The NYU School of Medicine is looking for a Manager of Information Management in New York, NY. According to the post, “Candidate will be responsible for developing and maintaining local institutional terminologies in a knowledge base; mapping, classifying and maintaining concept attributes and semantic links; maintaining national and worldwide standards to support institutional requirements for mapping terms to local and national standards; developing strategies to model relevant classification hierarchies; managing the impact of terminology data models on downstream storage, display systems, and applications; auditing knowledge base content; providing expert advice on terminology and ontology best practices; collaborating with owners of source systems, downstream systems and other existing terminology services; documenting the knowledge base and training others in its application; plus all related job functions.”

The NYU School of Medicine is “one of the nation’s leading centers of advanced biomedical learning, spans a history of excellence of nearly 160 years in the education and training of physicians, in patient care, and in scientific research.  The School of Medicine’s mission remains the same as was at its founding in 1841: “The pursuit and delivery of the highest quality patient care, medical training, and scientific research must be accomplished in a setting of excellence at the highest level of human achievement.’”

Learn more or apply here.

photo credit: NYU

A recent article comments, “The enterprise data centre is moving to the next stage of transformation. Server virtualisation technology has changed the way applications are provisioned and managed to improve cost-efficiency and business effectiveness. The fact is, though, some data centre architectures are still in phase one of the evolution. They are still built around complex, heterogeneous silos of servers and storage systems that result in poor utilisation and captive resources.”

The article continues, “These resources need multiple provisioning toolsets, data management processes and teams of people to manage them. In addition, massive data growth combined with power, cooling and space limitations exert extreme pressure on the responsiveness of IT. To ensure their business is still relevant, most organisations today have as a priority to boost business efficiency and cut costs. Unfortunately, 70% of IT budgets still go towards ‘keeping the lights on’ rather than innovation to ensure relevance.”

It goes on, “To a degree, this trend could be attributed to the fact that business does not know where to start. Many vendors and industry experts have published best practices and white papers on how to go about evolving the current state to a new-generation data centre. However, it is a different scenario when a CIO needs to take accountability and responsibly to establish an evolution roadmap that is not only realistic and within budget, but more importantly, executable with no risk to business continuity.”

Read more here.

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Brian McKenna recently wrote, “Master data management is like the quest for the Holy Grail. Just because the attainment of a pure state of perfectly integrated and flawless data is mythic does not invalidate the journey. Lloyds Banking Group has sustained a longstanding effort to put master data management at the heart of its business strategy, and gain a single customer view, through a history of mergers and acquisitions, including that of HBOS in 2009. Crucial has been a shift from an account-centric to a customer-centric approach to business common to financial services firms. The discipline of MDM is, for Lloyds Banking Group, crucial to that.”

He went on, “In the public sector, MDM has demonstrated value at the London Borough of Brent Council. Its master data management (MDM) hub has not only facilitated revenue collection and combated Council Tax and other forms of fraud, it has also helped respond to changing central government demands. Brent Chief Information Officer Tony Ellis is confident that the MDM programme he has driven since moving to the council in 2005 will continue to accrue benefits. So-called ‘big data’ could be seen as the problem to which MDM is a solution. It is a striking term, but there is the danger that it places the accent on sheer volume rather than on the value to be struck from the expanding diversity of an organisation’s data. Teradata’s chief technology officer Stephen Brobst explains the value of the term ‘big data’ in terms of a shift from transactions to interactions that capture customer experience or behaviour.”

McKenna continued, “Buzzwords like MDM and ‘big data’ are necessarily imperfect attempts to name management processes and technological phenomena that lie at the heart of how, and against which contexts, organisations succeed. But they can drive you nuts.”

photo credit: AlicePopkorn

The Economist recently weighed in on the subject of Big Data and its growing prevalence. The article states, “The data revolution is disrupting established industries and business models. IT firms are nosing their way into the health-care market: Google Health and Microsoft HealthVault both allow consumers to track their health and record their treatments. Manufacturers are hastening their transformation into service companies: all those sensors allow them to monitor their products and see if they need repairing long before they break down. BMW uses sensor-data to tell its customers when their cars need to be serviced, for example. Insurance firms can now monitor the driving styles of their customers and offer them rates based on their competence (or recklessness) rather than their age and sex.”

