Category Archives: Master Data Management
Every fall Informatica sales leadership puts together its strategy for the following year. The revenue target is typically a function of the number of sellers, the addressable market size and key accounts in a given territory, average spend and conversion rate given prior years’ experience, etc. This straight forward math has not changed in probably decades, but it assumes that the underlying data are 100% correct. This data includes:
- Number of accounts with a decision-making location in a territory
- Related IT spend and prioritization
- Organizational characteristics like legal ownership, industry code, credit score, annual report figures, etc.
- Key contacts, roles and sentiment
- Prior interaction (campaign response, etc.) and transaction (quotes, orders, payments, products, etc.) history with the firm
Every organization, no matter if it is a life insurer, a pharmaceutical manufacturer, a fashion retailer or a construction company knows this math and plans on getting somewhere above 85% achievement of the resulting target. Office locations, support infrastructure spend, compensation and hiring plans are based on this and communicated.
So why is it that when it is an open secret that the underlying data is far from perfect (accurate, current and useful) and corrupts outcomes, too few believe that fixing it has any revenue impact? After all, we are not projecting the climate for the next hundred years here with a thousand plus variables.
If corporate hierarchies are incorrect, your spend projections based on incorrect territory targets, credit terms and discount strategy will be off. If every client touch point does not have a complete picture of cross-departmental purchases and campaign responses, your customer acquisition cost will be too high as you will contact the wrong prospects with irrelevant offers. If billing, tax or product codes are incorrect, your billing will be off. This is a classic telecommunication example worth millions every month. If your equipment location and configuration is wrong, maintenance schedules will be incorrect and every hour of production interruption will cost an industrial manufacturer of wood pellets or oil millions.
Also, if industry leaders enjoy an upsell ratio of 17%, and you experience 3%, data (assuming you have no formal upsell policy as it violates your independent middleman relationship) data will have a lot to do with it.
The challenge is not the fact that data can create revenue improvements but how much given the other factors: people and process.
Every industry laggard can identify a few FTEs who spend 25% of their time putting one-off data repositories together for some compliance, M&A customer or marketing analytics. Organic revenue growth from net-new or previously unrealized revenue is what the focus of any data management initiative should be. Don’t get me wrong; purposeful recruitment (people), comp plans and training (processes) are important as well. Few people doubt that people and process drives revenue growth. However, few believe data being fed into these processes has an impact.
This is a head scratcher for me. An IT manager at a US upstream oil firm once told me that it would be ludicrous to think data has a revenue impact. They just fixed data because it is important so his consumers would know where all the wells are and which ones made a good profit. Isn’t that assuming data drives production revenue? (Rhetorical question)
A CFO at a smaller retail bank said during a call that his account managers know their clients’ needs and history. There is nothing more good data can add in terms of value. And this happened after twenty other folks at his bank including his own team delivered more than ten use cases, of which three were based on revenue.
Hard cost (materials and FTE) reduction is easy, cost avoidance a leap of faith to a degree but revenue is not any less concrete; otherwise, why not just throw the dice and see how the revenue will look like next year without a central customer database? Let every department have each account executive get their own data, structure it the way they want and put it on paper and make hard copies for distribution to HQ. This is not about paper versus electronic but the inability to reconcile data from many sources on paper, which is a step above electronic.
Have you ever heard of any organization move back to the Fifties and compete today? That would be a fun exercise. Thoughts, suggestions – I would be glad to hear them?
In his recent article: “The catalog is dead – long live the catalog,” Informatica’s Ben Rund spoke about how printed catalogs are positioned as a piece of the omnichannel puzzle and are a valuable touch point on the connected customer’s informed purchase journey. The overall response was far greater than what we could have hoped for; we would like to thank all those that participated. Seeing how much interest this topic generated, we decided to investigate further, in order to find out which factors can help in making print publishing successful.
5 key Factors for Successful Print Publishing Projects
Today’s digital world impacts every facet of our lives. Deloitte recently reported that approximately 50% of purchases are influenced by our digital environment. Often, companies have no idea how much savings can be generated through the production of printed catalogues that leverage pre-existing data sources. The research at www.pim-roi.com talks of several such examples. After looking back at many successful projects, Michael and his team realized the potential to generate substantial savings when the focus is to
optimize “time to market.” (If, of course, business teams operate asynchronously!)
