Posts Tagged ‘Software as a Service’
Friday, April 23rd, 2010
When it comes to Master Data Management, the implied definition is an à la carte of detailing and normalizing activities including data cleansing, data verification, data profiling, data governance, de-duplication, data enrichment and data provenance among other tasks. If you are managing or participating in the activities of a Master Data Management program, you are progressing in the right direction of achieving data quality. If you are NOT participating in the activities of MDM then you are part of a company wide initiative of “Garbage In, Garbage Out (GIGO)”. By the way, GIGO, in this case is not environmentally responsible or a “green” behavior.
Wikipedia’s definition for “Garbage In, Garbage Out, is a phrase in the field of computer science or information and communication technology. It is used primarily to call attention to the fact that computers will unquestioningly process the most nonsensical of input data (Garbage in) and produce nonsensical output (Garbage out).”
If you enter “garbage in” to a computer system, having the data passed through some very expensive ERP or CMMS software, isn’t going to change the data quality, the business results are equivalent to “garbage out”, which will be apparent in the day to day business activities and subsequent reporting used to determine the health of your business. Is it obvious that data should just not be moved from one system to a new system without a MDM program?
Let us now explore the concept of data migration. Wikipedia’s definition for Data Migration is the process of transferring data between storage types, formats, or computer systems. Data migration is usually performed programmatically to achieve an automated migration, freeing up human resources from tedious tasks. It is required when organizations or individuals change computer systems or upgrade to new systems, or when systems merge.
If an MDM program is not in process when implementing a new software or upgrading an existing software, the project should include an evaluation of the data and/or an evaluation of the additional functionality of the “to be” model of the new software identifying the new data required for improved business processes, reporting and the plan for legacy data clean up. A data migration project needs to be more than moving data from a legacy system to the new system.
I asked the question to one user of a maintenance software implemented a number of years earlier as I had the opportunity during a site visit at a plant. The software had awesome abilities to create and manage the relationships between equipment and spare parts, supplier contacts as well as the potential to improve processes, reporting and streamlining the information required for a maintenance organization. The company invested in the software / hardware, understood the ROI but lack the understanding of the data needs or management. The software was implemented however the majority of the functionality was not used, therefore the ROI was never achieved. When I asked why, I was told “no data and we don’t have time to add the data.”
Another scenario I came across, purchasing moved data from a legacy system to a new ERP system. The data wasn’t set up to a data governance or MDM procedure, legacy data riddled with duplication, obsolete information, unstructured descriptions and so forth. Different system, same legacy data quality and the ROI was never achieved.
I have one simple question, why invest in a software product if the data is not going to be treated as an asset? The results of a successful implementation are that the business processes are streamlined; simplified and reporting capabilities are enhanced through enabling both Master Data Management and Software functionality.
Garbage In, Garbage Out or Garbage Extracted, Garbage Migrated as we are moving to the next generation of technology. Are we relying on a skewed nonsensical output based on low quality data to make our critical business decisions?
Tags: Business Intelligence, Data Cleansing, data management, Data Profiling, data quality, DATAForge, dataquality, linkedin, masterdata, mdm, MRO, Software as a Service, spare parts
Posted by Jackie Roberts | 2 Comments to View »
Thursday, February 4th, 2010
Dear Andrew White,
Thank you for your comments in “Something beyond MDM is coming your way – would MDM 2.0 fly?” and starting the discussion to expand the definition of MDM to include data integrity, data quality, entity resolution, matching, data integration, governance, metrics and analysis. The topics discussed should also include work flow (management of data and analysts), translation management, data structuring, data profiling, duplication removal, data change management, verification contact management, etc.
The MDM and PIM software industry needs to take a step back to understand actual day to day business requirements of data management to achieve Master Data Quality. Lesson one is that data is created and supplied by many sources in many different formats at various quality levels. Data is created by engineering, submitted by integrators, manufacturers and suppliers. To add to the complexity of the information flow, data is introduced into businesses systems in different departments (engineering or purchasing or maybe plant from maintenance) with different data requirements to meet the needs of that job function. Now the next dynamic is mashing new data to existing legacy data in a number of systems to ensure no duplicates are created, managing obsolete / recommended use and functional equivalents. The old philosophies of a PIM or MDM software to “hold, provide search functionality and maybe a shopping cart” isn’t going to meet the true requirements of the new definitions of Master Data Management.
