Links from my eLearning Webinar

I recently delivered a webinar on the Secrets of Affordable Data Governance. In the webinar, I promised to deliver links for lowering the costs of data management.  Here are those links:

• Talend Open Source - Download free data profiling, data integration and MDM software.
• US Census - Download census data for cleansing of city name and state with latitude and longitude appends.
• Data.gov - The data available from the US government.
• Geonames - Postal codes and other location reference data for almost every country in the world.
• GRC Data - A source of low-cost customer reference data, including names, addresses, salutations, and more.
• Regular Expressions - Check the shape of data in profiling software or within your database application.

If you search on the term "download reference data", you will find many other sources.

Donating the Data Quality Asset

If you believe like I do that proper data management can change the world, then you have to start wondering if it’s time for all us data quality professionals to stand up and start changing it.

It’s clear that everyone organization, no matter what the size or influence, can benefit from properly managing their data. Even charitable organizations can benefit with a cleaner customer list to get the word out when they need donations.  Non-profits who handle charitable goods can benefit from better data in their inventory management.  If food banks had a better way of managing data and soliciting volunteers, wouldn’t more people be fed? If churches kept better records of their members, would their positive influence be more widespread?  If organizations who accept goods in donation kept a better inventory system, wouldn’t more people benefit? The data asset is not limited to Fortune 1000 companies, but until recently, solutions to manage data properly were only available to the elite.

Open source is coming on strong and is a factor that eases us to donate the data quality.  In the past, it many have been a challenge to get mega-vendors to donate high-end solutions, but we can make significant progress on the data quality problem with little or no solutions cost these days. Solutions like Talend Open Profiler, Talend Open Studio, Pentaho and DataCleaner offer data integration and data profiling.

In my last post, I discussed the reference data that is now available for download.  Reference data used to be proprietary and costly. It’s a new world – a better one for low-cost data management solutions.

Can we save the world through data quality?  If we can help good people spread more goodness, then we can. Let’s give it a try.

Referential Treatment - The Open Source Reference Data Trend

Reference data can be used in a huge number of data quality and data enrichment processes.  The simplest example is a table that contains cities and their associated postal codes – you can use an ETL process to make sure that all your customer records that contain 02026 for a postal code always refer to the standardized “Dedham, MA” for the city and state, not variations like “Deadham Mass”  or “Dedam, Massachusetts”.

Reference data is not limited to customer address, however. If everyone were to use the same reference data for parts, you could easily exchange procurement data between partners.  If only certain values are allowed in any given table, it would support validation.  By having standards for supply chain data, procurement, supply chain, finance and accounting data, processes are more efficient.  Organizations like the ISO and ECCMA are working on that.

Availability of Reference Data
In the past, it was difficult to get your hands on reference data. Long ago, no one wanted to share reference data with you - you had to send your customer data to a service provider and get the enriched data back.  Others struggled to develop reference data on their own. Lately I’m seeing more and more high quality reference data available for free on the Internet.   For data jockeys, these are good times.

GeoNames
A good example of this is GeoNames.  The GeoNames geographical database is available for download free of charge under a creative commons attribution license. According to the web site, it “aggregates over 100 different data sets to build a list containing over eight million geographical names and consists of 7 million unique features whereof 2.6 million populated places and 2.8 million alternate names. The data is accessible free of charge through a number of web services and a daily database export. “

GeoNames combines geographical data such as names of places in various languages, elevation, population and others from various sources. All lat/long coordinates are in WGS84 (World Geodetic System 1984). Like Wikipedia, users may manually edit, correct and add new names.

US Census Data
Another rich set of reference data is the US Census “Gazetteer” data. Courtesy of the US government, you can download a database with the following fields:

• Field 1 - State Fips Code
• Field 2 - 5-digit Zipcode
• Field 3 - State Abbreviation
• Field 4 - Zipcode Name
• Field 5 - Longitude in Decimal Degrees (West is assumed, no minus sign)
• Field 6 - Latitude in Decimal Degrees (North is assumed, no plus sign)
• Field 7 - 2000 Population (100%)
• Field 8 - Allocation Factor (decimal portion of state within zipcode)

So, our Dedham, MA entry includes this data:

• "25","02026","MA","DEDHAM",71.163741,42.243685,23782,0.003953

It’s Really Exciting!
When I talk about reference data at parties, I immediately see eyes glaze over and it’s clear that my fellow party-goers want to escape my enthusiasm for it.  But this availability of reference data is really great news! Together with the open source data integration tools like Talend Open Studio, we’re starting to see what I like to call “open source reference data” becoming available. It all makes the price of improving data quality much lower and our future much brighter.

There’s so much to talk about with regard to reference data and so many good sources.  I plan to make more posts on this topic, but feel free to post your beloved reference data sources here in the comments section.

