligence Conference 2015 [1], this session was part of the companion conference: IRM(UK) Master Data Management Summit / and Data Governance Conference 2016.

This article looks to highlight some of the areas that were covered in the forum, but does not attempt to be exhaustive, instead offering an impressionistic view of the meeting. One reason for this (as well as the author’s temperament) is that – as previously – in order to allow free exchange of ideas, the details of the meeting are intended to stay within the confines of the room.

Last November, ten themes emerged from the discussions and I attempted to capture these over two articles. The headlines appear in the box below:

Themes from the previous Forum:
  1. Chief Data Officer is a full-time job
  2. The CDO most logically reports into a commercial area (CEO or COO)
  3. The span of CDO responsibilities is still evolving
  4. Data Management is an indispensable foundation for Analytics, Visualisation and Statistical Modelling
  5. The CDO is in the business of driving cultural change, not delivering shiny toys
  6. While some CDO roles have their genesis in risk mitigation, most are focussed on growth
  7. New paradigms are data / analytics-centric not application-centric
  8. Data and Information need to be managed together
  9. Data Science is not enough
  10. Information is often a missing link between Business and IT strategies

One area of interest for me was how things had moved on in the intervening months and I’ll look to comment on this later.

By way of background, some of the attendees were shared with the November 2015 meeting, but there was also a smattering of new faces, including the moderator, Peter Campbell, President of DAMA’s Belgium and Luxembourg chapter. Sectors represented included: Distribution, Extractives, Financial Services, and Governmental.

The discussions were wide ranging and perhaps less structured than in November’s meeting, maybe a facet of the familiarity established between some delegates at the previous session. However, there were four broad topics which the attendees spent time on: Management of Change (Theme 5); Data Privacy / Trust; Innovation; and Value / Business Outcomes.

While clearly the second item on this list has its genesis in the European Commission’s recently adopted General Data Protection Regulation (GDPR [2]), it is interesting to note that the other topics suggest that some elements of the CDO agenda appear to have shifted in the last six months. At the time of the last meeting, much of what the group talked about was foundational or even theoretical. This time round there was both more of a practical slant to the conversation, “how do we get things done?” and a focus on the future, “how do we innovate in this space?”

Perhaps this also reflects that while CDO 1.0s focussed on remedying issues with data landscapes and thus had a strong risk mitigation flavour to their work, CDO 2.0s are starting to look more at value-add and delivering insight (Theme 6). Of course some organisations are yet to embark on any sort of data-related journey (CDO 0.0 maybe), but in the more enlightened ones at least, the CDO’s focus is maybe changing, or has already changed (Theme 3).

Some flavour of the discussions around each of the above topics is provided below, but as mentioned above, these observations are both brief and impressionistic:
 
 
Management of Change

Escher applies to most aspects of human endeavour

The title of Managing Change has been chosen (by the author) to avoid any connotations of Change Management. It was recognised by the group that there are two related issues here. The first is the organisational and behavioural change needed to both ensure that data is fit-for-purpose and that people embrace a more numerical approach to decision-making; perhaps this area is better described as Cultural Transformation. The second is the fact (also alluded to at the previous forum) that Change Programmes tend to have the effect of degrading data assets over time, especially where monetary or time factors lead data-centric aspects of project to be de-scoped.

On Cultural Transformation, amongst a number of issues discussed, the need to answer the question “What’s in it for me?” stood out. This encapsulates the human aspect of driving change, the need to engage with stakeholders [3] (at all levels) and the importance of sound communication of what is being done in the data space and – more importantly – why. These are questions to which an entire sub-section of this blog is devoted.

On the potentially deleterious impact of Change [4] on data landscapes, it was noted that whatever CDOs build, be these technological artefacts or data-centric processes, they must be designed to be resilient in the face of both change and Change.
 
 
Data Privacy / Trust

Data Privacy

As referenced above, the genesis of this topic was GDPR. However, it was interesting that the debate extended from this admittedly important area into more positive territory. This related to the observation that the care with which an organisation treats its customers’ or business partners’ data (and the level of trust which this generates) can potentially become a differentiator or even a source of competitive advantage. It is good to report an essentially regulatory requirement possibly morphing into a more value-added set of activities.
 
