Category Archives: Master Data Management
Working for Informatica has many advantages. One of them is that I clearly understand the difference between Product Information Management (PIM) and Master Data Management (MDM) for product data[i]. Since I have this clear in my own mind, it is easy to forget that this may not be as obvious to others. As frequently happens, it takes a customer to help us articulate why PIM is not the same as Product MDM. Now that this is fresh in my mind again, I thought I would share why the two are different, and when you should consider each one, or both.
In a lengthy discussion with our customer, many points were raised, discussed and classified. In the end, all arguments essentially came down to each technology’s primary purpose. A different primary purpose means that typical capabilities of the two products are geared towards different audiences and use cases.
PIM is a business application that centralizes and streamlines the creation and enhancement of consistent, but localised product content across channels. (Figure 1)
Figure 1: PIM Product Data Creation Flow
Product MDM is an infrastructure component that consolidates the core global product data that should be consistent across multiple and diverse systems and business processes, but typically isn’t. (Figure 2)
Figure 2: MDM Product Data Consolidation Hub
The choice between the two technologies really comes down the current challenge you are trying to solve. If you cannot get clean and consistent data out through all your sales channels fast enough, then a PIM solution is the correct choice for you. However, if your organisation is making poor decisions and seeing bloated costs (e.g. procurement or inventory costs) due to poor internal product data, then MDM technology is the right choice.
But, if it is so simple – why I am even writing this down? Why are the lines blurring now?
Here is my 3-part theory:
- A focus on good quality product data is relatively recent trend. Different industries started by addressing different challenges.
- PIM has primarily been used in retail B2C environments and distributor B2B or B2C environments. That is, organisations which are primarily focused around the sale of a product, rather than the design and production of the product.
- Product MDM has been used predominately by manufacturers of goods, looking to standardise and support global processes, reporting and analytics across departments.
- Now, manufacturers are increasingly looking to take control of their product information outside their organisation.
- This trend is most notable in Consumer Goods (CG) companies.
- Increasingly consistent, appealing and high quality data in the consumer realm is making the difference between choosing your product vs. a competitor’s.
- CG must ensure all channels – their own and their retail partner’s – are fed with high quality product data.
- So PIM is now entering organisations which should already have a Product MDM tool. If they don’t, confusion arises.
- When Marketing buys PIM (and it normally is Marketing), quite frankly this shows up the poor product data management upstream of marketing.
- It becomes quite tempting to try to jam as much product data into a PIM system as possible, going beyond the original scope of PIM.
The follow-on question is clear: why can’t we just make a few changes and use PIM as our MDM technology, or MDM as our PIM solution? It is very tempting. Both data models can be extended to add extra fields. In Informatica’s case, both are supported by a common, feature-rich workflow tool. However, there are inherent risks in using PIM where Product MDM is needed or Product MDM where PIM is needed.
After discussions with our customer, we identified 3 risks of modifying PIM when it is really Product MDM functionality that is needed:
- Decrease speed of PIM deployment
- Reduce marketing agility
- Risk of marketing abandoning the hybrid tool in the mid-term
The last turned out to be the least understood, but that doesn’t make it any less real. Since each of these risks deserves more explanation, I will discuss them in Part 2 of this Blog. (Still to be published)
In summary, PIM and Product MDM are designed to play different roles in the quest for the availability of high quality product data both internally and externally. There are risks and costs associated with modifying one to take on the role of the other. In many cases there is place for both PIM and MDM, but you will still need to choose a starting point. Each journey to high quality product data will be different, but the goal is still the same – to turn product data into business value.
I (or one of my colleagues in a city near you) will be happy to help you understand what the best starting point is for your organisation.
[i] In case you were wondering, this is not the benefit that I joined Informatica for.
Every two years, the typical company doubles the amount of data they store. However, this Data is inherently “dumb.” Acquiring more of it only seems to compound its lack of intellect.
When revitalizing your business, I won’t ask to look at your data – not even a little bit. Instead, we look at the process of how you use the data. What I want to know is this:
How much of your day-to-day operations are driven by your data?
