Thursday Aug 28, 2014

How Do You Know if You Have a Data Quality Issue?

By: John Siegman – Applications Sales Manager for Master Data Management and Data Quality & Murad Fatehali – Senior Director with Oracle’s Insight team and leads the Integration practice in North America.

Big Data, Master Data Management, Analytics are all topics and buzz words getting big play in the press.  And they’re all important as today’s storage and computing capabilities allow for automated decision making that provides customers with experiences more tailored to them as well as provides better information upon which business decisions can be made.  The whole idea being, the more you know, the more you know.

Lots of companies think they know that they should be doing Big Data, Master Data Management and Analytics, but don’t really know where to start or what to start with.  My two favorite questions to ask any prospective customer while discussing these topics are: 1) Do you have data you care about? And, 2) Does it have issues?  If the answers come back “Yes” and “Yes” then you can have the discussion on what it takes to get the data ready for Big Data, Master Data Management and Analytics.  If you try any of these with lousy data, you’re simply going to get lousy results.

But, how do I know if I’ve got less than stellar data?  All you have to do is listen to the different departments in your company and they will tell you.  Here is a guide to the types of things you might hear.

You know you have poor data quality if MARKETING says:

1. We have issues with privacy management and customer preferences
2. We can’t do the types of data matching and enhancement we want to do
3. There’s no way to do data matching with internal or external files
4. We have missing data but we don’t know how much or which variables
5. There’s no standardization or data governance
6. We don’t know who our customer is
7. We’ve got compliance issues

You know you have poor data quality if SALES says:

1. The data in the CRM is wrong and needs to be re-entered and is outdated
2. I have to go through too many applications to find the right customer answers
3. Average call times are too long due to poor data and manual data entry
4. I’m spending too much time fixing data instead of selling

You know you have poor data quality if BUSINESS INTELLIGENCE says:

1. No one trusts the data so we have lots of Excel spreadsheets and none of the numbers match
2. It’s difficult to find data and there are too many sources
3. We have no data variables with consistent definitions
4. There’s nothing to clean the data with
5. Even data we can agree on, like telephone number, has multiple formats

You know you have poor data quality if OPERATIONS or FINANCE says:

1. The Billing report does not match the BI report

2.   1. Payment information and address information does not match the information in the Account Profile
3. Accounts closed in Financial Systems show up as still open in CRM system or vice versa where customers get billed for services terminated
4. Billing inaccuracies are caught during checks because there are no up-front governance rules
5. Agents enter multiple orders for the same service or product on an account
6. Service technicians show up on site with wrong parts and equipment which then requires costly repeat visits and negatively impacts customer satisfaction
7. Inventory systems show items sales deemed OK to sell while suppliers may have marked obsolete or recalled
8. We have multiple GLs and not one single version of financial truth

You know you have poor data quality if IT says:

1. It’s difficult to keep data in synch across many sources and systems
2. Data survivorship rules don't exist
3. Customer Data types (B2B, end user in B2B, customer in B2C, account owner B2C) and status (active, trial, cancelled, etc.) changes for the same customer over time and it’s difficult to keep track without exerting herculean manual effort
You know you have poor data quality if HUMAN RESOURCES says:
1. First have to wait for data, then when it is gathered and delivered we need to work to fix it
2. Ten-percent of our time is wasted due to waiting on things or re-work cycles
3. Employee frustration with searching, finding, and validating data results in churn, and will definitely delay re-hire of employees
4. Incorrect competency data results in: a) productivity loss in terms of looking at the wrong skilled person; b) possible revenue loss due to lack of skills needed; and c) additional hires when none are needed

You know you have poor data quality if PROCUREMENT says:

1. Not knowing our suppliers impacts efficiencies and costs
2. FTEs in centralized sourcing spend up to 20% of their time fixing bad data and related process issues
3. Currently data in our vendor master, material master and pricing information records is manually synched since the data is not accurate across systems.  We end up sending the orders to the wrong suppliers
4. Supplier management takes too much time
5. New product creation form contains wrong inputs rendering many fields unusable
6. Multiple entities: 1) Logistics, 2) Plants, 3) Engineering, 4) Product Management, enter or create Material Master information.  We cannot get spend analytics
7. We have no good way of managing all of the products we buy and use

