Thursday, 22 April 2010

How do you identify your strategic data?

As I mentioned towards the end of my last blog post, many organisations face "information overload". Over time there has been so much data collated, some of which is no doubt duplicated and used by different people in different ways. It may be stored on disparate systems around the organisation, and it may be touched by numerous systems or processes on it's journey from source system to the report on your desktop.

Firstly, How do we separate the data that is "nice to have" from the data that is "critical" to our business? Secondly, how do we govern this critical data accordingly, as a valuable business asset?

Think Strategically

To separate the "nice to have" from the "critical" we need to understand the aims & objectives of the business that we are serving. We need to align our strategy for data provision with the wider corporate strategy and attempt to understand exactly what data will be required to support and achieve the business goals.

If a strategic goal of the business is to enter a new geographical market, do we currently have the data within our organisation that will support the marketing, promotion and launching of a product in this market? If not, what data do we need to start collecting, and from where?

How do we know what we need?

Work with data stewards and subject matter experts within your business community. This network of enthusiastic and knowledgeable individuals were chosen especially because of their knowledge and influence within the business. Utilise them. If you don't have a network of Data Stewards and Subject Matter Experts within your organisation, it would be a worthwhile exercise to identify and involve them.

Create and sustain a 'Strategic Data Forum' that meets on a regular basis to discuss how strategic business goals can be supported by data. Again, we need to have a clear understanding of what data we currently have within the organisation, identify any gaps that need to be filled, and ensure a roadmap exists so that we can track activities and objectives.

There are also a number of other challenges that should be addressed within the forum, for instance:
  • How are data sources communicated to the business community?
  • How will our current technical infrastructure be able to cope with any additional data requirements?
  • Is strategic data currently produced by a 'cottage industry' on an unreliable server - do we see that this needs to move to a more strategic platform for business continuity, scalability & increased governance?
This is it(erative)

Like any strategic exercise, this isn't a one off. This is an extremely iterative process. It will grow and adapt based upon the strategic goals of the business. You may find that what is deemed as 'strategic data' will be re-defined further down the line, but the important thing is that you have the people, and the process in place to manage change.

Monday, 19 April 2010

The importance of research before bringing data to market

When we talk about 'bringing data to market' we are talking about the process of taking an idea, or a request, and turning it into a stable, trusted source of data that can be utilised by the business community in order to aid decisions, and support the strategic goals of the business.

If 'bringing data to market' was treated in a similar way to how you would bring a product to market, we would see data created that has been:
  • clearly defined and aligned to strategic goals
  • fully tested and quality assured
  • documented ready for use
Lets think for a minute about one of the most important steps undertaken prior to launching a new product. Research. In product terms, once an idea has been generated the product team would undertake detailed research. This research will provide good knowledge and enable a strong vision of how to take the product forward towards a successful launch.

It starts with an idea, or a request for data.

A member of the business community may request that "We need data on X in a format such as an OLAP cube, so that we can slice/dice the data".

The idea, or request for data is then benchmarked against a number of research tasks.

1. Why do the business need X?
  • What is driving the business requirement for the data?
  • How does this align to the wider business strategy?
  • Do they already have X but are unaware that it exists?
2. Who will use X?
  • Will X only be used by the requester?
  • Does X have the potential to be utilised across other business areas?
  • Do the potential users of X have the correct systems access/skillset to utilise the data?
3. Does similar data already exist?
  • Will Y meet the needs of the requester of X?
  • Why was Y not previously considered by the requester?
  • Do they actually want Y, but in a slightly different format?
This research will ensure that good knowledge exists of the requirement, and how it fits into the strategic goals and direction of the business. It will also help us to understand the potential user base of the data, and whether they have the required skills to utilise the data, or whether further user training will be required.

Finally, it will also aid to reduce a problem that occurs in many organisations. Business users are continually requesting data from BI, and in many cases the data actually already exists and is utilised by other parts of the business, but for whatever reason (poor communication? poor 'data launch'?) the requester was not aware of the existing data. In less governed and communicative organisations this can result in multiple data sources that essentially provide the same data. There is a risk that these data sources may not be used in the same way, or even reconcile with each other due to potential criteria imposed during their creation.

Do your research

Detailed research and knowledge, combined with a strong strategic vision will ensure that all data that you 'bring to market' will be a valuable enterprise asset. It will help prioritise business requests, and place context onto data in terms of which strategic goals the data is supporting. In an age where we often hear the words "information overload" it is crucially important that we don't lose sight of the bigger picture, or the business goals, that we are supporting through data.

Thursday, 15 April 2010

Utilising the DICE Framework in Data Governance Initiatives

Your Data Quality or Data Governance roadmap will undoubtedly contain a number of enterprise initiatives that you wish to implement. These initiatives will result in change within your organisation, be it a change of organisational culture, a change of process, or even a change of systems.

