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Setting up Data Analytics to Increase Profitability
To introduce data analytics effectively, enterprises need to develop a strategy that promotes both top-down and bottom-up initiatives at the same time.
Fremont, CA: Since its inception, data analytics has matured significantly and attracted a more extensive range of adopters. Over the past several years, enterprises of all types and sizes have transformed themselves into data-driven organizations. Data analytics now makes it possible to put customer and market data to work and obtain rewarding insights almost immediately. The numerous advantages of data analytics are well known. Yet, some companies refrain from leveraging the technology as they are concerned that the high costs of launching and running a data analytics project will outweigh the potential benefits. As data analytics technology grows ever more powerful and accessible, such enterprises may want to reconsider their decision.
To introduce data analytics effectively, enterprises need to develop a strategy that promotes both top-down and bottom-up initiatives at the same time. On the one hand, top-down or management-driven initiatives play an essential role in adopting the technology. Leading by example, is beneficial to the adoption of data analytics. At the same time, an excellent selection of bottom-up projects to be executed first has proven to help break initial reluctance and skepticism arising from different teams and well-established areas in the organization. The first step should be to build a strong foundation. Hire key experts and choose the best software.
This should be followed by building an inventory of all the existing resources and capabilities, including whatever is available in the current data warehouse, the organizational structure, and staff competence. Companies need to ensure that the experts heading the enterprise’s data analytics project should be versatile and competent to handle the entire spectrum of data-related fields, including data analytics, data processing, data collection, data warehouses, data blending, data visualization, and data preparation. Practically, it may be impossible to find a single person who can manage all those aspects. The effort needs to be distributed to hire a group of people to cover global expertise.
Companies often collect large amounts of data but do not have a clear idea of what needs to be done with it. As a result, the data gets soiled between two departments. If a company has not been able to do anything with the data analytics tool at disposal, it is highly likely that individual departments have taken the initiative to build or purchase their solutions. This creates boundaries that need to be demolished to create a better environment. Separating the analytics development process into stages can help new adopters accurately assess costs, requirements, and the potential value of developing a data-driven analytical solution.
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