Three ideas for communicating data quality success

You have kicked off a data quality initiative and had some great results. Your next goal is to build on this success and grow the influence of your team around the organisation.

To achieve this, you need to get creative and put on your marketing hat. Here are some ideas that past interviewees on Data Quality Pro have shared with me when quizzed about their communication strategy.

1) Host an annual data quality event or summit

One of my recent interviews was with a data architect in a large lifestyle media organisation. To help engage the business, they are creating a data summit to help the business learn more about the potential of its data and how they can get involved.

This is an excellent idea for any company that wants to showcase some of the data quality projects they’ve been successfully delivering.

For global organisations it also offers the opportunity to host virtual events or invite members from overseas to observe how the data quality vision is maturing. Read More »

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On data, risk and LeBron

Few cities use data as much as Las Vegas. Walk into any casino and you'll see nothing sort of a paean to data. For instance, blackjack dealers use zero discretion when flipping cards. Pit bosses scour for potential cheats looking to move the needle just a few degrees. And that means you, Ben Affleck.

It should be no surprise, then, that many Vegas casinos took extreme action in light of LeBron James' decision to become a free agent on June 24, 2014. As Darren Rovell of ESPN wrote in LeBron opt-out impacts Vegas books:

As news of LeBron James opting out of his contract with the Miami Heat on Tuesday morning spread, some insiders in the gambling world took the ultimate measure to minimize their risk. They pulled all future odds on all NBA teams to win next year's title. Read More »

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Data science and decision science

Data science, as Deepinder Dhingra recently blogged, “is essentially an intersection of math and technology skills.” Individuals with these skills have been labeled data scientists and organizations are competing to hire them.

“But what organizations need,” Dhingra explained, “are individuals who, in addition to math and technology, can bring in the right business perspective. These individuals must have the ability to artfully blend left-brained and right-brained thinking to solve complex business problems. They should possess the requisite analytical skills to understand, translate and generate insights that can then be consumed effectively.” Dhingra refers to them as decision scientists who complete the data-driven decision making process started by data scientists. Read More »

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Examples of using graph analytics

Over the past few weeks I have been discussing the use of graph models for analyzing interconnectivity and how entity characteristics can be inferred in relation to links and connections. While we looked at the social network domain for identifying influential individuals within a social community, there are numerous other opportunities for examining the relationship between entities and identifying actionable knowledge. Here are three common examples:

  • Mobile telecommunications network optimization: Mobile telecommunications companies seek to make the best use of their network in a number of ways, including ensuring the quality and continuity of calls (so that calls are not dropped in the middle of connections), providing equal distribution of bandwidth (to prevent any particular section of the network from being overloaded), and eliminating dead zones (where there is little or no connectivity). Read More »
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Cracking the code to successful conversions - review or define standards

It is important to review any existing documents (maybe from previous projects) before re-inventing the wheel (standards and guidelines). My preference is to review existing standards with my customer and recommend any enhancements or additions that may be required for the conversion project. Consider locating the following:

1. Infrastructure guidelines - Usually found via the technical staff (systems personnel). Review for any gotchas, and include a report on usage of the intended platforms.

2. Database guidelines - Usually found via the database administrators or keepers of the production databases. This could be internal or external people.

3. Data modeling standards and guidelines - Usually found by talking with the enterprise data modeler or the manager of data management, and should include naming standards and entity/attribute definitions. Read More »

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Getting clinical with data quality analysis

I have recently qualified as a volunteer first responder to assist ambulance crews in my rural community, which is an interesting break from the world of data.

But not a break entirely.

During my training, it occurred to me that we’re simply not equipping many data quality practitioners with the right techniques to get to the complex root causes of a data quality problem.

Tackling data quality defects is very similar to treating patients at the scene. Your first task is to assess for any serious difficulties that you can tackle immediately. The same applies to your data. If a customer order is incomplete and lacking the correct address details, you will actively chase down that information in order to keep the customer happy (not to mention the delivery driver!).

With the immediate danger over, you then look to perform some basic observations to discover a range of potential problems. In a data quality context this may involve data profiling and data quality assessment activities to help you gather some baseline statistics of the data and its lineage. Read More »

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The error paradox

How are sales going?

It's a frequent query that every author gets from time to time. Lamentably, though, that four-word question is difficult if not impossible to answer with any precision.

If this seems like a paradox, you're absolutely right. Back in the Mad Men days, real-time sales numbers for authors were notoriously difficult to get. Ditto for sales of records, movie tickets and other pop-culture items. In 2014, you'd think that authors and publishers would have access to this type of information. And you'd be wrong, for the most part. (AppAnnie and a few other companies are trying to change that.)

Small-data problems

The issue is two-fold. While most sales may take place on Amazon, the company isn't terribly forthright about sharing much if not most this data with John Q. Author, not that I blame Bezos et. al. (Amazon's Author Central is decent, but nowhere nearly as robust as I would like.) Few companies are as guarded as Amazon, especially when it comes to data-related matters. Read More »

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The data that supported the decision

Data-driven journalism has driven some of my recent posts. I blogged about turning anecdote into data and how being data-driven means being question-driven. The latter noted the similarity between interviewing people and interviewing data. In this post I want to examine interviewing people about data, especially the data used by people to drive their business decisions, which are not supposed to be driven by anecdote or intuition.

This subject reminds of the classic American Western film The Man Who Shot Liberty Valance, which was adapted from a short story written by Dorothy Johnson. Liberty Valance (played by Lee Marvin) was an outlaw terrorizing the frontier town of Shinbone. Ransom Stoddard (played by James Stewart) accepts Valance’s challenge to a gun duel, during which, despite his lack of gunslinging skills, Stoddard kills Valance with one shot. Heralded as a hero, Stoddard goes on to have a long and successful political career as a governor and United States senator. Read More »

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Knowledge embedded in network organization

In our previous posts along this thread, I have suggested that graph analytics provides benefits in identifying actionable knowledge inherent in the relationships between and among entities, as opposed to typical analyses that focus on characterizing individual entities. I have to admit, that suggestion is a little bit misleading. What I really mean is that the process of analyzing the relationship among entities allows you to infer characteristics about entity behaviors that you might not have been able to identify otherwise.

A good example involves analyzing the level of influence that one individual exerts over others in a social network. Analyzing the sequence of transactions that a single customer makes over time provides one view of that person’s value as a customer. By examining the purchase patterns in the past, you can estimate (or perhaps even predict) what that customer’s projected consumption of products will be in the future. Read More »

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Cracking the code to successful conversions - scope

I don't know about you, but I've been on multiple conversion projects where the scope changes – especially during development. It's not that the requirements were not gathered properly; the requirements changed!

The business changes and people change, so the requirements can change on large conversion projects. I like to create scope documents for any project I am doing. Within this document I like to state exactly what business questions I'm solving for in this conversion. Even more, I like to state what I am not solving for in this project.

Here are some examples: Read More »

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