It continues, “The data revolution is clearly handing power to the little people as well as the big ones. You can now buy a device that will store all the world’s recorded music for just $600. Shoppers can use their mobile phones to scan bar codes to see if there is a better deal elsewhere. Citizens can use publicly available information to demand better public services. Britain’s Open Knowledge Foundation has used government databases to develop a useful site called wheredoesmymoneygo.org. Dr Foster Intelligence provides patients with information about the quality of health care.”

The article goes on, “But on the second question, they are silent. Big data has the same problems as small data, but bigger. Data-heads frequently allow the beauty of their mathematical models to obscure the unreliability of the numbers they feed into them. (Garbage in, garbage out.) They can also miss the big picture in their pursuit of ever more granular data. During the 2008 presidential campaign Mark Penn provided Hillary Clinton with reams of micro-data, thus helping her to craft micro-policies aimed at tiny slices of the electorate. But Mrs Clinton was trounced by a man who grasped that people wanted to feel part of something bigger. The winning slogans were vague and broad (‘hope’ and ‘change’).”

Continue reading here.

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Altisource Portfolio Solutions is looking for a Vice President of Data Management and Business Intelligence in Atlanta, GA. According to the post, “This position reports into the senior executive organization of Technology Services and will be responsible for managing the data quality and data intelligence organization that supports the entire application and data infrastructure of Altisource and our clients. The VP of Data Management and Business Intelligence has a dual charter. First, this position will have the responsibility for being the steward of data hygiene and data quality including the development and management of data policies, procedures, reporting and governance with the goals of creating competitive differentiation for our application solutions and creating a world-class application infrastructure with high quality and low operating costs.”

The post continues, “Secondly this position will own the goal of creating and managing a world-class business intelligence infrastructure and supporting Altisource and its clients with exceptional reporting and analytics solutions. This role has considerable interaction and visibility with senior executives of the company. As the leader of this organization, the VP will have the opportunity to establish, promote and institutionalize the following principles as it relates to the data quality and management dimensions of this role.”

Learn more about this position and apply here.

photo credit: Altisource

A recent article takes a look at the rise of text analytics and asks the question, is text data processing part of your enterprise information management framework? The writer states, “The current prominence [of text analytics] is due to the number of different organizations using text analytics to gain insight into how the masses use social media and the Web. Customer service and support, brand-reputation management, competitive intelligence, and market research applications are a few examples that highlight the main-stream adoption of text analytics. These applications and others use text analytics for automated, natural-language processing techniques to identify and extract names, facts, relationships, and sentiment, etc. from a range of data sources, including blogs, forums, news, twitter/social updates, and e-mail (going forward I will refer to these as Web/social data).”

The article continues, “No one questions that significant insight lays hidden within Web/social data. Many organizations use it as described above. Others want to dig deeper, and often that requires a well-thought-out EIM framework to handle text data and help derive insights from across the enterprise, including back-office processes.”

It goes on, “To understand the use of text analytics (depth/breadth) within your organization and the maturity of your EIM framework to process text data, ask the following questions: (1) Can your organization track sentiment around its top-10 customers based on amount of revenue generated over the past four quarters and their top 3 products, including all name variations for their company, products, and subsidiaries? (2) Can your organization then effectively use the information above to develop a revenue risk mitigation strategy? (3) Can your organization track sentiment around its top-10 suppliers and their products – including all the name variations – and then use that information to mitigate any risks associated with product quality and the impact it might have on your customers?”

Read more here.

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In a recent article, Bob Gourley reports that Big Data is essential to the Department of Defense Science and Technology investment agenda. Gourley writes, “The Secretary of Defense signed a memorandum on 19 April 2011 which articulates the Science and Technology (S&T) priorities for the Department of Defense (DoD). This memo flows from extensive planning including reviews of all defense missions and architectures to support those missions. The result: seven S&T priorities have been identified for strategic investment.”

