Information Quality Aggregator

Build Your Data Quality Castle First

2012-05-25T14:00:42Z

I came across a story recently about Walt Disney that really resonated with the way I’ve seen organisations succeed – or fail – when launching their initial “drive to data quality.”

Apparently, when Walt Disney was first creating Disneyland he told the designers to construct the castle first. The reason? He wanted everyone involved to witness the incredible sight of the castle and use that as a guiding vision for the quality and imagination that the rest of the part should aspire to.

This is a smart tactic for those fledgling data quality initiatives because you need to demonstrate that this stuff truly works before you start building committees, councils and all the other plumbing that goes into scaling up your data quality efforts.

In a recent interview with Jill Dyché (Data Governance and Data Quality Advice for Leaders) she talked about the need for avoiding the “kick-off and cold-cuts syndrome.” This is where organisations kick-off a data governance initiative only to complain about data quality without moving forward (and eating cold-cuts in endless meetings!). This is exactly why I believe you need to build your “data quality castle” first. Create an initiative that is within your grasp and will become a guiding light for future participants.

My first employer achieved this with scarce funding as we basically used our lunch breaks for training and some SQL and C scripts, the only data quality tools available at the time! We focused on one tightly defined problem and when we could demonstrate huge benefits to the organisation and individuals involved it became our version of data quality castle, other teams wanted to learn more and senior management could see that data quality process improvement could have a dramatic business impact.

Did you create a data quality castle to launch your initiative? Do you agree with this approach? Welcome your views.

Most Times the Home Team Wins

2012-05-25T12:20:31Z

This summer is going to be huge if you like sports. The Olympics is coming to London and only 14 days away from now we have the European football (soccer) championship in Poland and Ukraine.

As usual hopes are high for the England soccer team. But statistics doesn’t support the hopes. The England team haven’t really succeeded since the World Cup victory on home ground at Wembley in 1966. That victory was mainly (and now I’m going to be shot in the streets of London) due to a ghost goal.

In business, and in data quality and MDM business too, the home team usually also wins.

Yesterday I noticed a tweet telling that the MDM tool vendor Orchestra Network has been selected as tool vendor by a large bank. The bank is Credit Agricole, a big financial service provider based in France. Orchestra Networks is also based in France. A home win so to say.

In the post The Pond it was told how else dominating American tool vendors may in the first place succeed in expansion to Europe by coming to London, but in fact having a hard time competing in continental Europe due to diversity issues.

European tool vendors going to North America often tries to disguise as a home team. Orchestra Network for example uses Boston & Paris as place of origin in the messaging. Other examples are the leading open source data management tool vendor Talend with dual head quarter in Paris and California, hot Danish MDM vendor Stibo Systems messaging out of Atlanta and the Swedish business intelligence success QlikTech who officially has moved to Pennsylvania.

Filed under: Information Quality, Sport

Publishing and the Data Establishment

2012-05-24T14:00:29Z

A few weeks ago, I wrote a post on this site about the data-driven approach to editing I took on 101 Lightbulb Moments in Data Management. Call me a geek (you wouldn’t be the first), but using tools like pivot tables and advanced filters in Excel made curating the book easier.

Now, if you’re reading this, you’re probably not an author, but I’ll bet that you’re at least vaguely interested in data management. So keep reading. There’s a payoff at the end of this post.

It turns out that other writers believe in the data-driven approach to publishing. Case in point: Eric Ries. Now a rock star, Ries didn’t take a traditional approach to publishing his first book, The Lean Startup. As he explains in the following interview, he conducted tests well before the book was ready for publication:

Many of his tests were about the cover and the subtitle – the two things he thought would be most impactful, and two things that are traditionally hard to analyze to determine the best of each. He describes how he fought with his publisher over “horrendous” covers that were presented to him. He was thrilled to have empirical data to show them, based on what people reacted most favorably to – what actually drove book sales. In the end, they tested and tested until he found something that he liked, the publisher liked, and actually sold books.

What’s more, he shared the data on his experiments, and used this too to sell books. One idea that really sold a lot of books for him was offering people to see this data if you pre-ordered a book. So Eric offered people a chance to go behind the scenes to see the book marketing testing that he was doing on the site. Due to the nature of the book, testing ideas that work, this really resonated with his audience. All the data can still be accessed on his website, including experiments and trends among his book-buying customers.