To meet the new definitions the MDM or PIM software needs to provide horse power to electronically and intelligently processing data to identify exceptions for manual intervention by an analyst. Data should be processed one time to ensure that the data record will be enriched to meet the requirements of the enterprise and then the record is moved to a maintenance program (managed also by the MDM or PIM software). The processing of data needs to be efficient and cost effective, from my perspective the cost of data management should be covered by the cost saving achieved by MDM management.
I look forward to the discussions as the definition of MDM is expanded to include data quality, data governance, data provenience as the software industry provides the intelligence, functionality and business processes to cleanse, enrich and management data for my client to ensure their ability to make confident business decisions based on data integrity and accuracy.
Here is to the future of PIM and MDM!
Jackie Roberts
Tags: Andrew White, BPO, Business Intelligence, data, Data Cleansing, data management, data quality, DATAForge, development, Gartner, linkedin, maintenance, manufacturing, masterdata, Maximo, mdm, MRO, SaaS, Software as a Service, system implementation, Technology
Posted by Jackie Roberts | 2 Comments to View »
Friday, January 8th, 2010
With the ever increasing emphasis on finding ways to reduce cost, one of the clear targets is IT and more specifically data management systems. On the surface it can seem like there is real fat to trim, and many times this is true. But it is easy to become lost in the details and eliminate or negate some of the potential savings. Some of these ideas may seem obvious but are often forgotten. The evidence is clear with missed timing and over budget issues seen.
If we’re talking about a large company then inevitably with this new system comes the monolith project with whole organizations of people and processes, projects and documentation. The compulsion is to be sure that everyone, everywhere who has any relationship to it has their input and their needs accounted for. Along the way, the cost of implementation and other peripheral indirect costs have likely negated a great deal of at least any short term savings. Not to mention the potential increase in continuous maintenance costs and loss in performance. These are a few things I’ve learned from experience and I welcome yours.
Always have a specific objective when planning for development or evaluating software to purchase that overrides all others. Start with something like a mission statement, “We need this new system for….”
Determine the Real Needs. Try to separate the “must haves” from the “nice to haves”. Bells and whistles are great but there needs to be a true benefit. Seek a balance between development time, software performance, hardware performance and user experience. I always try to put special emphasis on the user group which stands to benefit the most. Having many users who can do their job faster and more efficiently can add up to real savings versus the few users who have a special need which bogs down the project and performance.
Change is inevitable. If some requests for additional features come along, evaluate them against the mission objective. There is nothing wrong with listening and investigating ideas for project add-ons as long as the benefits outweigh the costs in time and money, but there needs to be a limit or you’ll never complete the project. Good ideas can always be implemented later if it makes sense then you’ll have the benefit of the research already done, but be quick with the research. Evaluate the impact for doing it now or waiting. Here are some good questions to start with: 1) How much more money? 2) Would this be faster/cheaper for programming to do it now versus waiting and doing a more complicated enhancement? 3) Is the impact to the users great enough to warrant it?
Know the roles. Good ideas can come from anyone. Every project must have a project champion who makes the final decisions (and live with them) and also eliminate roadblocks. You need a user advocate who has done the job and knows what it takes. Have programmers who possess both talent and vision, not just code crunchers, and listen to them.
Have good documentation, and “Good” is subject to interpretation. This is another area where the KISS principle is very often not utilized. If you have to hire ten people to sit in meetings just to maintain your documentation you’ve probably overcomplicated it and certainly increased your project cost. I try to start with these principles:
- Document the people on the project and their responsibilities. Let there be no question as to who does what.
- Everyone who has a job to do needs to understand what they need to do and have the documentation to reference.
- Keep the language simple. Focus on getting the point across. If it takes a rocket scientist to understand it you’ve failed.
- Of course, document the issues, decisions made, by whom etc. but be sensible. Document enough to cover for the “he said/she said” but content is most important. No bonus points for flash.
- Know who is supposed to have what done and when. Another obvious one here but I see too often where target dates are determined top down with little or no thought to cost or the tasks. Don’t let the tail wag the dog. Pushing hard to get the job done is fine but be realistic. Listen to the people who know before making bold predictions.
