Tag <span class=data governance" src="/wp-content/uploads/2014/04/cropped-office-building-secondary-1.jpg">

Tag data governance

Data Management Concepts for Sustainability, Pt. 4

The SSC Team August 20, 2015 Tags: , , , , , , , , , , , , Strategic Sustainability Consulting No comments
This article was written as an expansion of our white paper “Choosing Sustainability Management Software for your Business” published in July 2011.  If you’re looking for information on how to make your software selection, check out the full article.  If you just want to make sense of this particular topic, keep reading.  Whether you like this article or not, we want to hear from YOU so that we can continue to provide the best insight for YOU, our readers…  Our series on Sustainability Software continues with “Data Management Concepts for Sustainability”.  In this article (Part 4 of 4), we’ll complete the introduction and definition of key Data Management terms (read Part 3 here).  Our end goal with this series is to enable YOU, as the Business Leader, to feel more comfortable in a technical discussion related to the various areas of Data Management, especially as related to the care and feeding of Sustainability Software packages. Being able to “talk the talk” is the best defense in the technology wilderness.  Just remember, at the basis of any technical term is a common sense business notion, and staying grounded to this notion will help keep your conversations from drifting astray.

Data Integration

Data Integration is one of the most difficult of the activities covered in this series because it involves most of the different activities working in concert with each other.  For example, it is implicit in Data Movement between systems where the Data models are different.  Suppose we have data in our Accounting system that will be used in a cost calculation algorithm (method) in our Sustainability Software.  To do this, we need to copy the Accounting data, then reshape it to conform to the load utilities for our package and proceed with the load.  This setup entails numerous subtleties including the cross referencing of the source data model in the Accounting System with the format of the import utility.  This is called Field Mapping and it’s not just an easy matching question where you can get the first few right and guess the rest.  Examples will help us here.
  • Suppose we need to deal with quantity shipment data and the target model is asking for unit prices and volumes.  We might need to deduce the carbon content per gallon from the available carbon content per fifty five gallon barrel, or just divide by 55.
  • A more complex example involves translation from the English System to the Metric System (raise your hand if you can do this without a calculator).
  • Another example would be the rules concerning the potential for rounding errors for large quantities.
  • A final classic example is how to deal with Asian names (commonly listed with the surname first) being transferred into a system with a European paradigm (where the surname is listed last).
Data Integration is expensive to build and more expensive to operate.  SaaS is a way to avoid the Integration Tax to the extent possible since it has already been built into many of the downstream systems you’ll be using.

Data Mining

Data Mining is the last major topic to be introduced.  It also involves smatterings of the others, but has a unique ad-hoc character at its essence. Suppose we have a database that describes product production events in a manufacturing setting.  Suppose also that we wish to learn more about root causes of some recurring problem that has escaped previous attempts to solve it and choose to “look at all the occurrences at once”.  Someone who is expert in the data itself, as well as all the business processes it describes could attempt to construct queries that will reveal common conditions that led to the problem occurrences.  For example, he might notice they all tend to fall in the first half hour of their respective production runs.  Further drill down might reveal they all involve late shipments from the same raw material vendor, while production runs with timely shipments from the same vendor seem to go without mishap.  This would lead us to suspect potential spoilage or lack of maturity in the late arriving material.  Data Mining is a spiral learning discipline.  One spirals in to a common cause, or spirals out to learn the nature of the Cosmos.

Conclusion

We hope that as a result of this information, albeit somewhat high-level, you’ll find a greater degree of ease in approaching Data Management problems and their solutions with any Sustainability Software package that you may be considering.   As with the rest of this series, our goal is to guide you through each of these complex topics and bring them safely toward a solution that will provide you with robust and accurate data and data management practices that will last for years to come. Now that you’ve read this article, tell us what you think!  And be sure to check out the full white paper.

Data Management Concepts for Sustainability, Pt. 1

The SSC Team August 11, 2015 Tags: , , , , , , , , , , Strategic Sustainability Consulting No comments
This article was written as an expansion of our white paper “Choosing Sustainability Management Software for your Business” published in July 2011.  If you’re looking for information on how to make your software selection, check out the full article.  If you just want to make sense of this particular topic, keep reading.  Whether you like this article or not, we want to hear from YOU so that we can continue to provide the best insight for YOU, our readers…  Our series on Sustainability Software continues with “Data Management Concepts for Sustainability”.  In this article (Part 1 of 4), we’ll begin introducing and defining key Data Management terms.  Our end goal with this series is to enable YOU, as the Business Leader, to feel more comfortable in a technical discussion related to the various areas of Data Management, especially as related to the care and feeding of Sustainability Software packages. Being able to “talk the talk” is the best defense in the technology wilderness.  Just remember, at the basis of any technical term is a common sense business notion, and staying grounded to this notion will help keep your conversations from drifting astray.

Data Management

The definition provided in the Data Management Association (DAMA) Data Management Body of Knowledge (DAMA-DMBOK) is: "Data Management is the development, execution and supervision of plans, policies, programs and practices that control, protect, deliver and enhance the value of data and information assets."  This term is the most general description of the collection of activities involved with data and broadly includes all the areas that we’ll introduce in this article.  If you’re really interested in more detail, check out the DAMA site at http://www.dama.org.

Data Processing

This is another very broad term representing the collection of plans, processes, people and technology tasked with the collection of transactional data (e.g. item sales in a company's retail outlets) and the subsequent calculation of summary data that has meaning to your business such as periodic sales reports.  This includes the routine computational work performed by your company's people and computers that generate output like your monthly customer invoices or accounting reports, for example. Your Sustainability Software, in the ongoing state, would be supplied with data such as rigorous measurements of weights and volumes of raw materials and products (Collected Data) and the software installation will calculate the various indicators and reports for their respective uses (Calculated Data).  When discussing Data Processing, it is always a good grounding exercise to distinguish the Collected Data vs. Calculated Data being considered.  The two have different types of rules around them, which brings us to the next category of Data Management.

Data Governance

Data Governance is the management aspect of Data Management and has to do with identification and life cycle management of Business Rules connected with Data Management.  These rules might be driven by law, profit motivation, social norms or a myriad of other factors, but the establishment of definitions of terms and their existence in your company's soft assets is the foundation of Data Governance.  Examples of such rules include the following:
  • Meta-data Management is the collection of rules and definitions of the data elements used in your company.  It could be stored in a rigorous set of spreadsheets, or in an exotic, purpose-built system like Rochade from ASG Software.  Meta-data should have a dedicated team devoted to its maintenance and secure distribution to interested parties.  This team should include representation from both the technical side and the business side of your firm.
  • Business and technical ownership of data quality standards for things like customer mailing addresses and formulae used in reporting.
  • The clear specification of things like sales transactions and revenue classifications in the company's data streams.
  • The identification and lifecycle management of your company's master lists such as store locations, product names and their reporting rollups, and a consolidated customer contact list across all lines of business.  This activity is referred to as "Master Data Management" and has taken on a life of its own by numerous software companies and consultancies but it is based on the common sense notion to "Keep your lists straight."
Data Governance is like going to church, in that it is often postponed until there is enough confusion in the Business to make people desperate enough to try it.  It is definitely an endeavor that can start small, but requires the organization’s highest level of support.  Unlike some of the other topics presented here. Data Governance must be practiced within the confines of your corporate headquarters by your employees, perhaps augmented by technical consultants from time to time. (TO BE CONTINUED…) Now that you’ve read this article, tell us what you think!  And be sure to check out the full white paper.