Tag Archives: Informatica data quality

At Valspar Data Management is Key to Controlling Purchasing Costs

Steve Jenkins, Global IT Director at Valspar

Steve Jenkins is working to improve information management maturity at Valspar

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

Data management at Valspar

“We realized our buying power was limited by the age and quality of available vendor and raw materials data.”

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

Informatica MDM supports vendor and raw materials data management at Valspar

The team chose Informatica MDM as their enterprise hub for vendors and raw materials

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 Chameleon Jon

Valspar will gain benefits by fueling applications with clean, consistent, connected and enriched data

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.”

Total Supplier Information Management eBook

Click here to download the Total Supplier Information Management eBook

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

 

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Posted in Business/IT Collaboration, Data Integration, Data Quality, Manufacturing, Master Data Management, Operational Efficiency, PowerCenter, Vertical | Tagged , , , , , , , , , , , , , , , , , , | Leave a comment

Don’t Take the Easy Way Out – Be a Data Quality Hero

When I talk to customers about dealing with poor data quality, I consistently hear something like, “We know we have data quality problems, but we can’t get the business to help take ownership and do something about it.” I think that this is taking the easy way out. Throwing your hands up in the air doesn’t make change happen – it only prolongs the pain. If you want to affect a positive change in data quality and are looking for ways to engage the business, then you should join Barbara Latulippe, Director of Enterprise Information Management for EMC and and Kristen Kokie, VP IT Enterprise Strategic Services for Informatica for our webinar on Thursday October 24th to hear how they have dealt with data quality in their combined 40+ years in IT.

Now, understandably, tackling data quality problems is no small undertaking, and it isn’t easy. In many instances, the reason why organizations choose to do nothing about data quality is that bad data has been present for so long that manual work around efforts have become ingrained in the business processes for consuming data. In these cases, changing the way people do things becomes the largest obstacle to dealing with the root cause of the issues. But that is also where you will be able to find the costs associated with bad data: lost productivity, ineffective decision making, missed opportunities, etc..

As discussed in this previous webinar,(link to replay on the bottom of the page), successfully dealing with poor data quality takes initiative, and it takes communication. IT Departments are the engineers of the business: they are the ones who understand process and workflows; they are the ones who build the integration paths between the applications and systems. Even if they don’t own the data, they do end up owning the data driven business processes that consume data. As such, IT is uniquely positioned to provide customized suggestions based off of the insight from multiple previous interactions with the data.

Bring facts to the table when talking to the business. As those who directly interact daily with data, IT is in position to measure and monitor data quality, to identify key data quality metrics; data quality scorecards and dashboards can shine a light on bad data and directly relate it to the business via the downstream workflows and business processes. Armed with hard facts about impact on specific business processes, a Business user has an easier time affixing a dollar value on the impact of that bad data. Here’s some helpful resources where you can start to build your case for improved data quality. With these tools and insight, IT can start to affect change.

Data is becoming the lifeblood of organizations and IT organizations have a huge opportunity to get closer to the business by really knowing the data of the business. While data quality invariably involves technological intervention, it is more so a process and change management issue that ends up being critical to success. The easier it is to tie bad data to specific business processes, the more constructive the conversation can be with the Business.

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Posted in Business/IT Collaboration, Data Governance, Data Integration, Data Quality, Pervasive Data Quality, Scorecarding, Uncategorized | Tagged , , , , | Leave a comment

Retail Case Study: Printemps Department Store Builds a Trusted Customer Data Foundation with MDM and Data Quality

If you have never traveled to France, you have missed the unique and exciting shopping experience offered at Printemps, a luxury fashion retailer. Its flagship store in Paris drives 60% of the company’s revenue. More than 1.5 million customers who love fashion visit this store as well as the retailer’s 15 other high-end stores around the country.

Printemps Haussmann, flagship department store in Paris, France.

Printemps’ goal is to cultivate long-term personal relationships with their high value customers by delivering exceptional services. Their strategy to accomplish this goal is to continuously meet their high value customers’ needs and expectations and create compelling incentives for customers to visit their stores.

Printemps’ marketing team is continually striving to be more customer-centric and improve campaign effectiveness. They are using customer analytics to segment their customers and better understand their preferences. For example:

  • Which customers prefer fashion, beauty or accessories?
  • Which customers prefer communications through the mail, email, mobile phone, social media channels?

Printemps has plenty of information about their 1.5 million customers. So what
was standing in their way? They lacked a 360-degree view of their high value
customers. The key culprit was duplicate customer information across multiple
systems.

I had the honor of introducing Olivier de Compiègne, who is responsible for Project Services and Customer Relationships at Printemps at Informatica World. Olivier’s main message: if your goal is to attract high value customers and boost customer loyalty, first you must invest in a solid customer data foundation.

To build their solid customer data foundation, Printemps’ team is leveraging Informatica Data Quality to ensure their customer information is as accurate and complete as possible across all key sources. They are using Informatica MDM, master data management (MDM) technology to rationalize customer information from numerous data sources to create a single customer view as well as a 360-degree customer view, which includes each customer’s purchase history.

Printemps’ solid customer data foundation is maintained on an ongoing basis, which allows Printemps’ marketing team to have confidence in the data they use for customer analytics and campaign management. Now they can truly support personalized relationships with customers and optimize their marketing by sending tailored messages to targeted customer segments.

If you are trying to cultivate long-term personal relationships with your customers and lack a 360 degree customer view, I hope Olivier’s story was helpful.  Do you have similiar goals? Please share your thoughts. I’m interested in hearing from you.

If you want to learn more about how Printemps’ is using Informatica Data Quality and Informatica MDM, please:

 

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Posted in Customer Acquisition & Retention, Data Quality, Master Data Management, Retail | Tagged , , , , , , , , , , , , , , , | Leave a comment

MDM – What’s The Cost Of Bad Data In Financial Services?

One of the most critical first steps for financial services firms looking to implement multidomain master data management (MDM) is to quantify the cost savings they could achieve.

Unfortunately, a thorough analysis of potential ROI is also one of the steps least followed (a key culprit being disconnects between business and IT).

This shortcoming is spotlighted in a new Informatica white paper, “Five Steps to Managing Reference Data More Effectively in Investment Banking,” which outlines key questions to ask in sizing up the cost implications of bad data and antiquated systems, such as:

  • How long does it take to introduce a new security to trade?
  • How many settlements need to be fixed manually?
  • How many redundant data feeds does your firm have to manage?
  • How accurate and complete are your end-of-day reports?
  • Do you have the data you need to minimize risk and exposure? (more…)
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Posted in Enterprise Data Management, Financial Services, Governance, Risk and Compliance, Master Data Management, Operational Efficiency | Tagged , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , | Leave a comment