Tags: Agile, automotive, BPO, Business Intelligence, data, Data Cleansing, data management, data quality, DATAForge, dataquality, development, eOTD, ERP, linkedin, maintenance, manufacturing, masterdata, Maximo, mdm, MRO, project management, SaaS, Software as a Service, spare parts, system implementation, Technology
Posted by Carl Hamlett | Post a Comment! »
Wednesday, December 2nd, 2009
As the Master Data Management industry matures, the industry focus is not only on the development of software to collect product records but software to implement the data quality process solutions supporting data governance and provenance including record history, structure, completeness and accuracy to ensure our customers are able to make confident, informed and accurate business decisions based on data accuracy. The first step of implementing a data governance program is implementing a naming classification system.
I have had experience working with single business home-grown classification structures and third party developed structures for purchase, currently I have chosen an open and public classification structure provided by ECCMA (www.eccma.org). This is beneficial to the customers that I support ensuring that they will always have access to the classification structure sometime referred to as the schema used to classify their data.
Implementing a classification requires setting up Identification Guide (IG) to establish the template definition to technically describe the product or service with enough information to support engineering, maintenance or purchasing while recognizing the limitation of software short and long description required character lengths. The IG template supports and simplifies the required information request to the manufacturer and suppliers to verify all information by our analysts to standardize the description.
To create an IG, we search the ECCMA class list; fortunately many of the classes are established. As the IG is set up we will use the ECCMA established class name convention; this will ensure that every item will be setup with the same name and format, every ball bearing item submitted will be classified as a BEARING, BALL.
The next step is to set up the properties required to describe the BEARING, BALL and for each property designated the data type requirements such as numeric, text string or designated unit of measure. The property value requirements for a BEARING, BALL might include TYPE, BORE DIAMETER, OUTSIDE DIAMETER, WIDTH, DYNAMIC LOAD CAPACITY, STATIC LOAD CAPACITY, MATERIAL and so forth. Our analysts will verify the data to the original manufacturer sometimes using xml to exchange the product information referred to as “Cataloging at Source”, the information requests are standardized and remove much of the quality issues commonly found in a non-standardized data verification or description process.
The property value description build is controlled by the sequence number of each property Item data that will make it’s way into a length restricted description field we place the most important information in the begin of the auto generated description.
Setting up the Identification Guides requires upfront strategic planning and detailed work, as you can imagine that a classification schema can be up to 10,000 classes depending on the industry but it provides a multitude of benefits including standardized requirements, a road map for our analysts to facilitate the process, improved data management reporting / metrics and enhances language translation for the global organization.
Tags: BPO, Business Intelligence, data, Data Cleansing, dataquality, eOTD, linkedin, masterdata, mdm, Software as a Service, spare parts
Posted by Jackie Roberts | Post a Comment! »
Thursday, October 1st, 2009
I am all about the data, location management (to location and equipment), data quality, and methods to improve auto-processing, enhancing data, providing data reports and results that support our customer’s data requirements in their day to day activities.
Here is the million dollar question, this is one scenario: Over a million records in a year, legacy and new records submitted for processing from 2,500 different users and two different business processes (single submit and BOM extract). What technology would be required to intelligently automate the processing of these records to a Master Data Quality Standard?
Remember this is an on-going maintenance process, not a one time migration of non-cleansed data to a new ERP or maintenance system, nor am I referring to parsing the records into different fields of the new ERP system but ensuring that the records are verified, structured, properly attributed with full descriptions and additional information to support the business needs.
First, let’s look at the Wikipedia definition of Product Information Management (PIM) “PIM systems generally need to support multiple geographic locations, multi-lingual data, and maintenance and modification of product information within a centralized catalog to provide consistently accurate information to multiple channels in a cost-effective manner.”
Future PIM software purchasers, what evaluation methods are you using to ensure that your PIM software purchase will support the continuous update and flow of data for your entire enterprise system? Here are some items to take into consideration during your evaluation, these are all items that I ask about and would recommend that you request the answers in writing:
1. How is the change history of the data stored in the system and how easily can it be retrieved?
2. Has the performance of all modules of the software been tested and what is the base line?
3. Request references (at least three) for each module of the software.
4. What is the software product work flow and how is the data processing assigned to employees?
5. Ask to review the documentation and take the time to review; this should be a window into the complexity of the system.
