2 items tagged "data management"

  • Master Data Management and the role of (un)structured data

    MasterDataManagementTraditional conversations about master data management’s utility have centered on determining what actually constitutes MDM, how to implement data governance with it, and the balance between IT and business involvement in the continuity of MDM efforts.

    Although these concerns will always remain apposite, MDM’s overarching value is projected to significantly expand in 2018 to directly create optimal user experiences—for customers and business end users. The crux of doing so is to globalize its use across traditional domains and business units for more comprehensive value.

    “The big revelation that customers are having is how do we tie the data across domains, because that reference of what it means from one domain to another is really important,” Stibo Systems Chief Marketing Officer Prashant Bhatia observed.

    The interconnectivity of MDM domains is invaluable not only for monetization opportunities via customer interactions, but also for streamlining internal processes across the entire organization. Oftentimes the latter facilitates the former, especially when leveraged in conjunction with contemporary opportunities related to the Internet of Things and Artificial Intelligence.

    Structured and Unstructured Data

    One of the most eminent challenges facing MDM related to its expanding utility is the incorporation of both structured and unstructured data. Fueled in part by the abundance of external data besieging the enterprise from social, mobile, and cloud sources, unstructured and semi-structured data can pose difficulties to MDM schema.

    After attending the recent National Retail Federation conference with over 30,000 attendees, Bhatia noted that one of the primary themes was, “Machine learning, blockchain, or IoT is not as important as how does a company deal with unstructured data in conjunction with structured data, and understand how they’re going to process that data for their enterprise. That’s the thing that companies—retailers, manufacturers, etc.—have to figure out.”

    Organizations can integrate these varying data types into a single MDM platform by leveraging emerging options for schema and taxonomies with global implementations, naturally aligning these varying formats together. The competitive advantage generated from doing so is virtually illimitable. 

    Original equipment manufacturers and equipment asset management companies can attain real-time, semi-structured or unstructured data about failing equipment and use that to influence their product domain with attributes informing the consequences of a specific consumer’s tire, for example. The aggregation of that semi-structured data with structured data in an enterprise-spanning MDM system can influence several domains. 

    Organizations can reference it with customer data for either preventive maintenance or discounted purchase offers. The location domain can use it to provide these services close to the customer; integrations with lifecycle management capabilities can determine what went wrong and how to correct it. “That IoT sensor provides so much data that can tie back to various domains,” Bhatia said. “The power of the MDM platform is to tie the data for domains together. The more domains that you can reference with one another, you get exponential benefits.”

    Universal Schema

    Although the preceding example pertained to the IoT, it’s worth noting that it’s applicable to virtually any data source or type. MDM’s capability to create these benefits is based on its ability to integrate different data formats on the back end. A uniformity of schema, taxonomies, and data models is desirable for doing so, especially when using MDM across the enterprise. 

    According to Franz CEO Jans Aasman, traditionally “Master Data Management just perpetuates the difficulty of talking to databases. In general, even if you make a master data schema, you still have the problem that all the data about a customer, or a patient, or a person of interest is still spread out over thousands of tables.” 

    Varying approaches can address this issue; there is growing credence around leveraging machine learning to obtain master data from various stores. Another approach is to considerably decrease the complexity of MDM schema so it’s more accessible to data designated as master data. By creating schema predicated on an exhaustive list of business-driven events, organizations can reduce the complexity of myriad database schemas (or even of conventional MDM schemas) so that their “master data schema is incredibly simple and elegant, but does not lose any data,” Aasman noted.

    Global Taxonomies

    Whether simplifying schema based on organizational events and a list of their outcomes or using AI to retrieve master data from multiple locations, the net worth of MDM is based on the business’s ability to inform the master data’s meaning and use. The foundation of what Forrester terms “business-defined views of data” is oftentimes the taxonomies predicated on business use as opposed to that of IT. Implementing taxonomies enterprise-wide is vital for the utility of multi-domain MDM (which compounds its value) since frequently, as Aasman indicated, “the same terms can have many different meanings” based on use case and department.