The Case for Smart Data
I recently learned that 7-Eleven Japan has pushed decision-making down to the store level – in fact, to the level of clerks. Store clerks decide what goes on the shelves in their individual 7-Eleven stores. These clerks push incredible inventory turns. Some 70% of the products on the shelves are new to stores each year. As a result, this chain has been the most profitable Japanese retailer for 30 years running.
Instead of just reading the data and making wild guesses on why something works and why something doesn’t, these clerks acquired the skill of looking at the quantitative and the qualitative and connected dots. Data told them what people are talking about, how it’s related to their product and how much weight it carried. You can achieve this as well. To do so, you must introduce a culture that emphasizes discipline around processes. A disciplined process culture uses:
- A template approach to data with common processes, reuse of components, and a single face presented to customers
- Employees who consistently follow standard procedures
If you cannot develop such company-wide consistency, you will not gain benefits of ERP or CRM systems.
Make data available to the masses. Like at 7-Eleven Japan, don’t centralize the data decision-making process. Instead, push it out to the ranks. By putting these cultures and practices into play, businesses can use data to run smarter.
“Raw materials costs are the company’s single largest expense category,” said Steve Jenkins, Global IT Director at Valspar, at MDM Day in London. “Data management technology can help us improve business process efficiency, manage sourcing risk and reduce RFQ cycle times.”
Valspar is a $4 billion global manufacturing company, which produces a portfolio of leading paint and coating brands. At the end of 2013, the 200 year old company celebrated record sales and earnings. They also completed two acquisitions. Valspar now has 10,000 employees operating in 25 countries.
As is the case for many global companies, growth creates complexity. “Valspar has multiple business units with varying purchasing practices. We source raw materials from 1,000s of vendors around the globe,” shared Steve.
“We want to achieve economies of scale in purchasing to control spending,” Steve said as he shared Valspar’s improvement objectives. “We want to build stronger relationships with our preferred vendors. Also, we want to develop internal process efficiencies to realize additional savings.”
Poorly managed vendor and raw materials data was impacting Valspar’s buying power
The Valspar team, who sharply focuses on productivity, had an “Aha” moment. “We realized our buying power was limited by the age and quality of available vendor data and raw materials data,” revealed Steve.
The core vendor data and raw materials data that should have been the same across multiple systems wasn’t. Data was often missing or wrong. This made it difficult to calculate the total spend on raw materials. It was also hard to calculate the total cost of expedited freight of raw materials. So, employees used a manual, time-consuming and error-prone process to consolidate vendor data and raw materials data for reporting.
These data issues were getting in the way of achieving their improvement objectives. Valspar needed a data management solution.
Valspar needed a single trusted source of vendor and raw materials data
The team chose Informatica MDM, master data management (MDM) technology. It will be their enterprise hub for vendors and raw materials. It will manage this data centrally on an ongoing basis. With Informatica MDM, Valspar will have a single trusted source of vendor and raw materials data.
Informatica PowerCenter will access data from multiple source systems. Informatica Data Quality will profile the data before it goes into the hub. Then, after Informatica MDM does it’s magic, PowerCenter will deliver clean, consistent, connected and enriched data to target systems.
Better vendor and raw materials data management results in cost savings
Valspar expects to gain the following business benefits:
- Streamline the RFQ process to accelerate raw materials cost savings
- Reduce the total number of raw materials SKUs and vendors
- Increase productivity of staff focused on pulling and maintaining data
- Leverage consistent global data visibly to:
- increase leverage during contract negotiations
- improve acquisition due diligence reviews
- facilitate process standardization and reporting
Valspar’s vision is to tranform data and information into a trusted organizational assets
“Mastering vendor and raw materials data is Phase 1 of our vision to transform data and information into trusted organizational assets,” shared Steve. In Phase 2 the Valspar team will master customer data so they have immediate access to the total purchases of key global customers. In Phase 3, Valspar’s team will turn their attention to product or finished goods data.
Steve ended his presentation with some advice. “First, include your business counterparts in the process as early as possible. They need to own and drive the business case as well as the approval process. Also, master only the vendor and raw materials attributes required to realize the business benefit.”
Want more? Download the Total Supplier Information Management eBook. It covers:
- Why your fragmented supplier data is holding you back
- The cost of supplier data chaos
- The warning signs you need to be looking for
- How you can achieve Total Supplier Information Management
A few months ago, while addressing a room full of IT and business professional at an Information Governance conference, a CFO said – “… if we designed our systems today from scratch, they will look nothing like the environment we own.” He went on to elaborate that they arrived there by layering thousands of good and valid decisions on top of one another.