Two things strike me about Ries’ approach. First, it’s very much a Black Swan. That is, his approach may explain his success in hindsight, but that doesn’t mean that any aspiring author can now follow his formula and expect anywhere near the same results. What’s more, no one could have predicted it in advance.

Second, far too many people in the Establishment (in this case, traditional publishers) by default don’t even think about data most of the time. Only when Ries “proved” that the aforementioned “horrendous” covers deterred potential buyers did his publisher consider alternatives. (Lamentably, I know exactly he’s talking about.)

See the results of one of his early experiments below:

The results of Ries’ efforts are hard to understate. The book has become a best-seller and Ries has gained tremendous leverage with his publisher for future books. After all, he has broken through and become an A-list author.

Simon Says:

To that end, learn from Ries’ strategy here. Collect and maintain our own data if you’re trying to convince your organization to do something. To be sure, naysayers and data skeptics will always pooh-pooh the importance of good, clean data. If confronted with obstinate, data-averse folks, go get your own data and prove them wrong.

Feedback

What say you?

Analyze Customer Profiles and Behavior for Better Decision Making

2012-05-24T13:00:00Z

Dave Loshin explains how anaylzing customer behavior and profiles helps you optimize your business decisions.

Articulating Why Data Quality Matters

2012-05-23T20:49:33Z

Don't overlook the importance of data quality in your information governance strategy

When companies talk about information governance, they often mention data warehousing, analytics or master data management – all of which have business value that can be articulated pretty clearly.

Often, though, these same organizations overlook the importance of data quality. But without trusted data quality, all those insights coming from analytics are of dubious value.

OCDQ blogger Jim Harris wrote a case study that still sticks in my mind: how the US Postal Service, a very traditional, very big, very entrenched institution, improved its focus on data quality. Since mail is routed based on the accuracy of address information, data quality is obviously important to USPS. But changing information governance processes for an organization like the USPS is not for the faint of heart.

So when the USPS wanted to add four digits to their postal codes to improve data quality, they had to make a very clear business case that identified the ROI of better, more complete data. At the same time, they had to articulate the potential cost of doing nothing and not investing in data quality.

As organizations embark on information governance, they need to have these conversations about the role and impact of data quality – before getting in the weeds.

Automated systems can help identify, fix and monitor data quality issues, but you still have to ask the right questions. Philip Howard, Research Director at Bloor Research, helps clients frame these conversations, making sure they’re poised to manage quickly growing data volumes.

Join Philip on Thursday, May 31 for a webinar and learn how to articulate the business case for data quality so you can get more out of your data warehousing and analytics investments. He’ll discuss linking business and IT while sharing several great cases from his experience, plus some real metrics about the cost of poor data quality. (I’ll be tweeting the session – follow along with #IBMDataQuality.)

I’m always on the lookout for examples of how organizations build business cases – or fail to make the case. Do you have any good stories to share of companies that failed to articulate the need for data quality – and the consequences?

Books That Influenced My Thinking: Management

2012-05-23T19:00:16Z

As those who follow this space will know, I’m reviewing a series of books that I view as classics – books that profoundly influenced my thinking. Up today is Peter Drucker’s Management: Tasks, Responsibilities, and Practices.

I hope that every reader has a favorite Drucker. Or has at least heard of him. For Peter Drucker is the pre-eminent management scholar of all time. The Los Angeles Times proclaimed him the “founding father of the science of management,” richly deserved praise if ever there was any.

Management, first published in 1973, covers the whole gamut, from first principles, to social responsibility, to the manager’s jobs, to required skills and organization, to top management. It’s a timeless classic. It’s also long, so don’t try to tackle it all at once. But do get through the first ten Chapters as quickly as you can.

The Chapters titled “What is a Business?” and “Business Purpose and Mission” had the most impact on me. This quote (from page 61 of my copy):

“There is only one valid definition of business purpose: To create a customer.”

may be my favorite simple business quote of all-time. The Business Purpose and Mission (pp74-94) chapter is the most lucid discussion of customers, understanding what they need, the distinction between a need and an unmet need, and the connection of customers to the business I’ve ever come across.

