The cause of poor customer service ratings, ineffective marketing initiatives, faulty financial planning, and the increase in fraudulent activity can, in many cases, relate back to an organization's management of its data. As the data collected and stored in organizations has grown exponentially over the past few years, its proper management has become critical to the successful implementation of such business initiatives as product marketing and corporate planning. Additionally, as fraud and acts of terror receive greater attention, it has become essential to use data to identify people and their relationships with one another.
This article will define master data management (MDM) and explain how customer data integration (CDI) fits within MDM's framework. Additionally, this article will provide an understanding of how MDM and CDI differ from entity analytics, outline their practical uses, and discuss how organizations can leverage their benefits. Various applications of entity analytics, including examples of its application to different types of organizations, will be highlighted along with the benefits it offers organizations in such service industries as government, security, banking, and insurance.
Data Management—Its Broad Spectrum
MDM has emerged to provide organizations with the tools to manage data and data definitions effectively throughout an organization in order to present a consistent view of the organization's data. In essence, MDM overcomes the silos of data created by different departments and provides an operational view of the information so that it may be leveraged by the entire organization. It focuses on the identification and management of reference data across the organization to create one consistent view of data. MDM's application identifies how different subsets of MDM address separate aspects of an organization's needs.
MDM manifests its importance when a customer service representative (CSR) cannot access customer information due to inconsistencies introduced by a corporate acquisition or a new system implementation, which may lead to the frustration (or even alienation) of the customer. Add to this the extra time the CSR spends accessing the appropriate data, and the issue extends to wasted time and money. MDM focuses on the identification and management of reference data across the organization to create one consistent view of data.
CDI is a subset of MDM, and serves to consolidate the many views of a customer within the organization into one centralized structure. This data consolidation provides the CSR with the information required or the ability to link to the required information, which may include billing, accounts receivable, etc. Once the data is consolidated, references to each customer file are created that link to one another and assign the "best" record from the available information. Consequently, data inconsistencies that occur across disparate systems, such as multiple address formats, are cleansed based on defined business rules to create one version of customer data that will be viewed across multiple departments within the organization.
The creation of "one version of the truth" presents unique challenges to organizations In many organizations, there are multiple views of the customer, such as accounts payable, call center, shipping, etc. Each profile may have the same customer name, but different addresses or other associated information such as unique customer numbers for each department, making it difficult to link one person to multiple processes. The difficulty comes when determining which view is the most correct. For example, if four versions of the same customer name and associated address exist, one version should be chosen from the four files to represent the most correct view in order to create a consolidated profile of that customer. The issue that arises here is that each department may have a different definition of "customer," making reconciliation of customer data an enormous task. For instance, organizations often profile their customers differently in systems across the organization, giving employees an incomplete view of the customer. The resolution of this issue allows the redundant or inaccurate customer records to be purged.
Aside from incomplete records, as the customer information is entered into the system multiple times, more silos are created, amplifying the problem. In addition to CSRs and employees having direct contact with the customer, marketing is another department that may have a different or incomplete view of the customer. This can translate into ineffective marketing campaigns and missed revenue opportunities. Although this last example may seem farfetched, the reality is that poor management of data within an organization affects the bottom line. CDI, when implemented properly, can not only reduce costs, but also increase sales, customer service ratings, and customer loyalty.
Data Complexity
As data becomes more complex, management strategies have been applied differently and used more widely to address not only organizational needs, but those concerning fraud detection and security. IBM's Entity Analytics Solution (EAS) addresses the needs of such organizations as government agencies and financial and insurance institutions to combat fraud and terrorism by applying data management techniques in a different way than CDI. Essentially, the concept surrounding the EAS platform translates into "the more data collected, the better". Instead of discarding extra information, as CDI does, the opposite direction is taken by aggregating, grouping, and resolving identity information attributes to use new, old, accurate, inaccurate, and seemingly attributes. This helps with the development of pattern recognition. For example, if a person collects more than one social security check using two or more separate addresses, EAS will identify the fact that a particular individual collects multiple checks sent to various addresses, and will create an alert in the system.