Similarly, Information Governance has also evolved out of the good work that was done by those who preceded us. These items evolve into something that only a few can envision today. Along the way, technology evolved and changed the way we interact with data to manage our daily tasks. What started as good engineering practices for mainframes gave way to data management.
Then, with technological advances, we encountered new problems, introduced new tasks and disciplines, and created Information Governance in the process. We were standing on the shoulders of data management, armed with new solutions to new problems. Now we face the four Vs of big data and each of those new data system characteristics have introduced a new set of challenges driving the need for Big Data Information Governance as a response to changing velocity, volume, veracity, and variety.
Before I answer this question, I must ask you “How comprehensive is the framework you are using today and how well does it scale to address the new challenges?”
While there are several frameworks out in the marketplace to choose from. In this blog, I will tell you what questions you need to ask yourself before replacing your old framework with a new one:
Q. Is it nimble?
The focus of data governance practices must allow for nimble responses to changes in technology, customer needs, and internal processes. The organization must be able to respond to emergent technology.
Q. Will it enable you to apply policies and regulations to data brought into the organization by a person or process?
- Public company: Meet the obligation to protect the investment of the shareholders and manage risk while creating value.
- Private company: Meet privacy laws even if financial regulations are not applicable.
- Fulfill the obligations of external regulations from international, national, regional, and local governments.
Q. How does it Manage quality?
For big data, the data must be fit for purpose; context might need to be hypothesized for evaluation. Quality does not imply cleansing activities, which might mask the results.
Q. Does it understanding your complete business and information flow?
Attribution and lineage are very important in big data. Knowing what is the source and what is the destination is crucial in validating analytics results as fit for purpose.
Q. How does it understanding the language that you use, and can the framework manage it actively to reduce ambiguity, redundancy, and inconsistency?
Big data might not have a logical data model, so any structured data should be mapped to the enterprise model. Big data still has context and thus modeling becomes increasingly important to creating knowledge and understanding. The definitions evolve over time and the enterprise must plan to manage the shifting meaning.
Q. Does it manage classification?
It is critical for the business/steward to classify the overall source and the contents within as soon as it is brought in by its owner to support of information lifecycle management, access control, and regulatory compliance.
Q. How does it protect data quality and access?
Your information protection must not be compromised for the sake of expediency, convenience, or deadlines. Protect not just what you bring in, but what you join/link it to, and what you derive. Your customers will fault you for failing to protect them from malicious links. The enterprise must formulate the strategy to deal with more data, longer retention periods, more data subject to experimentation, and less process around it, all while trying to derive more value over longer periods.
Q. Does it foster stewardship?
Ensuring the appropriate use and reuse of data requires the action of an employee. E.g., this role cannot be automated, and it requires the active involvement of a member of the business organization to serve as the steward over the data element or source.
Q. Does it manage long-term requirements?
Policies and standards are the mechanism by which management communicates their long-range business requirements. They are essential to an effective governance program.
Q. How does it manage feedback?
As a companion to policies and standards, an escalation and exception process enables communication throughout the organization when policies and standards conflict with new business requirements. It forms the core process to drive improvements to the policy and standard documents.
Q. Does it Foster innovation?
Governance must not squelch innovation. Governance can and should make accommodations for new ideas and growth. This is managed through management of the infrastructure environments as part of the architecture.
Q. How does it control third-party content?
Third-party data plays an expanding role in big data. There are three types and governance controls must be adequate for the circumstances. They must consider applicable regulations for the operating geographic regions; therefore, you must understand and manage those obligations.
This is the story about a great speaker, a simple but funny product and the idea of a Ventana Award winning company which does “Brandspiration”.
When I invited Dale Denham, CIO from Geiger to speak on his at Informatica World this year, I was not sure what I will get. I only knew that Dale is known as an entertaining speaker. What could we expect from a person who, calls himself “the selling CIO”?
And Dale delivered. He opened his session “How product information in ecommerce improved Geiger’s ability to promote and sell promotional products” with a video.