You know you have poor data quality if PRODUCT MANAGEMENT says:

1. Product development and life-cycle management efforts take longer and cost more
2. We have limited standards and rules for product dimensions.  We need to manually search missing information available elsewhere
3. Our product data clean-up occurs in pockets across different groups, the end result of these redundant efforts is duplication of standards
4. We make status changes to the product lifecycle that don't get communicated to Marketing and Engineering in a timely manner.  Our customers don’t know what the product now does

All of these areas suffer either individually or together due to poor data quality.  All of these issues impact corporate performance which impacts stakeholders which impacts corporate management.  If you’re hearing any of these statements from any of these departments you have a data quality issue that needs to be addressed.  And that is especially true if you’re considering any type of Big Data, Master Data Management or Analytics initiative.

Thursday Aug 07, 2014


Author: John Siegman 

How do you know if you have a Master Data Management (MDM) or Data Quality (DQ) issue on your campus? One of the ways is to listen to the concerns of your campus constituents. While none of them are going to come out and tell you that they have a master data issue directly, by knowing what to listen for you can determine where the issues are and the best way to address them.

What follows are some of the key on-campus domains and what to listen for to determine if there is a MDM or DQ issue that needs to be resolved.

Student: Disconnected processes lacking coordination

· Fragmented data across disparate systems, disconnected across groups for:

- data collection efforts (duplicate/inconsistent student/faculty surveys)

- data definitions, rules, governance

- data access, security, and analysis

· Lack of training around security/access further complicated due to number of sources

· No information owner/no information strategy

· Student attributes maintained across many systems

Learning: Does not capture interactions

· Cannot identify students at risk. Do not capture interactions with students and faculty, and faculty interactions for research support, etc.

· No way to track how many undergraduates are interested in research

· Don't do any consistent analytics for course evaluations

· Difficult and time consuming to gather information because of the federated nature of the data – for example, job descriptions in HR are different than what is really being used

· There is no view of Student experience

HR: Process inconsistencies, lack of data standards complicates execution

· Faculty not paid by the university are not in the HCM system, while students receiving payments from the university are in the HCM system

· Disconnected process to issue IDs, keys, duplicate issues

· Given multiplicity of data sources, accessing the data is a challenge

· Data analytics capabilities and available reports are not properly advertised, so people do not know what is available. As a consequence an inordinate amount of time is spent generating reports

· Faculty/Staff information collection is inconsistent, sometimes paper-based. Implication: lose applicants because it is too difficult to complete the application process

Research: Getting from data to insight is a challenge

· Very time consuming to determine: Which proposals were successful? What type of awards are we best at winning?

· Difficult to understand: number of proposals, dollar value, by school, by department, by agency, by time period

· Data challenges in extracting data out of the system for grants, faculty, and making it centrally available

Deans & Officers: Reporting is a challenge

· Significant use of Excel, reporting is becoming unstable because of the amount of data in the files

· Information charter, a common retention policy does not exist

· A lot of paper is generated for the domains we are covering. Converting paper to digital is a challenge

· Collecting information on faculty activity (publications) is a challenge. Data in documents requires validation

· Data requests result in garbage. Donors receiving the wrong information.

Finance: Has little trust in data

· Do not have workflow governance processes. Implication, information goes into the system without being reviewed, therefore errors can make it into the records

· Systems connected to ERP systems do not always give relevant or requested info

· Closing the month or quarter takes too long as each school and each department has its own set of GLs.

Facilities: Efficiencies are hampered due to data disconnects

· Do not have accurate space metrics due to outdated system, schools not willing to share their info with Research Administrators and Proposal Investigators

· Do not have utility consumption, building by building

· No clear classroom assignment policy (a large room may be assigned to a small number of students)

· Not all classes are under the registrar's control

· No tool showing actual space for planning purposes

· Difficult to determine research costs, without accurate access to floor plans and utilization

· Cannot effectively schedule and monitor classrooms

If your campus has data, you have data issues. As the push for students becomes more competitive, being able to understand your current data, mine your social data, target your alumni, make better use of your facilities, improve your supplier relationships, and increase your student success will be dependent on better data. The tools exist to take data from a problem filled issue to a distinct competitive advantage. The sooner campuses adopt these tools, the sooner they will receive the benefits of doing so.


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