For this new blog entry, we interviewed Michael Giesen, IT Consultancy and Project Management at Laudert to get his thoughts and opinion on the key factors behind the success of print publishing projects. We asked Michael to share his experience and thoughts on the leading factors in running successful print publishing projects. Furthermore, Michael also provides insight on which steps to prioritize and which pitfalls to avoid at all costs, in order to ensure the best results.
1. Publication Analysis
How are objects in print (like products) structured today? What about individual topics and design of creative pages? How is the placement of tables, headings, prices and images organized nowadays? Are there standards? If so, what can be standardized and how? To get an overall picture, you have to thoroughly examine these points. You must do so for all the content elements involved in the layout, ensuring that, in the future, they can be used for Dynamic Publishing. It is conceivable that individual elements, such as titles or pages used in subject areas, could be excluded and reused in separate projects. Gaining the ability to automate catalog creation potentially requires to compromise in certain areas. We shall discuss this later. In the future, product information will probably be presented with very little need to apply changes, 4 instead of 24 table types, for example. Great, now we are on the right path!
2. Data Source Analysis
Where is the data used in today’s printed material being sourced from? If possible or needed, are there several data sources that require to be combined? How is pricing handled? What about product attributes or the structure of product description tables in the case of an individual item? Is all the marketing content and subsequent variations included as well? What about numerous product images or multiple languages? What about seasonally adjusted texts that pull from external sources?
This case requires a very detailed analysis, leading us to the following question:
What is the role and the value of storing product information using a standardized method in print publishing?
The benefits of utilizing such processes should be clear by now: The more standards are in place, the greater the amount of time you will save and the greater your ability to generate positive ROI. Companies that currently operate with complex systems supporting well-structured data are already ahead in the game. Furthermore, yielding positive results doesn’t necessarily require them to start from scratch and rebuild from the ground up. As a matter of fact, companies that have already invested in database systems (E.g. MSSQL) can leverage their existing infrastructures.
3. Process Analysis
In this section of our analysis, we will be getting right down to the details: What does the production process look like, from the initial layout phase to the final release process? Who is responsible for the “how? Who maintains the linear progression? Who has the responsibilities and release rights? Lastly, where are the bottlenecks? Are there safeguards mechanisms in place? Once all these roles and processes have been put in place and have received the right resources we can advance to the next step of our analysis. You are ready to tackle the next key factor: Implementation.
Here you should be adventurous, creative and open minded, seeing that compromise might be needed. If your existing data sources do not meet the requirements, a solution must be found! A certain technical creative pragmatism may facilitate the short and medium planning (see point 2). You must extract and prepare your data sources for printed medium, such as a catalog, for example. The priint:suite of WERK II has proven itself as a robust all-round solution for Database Publishing and Web2Print. All-inclusive PIM solutions, such as Informatica PIM, already has a standard interface to priint:suite available. Depending on the specific requirements, an important decision must then be made: Is there a need for an InDesign Server? Simply put, it enables the fully automatic production of large-volume objects and offers accurate data preview. While slightly less featured, the use of WERK II PDF renderers offers similar functionalities but at a significantly more affordable price.
Based on the software and interfaces selected, an optimized process which supports your system can be developed and be structured to be fully automated if needed.
For individual groups of goods, templates can be defined, placeholders and page layouts developed. Production can start!
5. Selecting an Implementation Partner
In order to facilitate a smooth transition from day one, the support of a partner to carry out the implementation should be considered from the beginning. Since not only technology, but more importantly practical expertise provides maximum process efficiency, it is recommended that you inquire about a potential partner’s references. Getting insight from existing customers will provide you with feedback about their experience and successes. Any potential partner will be pleased to put you in touch with their existing customers.
What are Your Key Factors for Successful Print Publishing?
I would like to know what your thoughts are on this topic. Has anyone tried PDF renderers other than WERK II, such as Codeware’s XActuell? Furthermore, if there are any other factors you think are important in managing successful print publishing, feel free to mention them in the comments here. I’d be happy to discuss here or on twitter at @nicholasgoupil.
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.