What I liked about it was: It is a simple product, addressing a everyday problem, everybody knows. And this is the business of Geiger & Crestline, two brands in one company which sell promotional products to help companies inspire with their brand. They call is “Brandspiration”.
What this has to do with PIM?
Well the business need for Geiger was to sell 100,000s of products more efficient. Which includes update products faster and more accurately and add more products. But also Geiger was planning to
- Eliminate reliance on ERP
- Launch new web properties
- Improve SEO
- Centralize product management & control
- Standardize business processes & workflows
- Produce print catalog more faster
Before working with Informatica PIM it took a week to launch a new product. And Geiger/ Crestline has a complex price management for bundles, brands, packages and more under their two own brands for two different target groups: low price products with aggressive pricing and more high quality promotional products.
With PIM the product entry time could be reduced by about an hour. Geiger achieved 25% time saving for catalog creation and implemented PIM in about six months. (btw with the integrator “Ideosity“.) Another fact which made me proud on our offering was, that Dale told me his company was able to upgrade on the latest PIM version within hours.
“PIM has allowed us to be more proactive Instead of being handcuffed to a system that made us reactive. A great invest for this company. I can’t believe we survived for as long as we did without this software.”
Dale Denham, CIO
Whatch the video of Dale and how his company Geiger realizes Brandspiration with Informatica PIM. Did you know, Geiger is a proud winner of the Ventana Research Innovation Award for their PIM initiative?
Are you a manager dedicated to fashion, B2C or retail? This blog provides an overview what companies can learn on omnichannel from SportScheck.
SportScheck is one of Germany’s most successful multichannel businesses. SportScheck (btw Ventana Research Innovation Award Winner) is an equipment and clothing specialist for almost every sport and its website gets over 52 million hits per year, making it one of the most successful online stores in Germany.
Each year, more than million customers sign up for the mail-order business while over 17 million customers visit its brick and mortar stores (Source). These figures undoubtedly describe the success of SportScheck’s multichannel strategy. SportScheck also strives to deliver innovative concepts in all of its sales channels, while always aiming to provide customers with the best shopping experience possible. This philosophy can be carried out only in conjunction with modern systems landscapes and optimized processes.
Complete, reliable, and attractive information – across every channel – is the key to a great customer experience and better sales. It’s hard to keep up with customer demands in a single channel, much less multiple channels. Download The Informed Purchase Journey. The Informed Purchase Journey requires the right product, to right customer at the right place. Enjoy the video!
What is the Business Initiative in SportScheck
- Providing the customer the same deals across all sales channels with a centralized location for all product information
- Improve customer service in all sales channels with perfect product data
- Make sure customers have enough product information to make a purchase without the order being returned
Intelligent and Agile Processes are Key to Success
“Good customer service, whether online, in-store, or in print, needs perfect product data” said Alexander Pischetsrieder in an interview. At the Munich-based sporting goods retailer, there had been no centralized system for product data before now. After extensive research and evaluation, the company decided to implement the product information management (PIM) system from Informatica.
The main reason for the introduction of Informatica Product Information Management (PIM) solutions was its support for a true multichannel strategy. Customers should have access to the same deals across all sales channels. In addition to making a breadth of information available, customer service still remains key.
In times where information is THE killer app, key challenges are, keeping information up to date and ensuring efficient processes. In a retail scenario, product catalog onboarding starts with PIM to get the latest product information. A dataset in the relevant systems that is always up-to-date is a further basis, which allows companies to react immediately to market movements and implement marketing requirements as quickly as possible. Data must be exchanged between the systems practically in real time. If you want to learn more details, how SportScheck solved the technical integration between SAP ERP and Informatica PIM?
Product Data Equals Demonstrated Expertise
“I am convinced that a well-presented product with lots of pictures and details sells better. For us, this signals knowing our product. That sets us apart from the large discount stores,” notes Alexander Pischetsrieder. “In the end, we have to ask: who is the customer going to trust? We gain trust here with our product knowledge and our love of sports in general.” Just like our motto says, “We get our fans excited.” By offering a professional search engine, product comparisons, and many other features, PIM adds value not only in ecommerce – and that gets us excited!”