 
Innovation

Innovation

It might be expected that discussions around this topic would focus on perennials such as Big Data or Advanced Analytics. Instead the conversation was around other areas, such as distributed / virtualised data and the potential impact of Block Chain technology [5] on Data Management work. Inevitably The Internet of Things [6] also featured, together with the ethical issues that this can raise. Other areas discussed were as diverse as the gamification of Data Governance and Social Physics, so we cast the net widely.
 
 
Value / Business Outcomes

Business Value

Here we have the strongest link back into the original ten themes (specifically Theme 6). Of course the acme of data strategies is of little use if it does not deliver positive business outcomes. In many organisations, focus on just remediating issues with the current data landscape could consume a massive chunk of overall Change / IT expenditure. This is because data issues generally emanate from a wide variety of often linked and frequently long-standing organisational weaknesses. These can be architectural, integrational, procedural, operational or educational in nature. One of the challenges for CDOs everywhere is how to parcel up their work in a way that adds value, gets things done and is accretive to both the overall Business and Data strategies (which are of course intimately linked as per Theme 10). There is also the need to balance foundational work with more tactical efforts; the former is necessary for lasting benefits to be secured, but the latter can showcase the value of Data Management and thus support further focus on the area.
 
 
While the risk aspect of data issues gets a foot in the door of the Executive Suite, it is only by demonstrating commercial awareness and linking Data Management work to increased business value that any CDO is ever going to get traction. (Theme 6).
 


 
The next IRM(UK) CDO Executive Forum will take place on 9th November 2016 in London – if you would like to apply for a place please e-mail jeremy.hall@irmuk.co.uk.
 


 
Notes

 
[1]
 
I’ll be speaking at IRM(UK) ED&BI 2016 in November. Book early to avoid disappointment!
 
[2]
 
Wikipedia offers a digestible summary of the regulation here. Anyone tempted to think this is either a parochial or arcane area is encouraged to calculate what the greater of €20 million and 4% of their organisation’s worldwide turnover might be and then to consider that the scope of the Regulation covers any company (regardless of its domicile) that processes the data of EU residents.
 
[3]
 
I’ve been itching to use this classic example of stakeholder management for some time:

Rupert Edmund Giles - I'll be happy if just one other person gets it.

 
[4]
 
The capital “c” is intentional.
 
[5]
 
Harvard Business Review has an interesting and provocative article on the subject of Block Chain technology.
 
[6]
 
GIYF

 

 

Aphorism of the week

“Just because Jeffrey Archer exists, it doesn’t follow that Joseph Conrad can’t have existed”

Jeffrey Archer Joseph Conrad
Jeffrey Archer Joseph Conrad

Introduction

The context of the above bon mot was – as is often the case – a discussion on LinkedIn.com. I have been rather absent from the LinkedIn.com discussion groups for the same reasons that I have not been blogging and tweeting. In this case, my attention was drawn to the debate by a colleague.

linkedin CIOs.com: Chief Information Officer Network

The particular thread was posted by Andy McKnight and is entitled What’s missing from Business Intelligence? and at the time of writing has attracted nearly 60 responses (you have to be a member of the group to view the discussion). It referred to an article published by EMC2 which has the strap-line How CIOs can Reap the Benefits of BI Technology (note: this is a PDF document). Here is a pertinent quote:

The bad news is that only twenty-seven percent of respondents [to a survey of CIOs carried out by IDG Research] who use a BI solution report being extremely successful or very successful with it. Forty-five percent report being only somewhat successful, while seventeen percent say that they are not very, or not at all successful.

I’m not sure what happened to the other 11% of respondents, maybe they just hung up the ‘phone.
 
 
Blaming the users

"Users are the root of all evil" - anonymous [failed] BI Project Manager

Having stated that “BI has, too often, not lived up to expectations”, the paper goes on to list some reasons why. First on the list is the following:

  • lack of adoption by users

You don’t have to be Einstein to realise that this is the result of a BI project failing, not the cause of it. The equivalent in athletics terms would be to say that you came last in the race because everyone else was faster than you. While obviously true this observation doesn’t help a lot with how to do better next time.

