Blog Entries by Jackie Roberts

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Data Quality: Software Innovation Please

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?

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Who Represents the Data in your Master Data Management Software Systems Designs?

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.

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Life Cycle Data Management Strategy

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.

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Why Data Cleansing?

Thursday, August 27th, 2009

The statistics around data cleansing are overwhelming and there are mountains of discussions, white papers and tweets available pertaining to Data Quality, Data Profiling and Master Data Management. I think we need to take a step back and try to understand how and why data cleansing has become such a hot topic. You may have realized that business data typically isn’t as streamlined and efficiently maintained as we thought it was. Your organization may have shipped purchased items back because they were not what you thought you had ordered. In some cases another department was found to have the item in inventory, even though we have the item on urgent delivery status from a supplier because the item is set up under a different number or description, you couldn’t have possibly known the item was actually available from existing inventory.

The data quality issues that industries around the world are experiencing have occurred as a result of many years of manual inventory and purchasing record maintenance, through mergers and acquisitions of companies and business units as well as data migrations from various legacy systems into new fangled ERP black holes. There are a number of reasons why.

A common data trap frequently fallen into is assuming that just because you are implementing a new ERP system your organization will now have quality data. Remember the old computer motto – “Garbage In, Garbage Out”. Let me tell you based on first hand experience that there is nothing “sexy” about bad data when the production line is down or any other time.

Data Cleansing and Data Profiling is a very tedious and detailed oriented service. There are a number of key rules to follow whether the profiling and cleansing work is done internally or outsourced to someone who specializes in data cleansing. Here are some rules to consider before a project is started:

1) Conduct a detailed and comprehensive data mapping through all internal systems including engineering, purchasing, asset management, plant inventory management, etc. The goal is to standardize and document all data sources within the enterprise one time and ensure that each department is accounted for and determines what data elements are required to complete their business required tasks.

2) Build a central data cleansing database and make sure all locations using each item are referenced. This ensures that updated information will be passed back to the various legacy systems. You will need old information and updated information for this stage of the process.

3) The data cleansing database should include a balance of electronic scripting for data corrections and manual auditing. A solid process for answering questions needs to be set up. My preference is that the system should use a web utility that tracks data change history and other data related information such as contact information, issue resolution status, classification, questions and answers, etc.

4) The data needs to be referenced to a classification schema and a standard implemented for descriptions and properties. 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.

5) Free text is not our friend in the data standardization world. If all possible use a system that has built in data rules and ensure anyone entering data into the system understands the standards and the importance of quality data in addition to the high cost to businesses using bad data.

6) Data Cleansing and Profiling the proper way is not “cheap”, but the cost of cleaning the bad data is always less than the expenditures incurred by cleansing your data multiple times or continuing to operate your organization based on erroneous information generated from one or multiple dirty databases.

Cleansed data permits the removal of duplicated inventory items, an internal purchase philosophy that puts a priority on inventory sharing before issuing supplier purchase orders, standardizing inventory with predefined stocking levels, identifying critical pieces of inventory, identifying functionally equivalent items, use of engineering component standardization libraries and facilitates purchasing analytics as well as enhanced vendor management.

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The Spare Parts World

Tuesday, August 11th, 2009

Spare parts management at a high level is perceived and often approached as a process that should be simple. Looking at it from perspectives of the many different entities that form the supply chain and are required to work together – component manufacturers, tier 1 suppliers, tier 2 suppliers, and manufacturers, the logistical expertise needed to coordinate the information flow is anything but simple.

To realize cost saving from new process efficiencies, these separate legal entities need to “integrate” the information flow to manufacturers and within each manufacturer to internal groups such as purchasing, manufacturing engineering, plant maintenance, facilities management, warehousing, commodity management, and asset sharing / recovery need to share the mission critical master data related to the spare parts. A truly integrated information flow could conceivably touch a number of business units that indirectly work together across the supply chain to deliver just one item to a manufacturer. The most common element needed by (and from) all involved in the supply chain of the spare parts that keeps the equipment running is data standardization, data quality and an electronic method of transmittal. A study of large companies, a majority of which have revenues of more than $1 billion, found that 31% believe that their costs for incorrect data are $1 million or more per year.1

Data standardization and data cleansing cost should be covered with cost saving initiatives. In addition to the initial data cleanup; strong data governance processes should be implemented for on-going data setups.

1Dave Waddington, “Growing Adoption of Master Data Management by Business?” citing an Information Difference survey of 112 companies, 65% of which had revenues of more than $1 billion, IT-Director.com, IT Analysis Communications Ltd., June 23, 2008.

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Outsourcing; how do I compete?