The Data Quality space is an area where investment may have traditionally been low due to the difficulty of demonstrating ROI, and although smarter executives and business users understand the need for Data Quality Management & Data Governance, unless bitten many executives are still unwilling to invest time & capital in preventative and governing measures.

Due to the above, we may be presented with the challenge of gaining business & executive-level support. The sensible way to build momentum in gaining support is to demonstrate quick wins. Look for business problems in the DQ space that could be solved within a reasonable amount of time, with a manageable level of effort. Add value to the organisation, and you will see the momentum, and support for your efforts grow.

With this in mind it would make sense to attempt to measure the likeliness of implementation success prior to diving into any initiative.

(Naturally, you will never truly be aware of the chances/degree of success until an initiative is underway. However, indepth planning, analysis & risk mitigation will lead to greater chances/degrees of success.)

We can use tools for this

This is where tools such as DICE come in. The DICE Framework was created by the Boston Consulting Group and can be used to evaluate the likeliness of change management success.

Rather than trying to evaluate likeliness of success by looking at "soft factors" involved in successful change management, such as motivations, leadership & current organisational culture, the DICE framework looks only at what it calls "hard factors" that influence change success.

uration - either the duration until completion or the time between key milestones
Integrity - ability of the team to successfully perform & complete the project
Commitment - backing from the executive team, and support from the business community
Effort - the amount of effort required above the regular workload of the business

By scoring each of these factors, the framework will allow you to chart your change management project success likeliness, much like on the online tool provided by BCG.

Lets look at an example

Lets take the following initiative that features on your Data Quality Roadmap as an example - "Establish a Data Quality Resolution Centre".

The aim of the DQ Resolution Centre is to:
  • provide a single point of contact to raise data quality issues
  • promptly resolve all data issues
  • allow the business to track issue resolution progress
  • provide expertise & education to the business
  • create regular KPI reporting relating to data issues
It has been estimated that it would take around 1 month to create the resolution centre, establish the key processes, identify the key people, communicate the change, and agree KPI reporting. The newly employed 'Data Quality Manager', a person with solid experience in data quality resolution, and a strong grasp of the business will be taking ownership of the change.

In addition, there is support from senior management, although they refused to sign off the supporting 'Data Quality Analyst' role requested by the Data Quality Manager. The business community are extremely supportive, as previously their data issues have become lost in the world of IT. They've longed for someone "on the ground" to support them in their requirement for high quality and meaningful data.

Using the previously mentioned online tool and the information above, we can see that:

Duration: less than 2 months
Team Performance Integrity: Very Good
Commitment (Senior mgmt) : Seem to want success
Commitment (Local): Eager
Effort: Less than 10% additional

This suggests that the initiative is likely to be "highly successful".

Whether the Data Quality Resolution Centre is seen as "highly successful" by the business community six months down the line is however a further challenge, and a story for another day.

In Conclusion

By utilising the DICE framework, alongside evaluation of the softer factors that influence change success, you will be more prepared to aid in the selection, planning & prioritisation of change projects. This is particularly important if you are under pressure to succeed or risk having funding reassigned to another project. If 4 items of your roadmap are deemed to be of equal importance to the business perhaps it would be worth tackling an item which seems to have a higher chance of success first?

Tuesday, 6 April 2010

What makes a successful Business Intelligence Leader?

While reading "On the Good Life", a collection of some of the works by the Roman philosopher & statesman, Cicero, I came across a couple of statements that made me think about Business Intelligence leaders. I use the term "Business Intelligence leaders" but this can apply to any leader responsible for data and information management/usage within an organisation, such as BI Heads, Customer Insight Managers, Data Quality Managers and so on.

"A successful statesman, the person who guides the nation and controls its policy, may be defined as an individual who knows and employs the means of securing and promoting the interests of his country."

Lets break this down a bit:

"A successful statesman"

You - the Business Intelligence leader.

"the person who guides the nation"

You, and your team, should be the authority on data, and it's associated usage, meaning and quality, within your organisation. You should be able to guide the business in maximising the benefit from data, including ensuring that they are using the correct data sources, and the correct tools for the information exploitation they are performing. The business users should be aware of who to speak to if they have issues/problems, and communication channels should be open, with regular two-way communication undertaken. Listening to the business is essential.

"and controls its policy"

You, and your team, should ensure that all policy is documented, and adhered to. This will include policy relating, but not limited, to such things as:
  • Security & Access
  • Reporting Tool Usage
  • Regulatory Compliance
In addition to creating & maintaining policy, you should also ensure that the business community is aware of policy, through training sessions or communication bulletins.

For example, to ensure that security & access control policy is adhered to, and that the business has access to the data they require, you should communicate the policy and process surrounding systems access effectively. I have seen examples of people sharing system access logins as they either did not understand security & access policy, or the process to request system access was complicated, or slow. Efficient process surrounding policy will aid adherence.

Or similarly, how can you ensure that your business community adhere to Data Protection legislation if you do not make your community aware of what the legislation involves?

