CIOs Establish Data-to-Value Ecosystem

For effectively managing the increasing volume of data coupled with disruptive technologies, CIOs should establish a center of competency.

With the increased reliance on digital technologies, the volume of data has significantly surged over the past two years. This has led enterprises to utilize machine learning (ML) and artificial intelligence (AI) as the core enabler in their data analytics projects. While they are continuously increasing their investment in AI, the harsh reality is that most of the data science projects are doomed to failure. The reasons for their failure often range from the complexity of AI/ML initiatives as well as the lack of skilled talent to deal with the challenges that exist in data security, governance, and data integration. The IDC “AI StrategiesView 2020 Executive Summary,” referred to these issues collectively as concerns for “data readiness”.

To make matters worse, even though most organizations routinely maintain a large set of data, they are often stockpiled in silos and are not easily accessible across these boundaries. Additionally, the rapid advances in data engineering tools, machine learning algorithms and cloud computing make it difficult for new processes to deploy. Furthermore, there are competitive challenges that come from both traditional and disruptive technologies.

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Therefore, it is essential that CIOs take steps to break down the silos and extract the value of data to its extent. They should foster a community and culture that can help them to accelerate and sustain the growth of data science as well as for analytics throughout the organization.

CIOs should integrate a hybrid approach where there is a focus on data science and analytics to close the divide between the traditional IT infrastructure as well as the functional domains required to enable success. This methodology can help CIOs to become successful in accelerating the development of data-to-value ecosystems along with the cultural shift needed for sustainable growth.

Here are a few foundational goals that will enable them to achieve these:

• Establish a center of competency 

One of the major reasons that many projects fail to take off is that they are built-in isolation, without the acknowledgment of the entire lifecycle of the model and lineage, digital thread and pipeline needs. Additionally, employees may hold or hide their data out of the belief that it may help them personally. Such an attitude also hurts the chances of creating value when seeking deeper insights. CIOs should understand that data science and analytics require a team effort. They should develop a center of competency that focuses on education, inclusion and collaboration to build trust among functional organizations.

• Nurture data literacy efforts

CIOs should build a virtual community across the entire organization where employees can discuss queries as simple as the basic concepts of data science to its design thinking constructs. Being a part of CoC, the resource hub, will drive the development and administration of curated plans of study that range from the ‘onboarding analytics’ skill level to more advanced data science certifications. The goal behind creating this virtual hub is to create a cross-functional community that supports everyone in their data literacy journey.

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• Build a team of diverse thinkers 

CIOs should create a platform for cross-functional teams with employees having unique perspectives. This will enable them to share ideas as well as identify projects with the highest potential. Team members can also leverage each other’s skills as well as domain knowledge to build new value for customers and shareholders.

Having the alignment between individual metrics and strategic KPIs enables organizations to reduce the friction for the desired culture shift. This helps CIOs to focus on the highest ROI projects. Ultimately, CIOs should understand that a data-to-value ecosystem will not be feasible without establishing trust in the integrity and security of the data pipeline as well as between stakeholders across the organization.

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