Once and for all, these makes clear why customers are central to any definition of quality! They demolish the claims of some that quality management is somehow tangential to the business or a luxury for the choice few. No matter what business you’re in, quality is front and center.

Next: Future Perfect

See more posts in Thomas Redman’s series “Books That Influenced my Thinking.”

The Problem with Multiple Purposes of Use

2012-05-23T15:28:59Z

Today I noticed this tweet by Malcolm Chisholm:

I agree.

The problem with the “fitness for use” or “fit for the purpose of use” definition of data quality has been a recurring subject on this blog starting with the post Fit for What Purpose? through to lately the post Inaccurately Accurate discussing the data quality of the British electoral roll seen from either a strict electoral point of view and the point of view from external use of the electoral roll.

The problem with “fitness of use” becomes clear when data quality has to be addressed within master data management. Master data has, per definition so to say, many uses.

My thesis is that there is a breakeven point when including more and more purposes where it will be less cumbersome to reflect the real world object rather than trying to align all known purposes.

Today Jim Harris made an (as ever) excellent post related to how data actually represents what it purports to represent – now and tomorrow too. Find the post called Syncing versus Streaming on the Data Roundtable.

Filed under: Master Data

Syncing versus Streaming

2012-05-23T14:00:04Z

In a previous post, I pondered, since unlimited data plans do not cause smartphone data silos, whether the enterprise data management equivalent of an unlimited data plan could provide users with access to unlimited data streaming services without allowing them to create local copies of enterprise data, thereby limiting the proliferation and retention of data silos.

With his comment on my blog post, Henrik Liliendahl Sørensen extended the discussion to include not only data silos within enterprises but also between enterprises by way of the cross-enterprise use of external reference data.

I responded that using external reference data as a service, just like we use data streaming services for music and movies is a far better, and far more sustainable, approach than copying external reference data into an internal data silo, which almost immediately starts to become out of alignment with the real-world precisely because it has be siloed. And then we waste a lot of subsequent time, money, and effort trying to keep the internally siloed data updated as the real-world changes.

Essentially, to extend my smartphone analogy, I am talking about Syncing versus Streaming.

For example, since I have created a personal data silo of music, syncing keeps my iTunes music library updated across multiple devices (smartphone, tablet, laptop), whereas streaming via a service like Pandora allows me to listen to an external reference database of music from any device.

Of course, putting my personal data silo of music in the cloud would enable streaming my music library on demand without creating more data silos and requiring syncing data across multiple devices.

That’s why I think the cloud combined with data streaming is the future of enterprise data management. However, one unavoidable challenge is this would mean streaming a Single Version of the Truth, which, because its concept is often resisted, also drives the proliferation of data silos.

Syncing allows for multiple versions of the truth by allowing you to choose not to sync your data silo with the single version of the truth. Streaming eliminates the data silos, but also eliminates the possibility of viewing data in a different way or using data for a different purpose.

Do you think that your enterprise data management could shift from syncing to streaming?

Read this related Jim Harris blog post: Could Unlimited Data Limit Data Silos?

Facing Maturity: Devaluing your Data Warehouse – No History

2012-05-22T19:00:49Z

Do you just do reporting in the data warehouse? You know the type of reporting that ONLY looks at the most current information. If so, then you may be devaluing the data warehouse. Here is how:

In the beginning, we gathered requirements based on comparative analysis over time by customer or product, etc. We built the data warehouse to support this analysis using the lowest level of granularity to collect the data, and the star schema or cube (also known as a data mart) as the presentation layer for the analysis tool (also known as the front-end tool). So what happened?

Overtime the staff changed, which brought in new ideas (or old ideas in some cases), and in turn (over time) changed the data model. Maybe you started out with a historical and integrated data warehouse that fed the data marts, but the new staff did not understand the need for persistent historical data, and started just building data marts from the staging area. Data marts that did NOT include history! They got their job completed, and probably within scheduled amount of time, but not within the existing architecture. This does, in some cases, place all the integration and complexity in the ETL not created in the data – which would then age gracefully.

So, where is this architecture heading for in the future? Probably, into an environment that doesn’t use (nor do they understand) the use of history.

Where is the Data Warehouse Key Keeper? You know, the person who makes sure every project understands and uses the data warehouse to its fullest extent.