Gourley continues, “These seven priorities are: (1) Data to Decisions – science and applications to reduce the cycle time and manpower requirements for analysis and use of large data sets. (2) Engineered Resilient Systems – engineering concepts, science, and design tools to protect against malicious compromise of weapon systems and to develop agile manufacturing for trusted and assured defense systems. (3) Cyber Science and Technology – science and technology for efficient, effective cyber capabilities across the spectrum of joint operations. (4) Electronic Warfare / Electronic Protection – new concepts and technology to protect systems and extend capabilities across the electro-magnetic spectrum. (5) Counter Weapons of Mass Destruction (WMD) – advances in DoD’s ability to locate, secure, monitor, tag, track, interdict, eliminate and attribute WMD weapons and materials. (6) Autonomy – science and technology to achieve autonomous systems that reliably and safely accomplish complex tasks, in all environments. (7) Human Systems – science and technology to enhance human-machine interfaces to increase productivity and effectiveness across a broad range of missions.”

Read more here.

photo credit: Chairman of the Joint Chiefs of Staff

A recent article discusses the relatively new concept of linked enterprise data or LED. The writer begins, “I first heard about LED a few years ago.  It’s continued to grab my interest because of its connection to semantic web technology: a linked data enterprise is one in which semantic web technology is used to address the fundamental issues that prevent enterprises from achieving the agility they require.  The primary issues that prevent enterprises from achieving this agility are information silos and the mountain of data enterprises generate.  Moreover, a growing number of enterprises are realizing that cross-organizational data integration cannot be achieved on a project-by-project basis or with any single data management tool or database. Rather, data integration has to be an ongoing part of data creation and utilization, where accessing and sharing data is as important as creating it.”

The article uses the following definition of LED: “A linked data enterprise is an organization in which the act of information creation is intimately coupled with the act of information sharing. By analogy to a learning organization, in which learning from an activity is as important as the activity itself, and documentation of a system is as important as the construction of the system, in the linked data enterprise, sharing data is as important as producing it. In a linked data enterprise, individuals and groups continue to produce and consume information in ways that are specific to their own business needs, but they produce it in a way that can be connected to other aspects of the enterprise. When the time comes for information to be shared, the investment required to connect it is minimal, and reduces the barriers to information exchange. In the linked data enterprise, the motto of information production is distributed but connectable.”

Learn more about LED here.

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by David Plotkin

As you build metadata expertise, document definitions in your metadata repository (or whatever tool you use) and gain credibility in the enterprise, you are likely to find that your effort includes gathering, stewarding, documenting, and providing lists of valid values (sometimes called “enumeration lists”). After all, having standardization for your name suffixes, gender, marital status, relationship type, and so on ensures that new systems (and existing systems that can handle the modification) will use the same values. This leads to standardization on reports and in the data warehouse as well.

I think most would agree that items like gender code should be tracked by Data Governance (or whatever you call it) and even consider the Metadata Repository the “system of record” for such value lists. These lists have wide usage, a short list of values that don’t (or shouldn’t) change often, and no real clear owner or system of record.

But how do you draw the line between “true” valid lists of values and the vast number of data elements that happen to have a finite list of values, but should be neither governed by Data Governance nor should be documented in the Metadata Repository? Items that potentially fall into this category are things like GL Account codes, office location codes, sale rep identifiers, and even Employee Ids. And trust me, you do need to draw that line, as analysts and project team members will start asking Data Governance for this information once they understand what we do. After all, its easier than trying to dig this stuff out for themselves! The key differentiator seems to be that data elements that are created as part of a common business process with a clear business function owner should NOT be part of the Data Governance deliverables. All of these examples fall into that category. For example, HR creates (and terminates) Employee Ids as part of the hiring and termination process. They are rightfully in control of that process, and the value set changes daily (and even continuously). No one with any sense would suggest that this constantly fluctuating value set belongs in the Metadata repository, or that the Metadata repository should be the system of record. There is a clearly defined system of record — the HR system which uses these values to do the processing required for employees — such as establishing their managerial structure, setting their service date, getting them paid, tracking their taxes and withholding, disciplinary actions, change of status, location, and so on. The same can be said of the other examples noted.

Note that this doesn’t imply that the maintenance/add/removal of the values is limited to the system of record or is simple to administer. Adding a Sales Rep Id, for example, involves not only adding it to the Sales system (probably the system of record), but the HR/Compensation system, establishing the location (which can change from day to day or even hour to hour), and so on. They key, as I said, is that a common business practice with a business function owner owns this process, and the system of record is highly likely to be the main system in which the value set is adjusted initially (with propagation as necessary) and which cannot function properly without having the most up-to-date list of these values.

