Tag Archive for 'Data Quality'

More actionable data

Published on June 8, 2010 in Data Governance, Data Quality, Geocoding and Spectrum Technology Platform. 0 Comments Tags: , , , .

Business users count on high-quality data every moment of every day.  

In practical terms, data needs to be “fit for use”.  That is, you data should add value to your existing operations and help users make better, more accurate decisions in the course of doing business.  For most organizations, ensuring that data is fit for use entails three distinct disciplines. 

  • Traditional data quality: the ability to cleanse, standardize and validate data
  • Location intelligence: the ability to see data in context of location
  • Spatial analysis: the ability to understand the relationship between two or more data points

Today, 70% of all records contain a location element and knowing “where” affects decisions in virtually every aspect of an organization.  Consider:

  • Marketing can gain insights into customer needs and buying habits
  • Insurance underwriters can identify potential risks (such as proximity to a flood zone)
  • Finance teams can assign proper tax jurisdictions
  • Sales managers can develop territories based on true market opportunity
  • Service teams can plan routes to minimize logistics costs
  • Facilities managers can develop optimal network strategies

Location and spatial analysis are so critical, in fact, that organizations are increasingly looking to incorporate geocoding, location intelligence and predictive analytics as part of their core data quality platforms and processes.

This month, Navin Sharma, our Director Global Product Strategy, will share how these market realities play out in the latest release of the Spectrum™ Technology Platform.  This informative webinar will include details on how you can integrate enterprise geocoding, location intelligence and a new geo-confidence model into your core CRM, ERP and legacy systems.  Ideal for data stewards, IT managers and business users, I invite you to register for this Webinar today and get the facts on today’s best practices in data quality, data management and more.

The value of WHERE

Published on December 2, 2009 in Data Quality, Geocoding and Uncategorized. 0 Comments Tags: , , , , , .

Did you know that approximately 70 percent of all business data contains a location component? As the amount of location-related data increases, organizations are finding new ways to capture and analyze this information to strengthen customer relationships and make smarter business decisions—decisions that can plan an important role in your future success.

This month, we are pleased to announce that David Loshin, president of Knowledge Integrity, Inc., will offer his insights and perspectives through a much-anticipated webinar:

Location Intelligence and Data Quality: Gain Maximum Value from Your Business Data

Thursday, December 17, 2009 @ 11AM ET.

There is no cost, but you must register.

Like all data-related procedures, the quality of any location-oriented analysis is dependent on the quality of your underlying data. In this webinar, you’ll learn the degree to which data quality management should and can be integrated with location intelligence and spatial analysis. By looking at the types of data used for spatial analysis and location intelligence, you’ll see what data quality and data cleansing practices can deliver more precise results and more reliable decisions.

Registration is required to join this event, which is brought to you with compliments by Pitney Bowes Business Insight.  Please take a moment to register today.

The Four Rings of dPIQ

Published on November 11, 2009 in Data Governance, Data Quality, Data Stewardship and Spectrum Technology Platform. 0 Comments Tags: , , , , , , .

The world probably does not need any more acronyms – especially ones that sound like science fiction movies – but I was particularly impressed with a recent webinar from The Data Warehouse Institute where research analyst Philip Russom talked about dPIQ (pronounced dee-pick).

The idea is that organizations can create more value by unifying data profiling (P), data integration (I) and data quality (Q).  On the one hand, the concept seems so obvious – yet in practice, these functions often report into different teams – each with their own goals and objectives.

When teams work in silos, the lack of coordination often results in redundant efforts – with no synchronization in terms of deliverables or schedules. Russom makes the case as to how data profiling, integration and quality depend on each other.  But more importantly, he provides insight into the iterative and cyclical nature of these disciplines. What he calls “the four rings of dPIQ”.

While you may or may not want to consolidate teams, the real value-add comes from an organization’s ability to coordinate multiple cycles – so that planned updates, changes and improvements to various data management efforts work in concert.  The “four rings” approach can help you understand more about the iterative and overlapping cycles that exist in your company.  If you are in the business of adding value, this webinar could be worth your time.

Managing your data assets

Published on November 11, 2009 in Data Governance, Data Quality and Spectrum Technology Platform. 0 Comments Tags: , , , , , .

There are certain times of the year when we must all put on our financial cap.

As managers of business units and functional groups, we have all become accustomed to the concept of budgeting and planning out expenses for the upcoming year.