6. Request the design process model and how the software company incorporates customer feedback?
7. What is the bug fix process? What is the quality system to implement a bug fix?
8. What is the software company’s philosophy on customizations at your cost?
9. How is language handled? Translations referenced to a master record?
10. If the software solution is multi module system, how are the master records referenced through
the entire solution?
11. What are the long term design strategies or road maps for each module of the software solution? Ask for the earlier road maps and the software release note to evaluate the how well the software company plans and implement updates to the systems.
And I can go on and on, the licensing; customizing and implementing software in your environment can be extremely costly and time consuming, does Caveat emptor “Let the buyer beware” work in the business world or is there a “Lemon Law” when purchasing software?
Tags: BPO, Business Intelligence, data, Data Cleansing, data quality, DATAForge, eOTD, linkedin, maintenance, manufacturing, masterdata, Maximo, mdm, Software as a Service, spare parts, Technology
Posted by Jackie Roberts | Post a Comment! »
Thursday, October 1st, 2009
My company’s fiscal year is based on the calendar year as many others are. So, customarily we start the budget planning process in October. It is a detailed process that all of my managers and business units participate in. We usually do a few iterations before it is finalized in mid December. Sound familiar? So here’s the question, after 2009, how do you plan for 2010? Everything we knew and could usually predict with some certainty in recent years went out the window in 2009. Where do you start to plan for the next year? Is it too early to plan for growth, if not, at what pace? What certainty can we count on when developing our plans? The simple fact is, for most of us, we don’t know enough at this stage in the recovery to forecast with certainty where our businesses will be, at least, through mid next year.
So what can be done to insure profitability, or least stability, until growth returns? Control and further reduce costs. Already been there, done that? You have cut staff, benefits, wages, renegotiated prices and terms with suppliers, cut services, slowed production, cut inventories, everything you can think of. Are you sure? How well do you manage your Enterprise wide Master Data Indirect Materials / Commodities spend? What? Everything you buy that supports your facilities and the build of your products. Most large manufactures manage direct material precisely but don’t have an organized approach to their full advantage throughout the Enterprise to strategically manage indirect materials. A solution, fully implemented, provides a number of benefits:
1. “Cleansed” data, eliminating duplication of the same item coded to several different part numbers.
2. Consistent pricing for each and every part / component verified to the OEM level with lead time and warranty information. Minimizing your need to buy spare parts / commodities from distributors or your build sources.
3. Enterprise-wide material management to the department level in every Manufacturing Operation.
4. A reuse or repurposing of excess inventory in Manufacturing Engineering.
5. Able to search inventory with standardized part naming conventions and in multiple languages.
Bottom-line, an aggressive Enterprise wide well executed strategy can and will save your company significant dollars in the first 12 months of implementation. That’s 2010 folks….
Tags: BPO, Business Intelligence, data, Data Cleansing, DATAForge, dataquality, linkedin, maintenance, manufacturing, masterdata, Maximo, mdm, MRO, Software as a Service, spare parts, Technology
Posted by Art Healan | Post a Comment! »
Thursday, September 17th, 2009
Those of us that are representatives of Master Data Management initiatives, data quality projects and the users working the processes developed by software makers have a difficult journey in front of us. It seems that for years software developers have designed cumbersome transactional data management systems that do not begin to understand real time data management and what effort it really takes to achieve an on-going Master Data Management program. I have two initial questions: Do these software companies toting one press release after another about Master Data Quality Management even understand the importance of on-going change management to a master data record? How does a business stay in front of the information flow if the software system does not dynamically adapt to the ebb and flow of data volumes and requirements? Software companies track updates and revisions to software code, data is of the same importance sometimes it is of greater importance; the number of data level updates can be monumental depending on the size of the company. Isn’t the end result of a multi-million dollar software system implementation supposed to drive efficiencies and streamline the activities to support their businesses? Cost saving and real time data management is the name of the game.
Here are a few data management tips:
1. Data needs a simple way to be imported into the system. Data comes from a number of sources so a dynamic mapping and import procedure to an internal processing area is useful for data analysis.
2. Yes, there needs to be an area to work on data before it is promoted to a Master Data Status. Software developers need to understand that data is never in a pristine state ready to be entered as a Master Data Record. Never!
3. Data processing requires a managed work flow through the system. Imagine the issue to have thousands of records for analyzing and many employees trying to manage who has what records outside the system. Just not functional work scenario.
