The Good, the Better and the Best Kinds of Data Quality Technology

If I look at my journey in data quality I think you can say, that I started with working with the good way of implementing data quality tools, then turned to some better ways and, until now at least, is working with the best way of implementing data quality technology.

It is though not that the good old kind of tools are obsolete. They are just relieved from some of the repeating of the hard work in cleaning up dirty data.

The good (old) kind of tools are data cleansing and data matching tools. These tools are good at finding errors in postal addresses, duplicate party records and other nasty stuff in master data. The bad thing about finding the flaws long time after the bad master data has entered the databases, is that it often is very hard to do the corrections after transactions has been related to these master data and that, if you do not fix the root cause, you will have to do this periodically. However, there still are reasons to use these tools as reported in the post Top 5 Reasons for Downstream Cleansing.

The better way is real time validation and correction at data entry where possible. Here a single data element or a range of data elements are checked when entered. For example the address may be checked against reference data, phone number may be checked for adequate format for the country in question or product master data is checked for the right format and against a value list. The hard thing with this is to do it at all entry points. A possible approach to do it is discussed in the post Service Oriented MDM.

The best tools are emphasizing at assisting data capture and thus preventing data quality issues while also making the data capture process more effective by connecting opposite to collecting. Two such tools I have worked with are:

·        IDQ™ which is a tool for mashing up internal party master data and 3rd party big reference data sources as explained further in the post instant Single Customer View.

·        Product Data Lake, a cloud service for sharing product data in the business ecosystems of manufacturers, distributors, retailers and end users of product information. This service is described in detail here.

DQ

When Excel is Stretched too Far

I guess we all have encountered examples on how Excel is used in an over-complicated way to solve business tasks that should have been solved with a tool much better suited for that kind of work.

My pet peeve is using Excel for exchanging product information between supply channel partners. This has been a main driver behind launching Product Data Lake.

What is your example about a too far stretched use of Excel?

Samsung 49 inch

Product Data Quality

The data quality tool industry has always had a hard time offering capabilities for solving the data quality issues that relates to product data.

Customer data quality issues has always been the challenges addressed as examined in the post The Future of Data Quality Tools, where the current positioning from the analyst firm Information Difference was discussed. The leaders as Experian Data Quality, Informatica and Trillium (now part of Syncsort) always promote their data quality tools with use cases around customer data.

Back some years Oracle did have a go for product data quality with their Silver Creek Systems acquisition as mentioned by Andrew White of Gartner in this post. The approach from Silver Creek to product data quality can be seen in this MIT Information Quality Industry Symposium presentation from the year before. However, today Oracle is not even present in the industry report mentioned above.

Multi-Domain MDM and Data Quality DimensionsWhile data quality as a discipline with the methodology and surrounding data governance may be very similar between customer data and product data, the capabilities needed for tools supporting data cleansing, data quality improvement and prevention of data quality issues are somewhat different.

Data profiling is different, as it must be very tightly connected to product classification. Deduplication is useful, but far from in same degree as with customer data. Data enrichment must be much more related to second party data than third party data, which is most useful for customer and other party master data.

Regular readers of this blog will know, that my suggestion for data quality tool vendors is to join Product Data Lake.

The Future of Data Quality Tools

When looking at the data quality tool market it is interesting to observe, that the tools available does pretty much the same and that all of them are pretty good at what they do today.

A visualization of this is the vendor landscape in the latest Information Difference Data Quality Landscape:

Data Quality Landscape 2017

As you see, the leaders as Experian Data Quality, Informatica, Trillium and others are assembling at the right edge. But that is due to market strength. Else the bunch is positioned pretty much equal.

This report does in my eyes also mention some main clues about where the industry is going.

One aspect is that: “Some data quality products are stand-alone, while others link to separate master data or data governance tools with varying degrees of smoothness.”