The Secret Ingredient in Major IT Initiatives

One of my first jobs was that of assistant cook at a summer camp.  (In this case, the term ‘cook’ was loosely applied meaning to scrub pots and pans for the head cook.) It was there I learned that most cooks have ingredients that they tend to use more often.  The cook at Camp Marlin tended to use honey where applicable.  Food TV star Emeril likes to use garlic and pork fat.  Some cooks add a little hot pepper to their chocolate recipes – it is said to bring out the flavor of the chocolate.  Definitely a secret ingredient.
For head chefs taking on major IT initiatives the secret ingredient is always data quality technology. Attention to data quality doesn’t make the recipe of an IT initiative alone so much as it makes an IT initiative better.  Let’s take a look at how this happens.

Profiling
No matter what the project, data profiling provides a complete understanding of the data before the project team attempts to migrate it. This can help the project team create a more accurate plan for integration.  On the other hand, it is ill-advised to migrate data to your new solution as-is, as it can lead to major costs over-runs and project delays as you have to load and reload it.

Customer Relationship Management (CRM)
By using data quality technology in CRM, the organization will benefit from a cleaner customer list with fewer duplicate records. Data quality technology can work as a real-time process, limiting the amount of typos and duplicates in the system, thus leading to improved call center efficiency.  Data profiling can also help an organization understand and monitor the quality of a purchased list for integration will avoid issues with third-party data.

Enterprise Resource Planning (ERP) and Supply Chain Management (SCM)
If data is accurate, you will have a more complete picture of the supply chain. Data quality technology can be used to more accurately report inventory levels, lowering inventory costs. When you make it part of your ERP project, you may also be able to improve bargaining power with suppliers by gaining improved intelligence about their corporate buying power. 

Data Warehouse and Business  Intelligence
Data quality helps disparate data sources to act as one when migrated to a data warehouse. Data quality makes data warehouse possible by standardizing disparate data. You will be able to generate more accurate reports when trying to understand sales patterns, revenue, customer demographics and more.

Master Data Management (MDM)
Data quality is a key component of master data management.     An integral part of making applications communicate and share data is to have standardized data.  MDM enhances the basic premise of data quality with additional features like persistent keys, a graphical user interface to mitigate matching, the ability to publish and subscribe to enterprise applications, and more.

So keep in mind, when you decide to improve data quality, it is often because of your need to make a major IT initiative even stronger.  In most projects, data quality is the secret ingredient to make your IT projects extraordinary.  Share the recipe.

A Data Governance Mission Statement

Every organization, including your data governance team has a purpose and a mission. It can be very effective to communicate your mission in a mission statement to show the company that you mean business.  When you show the value of your team, it can change your relationship with management for the better.

The mission statement should pay tribute to the mission of the organization with regard to values, while defining why the data governance organization exists and setting a big picture goal for the future.
The data governance mission statement could revolve around any of the following key components:

• increasing revenue
• lowering costs
• reducing risks (compliance)
• meeting any of the organization’s other policies such as being green or socially responsible

The most popular format seems to follow:

Our mission is to [purpose] by doing [high level initiatives] to achieve [business benefits]

So, let’s try one:

Our mission is to ensure that the highest quality data is delivered via company-wide data governance strategy for the purpose of improving the efficiency, increasing the profitability and lowering the risk of the business units we serve.

Flopped around:

Our mission is to improve the efficiency, increase the profitability and lower the business risks to Acme’s business units by ensuring that the highest quality data is delivered via company-wide data governance strategy.

Not bad, but a mission statement should be inspiring to the team and to management. Since the passions of the company described above are unknown, it’s difficult for a generic mission statement to be inspirational about the data governance program. That’s up to you.
 
Goals & Objectives
There are mission statements and there are objectives. While every mission statement should say who you are and why you exist, every objective should specify what you’re going to do and the results you expect.  Objectives include activities that can be easily tracked, measured, achieved and, of course, meet the objectives of the mission.  When you start data governance projects, you can look back to the mission statement to make sure we’re on track. Are you using our people and technology in a way that will benefit the company?

Staying On Mission
When you take on a new project, the mission statement can help protect us and ensure that the project is worthwhile for both the team and the company. The mission statement should be considered as a way to block busy-work and unimportant projects.  In our mission statement example above, if the project doesn’t improve efficiency, lower costs or lower business risk, it should not be considered.

In this case, your can clearly map three projects to the mission, but the fourth project is not as clear.  Dig deeper into the mainframe project to see if any efficiency will come out of the migration.  Is the data being used by anyone for a business purpose?

A Mission Never Ends
A mission statement is a written declaration of a data governance team's purpose and focus. This focus  normally remains steady, while objectives may change often to adapt to changes in the business environment. A properly crafted mission statement will serve as a filter to separate what is important from what is not and to communicate your value to the entire organization.

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ETL, Data Quality and MDM for Mid-sized Business

Is data quality a luxury that only large companies should be able to afford?  Of course the answer is no. Your company should be paying attention to data quality no matter if you are a Fortune 1000 or a startup. Like a toothache, poor data quality will never get better on its own.

As a company naturally grows, the effects of poor data quality multiply.  When a small company expands, it naturally develops new IT systems. Mergers often bring in new IT systems, too. The impact of poor data quality slowly invades and hinders the company’s ability to service customers, keep the supply chain efficient and understand its own business. Paying attention to data quality early and often is a winning strategy for even the small and medium-sized enterprise (SME).

