A lot has been spoken about the explosion of data in the world. As we generate the zettabytes of data, big data and analytics have cemented their place in the enterprise.
Today, most successful and forward-thinking organizations are banking on big data and analytics to power their decision making. Being data-driven is the only way to drive the enterprise because, in today’s environment where VUCA reigns supreme, there is no place for guesswork.
Amongst other things in the organization, the rise of big data and analytics has influenced the role of the CIO. The CIO was once considered a functional unit head, who made the promise of rapid delivery. Over the years, this role progressed further, and we saw the CIO become more of a strategic partner who enabled business convergence in the wake of technological evolution and adoption.
Today, the CIO’s role has evolved a little bit more and become more centered around business transformation. The CIO is almost like the change agent who ensures that all IT functions that support the business work smoothly to effect transformational change.
To implement transformational change, you need information and intelligence –something that you can get only with data and analytics. As with any area of IT, there are strategic considerations involved in selecting a big data and analytics strategy for an organization.
Here are some guidelines for CIO’s to follow that help in designing robust big data and analytics strategies that lead to organizational success.
Having a big data and analytics strategy is great. But what are its use cases? Where do you want it to deliver value first?
We must remember that a goal without a plan is just a wish. For great results, a big data and analytics strategy begins with identifying business outcomes where data has the potential to generate business value. For example, if an organization wants to increase its customer share of wallet because customer loyalty is no longer enough, then the focus of the data initiative has to be on the data that influences the customers potential to purchase products.
The CIO, thus, has to be aligned with organizational and business goals to map the data strategy to these for productive outcomes.
The CIO is also the promulgator of business-led analytics. While IT data infrastructure remains IT’s responsibility, the capacity to use data, and the ideas to best use data comes from the frontlines and the business users. The business users, along with the analytics and data science teams, have to be enabled to become more data-driven in their decision-making.
For this, it is important to democratize data and use advanced analytics platforms that allow business users to build and manage data flows and gain access to powerful data visualizations, create predictive models, and streamline and automate forecasting processes.
The CIO has to make the right technology choices that enable business users to become more data driven.
The objective of big data and analytics is also to enable collaboration to drive transformation and lead to successful business outcomes. When designing big data and analytics strategies, CIOs have to not only promote collaboration between technology teams but also between the business and the technology.
The big data and analytics strategy has to be tied closely to the business teams and what outcomes they want to achieve. The technology, tools, and platform selection have to support this and help them build useful information networks.
Technology choices here would involve looking at the frameworks the platform uses and identify the ease with which users can access the right data and build data sets, irrespective of their technological expertise. It also involves ensuring that the platform offers a range of ready-to-use or easy-to-build algorithms so that business users can create data models that serve business purposes and generate business value.
IT has become the backbone of the enterprise of today. As digital becomes an organization’s priority, CIOs have to design their big data and analytics strategies so that they can achieve a business impact.
For this, the CIO has to ensure buy-in and generate support amongst business leaders across the organization and make technology choices that help in fostering true partnering relationships based on common goals.
This involves scrutinizing technology decisions beyond the cost and high-level strategy parameters and evaluate those from a business point of view. This can be done by determining the data and analytics capabilities required to fulfill the organization’s business strategy. Pursuant to this, the CIO has to take a deep dive into technical assessments, system, and data platform compatibilities, and vendor capabilities to make the right decisions.
One of the greatest advantages of big data and analytics is that it helps organizations move from insight to foresight.
However, this involves making the right technology decisions and leveraging the right data platform that enables not just analysts and data scientists to make use of data but also enables the non-technical business user to become data driven.
Technology decisions would involve looking at platforms that help business users overcome the challenges that emerge when identifying and categorizing large data volumes, especially test data. Platforms that support faster data visualization turnaround, allow flexibility to gain insights into any subject area, and provide a consistent user experience aid the success of big data and analytics strategies. Technologies that help business users play with data without technical skills pave the way for becoming a data-driven organization and ensure the success of data strategies.
Along with this, the CIO also has to evaluate the extensions and apps and accelerators the platform can provide. Extensions involve evaluating parameters such as the capability to manage large volumes of data, the capability to do complex processing, in-memory storage, and security. The Apps and Accelerators for elements such as fraud detection, risk identification, demand forecasting, predictive intelligence, recommendation engines, etc., which make the process of becoming data-driven in decision making faster and more reliable.
Every strategy that the enterprise develops has to be open to evolution. A big data and analytics strategy also must be open to these considerations as well. As such, the CIO has to make sure that the data platform helps them create, deploy, and maintain predictive models easily. As technologies such as IoT mature and find their place in the enterprise, CIOs have to assess if the data platform can help them develop, deploy, and manage IoT and M2M applications, automate processes easily, store and manage sensor data, etc.
The CIO’s role has traversed a long journey. From being the ones who deployed technology into businesses to becoming technology integrators and then moving on to becoming the ones who design the architectures of the business, CIO’s are now the conductors of value.
Since the CIO’s role has become that of the transformation enabler, the CIO has to ensure that the technology implementations close the gap between business priorities and outcomes. For this, they almost have to constantly evaluate and answer which comes first – the business opportunity chicken, or the technology opportunity egg, and then decide on a platform that acts as a universal translator that supports both.
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