Data Management Concepts for Sustainability - Part 3

August 18, 2015 The SSC Team

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 3 of 4), we’ll continue introducing and defining key Data Management terms (read Part 2 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 Movement

Data Movement is one of the silent cost areas of Data Management. This entails the replication of data into a system and then out of it on to another system. For example, suppose you have selected the ideal Sustainability Software offered in a SaaS-based contract by a reputable vendor. A qualified consultant is hired to mastermind the installation and the ideal algorithms are determined, tested and approved. All we need now is to move the company transaction data into it and let it do its work to produce the outputs we desire. What can be so hard about that?

Strong vendors of Sustainability Software will provide robust utilities to import data into their system and to export data from it. These must receive special attention from your Consultant and from your IT staff who will need to know how they work, at least for diagnostic scenarios.

We list some additional considerations below.

Data In

Suppose your consultant determines your current operational control systems can supply the data your new Sustainability Software needs and a prototype has proven this to everyone’s satisfaction. It seems like all we need to do is to rerun the prototype code every day and everything will work.

Axiom of Design: Everything needs to be designed at least three times: Once to see if we even really want what we had in mind, once more to learn how to build the ongoing system, and once more to really build what we imagined. Then Continuous Improvement kicks in.

You are in the process of building what is called a Data Movement Application. Any process that is repeated will fail often in new ways not anticipated. Yes, computers can break and humans make mistakes frequently, but tornadoes and blizzards happen too. We want repeating processes to repeat as planned, and this is why the first design of any software will be replaced. Moreover, you are probably required to prove to an auditor that all your data is being transmitted and received with very few errors that are themselves being analyzed and accounted for. This is motivation for an Automated Balance and Control system that manages your Data Movement and assures its accuracy and timeliness. If you intend all the work to be “outsourced”, be sure to consider these factors in your negotiations. At minimum, be prepared to self-ensure for these events—they will happen.

Data Out

There are two main reasons to move data out of your Sustainability Software.

To provide a report for approved readers
To supply calculated data to another system

Reporting is technically “easy” now with all the Business Intelligence platforms that are available. Vendors include Microsoft, Oracle, IBM and many others. These tools are expensive but would be cost effective for SaaS providers because they can scale to serve many end users. They are being enhanced daily to also support information display on tablets and smart phones, so you can digest this information over the Internet from nearly any place in the world.

Data transfer to another system, however can be a different story. This will be a Data Movement Application and all the considerations we’ve raised above apply here as well, except your system is now the supplier of data and another system is the recipient. The complexities arise not only from engineering for repeatability, but from the likely need to translate source data so the target system can receive and interpret it appropriately.

(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.

Data Management Concepts for Sustainability - Part 2

August 13, 2015 The SSC Team

Our series on Sustainability Software continues with “Data Management Concepts for Sustainability”. In this article (Part 2 of 4), we’ll continue introducing and defining key Data Management terms (read Part 1 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 Modeling

This term is most commonly associated with Data Warehouse design, but is relevant to the construction of any database. If you elect to design and build your own Sustainability Software you will find the design of its underlying database (Data Modeling) to be one of the most labor intensive steps in the process, and because Sustainability is a rapidly evolving concept, it will seem that the database changes are boundless.

Data Modelers are not only IT-savvy, but are required to be subject matter experts in the business functions of the company. Data Modeling usually starts with vocabulary lists which are organized by a discipline called Taxonomy. These lists are then translated into abstractions called Logical Data Models which ideally constitute the rigorous definitions of, and relationships among all the data elements required for the enterprise to function. Then magic happens and database administrators interpret the Logical Data Models into real databases in software products such as Oracle, DB2 or SQL Server. There are software tools like ERWin and ERStudio that assist both the modelers and DBA’s in doing this.

These are lofty goals indeed and can be expensive to implement especially if you purchase expensive tools. Additionally, in a rapidly changing environment it can be difficult for the Modelers to keep pace with the Entrepreneurs, but if your Business requires databases to function, their models (designs) must either be purchased from vendors or created by the home team.

Since Analysis Paralysis can be costly, we encourage you to “buy” vs. “build” the database for your Sustainability Software, especially given the wide variety of SaaS solutions available in the market today. For small to midsized companies, this is by far the most cost effective option. If you elect a SaaS approach, all these issues will be completely hidden from view and their expenses will be shared among all the system’s users as part of the overall licensing cost.

Data Storage & Archiving

This is where the ongoing cost kicks in. Hardware for data storage is at an all time low and trending downward, but the software licenses required are costly to buy and to maintain going forward. Both must be periodically patched and upgraded which requires a sophisticated IT Infrastructure team. These costs and hassles furnish more strong arguments for SaaS.

There are also potential standards clashes with bringing in special purpose software. For example, SQL Server is an excellent database platform for a small to midsized company, but the Sustainability package you love most might be based on DB2 and Cognos. The benefits of the new system could easily be outrun by the cost of this big company software alone. Remember the notion of Total Cost of Ownership, wherein it often turns out that ongoing costs exceed the installation costs dramatically.

This is the area of Data Management concerned with backups, disaster recovery, test environments, complex operational change control, etc. Bear in mind that Sustainability is an emerging venture and that commercial and governmental influences are afoot to undermine your investment, no matter which way you start out. It’s best to adopt the conservative approach unless your industry has specific special needs that package software has not yet addressed.

If you feel you must support your own Sustainability Software on your own premises with your own team, then make platform compatibility one of your highly loaded criteria. If you have a SQL Server shop, try to adapt to a SQL Server-based package if possible.

One final significant consideration: regardless of who maintains the data storage servers, you will be at least partly responsible to assure all data privacy and audit best practices are followed. If these are not contemplated in the initial setup, it is possible you will enjoy fines and audits that will eventually motivate the re-design of the storage systems (or migration to a SaaS solution!)

(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.

Data Management Concepts for Sustainability - Part 1

August 11, 2015 The SSC Team

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.