The ability to link individuals to multiple data sets and determine their interconnectivity helps proactive identification of potential fraudulent or criminal activity. IBM, with its acquisition of Language Analysis Systems (LAS), has started to address these needs through IBM Global Name Recognition. Instead of taking a business intelligence data integration or customer relationship management (CRM) customer data integration approach (whereby data cleansing activities take place to create one version of the truth), Entity Analytics uses the opposite approach to identify recurring data patterns to address terrorism and fraud through its Terrorism and Fraud Intelligence Framework (T&FI). The software addresses the issues of searching and managing data on individuals across geographic regions, customers within financial institutions, etc. to meet the demands of managing data sets from diverse cultures and geographic regions. This goes beyond name recognition to analyze how names are interconnected through the identification of recurring data patterns and entity connections. These connections are flagged based on rules created to identify suspicious transactions or behavior.
IBM Entity Analytics Software Offering
IBM Entity Analytics Solutions Global Name Recognition provides four modules (see figure 1 below) to enable organizations to identify people, relationships, and data patterns, and to share that information anonymously to identify potential fraudulent or suspicious behavior. IBM's EAS consists of
* IBM Identity Resolution, which identifies an individual entity and connects the data associated to that individual across data silos; * IBM Relationship Resolution, which identifies non-obvious relationships to reveal social, professional, or criminal networks. This module also provides instant alerts once data connections are detected; * IBM Anonymous Resolution, which de-identifies sensitive data sets using proprietary preprocessing and one-way hashing to add additional layers of privacy, and link that data based on codes that enable entity relationship identification without jeopardizing individual privacy laws. Data is shared anonymously and remains with the data owner to ensure data security; * IBM Name Resolution solution includes name searching, variation generator, parser, culture classifier, and defining genders. Global Name Recognition's primary use is to recognize customers, citizens, and criminals across multiple cultural variations of name data. A practical application of the name variation generator is to learn the different spellings of names across various geographical regions.
This article will define master data management (MDM) and explain how customer data integration (CDI) fits within MDM's framework. Additionally, this article will provide an understanding of how MDM and CDI differ from entity analytics, outline their practical uses, and discuss how organizations can leverage their benefits. Various applications of entity analytics, including examples of its application to different types of organizations, will be highlighted along with the benefits it offers organizations in such service industries as government, security, banking, and insurance.
Data Management—Its Broad Spectrum
MDM has emerged to provide organizations with the tools to manage data and data definitions effectively throughout an organization in order to present a consistent view of the organization's data. In essence, MDM overcomes the silos of data created by different departments and provides an operational view of the information so that it may be leveraged by the entire organization. It focuses on the identification and management of reference data across the organization to create one consistent view of data. MDM's application identifies how different subsets of MDM address separate aspects of an organization's needs.
MDM manifests its importance when a customer service representative (CSR) cannot access customer information due to inconsistencies introduced by a corporate acquisition or a new system implementation, which may lead to the frustration (or even alienation) of the customer. Add to this the extra time the CSR spends accessing the appropriate data, and the issue extends to wasted time and money. MDM focuses on the identification and management of reference data across the organization to create one consistent view of data.
CDI is a subset of MDM, and serves to consolidate the many views of a customer within the organization into one centralized structure. This data consolidation provides the CSR with the information required or the ability to link to the required information, which may include billing, accounts receivable, etc. Once the data is consolidated, references to each customer file are created that link to one another and assign the "best" record from the available information. Consequently, data inconsistencies that occur across disparate systems, such as multiple address formats, are cleansed based on defined business rules to create one version of customer data that will be viewed across multiple departments within the organization.
