2 items tagged "master data management"

  • Business Intelligence Trends for 2017

    businessintelligence 5829945be5abcAnalyst and consulting firm, Business Application Research Centre (BARC), has come out with the top BI trends based on a survey carried out on 2800 BI professionals. Compared to last year, there were no significant changes in the ranking of the importance of BI trends, indicating that no major market shifts or disruptions are expected to impact this sector.
     
    With the growing advancement and disruptions in IT, the eight meta trends that influence and affect the strategies, investments and operations of enterprises, worldwide, are Digitalization, Consumerization, Agility, Security, Analytics, Cloud, Mobile and Artificial Intelligence. All these meta trends are major drivers for the growing demand for data management, business intelligence and analytics (BI). Their growth would also specify the trend for this industry.The top three trends out of 21 trends for 2017 were:
    • Data discovery and visualization,
    • Self-service BI and
    • Data quality and master data management
    • Data labs and data science, cloud BI and data as a product were the least important trends for 2017.
    Data discovery and visualization, along with predictive analytics, are some of the most desired BI functions that users want in a self-service mode. But the report suggested that organizations should also have an underlying tool and data governance framework to ensure control over data.
     
    In 2016, BI was majorly used in the finance department followed by management and sales and there was a very slight variation in their usage rates in that last 3 years. But, there was a surge in BI usage in production and operations departments which grew from 20% in 2008 to 53% in 2016.
     
    "While BI has always been strong in sales and finance, production and operations departments have traditionally been more cautious about adopting it,” says Carsten Bange, CEO of BARC. “But with the general trend for using data to support decision-making, this has all changed. Technology for areas such as event processing and real-time data integration and visualization has become more widely available in recent years. Also, the wave of big data from the Internet of Things and the Industrial Internet has increased awareness and demand for analytics, and will likely continue to drive further BI usage in production and operations."
     
    Customer analysis was the #1 investment area for new BI projects with 40% respondents investing their BI budgets on customer behavior analysis and 32% on developing a unified view of customers.
    • “With areas such as accounting and finance more or less under control, companies are moving to other areas of the enterprise, in particular to gain a better understanding of customer, market and competitive dynamics,” said Carsten Bange.
    • Many BI trends in the past, have become critical BI components in the present.
    • Many organizations were also considering trends like collaboration and sensor data analysis as critical BI components. About 20% respondents were already using BI trends like collaboration and spatial/location analysis.
    • About 12% were using cloud BI and more were planning to employ it in the future. IBM's Watson and Salesforce's Einstein are gearing to meet this growth.
    • Only 10% of the respondents used social media analysis.
    • Sensor data analysis is also growing driven by the huge volumes of data generated by the millions of IoT devices being used by telecom, utilities and transportation industries. According to the survey, in 2017, the transport and telecoms industries would lead the leveraging of sensor data.
    The biggest new investments in BI are planned in the manufacturing and utilities industries in 2017.
     
    Source: readitquick.com, November 14, 2016
  • 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

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