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    • Azendian
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    Azendian: Designing Strategies to Model Data

    CIO VendorBill Lee, Group Managing Partner

    Often, enterprises treat big data and analytics projects as "yet another IT project", employing existing IT resources. Such investments have a high probability of failure. Singapore-headquartered Azendian believes in a different approach. Data analytics projects should originate in boardrooms, pave the way for enterprise transformation projects, and should be treated as such. Valerie Nathan, Executive Director of Azendian, shares that successful analytics implementation projects are essentially organization transformation projects. Azendian services help organizations understand the real business challenges and transform themselves to leverage data-driven insights for business decision making.

    There is still some confusion over big data and analytics in the market and the benefits they bring. "Analytics helps the organization by providing it with new insights and foresight for organization transformation. From our experience, these techniques help organizations to enhance their revenue, optimize resource use, and manage risk more effectively. In short, we help organizations increase revenue, improve profits, reduce risk with analytics," says Valerie. The company drives organizational strategies towards analytics use cases and not technologies to measure RoI.

    For Azendian, data science is also an art. Advanced analytics generate new insights providing organizations with different responses to different questions, of which were not typically raised in the past. For example, an attrition prediction capability would traditionally require the number of people that have resigned current open positions and the cost to close them.


    We help organizations increase revenue, improve profits, reduce risk with analytics


    An advanced analytics solution will check the probability rate of employees who might quit in the next 12 months, the percentage of high performers amongst them, and HR management will need to determine the necessary steps and costs to retain them. HR organization will therefore need to change its processes and policies, and add new skills to reprioritize its focus and efforts in unlocking the benefits of the new insights.

    Azendian works on such organizational change and helps companies assimilate the output of analytics into their workflows, processes, governance, people, and culture. Building data warehouses and predictive models is relatively easier compared to getting an end-user to change their habits of solving a problem or pursuing opportunities. "Our transformation services help organizations understand the real challenges and achieve these objectives," says Valerie. The firm’s advanced analytics service covers the entire gamut from data preparation to modeling employing data science for producing the expected outcomes for business and organization transformation.

    Using this approach, Azendian enhances their clients’ data model, storage, and data transformation strategies to ensure they meet the performance requirements of advanced analytics. Traditional data storage is design and build for reporting and dashboarding while advanced analytics focuses on predictive modeling, forecasting, and text mining which require specific design, development, and deployment strategies.

    While Azendian provides consulting and system integration services, its key focus includes industry and domain specific analytics products and solutions which are cloud-based, fast to implement and easy to maintain. These solutions will be developed in their R&D lab. Azendian has developed smart building solutions that optimize energy use and reduce the need for unscheduled maintenance.

    Valerie informs that the firm has other industry roadmaps which they will be working on. "Not all analytics use cases can be ‘productized’, but there area number which are possible, and we will be developing these," she says. In the days to come, Valerie asserts that the firm's solutions will be cloud based, remove needs for a large number of data scientists in the field, and shorten implementation time.
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