Archive for the 'Data Quality' Category

Battling today’s global threats

Published on January 27, 2010 in Data Governance and Data Quality. 0 Comments Tags: , , , , , , , , , , .

Do you really know your customers?

Will someone try to defraud your business? Use their account with you to circumvent laws? Threaten your customers? Left to your own devices, it may be difficult to ascertain the true intentions of everyone who wants to be your customer.

To support this initiative, government agencies across the world have created watch lists to help identify individuals who may represent security risks. At the same time, the same governments often require companies to collect, validate and match key information related to a customer’s identity to support the global fight against terrorism, fraud, money-laundering and identity theft.

While anti-money-laundering requirements may only affect financial institutions, the broader requirements impact most every business.  Compliance with the USA Patriot Act, Red Flag Rules, Bank Secrecy Act, HIPAA and Sarbanes-Oxley, for example, all require a more accurate, more in-depth view of your customers.

Data quality is often the first line of defense.  For executives in charge of compliance, poor data can result in the company facing public embarrassment, damage to brand equity, significant fines and even lawsuits. 

Technologies such as Pitney Bowes SpectrumTM help automate the customer screening process with robust matching and scoring mechanisms.  Such solutions consolidate various agency and country lists – providing a single source for matching against both third-party and internal fraud databases.   

In any case, whether your company treats this as a security concern or a compliance issue, your company’s data steward may be the one person best equipped to mitigate the threat.

Data and the CTO Edge

Published on January 27, 2010 in Data Governance, Data Quality, Data Stewardship and Spectrum Technology Platform. 0 Comments Tags: , , , .

IT executives no longer need to be convinced about the high cost of poor data. Most will freely admit that customer records are incomplete, not always consistent, rarely consolidated and frequently contain errors. Each day, however, brings new challenges, new business requirements and time-of-the-essence business demands.

In a recent CTO Edge Corner podcast hosted by Mike Vizard, Jay Bourland from Pitney Bowes Business Insight explains how IT organizations need to bring good data governance practices to bear if they ever want to get a real handle data quality. 

I would like to invite you to take a few minutes this week to listen in as Mike and Jay discuss how some IT execs are dealing with long-overlooked challenges. For example:

  • Juggling access and security. Data becomes more valuable when accessible by more people, but every new approved user represents a threat to data quality and privacy.
  • Responsibilities for data governance. Is it a committee? A person?  Is this an IT function or something everyone needs to be accountable for?
  • Relationship between IT and business units. Where in the process should business users engage IT, and vice versa?  The good news is, effective collaboration is attainable.
  • Organizational roadblocks. Are data profiling, cleansing, updates, governance and integration all managed by the same person on the same schedule? If not, you can still get everyone on the same page.

In addition to these insights, Mike and Jay also touch on the capabilities organizations should look for to achieve the greatest ROI.  If you want to get more practical, you will also want to catch the new 8-minute data quality video from Kit Hamilton that outlines the day-to-day and business challenges and an innovative three-step solution.

10 ways to maximize value of customer data – Part III

Published on January 21, 2010 in Data Quality and Spectrum Technology Platform. 0 Comments Tags: , , , .

In 2010, many business initiatives will bring attention to data quality, as the completeness and integrity of customer information can have a clear impact on near-term profitability.  Previously, we examined how data quality affects marketing and operational efficiency.  Today we focus on compliance and risk management.

8. Risk Management. While companies in the insurance, financial services and healthcare arena may face new regulatory oversight when it comes to risk management, organizations of all sizes will be looking for ways to mitigate risk in these uncertain economic times—especially when it comes to compliance.  Million dollar fines, ongoing lawsuits and damaged reputations are just a few of the costs that can be mitigated when companies have confidence in their customer data.

9. Tax Management. With states and localities dealing with budget pressures themselves, there will be increased scrutiny on how businesses assess, collect and manage tax obligations. Unfortunately, tax jurisdictions change frequently, and jurisdictions are not aligned with ZIP Codes or U.S. census data—so companies will look to specialized applications in order to keep pace with ongoing changes, comply with government requirements and avoid penalties.

