Modernizing Data Ecosystem:
To be able to compete in the digital age, organizations must take advantage of internal and external, and stay relevant to new business requirements such as AI, Analytics, Automation, and Digital initiatives. To achieve this goal, the very first step for most companies is to modernize their data ecosystem and make data accessible for business exploration and analysis. The Data Transformation projects are extremely complex end to end undertaking, and stakes are often too high to fail. It is not a coincidence that we are seeing a rise in Chief Analytics and Chief Data officer’s roles to support CTO, CIO, and business executives.
In our opinion these are a few recommendations that organizations must consider before embarking on this journey:
Vision & Strategy: The organizations need to define a vision to modernize their data management system, along with a comprehensive implementation roadmap that doesn’t disrupt their operational needs and increase adoption. It is also important for businesses to not only evaluate the fit of various technologies within their Data Ecosystem but equally important is to assure it will maximize the return and minimize OPEX while implementing these solutions.
Organization Readiness: Data Transformation project cannot be seen as a technology project. If business leads own the data, which is the case in most advanced companies, and sponsor the technology upgrades. It also their responsibility to assure it has appropriate coverage (during and post-project) to support the technology team and actively participate in data modeling and governance activities.
Business Use Cases: The ideal implementation would include an inventory of Data-driven use cases that looks beyond what companies currently have in their arsenal. The focus should not only be on improving current reporting performance and quality but rather on what next assets they can develop to enhance their decision-making process. For example: if an organization’s fiscal priority is to reduce warehousing ops cost, that use case to drive optimize data-driven warehouse operations. It is not how quickly you can implement, but how quickly you can get the value out to the business folks.