4. Never copy data from one software module or grid to another, always reference. Cost per record to manage the data is increased every time a person needs to manually update an aspect of a record more than once.
5. Performance of the software is imperative. To really capitalize on software and technology reporting and analysis need to be done on thousands of records at a time. Time is money.
6. Provenance tracking is extremely imperative especially when “Cataloging @ Source” is the foundation to the quality of the record. Data should be identified with history: where the data originated, contact information, data and time, a revision level, file name, all associated records on the file, etc. MDM system developers, can you start to see the importance of this information?
7. Data needs to be cleansed and profiled; it is important that the software processing tools understand all aspects of the data. For instance search rules should not be so rigid that it takes an analyst manual actions to find a duplicate record because of an extra space or a slash. A worse case scenario is to take the data out of the system to work the data in excel, I am not going to even comment any more on that scenario except that it is totally unacceptable to remove data from a system to try to normalize it. Remember there is a lot of data brought into the business and the cost to manage the data is not core to the primary business, it is an indirect cost. The solution is not outsourcing to a “low cost, low skilled” worker in another country when much of the preprocessing can be done at the expense of CPU time.
8. Data changes, if you have a number of different modules in your software package what is the strategy to support aggregation of the changes to the different business units using the data? Does your software only update in one module and the other modules are in an out of sync situation? Again remember software should be designed to simplify the processes to support the business needs.
9. We live in a global economy language translation and localization of data is more important now than ever. What are the methods translate and maintain localized data?
10. Reporting and exporting of information is critical. It is a requirement to export a segment data set to send to a business customer or run a report of the activities of the work. A MDM system must be able audit data activities through the complete process of import through promotion to a master record.
I am a firm believer that software should not dictate a business process but should be designed to streamline and add efficiency to lower the cost the activity. If you are designing MDM systems, your team should include experts in data management, data quality and business process expertise with applicable experience. Businesses should not be paying for customizations to your software to be support basic 101 management of data.
Tags: BPO, Business Intelligence, data, data quality, DATAForge, dataquality, eOTD, ERP, linkedin, maintenance, manufacturing, masterdata, Maximo, MRO, Software as a Service, spare parts, Technology
Posted by Jackie Roberts | Post a Comment! »
Friday, September 11th, 2009
October 2009 is going to be a busy and exciting month for DATAForge. We are scheduled to present at two events and hope to see you all at both
DATAForge will be presenting at the FMMUG 2009 Best Practices to be held October 11th and 12th. This years event is to be hosted by Purdue University. The mission of the Facilities Management Maximo User Group (FMMUG) is to provide a forum for Maximo users in the facilities management industry to exchange information, methods and experiences. This exchange of information is designed to optimize the use of Maximo’s capabilities. For more information visit http://www.fmmug.org.
DATAForge will also be presenting on behalf of the Automotive Industry Content Standardization council at the 10th Annual ECCMA ISO 8000 Data Quality Conference on October 27th, 28th and 29th. This years event will be held at the historic Hotel Bethlehem in Bethlehem, Pennsylvania. Whether you are new to data quality or a seasoned professional, this conference will provide you with a unique opportunity to discuss the latest trends and check out the latest technology. If you have an interest in improving the accuracy of your vendor, material, service or asset masters, improving the descriptions in your ERP software or buy-side or sell-side catalogs or if you are looking for solutions to data integration challenges, the ECCMA conference is the place to be! Please visit the ECCMA website for more information.
Tags: Data Cleansing, DATAForge, dataquality, linkedin, maintenance, masterdata, Maximo, Purdue, Software as a Service, spare parts, XML
Posted by Industry News | Post a Comment! »
Wednesday, September 9th, 2009
At DATAForge we pride ourselves on designing simple, elegant, easy to use, web based software for a manufacturing demographic that has been flooded with overly complicated software, abound with options and restrictions, screens to control those options,restrictions and configurations. I’m tired of it. I don’t want you to get me wrong, there is certainly a time, place, and need for software that is configurable in every conceviable way. For example when a multi-state and international corporation is required by law to comply with one of the most complicated tax codes in the recorded history of Earth, then you get a pass for making an application complicated. In this case complication can and has saved many organizations millions or hundreds of millions of dollars, issues like The Sarbanes-Oxley Act of 2002 are not to be taken lightly.