"knows and employs the means of securing and promoting the interests of his country"

Two methods of securing and promoting interest that instantly came to mind are:

1. Aligning actions to Business Strategy

There is a common consensus that a single point of contact for business reporting is a good thing, and many people are aware of the associated benefits - however, a successful BI leader is able to translate and align these benefits to Business Strategy.
  • How does X fit into and support the overall business strategy?
  • How can Y help us achieve our strategic business goals?
  • Is Z most likely to assist in achieving a strategic business objective?
2. Having a solid Communications Strategy

Securing the support of the business is essential in your success as a BI leader. If the business have no confidence in your ability to deliver information to the right people, in the right place, in the right format, and at the right time, they will not support you. Having a solid communications strategy will aid in gaining, and sustaining business support.

Through effective communication you can ensure that the business are aware of any issues impacting your service. You can also provide measurement scorecards that allow the business to benchmark where we are against where we've been, alongside a view of where we're going. This strategic visibility, alongside a forum for business users to express their opinions, praise or concerns will aid in keeping the business supportive of your goals & objectives.

In closing, there is another Cicero quote that I feel should be shared here:

"No leader, either in war or in peace, could ever have performed important or beneficial actions unless he had gained the cooperation of his fellow men."

Build a great team around you, with proactive, knowledgeable and approachable people who share in your vision.

Finally, know, and remember, your audience - the business community - involve them in your actions, ensure you have their support, and continually communicate with them. Only then will you be able to maximise the benefit and performance of your objectives.

Monday, 5 April 2010

El Festival del IDQ Bloggers: April 2010

El Festival del IDQ Bloggers AKA The blog carnival for Information/Data Quality Bloggers is a monthly event devised by IAIDQ which compiles great data quality related blog posts. Each month the festival is hosted by a different data quality blogger, and I am privileged to host the festival during April. Lets get on with the show, and introduce the names behind the blog posts..

Henrik Liliendahl Sørensen

As well as making extremely frequent posts on his blog, Henrik is a Data Quality and Master Data Management Professional who is also engaged within Data Architecture solutions. He currently works for Omikron Data Quality. Click over to his blog for more information.

What better place to start in this edition of the Blog Carnival than this thought-provoking post posing the question of "What is Data Quality Anyway?". Like so many of Henrik's recent posts, this one leads to an interesting discussion within the comments.

Jim Harris

An Independent consultant, speaker, writer and blogger with over 15 years of professional services and application development experience in multiple data quality related disciplines, Jim is a prolific contributor to the Data Quality community. Ever innovative in his blog posts, from his use of song lyrics, to his use of Shakespeare - not to be missed.

The Circle of Quality sees Jim guiding us through his thoughts on the interconnected business context continuum and the associated challenges with measuring quality throughout the cycle.

Oughtn't you Audit, hosted on the DataFlux blog, asks why more organisations are not fully auditing their data on a daily basis, and questions the all or nothing approach that is often seen.

Daragh O'Brien

The first person I met during my visit to the DMIQ conference in 2007, Daragh maintains multiple blogs, including DoBlog, which has been serving up posts centered on the fun side of Information Quality Management from an insiders perspective since 2006. He is also the founder of Castlebridge Associates, a leading niche Information Asset Management consulting company based in Castlebridge, Co. Wexford.

His St Patricks Day Special hosts an interesting photograph and uses it as a metaphor for information quality.

Sometimes it is the simplest things tells a humorous tale of how a home improvement DIY task led to darkness, all for the want of a piece of metadata.

In his company blog, Obscured by Clouds discusses the need to avoid having our vision of what it actually means to manage the quality and privacy of information obscured by a goal rush mentality around Cloud Computing.

Dylan Jones

Aside from being the 1st person within the DQ Internet community that I spoke to, Dylan Jones is the editor of the fantastic online community resource - Data Quality Pro, which is dedicated to helping data quality professionals take their career or business to the next level. He is a prolific author on the subject of data quality and related disciplines.

Does Your Project Suffer From Data Quality Product Myopia? looks at the pitfalls of centering your Data Quality efforts around a technology toolset, and offers some advice to aid the cure of this common condition.

Ken O'Connor

Having been described as a "Grizzled Veteran", and with almost 30 years experience across the full development lifecycle, it's not hard to understand why. Ken started his blog to share his experience and to learn from the experience of others. More information about Ken is available on LinkedIn.

Applying Lateral Thinking to Data Quality takes inspiration from Edward De Bono and asks how applying an example of De Bono's work can help improve Information Quality.

Rich Murnane

A self-described "data geek" on his twitter account, Rich has been running his blog, and sharing great posts for 5 years. He specialises in Oracle, Data Architecture & Data Quality.

De-Duplication of names using DataFlux is a St Patricks Day special, where Rich decided to take a stab at de-duplicating a list of "Patrick" names, and sharing his experiments with de-duplication using the DataFlux toolset.