    The hierarchies implicit in taxonomies are infinitely utilitarian in this regard, since they enable consistency across the enterprise yet have subsets for various business domains. According to Aasman, the Financial Industry Bank Ontology can also function as a taxonomy in which, “The higher level taxonomy is global to the entire bank, but the deeper you go in a particular business you get more specific terms, but they’re all bank specific to the entire company.” 

    The ability of global taxonomies to link together meaning in different business domains is crucial to extracting value from cross-referencing the same master data for different applications or use cases. In many instances, taxonomies provide the basis for search and queries that are important for determining appropriate master data.

    Timely Action

    By expanding the scope of MDM beyond traditional domain limitations, organizations can redouble the value of master data for customers and employees. By simplifying MDM schema and broadening taxonomies across the enterprise, they increase their ability to integrate unstructured and structured data for timely action. “MDM users in a B2B or B2C market can provide a better experience for their customers if they, the retailer and manufacturer, are more aware and educated about how to help their end customers,” Bhatia said.

     

    Author: Jelani Harper

    Source: Information Management

  • Will the battle on data between Business and IT be ended?

     

    Business users have growing customer expectations, changing market dynamics, increasing competition, and evolving regulatory conditions to deal with. These factors compound the pressure on business decision makers to act now. Unfortunately, they often can’t get the data they need when they need it.

    Research shows that business managers often have to make data-driven decisions within one day. However, the time to build a single report using traditional BI methods can take six weeks or longer and a typical business intelligence deployment can take up to 18 mobusiness and ITnths.

    On the IT side, teams are feeling the pressure. They have a long list of items to do for the short run and long run. Regarding data management, IT has to try to combine data from multiple sources, ensure that data is secure and accurate, and deliver the data to the business user as requested.

    Given the need for “data now,” in relation to the bandwidth concerns placed on IT, many organizations find that their enterprise lacks the skills, technology, and support to use their corporate data to keep up with competitors, customer needs, and the marketplace.

    Adding to this existing challenge is the notion that companies are continuously adding new data sources, but each new data integration can take weeks or even months. By the time the work is complete, it’s likely that a newer, better source has already taken its place.

    Automation is a force that is driving change throughout the entire BI stack. Just look at the proliferation of self-service data visualization tools. But self-service analytics can quickly go awry without adequate governance.

    Companies that can integrate self-service BI and still maintain governance, security, and data quality will empower business users to make decisions on-demand, while relieving IT from these internal stakeholder pressures.

    Having the ability to store data in a place or a hub where it can be cleansed, reconciled, and made available as a consistent resource, on demand resource to business users can help solve the issue.

    When quality issues arise, or bad data is found, the error can be corrected once in the hub for all users – resulting in one single source of the truth. It is a place where data quality and consistency are maintained. This central repository enables the right person to have access to the right data at the right time.

    Business executives, managers, and frontline users in operations want the power to move beyond the limits of spreadsheets so that they can engage in deeper analysis by leveraging data insights to strengthen all types of decision needs. Today, newer tools and methods are making it possible for organizations to meet the demands of nontechnical users by enabling them to access, integrate, transform, and visualize data without traditional IT handholding.

    The age of self-service demands that business users have full and flexible access to their data. It also demands that business users be the ones who determine that data should be included in the system. And while business users need the expert help of IT to ensure the quality, consistency, and contextual validity of the data, business and IT can now work together more closely and more easily than ever before.

    Organizations can effectively “democratize” data by addressing the needs of nontechnical users including business executives, managers, and frontline users. This can transpire If they grant more power to those users, not just in terms of access and discovery, but also in terms of sourcing what goes into a central hub.

    In the end, giving more power to the people is one surefire way to help end the battle between business and IT.

    Author: Heine Krog Iversen

    source: Information management

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