Read more posts from Joyce Norris-Montanari.

Do You Want Social MDM?

2012-05-22T16:26:23Z

This weekend I noticed a tweet from the MDM tool vendor Orchestra Networks:

There is clearly something completely wrong with this tweet. Why on earth should a French company use an American date format?

Apart from that there is a very good point. Why should tool vendors work on solving imaginable future master data management issues as integrating social network profiles with traditional customer master data while there are plenty of issues that need a better solution today?

Personally I think social MDM is going to be huge. I had some of my first musings on the subject some years ago in the post Social Master Data Management. Probably we will start with some Lean Social MDM, and that is honestly also as far as I have explored this field until now.

What about you. Do you want social MDM?

Filed under: Social MDM

Lessons Learned: How CxOs Use MDM

2012-05-22T14:49:30Z

C-level officers can use MDM in a variety of ways.

As we have seen from the eyes of chief officers across many functional areas of an organization, master data management isn’t just an IT solution – it is a practice that must involve the business and be tied to business initiatives in order to succeed. Let’s take a look at what we have learned in this CxO series:

Chief Executive Officers leverage MDM to run the business, grow the business and ultimately to transform the business.
Chief Financial Officers understand that MDM can affect the bottom line by improving processes enabling adherence to regulations, managing customer privacy preferences, consolidating data, identifying credit risk and supporting system consolidation.
MDM can help Chief Information Officers do less with more by leveraging systems they already have.
MDM provides Chief Operating Officers assurance that enterprise data can be trusted and that processes empower the truth.
Chief Marketing Officers utilize MDM to segment and analyze customers, improve customer service and satisfaction, increase offer response rates and market more interactively.
MDM helps answer key questions for Chief Regulatory Officers: Is this customer related to anyone who has attempted to defraud us before? What is this vendor’s track record regarding compliance with ethical labor practices? Am I able to market to this customer via email based on their privacy preferences?
Chief Privacy Officers in healthcare benefit from MDM by automating provider information updates to ensure data in clinical and billing systems is reliable, establishing a single system to validate provider information and automatically populating billing information.

Thank you to the IBMers who contributed their experiences and thoughts around MDM. I know I learned a thing or two and look forward to speaking to many C-level executives about how MDM can help them achieve their corporate visions.

Have you seen any other C-level officers use MDM? What results have they experienced?

The Myth of Supplier Data Quality

2012-05-22T14:00:58Z

In a controlled environment where the data exchange protocols are negotiated and agreed to by both the supplier and the consumer of data, it is fair to suggest that the supplier must meet the data quality requirements of the consumer. However, in our evolving world in which multiple internal analysts and developers continually seek to use externally-acquired data, it is unlikely that there is any set of standards of use. This is abundantly clear when using data sets (anonymously) posted to a web site by the supplier and then (anonymously) downloaded or linked to by the consumer, as it the case with public data sets provided by government agencies at data.gov, or individuals posting their current statuses at social media web sites like Twitter and Facebook.

It goes back to the conflict between intent and inference. The quality of the data is suited to its original intent. Those providing the data have no interest or investment in its further repurposing. The consumers must reinterpret the usage details, and then assign their own sets of quality rules. And there is probably little chance of the consumer influencing the supplier to adjust their processes to improve the quality for their specific use, let alone the potential hundreds or thousands of alternate uses.

So when you hear an expert tell you that it is always the responsibility of the data supplier to ensure that the quality of the data meets the needs of the consumers, suggest that their recommendations are only valid within the controlled scenario. But in most cases of data reuse today, there are wide distances between supplier and consumer and that gap is only going to get wider.

Introducing the All New Informatica Certified Professional Program

2012-05-22T07:31:02Z

Last week at Informatica World 2012, Informatica University announced the launch of a completely redesigned and rebuilt Informatica Certified Professional (ICP) program. The ICP program has one goal – to provide Informatica customers with assurance that those consultants working on …
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An Enforcement Reality supporting my “Penalty Points” idea

2012-05-22T08:01:49Z

Over my morning coffee this morning I read this story from eConsultancy.com about the UK ICO beginning ‘soft enforcement’ of the ePrivacy regulations around cookies.