Of course hidden in the comment is the plaintive whine heard emanating from many an unsuccessful project (or indeed product launch), “the problem is the users”. This is arrant nonsense, returning to the start of article if you write a book that is panned by the critics and not bought by the public, then there is at least some chance that the fault lies with you and not them. It is the job of the IT professional to know their users, understand their needs and provide systems that cause delight, not disillusion.

A more interesting observation later on is:

Many BI initiatives falter because the analytics capabilities that are at the core of the system aren’t even used. Many users simply pull data from the warehouse and dump it in a spreadsheet. […] A true BI implementation includes both reporting and analytics. CIOs indicate a much higher success rate with BI when users embrace both.

I think that there is some truth in this. Some of the BI failures I have seen have gone to the bother of building a warehouse only to front it with flat reports that are only marginally better than what they replaced.

In my career I have taken the opposite approach. While many people warn against analysis paralysis, I have deployed OLAP tools to all users, with fixed format reports de-emphasised, or used mostly for external purposes. This does mean that more effort needs to be put into training, but this is necessary anyway if you want your BI system to be an agent of change (and why else would you be building one if this is not the case?). I cover my general approach to driving user adoption in a series of three articles as follows:

This approach was very successful and we achieved user adoption of 92% – i.e. of those people who attended training, 92% remained active users (defined as using the BI system on average for at least two extended periods each week). We actually felt that the OLAP tools we were implementing were pretty intuitive and easy-to-use and so focussed mostly on how to use them in specific business scenarios. Overall we felt that training was 25% technical and 75% business-related.
 
 
Aiming for simplicity

Simplicity - with apologies to whoever thought of the image first

Related to the above point, the EMC2 article also mentions the following reason for failure:

  • limited functionality/hard to use

This seems a little oxymoronic as normally it is depth of functionality that confuses people. I think I would disagree with both parts of this point. Out of the box, most BI tools have rich functionality and a reasonably intuitive to use. In one response to the LinkedIn.com thread I said the following:

I have been successful in getting users […] weaned […] off ad hoc reports, it wasn’t an easy process and required persistence and selling, but this paid off. […] It is illuminating seeing business managers (some of whom still dictate memos for their secretaries to type) “slicing and dicing”, drilling down/through and generally interacting away merrily and stating that if all IT was this easy to use and informative, they might have taken to it earlier.

My view here is that you can make the tool as complicated or a simple as you choose. Going back to my first warehouse project, in our somewhat naive early attempts at prototype cubes, we had all available dimensions and all available measures included. I think our idea is that the users could help us sift out the ones that were most important. Instead this approach caused the negative reactions that the article refers to.

We subsequently adopted a rule of having as few dimensions and measures as possible in a cube, without compromising the business need that the cube was trying to address. The second part of this rule was that every cube had to be focussed on answering business questions in at least one area and at most two.

Rather than having a small number of monolithic cubes, we went with the option of a slightly larger number of significantly clearer and simpler ones. I think that this was a factor in our success in driving business adoption.
 
 
Should the fact that some BI projects fail dissuade you from BI?

I won’t attempt to dissect the rest of the article, the areas that I comment on above are representative. There are some good points and some less good ones – just like any article, including of course my own. Take a look yourself and see whether the findings and recommendations chime with your own experience of success and failure. What I did want to do was to return to the context of the aphorism that starts this post.

The thesis of the original LinkedIn.com post was that because a significant number of organisations had failed to get enormous benefit from BI, BI itself was therefore somehow flawed. I think this is wrong-headed reasoning. If 1,000 people write a book, how many are likely to become acknowledged as great authors? How many are likely to have the lesser accolade of commercial success? The answer in both cases is “not many”. This is because writing well is a very difficult thing to do (I prove this myself with every blog post!). Not everyone who tries it will be successful. BI is also difficult to do well and a major cause of problems is underestimating this difficulty.