A key point here is that many times, true “valid values” (such as gender code) don’t have a well-defined system of record. You might make a case that Gender Code is “people data” and thus owned by the HR business function (and so the system of record should be the HR system). But what about all the people the Enterprise deals with who are not employees or contractors? Customers, suppliers, external agents, etc. “Solving” this by putting the data element into a generalized function like Customer Master (with a domain data steward) establishes ownership but does nothing for solving the issue of the SOR for these data elements. Most of the time, these values are used so generally across the enterprise that it is prudent to have an agreed-upon list documented in an easy-to-find place. The list of potential values is so small that it is reasonable and convenient to record and maintain the list in the Metadata repository, though it must be implemented identically (good luck with that) in every system which contains the data element.

To tell the truth, this whole discussion came up because of Product, and whether there should be a list of product codes supplied by Data Governance and kept for reference in the Metadata Repository. I have to admit, my initial inclination was to specify the codes and keep them in the repository, though not as the system of record. After all, we only have about 25 products, and we don’t add a lot of new ones very fast, since it takes a major effort to do that. And to reiterate, the system of record(s) has to be the product systems themselves because that is where you need to fully define the product in order for the system to work correctly (and enable you to sell the product). The fact that a bunch of other systems have to get major updates as well is more a failing of the integration and system design than anything else.

I have since changed my mind about even keeping product in the Metadata repository. To see the apparent insanity of recording the list of products in the Metadata Repository, generalize to take the example of a major retailer, such as Longs Drugs, where I worked for 7 years. Longs (now CVS) has a well-defined product hierarchy, each level is clearly specified and ALL products must fit into the hiearchy and populate under values all the way to the top. The hierarchy is a pyramid (as pretty much all  hierarchies are), with just a few values at level 2, 3, and 4. However, by the time you get down to the next-to-last level (SKU), the values have ballooned to well over 100,000; and at the bottom level (UPC), the list of values are numbered in the millions. This is because every single item that Longs sold had a separate product identifier (UPC) which differentiates by package size (8 oz. vs. 12 oz. of Gelatin), flavor (grape or cherry), brand (Jello or Royal), and even type of packaging (single boxes versus six-packs/bundles). And more.

Given all, this, it is clear that the Metadata repositoryis NOT the system of record for any part of the product hierarchy (that also has not changed from my initial dialog). But does it make sense to record the products in the repository? I would now say that it does not.  Basically, the list will be out of date almost immediately, because the business processes to keep it updated doesn’t exist — and really doesn’t need to.

However, just to be clear, I do think that the definition (the levels, what they are called, what they mean) of product hierarchy ITSELF (and probably any other hieararchy) SHOULD be documented in the Metadata repository.  If there is no agreement on the hierarchy, then reports based on the disparate hierarchies will not match. In addition, it is a very bad idea to have different versions/definitions of what is meant by “product” (the bottom level of the hierarchy). If one group defines product as (for example) an auto insurance policy, while another breaks it down to a specific set of coverages in an auto policy, then not only will reports not work properly, but the very systems that enable working with those products will not work the same and will require people using the systems to know the idiosyncracies of something as basic as what the product is. While little can be done when working with legacy systems, new systems should be designed with a common, governed, list of products and a common hierarchy.

So, that’s it! As always, comments are much welcomed, especially from those of you who have fought this fight before.

Johnson & Johnson Consumer Products Company is looking for a Manager of Master Data Management for their Enterprise Master Data team in Fort Washington, PA; Morris Plains, NJ; Los Angeles, CA; or Skillman, NJ: “The Manager, Master Data Management is responsible for the detailed design of central data management including standardized, documented central processes, regional execution and common processing.   This individual will oversee the efforts of the Master Data Site Leads, and will ensure common process is applied across all 12 international locations in scope and that processes are optimized and compliant with SOX and GxP requirements.”

The post continues, “The Manager, Master Data Management will work closely with site leads reporting to this manager will maintain local customer support to ensure that each of the locations continues to receive the support required for their location from the Enterprise Master Data organization.    Through these direct reports this position will be responsible for all types of change processing, from changes to existing commercial codes as well as changes to in process materials that are not yet commercialized.”

photo credit: Johnson & Johnson