Tuesday, July 28th, 2009

I get it, you operate globally and the cost of labor in the states is 4 to 5 times higher than the wages in the countries that typically receive outsourced work. I have only one question; is the only factor taken into account when deciding to outsource from the US to a foreign country cost? When the RFP is evaluated does intellectual property protection and security, quality of work product, time zone communication issues, the geopolitical climate or increasing price trends enter into the decision making process?

I once spoke with a purchasing agent employed by a Fortune 500 company and this is how outsourcing was explained to me…”even if takes someone in a foreign low wage country 3 attempts to get the work correct, we are still are saving 25% over their competitors in the US.” Of course, I had a number of responses, including: Was the cost to manage and audit the work 3 times included in the cost saving analysis? Of course not, the cost savings estimate is only documented at the RFP phase.

Each day our company evaluates our internal and customer processes to build automation and intelligent software applications that increase throughput, improve accuracy without manual intervention and provide our customers with a continuous stream of process improvements. I believe long term our cost are competitive, the challenge is educating new customers to understand the unique and beneficial processes that allow them to capitalize long term implementing  our data quality solutions.

My hope is that I will never see another response to an RFP “Need more competitive pricing or to include “off shore” solution – This is required for more competitive proposal and for further consideration”

How long will it take US salaries to race to the bottom so work can be outsourced back to the states? I hope that this is not the answer, let’s discuss what US vendors need to do offer the long term value add processes that off shore options do not?

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What is the Cost of Bad Data?

Friday, July 10th, 2009

How does a company apply a “cost” to bad data when the costs are so fragmented across the organization? There are obvious costs such as a part not being in inventory, purchasing has tried to buy the part but the supplier didn’t recognize the part number, now production is down and everyone is scrambling to find the replacement part. In this case the cost of the bad data can be assigned.

What about the other costs? What does it cost a global manufacturer the lack of visibility of the “spend” or the inability to manage vendors selling like or equivalent products?

It’s estimated that process failures and bad information cost $1.5 trillion or more in the U.S. alone.[i]

[i] Larry English, “Information Quality Tipping Point: Plain English about Information Quality,” DM Review, July 2007.

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Attending the National Summit

Friday, June 26th, 2009

First, I want to thank my company DATAForge LLC for suggesting that I attend the National Summit. My leadership understands the importance of participating in the comprehensive dialog around the direction of the US strategies in the Technology, Energy, Environmental and Manufacturing industries.

The Detroit Economic Club put together a dynamic three days of thought starting and panel discussions with business leaders, state, federal and education representatives. I am looking forward to reading the report of the action items gleaned from the National Summit to be presented by Bill Ford and Andrew Liveris to Secretary Locke in Washington.

The dialog was refreshing and un-stereotypic as prominent business leaders discussed the “how to” and the “what is” for the future direction needed to support “clean energy” and “smart technology” for our environment. The discussions were the first steps to create a roadmap to set the long term policies and standards needed to allow our innovative research universities and businesses to work together to develop and manufacture the next generation of global environmental and manufacturing technologies. This is the old fashion “American” spirit and I am thrilled to be a part of the revolution.

So let’s get the plan in motion, communicate and discuss our action items, support the research institutions, businesses and the startup ventures to make America the next generation innovators and competitors in this global economy.

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Data Quality – What is a good description?

Monday, June 1st, 2009

A spare part record, sounds fairly simple? Yes, I used the same concept earlier. Ask the questions, what is the end use for this record? Is this information only to purchase the item? What if we broaden the scope of Data Quality to enterprise wide, say Engineering or Plant Maintenance, is this description enough to describe the part technically?

Table

A full technical description provides many benefits that support engineering and maintenance such as identifying functional equivalents or using another part in inventory if the requested item is out of stock. The description will provide enough information for purchasing to ensure that the correct item is being purchased and also provides ability to reconcile legacy data.  

What additional elements should be included in a description?

table1

I am not sure what the legacy system search capabilities are but if these descriptive element are included in a description, it only ensures the ability to find the part quickly (or a substitute part) and this will minimize equipment down time and employee sourcing time.

Question: What level of description is required enterprise wide to optimize employee performance and inventory optimization?

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Data Integrity – How is this really achieved?

Thursday, May 21st, 2009

Data integrity is the assurance that data is consistent and correct. Spare parts, sounds fairly simple?
What are the basic elements of a part record; name, part number, description? Data Integrity is used way too much but is a very vague concept. Let’s just look at the purchasing department; it is easy if the part records are only used by the purchasing department where the main objective is to purchase the item. This example is all the data that the buyer will need to purchase this switch.

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How would a buyer know that these are the same parts? Two different manufacturer names and two different part numbers; this scenario will cause duplication in a purchasing system. The result is the additional work of creating and maintaining two contracts but also cause downstream effects such as excess inventory with more than 1 stocking location, lack of a volume purchase or a global view.

 Question: Is the answer to always to confirm the actual manufacturer and set up supplier references?

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