However, SME’s have challenges with the investment needed in enterprise level software. While it’s true that the benefit often outweighs the costs, it is difficult for the typical SME to invest in the license, maintenance and services needed to implement a major data integration, data quality or MDM solution.

At the beginning of this year, I started with a new employer, Talend. I became interested in them because they were offering something completely different in our world – open source data integration, data quality and MDM.  If you go to the Talend Web site, you can download some amazing free software, like:

• a fully functional, very cool data integration package (ETL) called Talend Open Studio
• a data profiling tool, called Talend Open Profiler, providing charts and graphs and some very useful analytics on your data
  

The two packages sit on top of a database, typically MySQL – also an open source success.

For these solutions, Talend uses a business model similar to what my friend Jim Harris has just blogged about – Freemium. Under this new model, free open source content is made available to everyone—providing the opportunity to “up-sell” premium content to a percentage of the audience. Talend works like this.  You can enhance your experience from Talend Open Studio by purchasing Talend Integration Suite (in various flavors).  You can take your data quality initiative to the next level by upgrading Talend Open Profiler to Talend Data Quality.

If you want to take the combined data integration and data quality to an even higher level, Talend just announced a complete Master Data Management (MDM) solution, which you can use in a more enterprise-wide approach to data governance. There’s a very inexpensive place to start and an evolutionary path your company can take as it matures its data management strategy.

The solutions have been made possible by the combined efforts of the open source community and Talend, the corporation. If you’d like, you can take a peek at some source code, use the basic software and try your hand at coding an enhancement. Sharing that enhancement with community will only lead to a world full of better data, and that’s a very good thing.

The World is Addicted to Data (and that's good for us)

In the famous book “The Transparent Society”, we are asked to consider some of the privacy ills we will be facing as technology improves and our society gains access to more data sets. The book was groundbreaking when it was written in 1999. It imagines the emergence of groups who are more powerful because they own the data. However, as we sit here ten years later with 20/20 hindsight, it’s clear that the existence and access to specialized data sets makes our life better, not worse.

There are countless examples of this daily improvement in our lives, but some personal ones:

• I was in the supermarket recently and per usual, there was a long line at the deli. On the other hand, there was no line at the “deli kiosk” so I gave it a try. Based on my frequent shopper card number and underlying database, the deli kiosk already knew my preferred brand and type of cheese and delicious deli meats. Ordering was a snap thanks to a database, and I didn’t even have to mispronounce “Deutschmacher” to the deli man, like I usually do.
• For Thanksgiving, I visited some relatives that I don’t often see. My GPS led me there thanks to a geospatial database. It told me how long it was going to take based on traffic data, which is often aggregated from several sources, including road sensors and car and taxi fleets. I also was informed about all the coffee shops along the way, thanks to the data set provided by the Dunkin Donuts. Before I left, I used Google Street View and Microsoft Bing’s Birds Eye view to see what the destination looked like. Ten years ago, all of this was pretty much unheard of, but thanks to the coming together of geospatial data, real-time traffic data, satellite and airplane imagery, street view imagery, Dunkin Donuts franchise data, and small, cheap processors, my trip was fantastic.
• Fantasy Football is a new phenomenon, made possible by data our addiction to data. We know exactly where we stand on any given Sunday as player stats are made available instantly during the games. When Wes Welker scores, I see the six points reflected on my score instantly. Companies like STATS not only cover football, but according to their web site - 234 sports.
• For iPhone users, there are tons of data-centric applications. For example, Wait Watchers is an app that uses user submissions to generate and display a table of the current ride wait times at major theme parks throughout the world. As this information is updated by users, other users at Disney can make decisions about whether to go to Space Mountain or It’s a small world, for example.

In the corporate world, it’s much of the same and even more important to our society. Marketing teams are addicted to information from web analytics and use marketing automation tools to track the success of their programs. Operations teams track assets like computers, buildings, trucks and people with data. Sales has been and will continue to track customers with data. Finance relies on the collision of credit scores data, invoice and payment data as well as making sure they have enough money in reserves to meet regulations. Executives will continue to rely on business intelligence and data. In fact, it’s hard to find anyone in the business world who doesn’t rely on data.

Of course, much of this is anecdotal. I haven’t found any specific study on the increase in database use, but we do know from an old IDC study that the number of servers in use worldwide, presumably some used for database, has roughly doubled from 2000 to 2005. A doubling of servers, combined with a typically bigger hard drive capacity, point to higher database use.

It was difficult to imagine us here ten years ago, and it’s even more difficult to imagine where we’ll be at the beginning of 2020.  It seems to me that we'll have more opportunity to create and use information with applications on our mobile devices. The collision of iPhone/Droid devices with increasing bandwidths of 3G and 4G networks on the major mobile phone carriers tells me that data in the future will let us do things we can only imagine today.

The world is addicted to data and that bodes well for anyone who helps the world manage it. In 2010, no matter if the economy turns up or down, our industry will continue to feed the addiction to good, clean data.