Data Acquisition and Quality Assessment

By Jamie Jones, Genentech Inc.

Presentation Abstract:

For many businesses, success is dependent upon their ability to react quickly based on accurate information. For Genentech, this is absolutely critical for effective decision-making in a marketplace that is rife with change. Their innovative sales and marketing data strategy is the pillar of the organization’s ability to remain nimble as the company grows at a lightning-fast pace. Tasked with supporting this strategy for Genentech’s Commercial organization, the Data Acquisition group has responded to a significant increase in demand for timely, high-quality data in the past three years without increasing headcount. With the high degree of visibility and responsibility placed on this data, the Data Acquisition group faces significant challenges with respect to data quality management and validation. The group is driven to continually reinvent its processes to find new, innovative ways of automating data validation to maintain quality while data volume increases and new types of data are required.

Jamie will discuss the business drivers for Genentech’s data strategy and its impact on decision making. From a systems standpoint, she will cover how Genentech’s IT systems evolved over time from a barely-performing stand-alone application into an extremely flexible core of applications that have allowed the Data Acquisition group to manage the rapidly-increasing need for sales and marketing data.

Speaker Biography:

Jamie Jones is a Senior Programmer / Analyst for Genentech’s Data Acquisition group. Jamie joined Genentech during a time of rapid change and increasing demand for quality data. To meet these demands, Jamie served as the subject matter expert and member of the core development team for Data Acquisition’s data validation initiatives. Her unique experience that combines technical expertise with in-depth knowledge of data validation issues and strategy helped her lead two major phases of the Data Validation and Audit Workbench project successfully. The DVAW project ultimately automated a completely manual data validation process, and helped the group meet steady increases in data volume without increasing headcount.

Before joining Genentech, Jamie was a Senior Consultant with Dimension Data, a global Internet consulting firm. She has played pivotal roles on eCommerce and Internet application development teams for clients such as Wellpoint, Lexus, Toyota and Amgen.

  

The board of directors met twice this month to plan for exciting and thought-provoking presentations at our monthly meetings, at DAMAday and even at special multi-day events. That means inviting well-known speakers. That also means incurring additional expenses. I encourage you to help us insure a heightened quality in our program, by renewing early.

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I asked last month for feedback on the topic of where we are going as a profession. I got an earful of feedback. Continuing what I talked about last month about the evolution of our profession. I need to talk about attitude. Our attitude is based on paradigms we have established for ourselves in the past and that we use nowadays to identify ourselves as professionals. These paradigms were based on the realities that existed then. Well, these realities have progressively shifted. So must our paradigms if we want to evolve as a profession.

Let's take the Zachmann Framework. John mentioned that, in addition to the cells shown in two dimensions by the 5 rows and 6 columns of his popular diagram, there were other cells in other dimensions representing 2-by-2, 3-by-3, etc. relationships between cells on the same row, and cells representing relationships between cells representing relationships till your head hurts in utter complexity. John doesn't talk much about these because he doesn't want to confuse executives.

That expansion of the Zachmann Framework talks directly to us. Many of us have had the attitude of being "data-only" to the point of being "process-adverse". How important or relevant to our work is process modeling to us? How about that offshoot of process modeling: data flow modeling? Are we saying we are in interested in what data is, but not where it goes? Isn't the data flow diagram part of the cells between the "Data" and the "Process" columns on the framework? By that attitude of narrowly concentrating on the "Data" column, only the "Data" column, and nothing but the "Data" column, are we not causing our own vision, and usefulness to the community to shrink?

As a case in point, look at where the wide usage of XML/XSD is leading the industry: to the transport of metadata with the data. This adds to those intermediary cells another subset: metadata flow diagrams. Is there a business advantage to map these flows? Who is going to do it? Who is going to decide how it is done? Pretty soon, we will be talking about "semantic flow diagrams", "ontology flow diagrams", and so on. How are we going to relate to them, and which are we going to incorporate in our professional world?

I encourage you to ask yourselves more questions, as I do myself. Together, we may formulate answers that help us swim these changing waters, and strengthen our position as professionals.