Data Quality Measurement: Looking Beyond the Obvious

In my blog post “Measuring data governance programs,” I discussed four distinct categories of measurement that apply to data governance programs: level of policy compliance (addressed in “Data Policy Compliance: Beyond Crime and Punishment”), level of data quality, impact on business performance, and performance of data governance processes. By measuring your program across multiple dimensions, you can better ensure that your organization reaps the greatest benefit from your investment in data governance. Read more

The Biggest Philosophical Debate in Data Management

In a recent webcast I did with Jim Harris, he talked about two views of data quality: provider centric and consumer centric. According to the provider centric view, data are just digital representations of real world things. If the representations are accurate, then you can use them for anything. In other words, data is good as long as data providers do their job right. The consumer centric view says, data is good only if it’s fit for use, i.e., if it meets the declared needs of consumers. Read more

Does Big Data Mean Good Data?

Welcome the newest, biggest, baddest buzzword to the technology stage – Big Data. The acceleration of data created through social media, tracking devices and the internet is giving companies a unique opportunity to learn more about their customers, target their marketing and sales campaigns more effectively and become more efficient. Read more

Noetix Enables End Users at McKee Foods to Access their Oracle Financials Data

Don Lastine from McKee Foods shares how Noetix has enabled end users to immediately access their Oracle Financials data without assistance from the Information Systems team, greatly speeding the operational reporting turn-around time.

McKee Foods, best known for its line of Little Debbie® snack cakes—America’s leading snack cake brand, has more than 6,000 employees and annual sales of about $1.2 billion.

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Business predictability from an ant’s viewpoint

“Been days when it pleased me,
To be on my knees…
Following ants as they crawled across the ground.”
–Willie Nelson

As does Willie Nelson, I live in Texas. For all I know, fire ants might be our state insect. But I sort of hate them. From Houston to Dallas, San Antonio to El Paso they are pretty much everywhere, and I have rarely escaped encounters with them without suffering a round of painful stings. Hence their name. I really do hate them. But I just can’t help but watch them. Read more

Customization Plays Key Role in Analytic Application Development – Research Study of 250 IT Professionals

By Elliot King, Ph.D.
Professor of Communication, Loyola University Maryland & Founder of the Digital Media Laboratory

The production and retention of data within organizations has grown on an exponential curve for at least the past decade. All that data jamming up companies’ storage infrastructures can be virtually useless if people can’t extract actionable information from it. The amount of data a company collects is not nearly as important as what it does with that data. Read more

Data Governance Should Not Be a Clandestine Effort

A few months ago, I talked about data governance with a data architect at a large retailer. He had built the rough outline of what’s in Kalido Data Governance Director — in Excel! Needless to say our views are pretty much aligned. When I asked him how he intended to roll out his Excel framework, he smiled and said, “I’m doing data governance by stealth.” Clandestine data governance. Read more

Establishing the Business Need for New Analytical Applications

By Elliot King, Ph.D.
Professor of Communication, Loyola University Maryland & Founder of the Digital Media Laboratory

Building a culture of analysis is an ongoing process for most organizations. As companies generate more data, the need to access and analyze data to measure and monitor performance and identify trends is no longer the sole domain of business analysts and senior executives. Line managers and customer-facing personnel can also take advantage of better data access and an opportunity to understand that data more fully.

However, developing and deploying analytic applications can be time-consuming and expensive. Custom-built analytic applications often take months to build, demanding a significant investment of money and resources. In many cases, companies don’t build these applications from scratch but customize packaged applications to a greater or lesser degree. (See post, Customization Plays Key Role in Analytic Application Development).

How should a company determine where to put resources to expand the number of analytical tools available to their employees? The wrong way to go about that decision is to try to oil the squeaky wheel. In every organization, there are business groups that voice their needs with more urgency than others. Responding to them is easy but not always the right choice.

The right approach is to establish the business need and business case for the proposed application, which requires answering four questions:

  • What is the current pain point? In other words, what is the business group not able to do that better access to data and analysis would allow it to do? Can the HR department produce accurate employee counts on a regular basis?
  • What business goal would be achieved by implementing the new application? Can the business benefit of having an accurate head count be quantified in some way?
  • Can you access the data you need to make the analysis in a cost-effective way?
  • How long would it take to create and deploy the application needed?

As requests for new kinds of data percolate up (and down), it is important to formulate a methodology to prioritize those requests. By asking the right questions and collecting the right information, the right answers will become clear.

What does Noetix mean by upgrade protection?

QUESTION:  What does Noetix mean by upgrade protection?

ANSWER:  Both NoetixViews and Noetix Analytics provide a level of insulation from database structural changes inherent with Oracle EBS upgrades. Because of that insulation, reports, queries, and ETL maps that are based on NoetixViews and Noetix Analytics are protected – they’ll continue to work even after you’ve gone through that application upgrade.

How does that work? It’s a little bit different for NoetixViews and Noetix Analytics, so let’s look at each separately.

In NoetixViews, each view joins together a collection of EBS tables. Users build reports and queries using those views. If Oracle changes the database schema, moving the data to new EBS tables, Noetix provides views that join together the new EBS tables, so those reports and queries continue to work. For example, in Release 12, Oracle moved information about Suppliers, Supplier Sites, and Contacts out of Purchasing tables into tables used by Trading Community Architecture (TCA). Consequently, several Noetix views, spanning Purchasing, Assets, and HR, were enhanced to work with the new version of Oracle, using these new tables so that the database schema changes were invisible to users who had built reports on Noetix views.

In Noetix Analytics, upgrade protection is provided in two forms. The ETL architecture maps that extract data from the EBS database use a layer of database views to access that data. These views shield the ETL maps from changes in the database tables introduced by new EBS releases. The extraction views can also be modified to include additional columns from Oracle EBS base tables via metadata. These customizations are also protected during upgrades. If you modify the Noetix ETL maps (or write your own), you can use that same layer of database views. When you upgrade to a new version of EBS, Noetix Analytics provides a version of those database views to match the new EBS database schema. The views will “look the same” to the ETL maps, so those ETL maps will continue to work.

The second form of upgrade protection with Noetix Analytics involves the data models of the Noetix Analytics data warehouse. Noetix commits that those data models – both for the Operational Data Store and the star schema Data Marts – will remain consistent across upgrades. Noetix may add facts, dimensions, attributes, and relationship sets to the model, but won’t change the ones that are already there. As a result, reports, queries, and dashboards that you build with Noetix Analytics will continue to work after you upgrade.

Why does all of this matter? Noetix makes your EBS upgrade easier, because it protects your Oracle reporting environment. You save time, you save money, and you reduce the risk of errors.

Submit a question to Noetix and check back for the answer in an upcoming blog post!


MDM and data quality for the data warehouse

A recent Information Management article raised a number of issues with data warehouses and why they are such time-consuming projects. According to the article, the main reasons are primarily around changing scope, data quality and ETL design. I’ve discussed how to handle the scope and design issues in earlier posts about business modeling. Over the next few posts, I’ll talk about how to deal with data quality in the data warehouse. Read more