The same logic of presenting every imaginable, option, configuration, button, screen, step, radio button, piece of information has been applied to many software packages. You would think in a large organization, simplicity would be king…not so…I am currently consulting with a large multi-national organization to help in the deploymentof a centralized system to house all product information for their MRO or Maintenance, Repair and Operations. Which, in practical terms, means that they are centralizing their databases of information required to order, maintain, and use any item that can potentially be purchased but does not go into their final product.
Not a small task by any measuring stick. Master Data Management, data cleansing, data normalization, intra-organization de-duplication are on the radar of most if not all large businesses. The most important part of the process is to choose application(s) that are the best fit for your organization, not the one that is made or owned by the largest company, and not the one who has the most clever marketing, not the one that appears in the latest report by the best marketed research firm (think about the ratings agencies who rated toxic subrime mortgage backed securities AA or AAA)
The software that was chosen xxxxxx (contractually obligated not to say the name) has one main screen for entering most of the data related to any given item, this screen contains no less than 50 possible fields in tabular form. There are also 3 additional screen each with less than 50 fields for data entry, these subsequent screens are used to associate ansillary information such as pictures to an item. The screens that DATAForge uses – one screen with 25 or less (depending on the type of data). The remainder of the information is gathered organically and seamlessly based on the way the application is used and who is using it.
When we design a solution the question on each team members mind is “How can I make this easier and faster to do for the end user?”
When evaluating an application force the vendor to show you how it will be used (not tell you), make them show you their solution is faster and more efficient. Lots of options, inputs, and fields are not always the users friend.
Tags: DATAForge, maintenance, manufacturing, masterdata, MRO, Software as a Service, Technology
Posted by Chris Roberts | 1 Comment to View »
Thursday, September 3rd, 2009
Life Cycle Management implies a single “cradle to grave” plan that integrates production support planning, acquisition and sustainment strategies. Think about the importance of data flow and the criticality of accurate data throughout the complete life cycle of a piece of equipment: design, build, install, spare part acquisition, inventory management, maintenance, spare parts sharing and finally, asset disposal. From a data perspective, remember the old computer motto: “Garbage In, Garbage Out”.
What is your Life Cycle Data Management Strategy?
1) Drawing Libraries – The items in the library need to be cleansed and profiled to a classification schema. The schema requires standard naming conventions and technical descriptions. The schema can be designed within your company, priority purchased from another vendor or you can opt for using an open classification dictionary for public use such as the ECCMA eOTD.
2) Common Component Listing – provides a listing of preferred components that support the inventory management strategies for your organization. All equipment designers and builder are required to use the common components identified. Note: common components are set up in the drawing libraries.
3) Spare Part Acquisition – Place the components on purchasing contacts at the beginning of design, this will facilitate the ease of spare parts planning and purchasing. An item on contract provides purchasing the data needed to run analytical algorithms in order to better negotiate pricing organization wide. If the item is set up accurately to a standardized classification dictionary with technical descriptions only one time the whole organization can realize the benefits of the Life Cycle Data Management Strategy.
4) Inventory – supports optimal inventory management by promoting the ability to plan stocking levels and strategies with nearby facilities. Think about the implementation of spare parts sharing or an internal purchase first program. The most important requirement is the standardization or normalization of the data; the part needs to be classified only one-way and should be shown in every system the same way.
5) Maintenance –The use of standardized components coupled with a data management strategy allows the organization to streamline the number of different components used to serve the same function on different equipment. Also reducing the number of parts in inventory and maintenance management tasks.
Life Cycle Data Management Plans starts with component standardization and cleansing the data in your equipment drawing libraries and all downward systems including maintenance. This strategy avoids duplicate inventory items and at the same time promotes an internal purchase philosophy that puts a priority on inventory sharing before issuing supplier purchase orders. Standardizing inventory with information elements such as predefined stocking levels, identification of critical inventory, functionally equivalent item identification and purchasing analytics as well as enhanced vendor management are all necessary steps for a manufacturing business to remain competitive in today’s world of lean low overhead manufacturing.
Tags: BPO, Business Intelligence, data, Data Cleansing, data quality, DATAForge, dataquality, eOTD, ERP, linkedin, maintenance, manufacturing, masterdata, mdm, MRO, SaaS, Software as a Service, spare parts, Technology
Posted by Jackie Roberts | Post a Comment! »