Maybe this is too recherché and example, and maybe if the chances of success with BI are as slim as winning the Purlitzer Prize then it is not worth the effort. So I’ll instead I’ll resort to my favourite area of the sporting analogy. Let’s take the same 1,000 people and say that they all take up a new sport – it is mostly immaterial what the sport is, let’s say tennis. How many of them will go on to become proficient in it? By this I don’t mean that they are the next Roger Federer, just that they become competent enough to serve adequately, master the dark arts of the backhand and sustain a few rallies. My feeling is that the stats would look something like those in the EMC2 report.
 
 
Is the prize worth it?

Alfred's gong

Given this, does it mean that some companies are just not cut out for BI and should ignore the area? Well the answer is “it depends”. Going back to tennis, if some one wants to be good, and has the determination to succeed, that is a necessary (though sadly not sufficient) condition. What may drive such a person on is the objective of achieving a goal, or maybe the pleasure of being able to perform at a certain level.

Focussing on business outcomes, I believe that BI can deliver substantial benefits. In fact I have argued elsewhere that BI can have the greatest payback of any IT project. Of course this presupposes that the BI project is done well. If the prize is potentially that great then maybe – like the aspiring tennis player who wants to become better – trying again makes sense. In recent recruitment I have heard frequent mention of organisations that were building their second warehouse as they didn’t get the first one quite right.

However the comparison with tennis breaks down in that business is a team game. If an organisation as a whole has struggled with BI, then this is not a question of simply accepting your genetic limitations. Companies can “evolve” capabilities by hiring people who have been successful in a field. They can also get benefit from targeted consultancy from practitioners who have a track record of success; this can help them to build an internal capability. This is an approach that I took advantage of myself in the initial six months of my first BI project [note: although I often seem to get mistaken for a BI consultant, I am not touting for business here!].

This means that if a company’s BI architecture is currently the equivalent of a Jeffrey Archer novel, it is still possible to transform it into Heart of Darkness. It will not be easy and will take time and effort, but there are people out there who have been successful and can act as guides.

Not the ideal end of a BI journey

In closing I should also mention that, if you take appropriate precautions, it is far from inevitable that the end of a BI journey will be finding your own version of Kurtz!
 

Maureen Clarry stresses the need for change skills in business intelligence on BeyeNetwork

The article

beyenetwork2

Maureen Clarry begins her latest BeyeNETWORK article, Leading Change in Business Intelligence, by stating:

If there was a standard list of core competencies for leaders of business intelligence (BI) initiatives, the ability to manage complex change should be near the top of the list.

I strongly concur with Maureen’s observation and indeed the confluence of BI and change management is a major theme of this blog; as well as the title of one of my articles on the subject. Maureen clearly makes the case that “business intelligence is central to supporting […] organizational changes” and then spends some time on Prosci’s ADKAR model for leading change; bringing this deftly back into the BI sphere. Her closing thoughts are that such a framework can help a lot in driving the success of a BI project.
 
 
My reflections

I find it immensely encouraging that an increasing number of BI professionals and consultants are acknowledging the major role that change plays in our industry and in the success of our projects. In fact it is hard to find some one who has run a truly successful BI project without paying a lot of attention to how better information will drive different behaviour – if it fails to do this, then “why bother?” as Maureen succinctly puts it.

Without describing it as anything so grand as a framework, I have put together a trilogy of articles on the subject of driving cultural transformation via BI. These are as follows:

Marketing Change
Education and cultural transformation
Sustaining Cultural Change

However the good news about many BI professionals and consultants embracing change management as a necessary discipline does not seem to have filtered through to all quarters of the IT world. Many people in senior roles still seem to see BI as just another technology area. This observation is born out of the multitude of BI management roles that request an intimate knowledge of specific technology stacks. These tend to make only a passing reference to experience of the industry in question and only very infrequently mention the change management aspects of BI at all.

Of course there are counterexamples, but the main exceptions to this trend seem to be where BI is part of a more business focused area, maybe Strategic Change, or the Change Management Office. Here it would be surprising if change management skills were not stressed. When BI is part of IT it seems that the list of requirements tends to be very technology focussed.