Examples among the leaders are Informatica, with data quality, MDM, PIM and other data management tools under the same brand, and Trillium with their partnership with the top data governance vendor Collibra. We will see more of that.

Another aspect is that: “Although name and address is the most common area addressed in data quality, product data is another broad domain requiring different approaches.”

I agree with Andy Hayler of Information Difference about that product data needs a different treatment as discussed in the post Data Quality for the Product Domain vs the Party Domain.

We Need More Product Data Lake Ambassadors

ambassador

Product Data Lake is the new solution to sharing product information between trading partners. While we see many viable in-house solutions to Product Information Management (PIM), there is a need for a solution to exchange product information within cross company supply chains between manufacturers, distributors and retailers.

Completeness of product information is a huge issue for self-service sales approaches as seen in ecommerce. 81 % of e-shoppers will leave a webshop with lacking product information. The root cause of missing product information is often an ineffective cross company data supply chain, where exchange of product data is based on sending spreadsheets back and forth via email or based on biased solutions as PIM Supplier Portals.

However, due to the volume of product data, the velocity required to get data through and the variety of product data needed today, these solutions are in no way adequate or will work for everyone. Having a not working environment for cross company product data exchange is hindering true digital transformation at many organizations within trade.

As a Product Information Management professional or as a vendor company in this space, you can help manufacturers, distributors and retailers in being successful with product information completeness by becoming a Product Data Lake ambassador.

The Product Data Lake encompasses some of the most pressing issues in world-wide sharing of product data:

The first forward looking professionals and vendors in the Product Information Management realm have already joined. I would love to see you as well as our next ambassador.

Interested? Get in contact:

Interenterprise Data Sharing and the 2016 Data Quality Magic Quadrant

dqmq2016The 2016 Magic Quadrant for Data Quality Tools by Gartner is out. One way to have a free read is downloading the report from Informatica, who is the most-top-right vendor in the tool vendor positioning.

Apart from the vendor positioning the report as always contains valuable opinions and observations about the market and how these tools are used to achieve business objectives.

Interenterprise data sharing is the last mentioned scenario besides BI and analytics (analytical scenarios), MDM (operational scenarios), information governance programs, ongoing operations and data migrations.

Another observation is that 90% of the reference customers surveyed for this Magic Quadrant consider party data a priority while the percentage of respondents prioritizing the product data domain was 47%.

My take on this difference is that it relates to interenterprise data sharing. Parties are per definition external to you and if your count of business partners (and B2C customers) exceeds some thousands (that’s the 90%), you need some of kind of tool to cope with data quality for the master data involved. If your product data are internal to you, you can manage data quality without profiling, parsing, matching and other core capabilities of a data quality tool.  If your product data are part of a cross company supply chain, and your count of products exceeds some thousands (that’s the 47%), you probably have issues with product data quality.

In my eyes, the capabilities of a data quality tool will also have to be balanced differently for product data as examined in the post Multi-Domain MDM and Data Quality Dimensions.

Sign Up is Open

Over the recent one and a half year many of the posts on this blog has been about Product Data Lake, a cloud service for sharing product data in the business ecosystems of manufacturers, distributors, retailers and end users of product information.

From my work as a data quality and Master Data Management (MDM) consultant, I have seen the need for a service to solve data quality issues, when it comes to product master data. My observation has been that the root cause of these issues are found in the way that trading partners exchange product information and digital assets.

It is the aim of Product Data Lake to ensure:

  • Completeness of product information by enabling trading partners to exchange product data in a uniform way
  • Timeliness of product information by connecting trading partners in a process driven way
  • Conformity of product information by encompassing various international standards for product information
  • Consistency of product information by allowing upstream trading partners and downstream trading partners to interact with in-house structure of product information
  • Accuracy of product information by ensuring transparency of product information across the supply chain.

You can learn more about how Product Data Lake works on the documentation site.

pdl-how-much-smallBecome a:

Sign Up is open on www.productdatalake.com

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