Good news: They are starting to enforce the law. They will be taking a balanced approach. I assume that the letters will take the form of Information Notices and possibly Enforcement Notices.

Bad news: The level of breach that not complying with the Cookie provisions of the ePrivacy Directive constitutes is not likely to meet the standard of severity required for the ICO to levy a fine.

So businesses will receive a letter. But we can be assured it will be a strongly worded one. But, given the mental discounting that management do in compliance situations, this is inevitably going to lead to precisely no change in compliance behaviour. When faced with the question “So, what’s the worst that is likely to happen?” Data Protection Officers or advisors will have nowhere to go in their persuasion. It is all carrot and no stick. And CxO level managers are pure carnivores, so carrots are not that enticing on their own.

There will be no financial penalty for the Cookie breach
Any penalty that might arise will be for failing to comply with an Enforcement notice or provide information requested under an Information Notice. But that would require another cycle or three of communication between the ICO and the infringing company.

There is no sting in the tail. The arc that must be travelled between Breach and Penalty is too long. And as every parent of a toddler knows, there is no point putting them on the naughty step days or weeks after their valiant but doomed attempt to juggle with kittens.

Hence the need, in my view, to have something else that allows a sting to be put in the tail, that wraps the polite letter from the ICO (or the Irish DPC for that matter) in a small brick that will get attention. In my opinion, if the EU is serious about changing attitudes to Data Protection amongst businesses it needs to ensure that the laws that are passed can be enforced with both carrot and stick so that culture and values in business will change.

Breaches of the Cookies rules fit the bill nicely for a structured penalty system that allows for cumulative penalties to build towards a more serious fine or enforcement action. Assume, for argument, that writing a non-essential cookie without notice and consent was a 1 point offence carrying a fixed penalty notice of €120/£100 for first offence (with higher penalties for subsequent offences). Audit tools such as those developed by CookieQ.com could be used to audit the site, tot up the number of cookies, an investigator could make a judgement as to the essentialness and generate a fixed penalty notice attached to the letter.

Perhaps the 1st offence would be a “freebie”, with a second failure leading to a penalty (after all, we want this to be fair and graduated). At some threshold (let’s say 20 points) more serious penalties would kick in (perhaps the €2million outlined in the proposed Regulation, or mandatory multi-year privacy audits such as being imposed on firms in the US by the FTC). As this is an evolving thought doodle I won’t waste time mapping specifics here.

If the penalty points for the Cookie infringement formed part of the overall “scorecard” that a company would accumulate, adding to the risk of a more severe penalty (and the inevitability for hard core recidivists). If, as with parking tickets and speeding fines, the Data Controller had the right to appeal the fixed penalty to the Courts (at the risk of a greater penalty and increased publicity), the “mental discounting’” would need to change. This would change the conversation for Data Protection Officers and advisors when the letter comes.

Boss: "What is the worst that they can do?

DP Team: “Well,50 cookies being written has already cost you €5000 in fixed price penalties. You can appeal them to Court, but that carries a risk of the penalty being increased further and a conviction being recorded against you.”

Boss: “OK, so pay the fine and then we keep going.”

Boss: “Oh shit. Let’s fix this then”

Just as cumulative breaches of Road safety lead to serious penalties, cumulative breaches of Data Protection rules could lead to more serious penalties.

The benefit of this approach is it would encourage and incentivise organisations to focus on the small stuff. And as repeated studies in risk management and accident investigation have shown, the major disasters are usually a result of an accumulation of small things.

According to econsultancy, the ICO is considering applying penalties based on a scale. It is not a significant jump from a scale for a specific penalty to a framework for levying administrative sanctions in a structured and transparent manner.

Introducing the new IBM Information Server for Data Integration bundle

2012-05-22T01:16:31Z

IBM has released new Information Server product bundles that offer great discounts and a great platform for batch or real time data integration.

Information Asymmetry versus Empowered Customers

2012-05-22T08:00:00Z

Information asymmetry is a term from economics describing how one party involved in a transaction typically has more or better information than the other party. Perhaps the easiest example of information asymmetry is retail sales, where historically the retailer has always had more or better information than the customer about a product that is about to be purchased.