In an earlier article, BI implementations are like icebergs I argued that, in BI projects, the technology – at least in the shape of front-end slice-and-dice tools – is not nearly as important as understanding the key business questions that need to be answered and the data available to answer them with. In “All that glisters is not gold” – some thoughts on dashboards, I made similar points about this aspect of BI technology.

I am not alone in holding these opinions, many of the BI consultants and experienced BI managers that I speak to feel the same way. Given this, why is there the disconnect that I refer to above? It is a reasonable assumption that when a company is looking to set up a new BI department within IT, it is the CIO who sets the tone. Does this lead us inescapably to the the conclusion that many CIOs just don’t get BI?

I hope that this is not the case, but I see increasing evidence that there may be a problem. I suppose the sliver lining to this cloud is that, while such attitudes exist, they will lead to opportunities for more enlightened outfits, such as the one fronted by Maureen Clarry. However it would be even better to see the ideas that Maureen espouses moving into the mainstream thinking of corporate IT.
 


 
Maureen Clarry is the Founder and President/CEO of CONNECT: The Knowledge Network, a consulting firm that specializes in helping IT people and organizations to achieve their strategic potential in business. CONNECT was recognized as the 2000 South Metro Denver Small Business of the Year and has been listed in the Top 25 Women-Owned Businesses and the Top 150 Privately Owned Businesses in Colorado. Maureen also participates on the Data Warehousing Advisory Board for The Daniels College of Business at the University of Denver and was recognized by the Denver Business Journal as one of Denver’s Top Women Business Leaders in 2004. She has been on the faculty of The Data Warehousing Institute since 1997, has spoken at numerous other seminars, and has published several articles and white papers. Maureen regularly consults and teaches on organizational and leadership issues related to information technology, business intelligence and business.
 

“Gartner sees a big discrepancy between BI expectations and realities” – Intelligent Enterprise

Doug Henschen
 
Doug Henschen at Intelligent Enterprise reports from the Gartner Business Intelligence Summit in Washington D.C., explaining that Gartner analysts John Van Decker and Kurt Schlegel cited five reasons why BI projects do not live up to expectations (article link here):
 

  1. No IT-Business Partnership – “We have to get away from thinking of it as a vendor-customer relationship,” said Schlegel.
  2. No Link to Corporate Strategy – BI team have to listen to the executives and develop metrics and measures that are aligned with their goals.
  3. No connection to the Process – “BI has been overtly disconnected for too long,” Schlegel proclaimed. It’s not enough to deliver the right information to the right users. You have to insert those insights into the processes and interfaces that business users live in every day.”
  4. No Governance or Too Much – It has to be just the right amount of governance. BI grew up departmentally, so it’s all too common to have many silos of BI. At the other extreme, some companies are so uptight about data standards that companies can’t get their data into the warehouse.
  5. No Skills – Business users often lack skills, chimed in Van Decker, citing the one area where the business side was said to be falling short. “Most companies have very sophisticated capabilities available that folks just aren’t taking advantage of,” he said, pointing to corporate performance management (CPM) software as a leading example.

 
I come close to the situation of words failing me when I read a list like this (though not close enough to prevent me penning an article of course). I appreciate that maybe a public seminar is not the easiest place to provide deep insights (I present a lot myself), but the commentary against each of the problem areas seems odd to me. I’m not sure that these are really the five reasons for BI continuing to disappoint in some organisations, while it continues to delight in others, but here are my thoughts on each headline.
 

