Tag Archive for 'Data Governance'

Managing your data assets

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 Rx for healthy data

Health care is certainly front and center in today’s news. Without taking sides on the issues in Washington, I’d like to share some of the learning we’ve done at Pitney Bowes regarding health care – and how I think it relates to data and data quality.

Organizations – Pitney Bowes especially – define their employees as company assets; and they make major efforts to increase the value through productivity. 

For example: Pitney Bowes makes a considerable investment in preventive healthcare for its employees, providing onsite monitoring and treatment for those with chronic conditions; and onsite care when employees are feeling under the weather. As a result, employees are better about getting the treatment they need:  they start treatment sooner, and typically feel better sooner too.  It’s a win-win: employees benefit; and productivity improves.

Every corporate decision and operation has reliance on the underlying data. In other words, good quality data is as much an asset to the organization as a hard-working employee. It’s time for businesses to recognize that data quality isn’t a place to cut corners. In fact, by taking a page out of the healthcare book and performing good preventive maintenance on data and quick treatment when data-quality issues arise – data quality will be better, and productivity will increase.

A few weeks ago, we talked about how data governance is everybody’s business.  Just as employees stay healthier when it’s easier to do so, we can expect that employees will do a better job of keeping data quality high if they recognize the value and find that its easy to do so.  I think one of the challenges is to make the process of data governance meaningful and straightforward.

Transforming Raw Data into Strategic Assets

Earlier this month, my colleague Dean Wiltshire gave a webinar to a number of our customers in the financial industry titled “Good Data, Smart Decisions”.

The feedback from attendees was so positive, I wanted to post a link to the archived webinar so you could view at your convenience.  Here’s a brief synopsis:

Data governance is proving to be one of the more difficult aspects of data management, although it is the only way an organization can know that data compliance and accountability requirements are being met. In addition, key stakeholders often have different perspectives on the state of their data, which can lead to incorrect assumptions and ultimately affect data quality. Now you can gain a shared view of enterprise data with integrated profiling, analysis and management reporting in one data governance solution.

Dean talks about hidden data quality issues, best practices and provides a thorough overview of the end-to-end data quality process-along with a close-up look at the suite of Data Governance solutions available through Pitney Bowes Business Insight.   If you have the time, this webinar is definitely worth a look.

Announcing the Pitney Bowes Spectrum™ Technology Platform

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.

 

 

Data governance: It’s everbody’s business

Governance is defined as the act, process or power of governing.  Stated a different way, governance is the system or method of management. For without governance, there would be chaos. Do you think there was appropriate governance on Wall Street?

Corporate governance is a term that refers broadly to the rules, processes or laws by which businesses are operated, regulated and controlled. The term can refer to internal factors defined by the officers, stockholders or constitution of a corporation, as well as to external forces such as consumer groups, clients and government regulations.

Well-defined and enforced corporate governance provides a structure that, at least in theory, works for the benefit of everyone concerned by ensuring that the enterprise adheres to accepted ethical standards and best practices as well as to formal laws. To that end, organizations have been formed at the regional, national, and global levels.

Similar to corporate governance, data governance is a term that refers broadly to rules, processes or practices by which data is collected, shared, utilized and updated across the enterprise.  Similar to corporate governance, stakeholders and custodians for the data assets need to be identified along with well-defined policies around the integrity and up-keep of the data assets.

If every decision and every operation relies on good quality data, is it safe to assume that the quality of that data is the best it can be? Who manages that data? Who has ownership of ensuring the quality of the underlying data that corporate systems rely on and every employee relies on for analysis and operation? Is it the role of IT? Not in my opinion…

The integrity of the data (completeness, accuracy, validity, reliability, fit for use) needs to be clearly understood by all-and accountability needs to lie with every employee within the organization, not just the data stewards or the data custodians. This is where I differ from most of the industry analysts.

You see, just as it is up to every corporate citizen to uphold the corporation’s ethical standards, best practices, and laws, it is up to every member of the corporation’s data community – those who generate it, refine it, analyze it, and use it – to adhere to best practices and data governance rules.  

Today, business practices across departments encourage poor data quality practices at the point of capture and entry. For example:

  • CSRs are measured on the speed and quantity of calls answered, but not the quality of the information captured.
  • Sales reps are measured on their quota achievement and there is no accountability in terms of what they enter into SFA systems that cause product shipment and billing invoice delays.
  • POS clerks, in retail or hospitality are incented to sign-up customers for royalty cards, regardless of whether they were an existing customer.

Such practices lie at the root of why many companies bleed cash and lose face with their customers and often are not even aware of such problems.

Data Governance and accountability needs to lie with every employee and business partner and not just one or two individuals who would otherwise end up fighting a losing battle and be set up for failure.

I believe data governance is everybody’s business. How about you?

Learning the Data Four-Step

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.