Generally speaking, information asymmetry is advantageous for the retailer, allowing them to manipulate the customer into purchasing products that benefit the retailer’s goals (e.g., maximizing profit margins or unloading excess inventory) more than the customer’s goals (e.g., paying a fair price or buying the product that best suits their needs). I don’t mean to demonize the retail industry, but for a long time, I’m pretty sure its unofficial motto was: “An uninformed customer is the best customer.”

Let’s consider the example of purchasing a high-definition television (HDTV) since it demonstrates how information asymmetry is not always about holding back useful information, but also bombarding customers with useless information. In this example, it’s about bombarding customers with useless technical jargon, such as refresh rate, resolution, and contrast ratio.

To an uninformed customer, it certainly sounds like it makes sense that the HDTV with a 240Hz refresh rate, 1080p resolution, and 2,000,000:1 contrast ratio is better than the one with a 120Hz refresh rate, 720p resolution, and 1,000,000:1 contrast ratio.

After all, 240 > 120, 1080 > 720, and 2,000,000 > 1,000,000, right? Yes — but what do any of those numbers actually mean?

The reality is that refresh rate, resolution, and contrast ratio are just three examples of useless HDTV specifications because they essentially provide no meaningful information about the video quality of the television. This information is advantageous to only one party involved in the transaction — the retailer — since it appears to justify the higher price of an allegedly better product.

But nowadays fewer customers are falling for these tricks. Performing a quick Internet search, either before going shopping or on their mobile phone while at the store, is balancing out some of the information asymmetry in retail sales and empowering customers to make better purchasing decisions. With the increasing availability of broadband Internet and mobile connectivity, today’s empowered customer arrives at the retail front lines armed and ready to do battle with information asymmetry.

The empowered customer changes the balance of power in the retail industry. Is your business ready for Smarter Commerce? Join the conversation on the IBM Mid-Market Smarter Commerce Twitter chat on Wednesday, May 23 at 2:00PM EST. Panelists will include some of the top Mid-Market influencers in the industry. IBM Experts, business partners, business owners and managers are all encouraged to join in, ask questions, and share their knowledge in a relaxed atmosphere. The chat can be followed on Twitter using the hashtag #mmSCchat or log on and access the chat on twebevent: twebevent.com/mmSCchat

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet.

The customer conundrum

2012-05-21T12:42:28Z

I’m a customer of a few on-online services. I have really liked using Tweetdeck for the past few months (hang on… years… eek). The problem is that I’m busy. Nuts busy. I’ve a business, a family, and a strange compulsion to sleep maybe a few minutes or three every day or so.

I’m a voracious reader and idea gatherer. This is the problem I’m facing now. Tweetdeck/twitter has put a massive pool of people at my disposal who are sticking post-it notes under my nose every few seconds saying “Hey, you might like this. Click through and read it”. And I do. And I get lost in clicks-ville as I wander through related content.

So I use Instapaper. A lot. It synchs to my kindle (I’m such a hipster). My good friend @tupp_ed put me onto it. I also use Evernote and Microsoft OneNote (depending on what I’m reading – different files for different contexts etc.). So, when I see something on a website I like I click the “Read Later” or “Send to …” widget and ping… I don’t need to read any more than the first few lines of the article. It even works on my iphone. And on the twitter client on my iphone at that. Often I don’t even open the link when it comes into me on my iOS client. I just “Read Later” and huzzah I am done until I download a tonne of stuff to read on Saturday morning over a gallon of coffee and a sausage or six.

But not on Tweetdeck on my computer. On Tweetdeck I have to open the link in a browser. This is the problem. I pull the post-it note my friend has sent me and then I pull up the reference. And I read it. And invariably I wind up reading all of it. And any related content. And then it is suddenly Thursday and the deadline that was small and manageable has become a big hairy gurner that I need to disengage from my family to tackle.

This is a weakness in Tweetdeck. One that I am finding it harder to work with. Other twitter clients integrate Instapaper (or Evernote or similar services). Tweetdeck is an isolated island that needs me to walk across a bridge into time-suck land to even begin to integrate it into the way that I want to work with my knowledge suck tendencies.