The creation of "one version of the truth" presents unique challenges to organizations In many organizations, there are multiple views of the customer, such as accounts payable, call center, shipping, etc. Each profile may have the same customer name, but different addresses or other associated information such as unique customer numbers for each department, making it difficult to link one person to multiple processes. The difficulty comes when determining which view is the most correct. For example, if four versions of the same customer name and associated address exist, one version should be chosen from the four files to represent the most correct view in order to create a consolidated profile of that customer. The issue that arises here is that each department may have a different definition of "customer," making reconciliation of customer data an enormous task. For instance, organizations often profile their customers differently in systems across the organization, giving employees an incomplete view of the customer. The resolution of this issue allows the redundant or inaccurate customer records to be purged.
Aside from incomplete records, as the customer information is entered into the system multiple times, more silos are created, amplifying the problem. In addition to CSRs and employees having direct contact with the customer, marketing is another department that may have a different or incomplete view of the customer. This can translate into ineffective marketing campaigns and missed revenue opportunities. Although this last example may seem farfetched, the reality is that poor management of data within an organization affects the bottom line. CDI, when implemented properly, can not only reduce costs, but also increase sales, customer service ratings, and customer loyalty.
Data Complexity
As data becomes more complex, management strategies have been applied differently and used more widely to address not only organizational needs, but those concerning fraud detection and security. IBM's Entity Analytics Solution (EAS) addresses the needs of such organizations as government agencies and financial and insurance institutions to combat fraud and terrorism by applying data management techniques in a different way than CDI. Essentially, the concept surrounding the EAS platform translates into "the more data collected, the better". Instead of discarding extra information, as CDI does, the opposite direction is taken by aggregating, grouping, and resolving identity information attributes to use new, old, accurate, inaccurate, and seemingly attributes. This helps with the development of pattern recognition. For example, if a person collects more than one social security check using two or more separate addresses, EAS will identify the fact that a particular individual collects multiple checks sent to various addresses, and will create an alert in the system.
The ability to link individuals to multiple data sets and determine their interconnectivity helps proactive identification of potential fraudulent or criminal activity. IBM, with its acquisition of Language Analysis Systems (LAS), has started to address these needs through IBM Global Name Recognition. Instead of taking a business intelligence data integration or customer relationship management (CRM) customer data integration approach (whereby data cleansing activities take place to create one version of the truth), Entity Analytics uses the opposite approach to identify recurring data patterns to address terrorism and fraud through its Terrorism and Fraud Intelligence Framework (T&FI). The software addresses the issues of searching and managing data on individuals across geographic regions, customers within financial institutions, etc. to meet the demands of managing data sets from diverse cultures and geographic regions. This goes beyond name recognition to analyze how names are interconnected through the identification of recurring data patterns and entity connections. These connections are flagged based on rules created to identify suspicious transactions or behavior.
IBM Entity Analytics Software Offering
IBM Entity Analytics Solutions Global Name Recognition provides four modules (see figure 1 below) to enable organizations to identify people, relationships, and data patterns, and to share that information anonymously to identify potential fraudulent or suspicious behavior. IBM's EAS consists of
* IBM Identity Resolution, which identifies an individual entity and connects the data associated to that individual across data silos; * IBM Relationship Resolution, which identifies non-obvious relationships to reveal social, professional, or criminal networks. This module also provides instant alerts once data connections are detected; * IBM Anonymous Resolution, which de-identifies sensitive data sets using proprietary preprocessing and one-way hashing to add additional layers of privacy, and link that data based on codes that enable entity relationship identification without jeopardizing individual privacy laws. Data is shared anonymously and remains with the data owner to ensure data security; * IBM Name Resolution solution includes name searching, variation generator, parser, culture classifier, and defining genders. Global Name Recognition's primary use is to recognize customers, citizens, and criminals across multiple cultural variations of name data. A practical application of the name variation generator is to learn the different spellings of names across various geographical regions.
SOURCE:http://www.technologyevaluation.com/research/articles/business-intelligence-and-identity-recognition-ibm-s-entity-analytics-18862/
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