Yet there is another financial concept that is often overlooked when it comes to data – and that’s the idea of asset valuation. What is the value of accessible, quality data to your organization?  Can you quantify that value?  More importantly – do you really manage data like an asset?

Your company balance sheet lists current and long-term assets, including inventory, accounts receivable, cash, property and equipment.  But your most important assets – the factors that define who you are and provide for your greatest competitive advantage – are sometimes less obvious. 

Like other corporate assets, data has measurable value that is integral to achieving your strategic objectives.  Likewise, the value of your data can increase or decrease depending on how effectively you manage this asset over time. 

 We’ve recently published a white paper that explores best practices in this area, showcasing where successful managers create environments where data is:

  • accurate and up-to-date
  • accessible and secure
  • usable and well-governed

You may already be looking at next year’s challenges and opportunities from an IT and business owner perspective.  But in today’s environment, it also pays to put on your finance cap as well.

Download this white paper and examine the factors that can maximize the value of your data assets and get a road map for how to increase the return on your investments.

The Business Case for Data Quality

Published on September 8, 2009 in Data Quality and Data Stewardship. 0 Comments Tags: , , , , .

Two recent studies highlight the significant cost of inadequate data quality.

Last month, we wrote about the State of Data Quality, an Information Difference research survey in which one-third of respondents rated their data quality as “poor at best.” A full 63% of organizations had no idea what poor data quality may be costing them.

This month, Gartner quantifies that cost.

In the latest Gartner study, participants estimated that poor data quality cost their organization an average of $8.2 million a year.  22% of respondents calculated their annual losses at $20 million or more.  And while losses of millions of dollars are significant, Gartner analysts believe these figures understate the true financial impact on most organizations.

A recent article in Tech Target added that much of this loss is due to lost productivity among workers who, realizing their data is incorrect, are forced to compensate for the inaccuracies or create workarounds when using both operational and analytic applications.

Tackling the challenges of data quality, however, requires not only an investment of time and money-but also a commitment from management. Given today’s economic pressures, it is more critical than ever for organizations to build a business case for data quality.  Yet that is easier said than done.

As data quality impacts every facet of your organization, calculating its impact can be a daunting task.  Consider for example, what is the value of: 

  • Stronger customer relationships
  • Improved targeting and higher sales
  • Streamlined billing and order processing
  • Compliance with state and federal regulations
  • Lower cost of communication and postage
  • Consolidated workflow processes
  • Better management decisions
  • Timely, accurate service

Fortunately, you can learn how to overcome these challenges and build the case for stronger data quality in your organization. On Friday, September 18th, Andy Hayler, the President and CEO of The Information Difference, will share his insights on what it takes to make a business case for data quality.  Using real-world stories that illustrate both successes and failures, this interactive webinar can help you gain the executive support you need to make a difference.

Best of all, as a guest of Pitney Bowes Business Insight, you can participate in this informative webinars at no cost to you.  You just need to register today.

Announcing the Pitney Bowes Spectrum™ Technology Platform

Published on August 19, 2009 in Spectrum Technology Platform. 1 Comment Tags: , , , , , , , , , .

In the next few weeks and months, you will be hearing a great deal about the Pitney Bowes Spectrum™ Technology Platform. This represents a renaming of our enterprise data quality and location intelligence solutions formerly called Customer Data Quality (CDQ) Platform. The new name will be used beginning with the next release. If you are currently using any of the Customer Data Quality Platform Modules, this new name will not require anything from you.

Next month, you will be hearing about our 6.0 release which further expands the capabilities of our enterprise data quality solution. This release will use our new naming and be called Pitney Bowes Spectrum™ Technology Platform 6.0.

The new name was created to clarify exactly what we offer. Essentially, we are using the name Pitney Bowes Spectrum™ Technology Platform as we reference the SOA plaform in its entirety. We then grouped modules together to create five core function areas, illustrated in this diagram.

This new module alignment is very flexible. We will continue to be the only vendor to offer functionality in modular fashion. In addition, we will now bundle modules into packages for those customers who desire it.

Below are some frequently asked questions. We welcome additional questions or comments.

Frequently Asked Questions

 

Do I have to uninstall CDQ Platform to install Pitney Bowes Spectrum™ Technology Platform?

Nothing about the Pitney Bowes Spectrum™ Technology Platform naming requires you to install or uninstall anything.