Benefits for SportScheck
- Centralized location for all product information across all sales channels
- An agile system that is capable of interweaving the different retail processes across sales channels into a smooth, cross-channel function
- Self-Service portal for agencies and suppliers with direct upload to the PIM system
PS: This blog is based on the PIM case study on SportScheck.
Last time I talked about how benchmark data can be used in IT and business use cases to illustrate the financial value of data management technologies. This time, let’s look at additional use cases, and at how to philosophically interpret the findings.
So here are some additional areas of investigation for justifying a data quality based data management initiative:
- Compliance or any audits data and report preparation and rebuttal (FTE cost as above)
- Excess insurance premiums on incorrect asset or party information
- Excess tax payments due to incorrect asset configuration or location
- Excess travel or idle time between jobs due to incorrect location information
- Excess equipment downtime (not revenue generating) or MTTR due to incorrect asset profile or misaligned reference data not triggering timely repairs
- Equipment location or ownership data incorrect splitting service cost or revenues incorrectly
- Party relationship data not tied together creating duplicate contacts or less relevant offers and lower response rates
- Lower than industry average cross-sell conversion ratio due to inability to match and link departmental customer records and underlying transactions and expose them to all POS channels
- Lower than industry average customer retention rate due to lack of full client transactional profile across channels or product lines to improve service experience or apply discounts
- Low annual supplier discounts due to incorrect or missing alternate product data or aggregated channel purchase data
I could go on forever, but allow me to touch on a sensitive topic – fines. Fines, or performance penalties by private or government entities, only make sense to bake into your analysis if they happen repeatedly in fairly predictable intervals and are “relatively” small per incidence. They should be treated like M&A activity. Nobody will buy into cost savings in the gazillions if a transaction only happens once every ten years. That’s like building a business case for a lottery win or a life insurance payout with a sample size of a family. Sure, if it happens you just made the case but will it happen…soon?
Use benchmarks and ranges wisely but don’t over-think the exercise either. It will become paralysis by analysis. If you want to make it super-scientific, hire an expensive consulting firm for a 3 month $250,000 to $500,000 engagement and have every staffer spend a few days with them away from their day job to make you feel 10% better about the numbers. Was that worth half a million dollars just in 3rd party cost? You be the judge.
In the end, you are trying to find out and position if a technology will fix a $50,000, $5 million or $50 million problem. You are also trying to gauge where key areas of improvement are in terms of value and correlate the associated cost (higher value normally equals higher cost due to higher complexity) and risk. After all, who wants to stand before a budget committee, prophesy massive savings in one area and then fail because it would have been smarter to start with something simpler and quicker win to build upon?
The secret sauce to avoiding this consulting expense and risk is a natural curiosity, willingness to do the legwork of finding industry benchmark data, knowing what goes into them (process versus data improvement capabilities) to avoid inappropriate extrapolation and using sensitivity analysis to hedge your bets. Moreover, trust an (internal?) expert to indicate wider implications and trade-offs. Most importantly, you have to be a communicator willing to talk to many folks on the business side and have criminal interrogation qualities, not unlike in your run-of-the-mill crime show. Some folks just don’t want to talk, often because they have ulterior motives (protecting their legacy investment or process) or hiding skeletons in the closet (recent bad performance). In this case, find more amenable people to quiz or pry the information out of these tough nuts, if you can.
Lastly; if you find ROI numbers, which appear astronomical at first, remember that leverage is a key factor. If a technical capability touches one application (credit risk scoring engine), one process (quotation), one type of transaction (talent management self-service), a limited set of people (procurement), the ROI will be lower than a technology touching multiple of each of the aforementioned. If your business model drives thousands of high-value (thousands of dollars) transactions versus ten twenty-million dollar ones or twenty-million one-dollar ones, your ROI will be higher. After all, consider this; retail e-mail marketing campaigns average an ROI of 578% (softwareprojects.com) and this with really bad data. Imagine what improved data can do just on that front.
I found massive differences between what improved asset data can deliver in a petrochemical or utility company versus product data in a fashion retailer or customer (loyalty) data in a hospitality chain. The assertion of cum hoc ergo propter hoc is a key assumption how technology delivers financial value. As long as the business folks agree or can fence in the relationship, you are on the right path.
What’s your best and worst job to justify someone giving you money to invest? Share that story.