10. Fraud Detection. One effect of the recent recession is an increase in fraud. Many companies, including those in the financial services and insurance industries, are looking for ways to integrate fraud detection applications into their core business processes. Here, data standardization, address validation, location intelligence and the ability to match against “watch list” files can greatly reduce risk and exposure.

Whether your organization is looking to pursue one, two or all ten of these strategies, Pitney Bowes offers solutions that can help, including our SpectrumTM Technology Platform.   As we start 2010, think about the ways you can leverage data quality to locate new opportunities, connect with customers and communicate more efficiently.

10 ways to maximize value of customer data – Part II

Published on January 13, 2010 in Data Governance, Data Quality, Data Stewardship and Spectrum Technology Platform. 0 Comments Tags: , , , , .

Like other corporate assets, customer data has measurable value that is integral to achieving strategic objectives. Likewise, the value of data can increase or decrease depending on how effectively you manage this asset over time.  Last week we examined how data quality affects marketing initiatives. Today we look at operational efficiency and in Part III we will examine compliance and risk management.

4. Point-of-Entry Correction. A recent Gartner study estimated that poor data quality cost organizations an average of $8.2 million a year. At that price tag, companies are taking steps to help ensure that “bad data” does not even reach their database. Real-time address validation tools are being integrated at any point where customer data may be entered into the system, including customer service, order processing and Web self-service apps.

5. Data Quality-Data Integration-Data Governance. Analysts from The Data Warehouse Institute have recently identified how organizations can create more value by unifying their data profiling, data integration and data quality initiatives. While the concept seems obvious, in practice these functions have traditionally reported into different teams.  In 2010, look for organizations to overcome the lack of coordination and redundant effort by leveraging new tools to synchronize deliverables across all aspects of data management.

6. Routing and Fleet Management. The increase in online purchasing has created challenges for manufacturers, retailers and shippers who must identify more cost-effective ways to plan distribution networks and manage their fleets. Fortunately, today’s leading data quality solutions provide for the geocoding and spatial analysis necessary to calculate routes, determine points within a boundary and move goods from one point to another at the lowest possible cost.

7. System Optimization. After investing millions in sophisticated CRM and ERP platforms, including Oracle® e-Business Suite, Salesforce.com®, SAP® and Siebel®, over two-thirds of all companies have issues with non-standard data and incomplete information.  Rather than re-invent the wheel, more organizations are increasing the returns on past IT investments by using out-of-the-box “connectors” that extract data, analyze information and provide users the visibility necessary to effectively run their business.

Clearly, the challenges associated with corporate data can overwhelm even the most sophisticated organization as each new system and customer initiative adds new levels of complexity. Solutions like our SpectrumTM Technology Platform complement your existing business platforms, making it easier to improve the quality, accessibility and value of your data assets. Obviously, there are many other ways data quality impacts customer service… how is your company using data more effectively?

10 ways to maximize the value of customer data – Part I

Published on January 7, 2010 in Data Quality and Spectrum Technology Platform. 0 Comments Tags: , , .

This year, a number of business initiatives will bring attention to data quality, as the completeness and integrity of customer information can have a clear impact on near-term profitability. Companies that take steps to improve the quality, integration, accessibility and value of their customer information may realize dividends across these ten initiatives in 2010.  This week, we look at areas that are especially critical for marketing. Next week we’ll look at operational efficiency, compliance and risk management.

1. Customer Onboarding. Given the cost of acquiring new customers, organizations will look to strengthen the quality and effectiveness of their customer onboarding experience. Studies show that getting the first 90 days right may produce greater returns than other comparable investments. In addition to significantly lower attrition rates, in some industries this initial period can account for as much as 70% of all cross-sell activity.

2. Predictive Analytics. CNN recently reported how companies are drowning in data and how gaining meaningful insight can take more effort than ever.  Each week, businesses waste 5.3 hours per employee due to inefficient processes.  It’s no wonder that one of the fastest growing fields involves predictive analytics—technologies that parse, compare and evaluate diverse, disconnected pieces of information in real time so users can make more informed decisions.