  1. No IT-Business Partnership – We have to stop talking about IT and Business as if they were two parties trying to negotiate a cease-fire. IT is a business department, it carries out business projects. It would be ridiculous to say that there needs to be a Sales-Business Partnership, it should be equally so with IT. I expand on this theme in the very first article on this blog.
  2. No Link to Corporate Strategy – There should not be a link to Corporate Strategy, BI does not exist as a separate entity that requires linkage. Instead BI work should be an expression of Corporate Strategy (as should any IT project), what else is it an expression of? This is not about listening to executives (though that is important) it is about IT being part of the senior management team of an organisation and not some semi-detached entity, focussed only on the beauty of its own navel. I give some indication of how to go about ensuring that this is the case in two articles, one focussed on a European environment and one spanning four continents.
  3. No connection to the Process – I agree that embedding good BI in a coporoate culture requires effort (indeed I have written a series of articles on the subject, starting with this one), however I struggle to see how any BI team could fail to realise this and pay the area due attention. If this is truly a reason for failure, then it is because of a lack of basic project and change management skills in BI teams.
  4. No Governance or Too Much – I’m not sure that achieving the Goldilocks approach to Governance is the issue here. BI’s impact on an organisation is directly proportional to how pervasive it is. This means that silos of BI reduce value and the walls need to be knocked in. Does this have to be all in one go? of course not, there are always benefits in a balance between incremental progress and paradigm shifts. Any organisation who has gatekeepers who refuse access to the warehouse because of and overly rigid approach to data standards is going to have problems. Equally where systems are developed with little thought to the information that they provide and data is simply thrown over the wall to the BI team, this is going to destroy value. As ever, there needs to be a sensible balance struck, one that yields the greatest business benefit for the lowest cost.
  5. No Skills – “Business users often lack skills”, this seems both incredibly patronising (are only IT people smart enough to get it?) and also a poor excuse for BI teams not paying enough attention to education (see point 3. above). If business people truly lack the skills to use good BI, then they are probably unfit to be in business as the tools are pretty intuitive (if not over-engineered by an approach that is too technology-focussed). More likely, training has been poor, or the BI deliveries have failed to be tailored to answering questions that the business wants to ask.

 
In summary, I don’t have five reasons for BI failing to live up to expectations, I have one. I firmly believe that BI done well is both the easiest of IT systems to sell to people and has one of the highest paybacks of any IT initiative. BI done badly (at the design, development, implementation or follow-up stages) will fail.

The issue is basically a simple one: just how good is your BI team? If a BI implementation fails to deliver significant business value, then instead of looking for scape-goats, the BI team should purchase a mirror and start using it.
 


 
Continue reading about this area in: Some reasons why IT projects fail
 
 
Doug Henschen joined Intelligent Enterprise as Editor in 2004 and was named Editor-in-Chief in January 2007. He has specialized in covering the intersection of business intelligence, performance management, business process management and rules management technologies within enterprise applications and architectures. He previously served as Editor-in-Chief of Transform Magazine, which covered content management and business process management challenges.

I comment on another Intelligent Enterprise article, this one by Cindi Howson, here.
 

Sustaining Cultural Change

This article is the final one in a trilogy focussed on enacting change. The previous instalments were as follows:

  1. Marketing Change
  2. Education and cultural transformation.

The first two pieces covered generating enthusiasm for change in advance of enacting it and then the role that professional training has in repositioning behaviours. I left off with the first training event having been a success. I will pick up the story from this point and seek to answer the question: how do you sustain initial success over the medium term?

Before starting to discuss some approaches that worked for me in this area, I should remind readers of the context. This was delivering a new BI system in a European insurance organisation with the explicit aim of enacting a cultural transformation; in this case making top-quality management information part of the corporate DNA.
 
 
Introduction

flying-buttress-h200

It is part of human nature to sometimes rest on your laurels. Having worked hard to make sure than something goes well, it is tempting to sit back and admire what you have done. Unfortunately gains are not always permanent and indeed may be quickly eroded. It is a useful to recall the adage that you are only as good as your last performance. As in a sporting contest, when you have made a good start, it is then the time to press home your advantage.

After our first successful training session, we had several other waves of training for our first report family – in fact we trained over 300 people in this first activity, 150 more than we had anticipated, such was the demand that we had generated and the positive feedback from people who had attended. But at this stage we had only won the first battle, the outcome of the war remained in the balance. We had made a good start, but it was important that the team realised that there was still a lot of work to be done. In this final article I want to talk about some of the ways in which we sustained our focus on the system and managed to embed its use in day-to-day activities.
 
 
Using new functionality to reinforce training

By the time the training team had come to the end of its first phase, the development team had produced its second report family. This was aimed at a slightly narrower group of people, so training was a less extensive task. Also we were showing new BI functionality to people who were already users, or at least had attended the training. The training for the second release was just a half day, but we asked people to book out a whole day. The extra time was spent in attending either a refresher course (for people who had not been confident enough to use the system much after initial training) or a master-class (for those who had taken to it like a duck to water). We also offered these two options to people who were not recipients of the second report family.