 

What about the modules?

As stated above, we are keeping the modules.  It is the preference of our customers and therefore will continue to be how we offer our solution.  The module names will not change. 

 

Does this new name change the frequency or format of database updates?

No.  This will not be changed due to the new naming.

 

Will I still find the databases in the same place on the support site?

Yes.

 

Do I have to upgrade to the new Spectrum release, or can I stay on CDQ Platform 5.7?

You can stay on v5.7, but we highly recommend you move to Pitney Bowes Spectrum™ Technology Platform as soon as possible so you can take advantage of the great new features we have added.

 

Will the programs I’ve already written with the CDQ API still work?

Yes. And that is true for all available APIs.

 

What do you mean by ‘Technology Platform’? What new features does that give me?

The Pitney Bowes Spectrum™ Technology Platform refers to the overall set of solutions and the actual SOA platform.  The features that are on the existing Customer Data Quality Platform are the same as those on the Pitney Bowes Spectrum™ Technology Platform.

 

 

Is data quality old news?

Published on August 5, 2009 in Data Quality. 0 Comments Tags: , , .

There’s an old saying: “You know its time to get out of the market when the shoeshine guy is giving out stock tips.” So, does all the chatter in the press and around the blogosphere about data quality mean that its issues have already been recognized and addressed by most businesses today?

The answer is a resounding “no”. Data quality is still a substantial issue for businesses large and small; and, most businesses don’t even have their arms around how big an issue it really is.

A new Information Difference Research Study, The State of Data Quality Today, released in July provides a stark picture of the data quality dilemma still faced today. This study, sponsored by Pitney Bowes Business Insight and Silver Creek Systems, found, in a survey of 193 businesses across Europe and North America, that: 

  • Fully one-third of respondents rate their data quality as “poor at best” – and only 4% indicated that it was “excellent”
  • 42% have made no effort to measure or monitor the quality of their data
  • 63% have no idea what poor data quality may be costing them

The study also includes insights into the types of data challenges companies face; the reasons they still struggle to get the funding and focus on data quality; and the types of initiatives companies do have underway to improve their data.

It’s an illuminating read – and clearly data quality is not old news.  You can view the study today.

Learning the Data Four-Step

Published on July 15, 2009 in Data Governance, Data Quality and Data Stewardship. 1 Comment Tags: , , , , , .

According to a recent SiriusDecisions Research Brief, 10-25 percent of the records for the average B2B company contain critical errors. The same study reports that 66% more revenue goes to the company with high quality data management.

So, why does good data make such a difference?

Bad data hurts your image, your operations, and your bottom line – and just gets worse over time. 

  • It means more returned mail and redundant processes, reduced access to postal discounts, and greater susceptibility to fraud.
  • It limits service quality, and that causes lower rates of customer satisfaction and loyalty. 
  • It impacts so many businesses and business areas from insurance to financial services; telco to utilities; public and private companies. What’s more, departments within these businesses – from customer service, sales and marketing; to billing and resource planning; to sales-force automation – are all data-reliant.

Companies today, however, are making marked improvements in data quality. Data Governance – an exercise of people, process and tools, typically a committee that represents every level of the organization, defines clear standards for data management, security and use – and Data Stewardship, an expanding role that ensures the business rules set up by the Data Governance committee data are enforced – play important parts in a four-step process that improves both data quality and data usage.  

1 – Access and integrate: All too often data is kept in a different database in every department – you want everyone across your organization to be, literally, on the same page.

2 – Profile and Monitor: Assess where you are, analyze your data and pinpoint issues. Determine what you have, where it’s coming from, and how it does-and doesn’t-work in concert.  As part of this assessment, determine where you can enhance your approach to Data Governance, and set firm rules and requirements going forward.

3 – Remediate: Clean up your data. Validate it. Standardize it. Match and de-dupe it. Enrich it with spatial, credit and/or marketing data that will enable your organization to use it better.

4 – Deliver/Federate: Empower your Data Governance Committee, specifically your data stewards to advocates proper collection, management and use of data.  First ensure it is fit for use, then ensure it used as intended.

With the new modular tools out there today that work across platforms and address each and every one of these steps in an integrated fashion, getting to better data quality is certainly getting easier. 

What steps are you taking to improve your data quality – and your bottom line? Comments and questions welcome.