3. Postal Savings. While postal rates will not increase in 2010, the US Postal Service will assess fees against companies that do not update addresses when customers move—and that could cost mailers as much as seven cents per piece.  At the same time, new postal discounts are being offered to those who convert address data and mailing specifications into barcodes under the new Intelligent Mail® program.

To be successful, organizations need to find effective ways to deal with long-term issues such as ownership, disparate systems and overall data quality—while being opportunistic when it comes to low-hanging fruit and critical business issues.  Solutions like our SpectrumTM Technology Platform complement your existing business platforms, making it easier to improve the quality, accessibility and value of your data assets.   Obviously, there are many other ways data affects marketing results… what initiatives are critical for your business?

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 marriage of data quality and governance

Published on December 2, 2009 in Data Governance, Data Quality and Data Stewardship. 0 Comments Tags: , , , , .

In business, you make the best decisions when your data reflects reality.

This applies to the call center rep who needs to understand the scope of your relationship with a customer. The manager who is looking to assess the risk of a pending deal. And the executive who must sign-off on a multi-million dollar investment.

In a recently published article, our own data quality expert Navin Sharma writes: every corporate decision and operation relies on the quality of the organization’s underlying data. 

How true.  Often, however, we hear about organizations who fall short in their data quality efforts because the fail to spell out clear guidelines for data governance.  To avoid this trap, take a moment to read Navin’s article, The role of data governance in the enterprise.

Here, you can learn more about the three fundamental best practices to ensure that your data is complete and up-to-date:

 1. Data profiling

2. Data cleansing

3. Data governance

Poor data quality can cost organizations millions of dollars a year, and as many expect the volume of data managed by businesses to double over the next 18 months, there’s never been a better time to review your approach.  Once you read Navin’s article on best practices, be sure to come back and learn more about the many ways we can help you improve data management.

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.

Bigger than the sum of its parts

Published on October 28, 2009 in Data Quality and Spectrum Technology Platform. 0 Comments Tags: , , , , , , .

Filling a gap in intelligence needs

Business intelligence. Predictive analytics. Data mining. Businesses today are truly recognizing the power that lies in their customer data.

The challenge is in harnessing that power, and doing so efficiently.  BeyeNetwork blogger Krish Krishnan captures this issue – particularly as it pertains to the gap in solutions that can help to transform data into actionable information: 

“With market consolidation, companies are left with a mixed bag of solutions and now need to reassess their investments, new market offerings have not reached enough maturity and open source is not accepted yet as enterprise capable in BI. Where we need to go with this situation is to setup an interoperable solution where the vendor consolidation will not impact current investments. There were third party companies that used to offer these kind of solutions and we need a new series of such technologies to be recreated.”

He’s right in some ways: companies that want to better understand their customers have in many cases assembled a hodgepodge of different solutions to address different challenges. As the sources of those solutions thrive and consolidate (or wither and disappear), companies that have acquired them are left with a collection of parts built in silos that are condemned to stay in silos unless they find an “interoperable” solution to enable those parts to work in concert.

However, there are solutions providers, including Pitney Bowes Business Insight, who’ve recognized this issue and have been working steadily to create options – such as the Pitney Bowes Spectrum™ Technology Platform – to address this very issue.

Rather than sending companies back to “square one”, solutions like Spectrum work in tandem with companies’ core data quality platforms. Spectrum is designed to facilitate improvements in customer data quality, and augment/assess data for a range of purposes, from Enterprise Tax Management to Global Sentry Watchlist Monitoring to Enterprise Routing for Fleet Logistics. It also provides data quality connectors for SAP and Siebel – again, enabling companies to make the most of the systems they may already have.

We agree with Krish and the others who tout the need for solutions that can grow and change with the companies they serve – and help to provide the tools for enterprise-wide business intelligence and predictive analytics.  Today’s leading-edge data and software technologies should continue to grow and change to address those specific needs.

To learn how a common data enrichment and management platform can support needs across your entire enterprise, you can read more about our Spectrum™ Technology Platform.