Tag Archives: Single View of Customer
Are You Planning To Be There?
Based on the overwhelming interest we’ve seen in master data management (MDM), we just announced a Customer Centricity Seminar Roadshow, featuring presentations from Citrix, Vertex, St. Jude Medical, AutoTrader.com, InfoSys, Wipro, and 3sage Consulting in select cities.
Learn how you can eliminate the time your sales, marketing, and channel operations teams spend searching for and reconciling inconsistent customer and channel partner data from multiple applications. Empower your customer-facing teams to maximize account revenue and optimize channel operations with single view of customer and channel partner extensions using MDM.
Don’t miss this opportunity to find out how MDM can make a difference for your company and your career. Check here to find a seminar in a city near you.
The Holy Grail Of Data Quality – Linking Data Quality To Business Impact
“We have 20% duplicates in our data source”. This is how the conversation began. It was not that no one cared about the level of duplicates, it’s just that the topic of duplicate records did not get the business excited – they have many other priorities (and they were not building a single view of customer).
The customer continued the discussion thread on how to make data quality relevant to each functional leader reporting to C-level executives. The starting point was affirmation that the business really only care about data quality when it impacts the processes that they own e.g. order process, invoice process, shipping process, credit process, lead generation process, compliance reporting process, etc. This means that data quality results need to be linked to the tangible goals of each business process owner to win them over as data advocates. (more…)
The Win-Win Of Enabling Customer Centricity with MDM
One of the world’s largest financial services providers assessed the workload and productivity of its 16,000 financial advisors, and the results were not pretty. This Fortune 50 firm found that its customer-facing representatives were spending:
- 70% of their time searching for and reconciling data on customers and products
- 30% of their time on revenue-generating activities, such as cross-sell and up-sell
That’s a pretty poor ratio, and it hurt the bottom line with subpar productivity and lost revenue opportunities. Unfortunately, it’s not uncommon in financial services and other industries. And experience has shown that whatever you think your company’s ratio is, the reality is probably worse.
Attract and Retain Customers with Trusted and Complete Data
But it’s also a solvable problem, as you can find in our new white paper, “Get on the Fast Track to Maximizing Customer Value.”
For this financial services firm, the root cause of the problem was that business-critical data about customers, their products and services was spread across account-centric system silos, which prevented them from gaining a customer-centric view. Financial advisors were unable to readily identify which clients had which products. Without that insight, they couldn’t effectively cross-sell and up-sell the right products to the right customers. So they were forced to spend excessive time manually pulling information from all these separate systems into a spreadsheet.
To make matters worse, advisors couldn’t accurately measure each client’s value to the institution. The firm couldn’t tailor service levels to customer profitability, meaning that expensive services might be provided to customers of marginal profitability—while high-value customers didn’t receive the attention they deserved.
Rethinking Data Virtualization
I’m looking forward to doing a Webinar on data virtualization this Thursday, April 22nd. Why? Because this is the single most beneficial concept of architecture, including SOA, and it’s often overlooked by the rank-and-file developers and architects out there. I’m constantly evangelizing the benefits of data virtualization, including integrating data from many and different data sources in real-time, and enabling query-based applications to get data from multiple systems.
The idea is pretty simple, really. Considering that there are many physical database schemas within most enterprises, and typically no common view of the data, data virtualization allows you to map many physical schemas to virtual schemas that are a better representation of the business. For example, a single view of customer data, sales data, and other data that has the same logical meaning, but may be scattered amongst many different physical database systems, using any number of implementation models. (more…)
Informatica 9 Sweet Spots
There’s been a lot of hoopla this week about Informatica 9, and rightfully so. A lot of people at Informatica have been working on this release for a long time, and we’re all excited about the potential it has to help our customers get to the next level. Informatica 9 can be used in many different ways to help organizations deliver more value from their data to the business. But there are several business use cases which are really a sweet spot for Informatica 9, based on the traction we have seen already with real-world customers.
- Regulatory/compliance/financial reporting. Informatica’s heritage is in data warehousing, which is a fundamental underpinning to reporting systems. But even with all the great advances that both business intelligence and data integration technologies have achieved, CFOs are still challenged in many ways. Hidden data quality issues put them at risk for non-compliance, leading to penalties, brand degradation, or even jail time. Minor changes in data sources—say a business rule that has been changed or a format has been adjusted—if not handled correctly and quickly also can mean that reports are incomplete or erroneous. And it simply takes too long for any new sources of relevant information to be incorporated into the reporting structure. Informatica 9 can really help in these areas by making data quality issues much more visible, and by drastically reducing the amount of time it takes to accommodate changes in the data underlying the reports. (more…)