Inevitably there were initially some people who were not 100% converts to the new system at first; crucially less than half of users fell into this category. Over time both the enthusiasm of their peers and the fact that early adopters could present information that was not generally available at internal and external meetings began to exert pressure on even the most sceptical of people.
 
 
Travelling to the users

With later report families, which were again aimed at the mass market, we change our approach and travelled to give training in different countries. This helped to tailor our training to local needs and prevented anyone becoming isolated by language issues. Again when we travelled we would go for two days and have two half-day formal classes. The other half days were taken up with refresher courses, master classes or – something that started to become more and more requested – one-on-one sessions. These are in many ways ideal as the user can go at their own pace and focus can be on compiling and saving reports that are directly pertinent to them – classroom work has of its very nature to be more general.

Sadly we did not have unlimited funds to travel round Europe, so these one-on-one sessions morphed into using the telephone and network facilities with the trainer “taking over” the PC of the delegate to work together. This approach has also been very successful on our Help Desk.
 
 
The importance of the Help Desk

Speaking of the Help Desk – because the BU systems was very business-focussed people tended to raise business-focussed questions (as opposed to “when I click on this button, the system locks up”). This meant that the Help Desk needed to understand both the technology and the business and we used our business analysts and trainers to staff it – this is high-end resource to apply, but they were just as proud of the system as the extended team and wanted to help people to get the best of it.
 
 
Summary

So, we were relentless. We didn’t really ever lower the intensity we had established when launching the system; business adoption and retention both reflected this. Even once our cultural change had been mostly achieved and BI had become as much part of everyday life as the ‘phone or e-mail, the team continued to put just as much effort into new releases. The contributions of professional training and a business-focussed Help Desk functions were both indispensable in sustaining our success.
 

BI and a different type of outsourcing

outsourcing

The current economic climate seems to be providing ammunition for both those who favour outsourcing elements of IT and those who abjure it. I’m not going to jump into the middle of these discussions today (though I am working on an article about the pros and cons of outsourcing BI which will appear here at some future point). Instead I want to talk about another type of outsourcing, one that ended up being a major success in a BI project that I recently led. The area I want to focus on is outsourcing analysis to the business.

The project was at an Insurance company and in these types of organisations one hub for business analysis is the actuarial department. These are the highly qualified and numerate people who often spend a lot of their time in simple number crunching with the aim of ensuring that underwriters have the data they need to review books of business and to take decisions about particular accounts. As with many such people, they have both the ability and desire to operate at a more strategic level. They are sometimes prevented from doing do by the burden of work.

As I have explained elsewhere, an explicit aim of this project was cultural transformation. We wanted to place reliance on credible, easy-to-use, pertinent information at the heart of all business decisions; to make it part of the corporate DNA. One approach to achieving this was making training programmes very business focussed. One exercise that the trainers (both actuarial and indeed me) took delegates through was estimating the future profitability of a book of business based on performance in previous years (using loss triangulation if you are interested). This is a standard piece of actuarial work, but the new BI system was so intuitive that underwriters could do this for themselves. Indeed they embraced doing so, realising that they could get a better and more frequently updated insight into their books of business in this way.

This meant two things. First the number-crunching workload of actuarial was reduced. Second when underwriters and actuarial engaged in discussions, for example around insurance estimates to be included in year-end results, the process was more of an informed dialogue than the previous, sometimes adversarial, approach. Actuarial time is freed-up to focus on more complex analysis, underwriters become more empowered to manage their own portfolios and the whole organisation moves up the value chain.

This is what I mean by the idea of outsourcing analysis to the business. In some ways it is the same phenomenon as companies outsourcing internal administrative tasks to customers via web applications. However, it is more powerful than this. Instead of simply transferring costs, knowledge and expertise is spread more widely and the whole organisation begins to talk about the business in a different and more consistent manner.

It’s nice to be able to report a success story for at least one type of outsourcing.