As the big data ecosystem grows every year, it is important to understand some of the prerequisites that are critical in embracing big data and data-driven decision-making culture in an organization.
Big data is not a “Plug and Play” technology. It involves a number of managerial and technological steps that are critical for success. Like any other organizational technology, big data implementation requires the full endorsement of an organization’s senior management, finding the right talent, and introducing a measurement culture.
From the top on down
The lessons learned from implementation failures of enterprise-wide systems such as ERP, CRM and SCM in many large or mid-size organizations indicate that executive sponsorship is the starting point for such large-scale undertakings. Big data introduces a whole new decision-making culture to an organization that cannot happen overnight.
Depending on the history of the organization, this change management journey could be very long. Nevertheless, senior management can play an important role in this journey. The management must lead by example with a clear understanding of the huge impact of big data on the organization’s information sharing and decision-making culture.
The alignment of the organization’s strategy, operational visibility, and investments in big-data related technologies can only be materialized when senior management fully understands the scope and challenges of this process and makes it easy for employees and stakeholders to embrace it.
Data-driven culture transforms organizations from a reactive to a proactive mode of operations, and brings transparency, accountability and speedy decisions. Incorporating this culture is not feasible through a bottom-up approach.
It is unlikely that individual employees or even division managers can change the whole organization by being the main advocates and activists. This approach may in fact lead to isolated or disconnected data-driven initiatives in organizations. Therefore, senior management must be ready to bring these fundamental changes from the top.
Investment is critical
Another critical area of readiness is the investment required to adopt big data technologies. Long-term vision and the need to know the scope of implementation are important here.
Yet, the cost of big data technologies may not be as big as the name sounds. This is mainly because there is a major shift of burden from hardware to software requirements due to the parallel and distributed processing architecture of big data and the use of commodity machines in the process.