So. I’ll keep using Tweetdeck for a few months but will be trying out other services in the meantime. Once I find a service that can be a Platform-For-Me I’ll blog about why. But right now my general wish list is:

Needs to let me manage business, personal, and volunteer tweeting in one place
Needs to let me sling links to Instapaper, Evernote etc. easily (no double lifting to get the content to my kindle for reading days)
If it can hook into the other tools I’m using to help run my business and my personal life all the better.
In using it I need to be able to ensure that my company isn’t going to fall foul of any personal data winding up in places it shouldn’t, so Safe Harbor/Safe Country requirements under EU Data Protection laws are a consideration.
It must not have a learning curve that requires mountaineering gear or a rocket pack to climb

Not too much to ask I hope.

Solve Unscheduled Fleet Maintenance with Business Analytics

2012-05-21T13:00:00Z

James Langley explains the various steps you can take to minimize unscheduled fleet maintenance exposure and save money.

Avoiding Contact Data Entry Flaws

2012-05-19T10:51:12Z

Contact data is the data domain most often mentioned when talking about data quality. Names and addresses and other identification data are constantly spelled wrong, or just different, by the employees responsible of entering party master data.

Cleansing data long time after it has been captured is a common way of dealing with this huge problem. However, preventing typos, wrong hearings and multi-cultural misunderstandings at data entry is a much better option wherever applicable.

I have worked with two different approaches to ensure the best data quality for contact data entered by employees. These approaches are:

Correction and
Assistance

Correction

With correction the data entry clerk, sales representative, customer service professional or whoever is entering the data will enter the name, address and other data into a form.

After submitting the form, or in some cases leaving each field on the form, the application will check the content against business rules and available reference data and return a warning or error message and perhaps a correction to the entered data.

As duplicated data is a very common data quality issue in contact data, a frequent example of such a prompt is a warning about that a similar contact record already exists in the system.

Assistance

With assistance we try to minimize the needed number of key strokes and interactively help with searching in available reference data.

For example when entering address data assistance based data entry will start with the highest geographical level:

If we are dealing with international data the country will set the context and know about if a state or province is needed.
Where postal codes (like ZIP) exists, this is the fast path to the city.
In some countries the postal code only covers one street (thoroughfare), so that’s settled by the postal code. In other situations we will usually have a limited number of streets that can be picked from a list or settled with the first characters.

(I guess many people know this approach from navigation devices for cars.)

When the valid address is known you may catch companies from business directories being on that address and, depending on the country in question, you may know citizens living there from phone directories and other sources and of course the internal party master data, thus avoiding entering what is already known about names and other data.

When catching business entities a search for a name in a business directory often leads to being able to pick a range of identification data and other valuable data and not at least a reference key to future data updates.

Lately I have worked intensively with an assistance based cloud service for business processes embracing contact data entry. We have some great testimonials about the advantages of such an approach here: instant Data Quality Testimonials.

Filed under: Big Reference Data, Business Directories, Master Data

Capturing Change Data for Better Decisions

2012-05-18T18:44:19Z

Businesses want their change data capture to be fast and accurate - like a race car.

The power of NOW is on the rise. Gone are the days when patience was a virtue; today it is a disadvantage.

Businesses seek to adapt quickly to change and gain first mover advantages. They want to make quick decisions based on accurate information.

It is also vital that any change in the data that generates management information is also quickly and accurately reflected so that the reference always reflects the rapidly changing business reality.

Accuracy with speed is a potent combination but one not easily achievable. Any Formula One race car driver would agree!

Technologies such as InfoSphere Change Data Capture (CDC) enable businesses to overcome this challenge by capturing only relevant operational data (which has been updated or modified) and transmitting it across the enterprise. This ensures that everyone has a single source of truth.

IBM InfoSphere Data Replication CDC is powerful tool which can power you ahead in this race for accurate information.

Read our new Redbook, ''Smarter Business: Dynamic Information with IBM InfoSphere Data Replication CDC '' to learn more about how it fits into optimized data integration. Go on, race ahead by downloading your copy now.

Table of Contents:

Chapter 1: Introduction
Chapter 2: InfoSphere CDC: Empowering Information Management
Chapter 3: Business Use Cases for InfoSphere CDC
Chapter 4: Solution Topologies
Chapter 5: InfoSphere CDC Features & Functionality
Chapter 6: Understanding the Architecture
Chapter 7: Environmental Considerations
Chapter 8: Performance Analysis and Design Considerations
Chapter 9: Customization and Automation