Championing the Capabilities of Effective Analytics Leadership to Drive Efficiency

Driven by potentials of data, all organizations and their respective leadership are betting big on analytics and other data science technologies. The ability of data analytics to provide hidden and never-explored-before insights pushes such organizations to take a lead in this arena. When it comes to leading the industry, the experience, vision, goals and competitive attributes of their Analytics Leaders turn nifty. Yes! The probability of success of an analytics project is directly proportional to the innovative vision of the leadership and their ability to frame productive strategies.

The leadership of an organization impersonates the success factor of a data-driven analytics project through their guidance and directing their efficient team towards fetching the unthinkable. If spurred right, the innovation brought in by analytics can have a positive impact on company revenues, margins, and organizational efficiency. And for those who lag behind, the loopholes in the strategy and wrong tool selection aren't necessarily to blame; rather, industry reports suggest that the most influential hurdles to an effective analytics program is the lack of leadership support and communication.

Today almost all organizations pursue data and analytics activities for a range of reasons, majorly to build competitive advantage or improve the customer experience. Regardless of the motivation, companies find mixed success depending on the factors their approaches tend to satisfy. Many professionals believe that greater effectiveness and more developed analytics capabilities are related to their leadership. Compared with the lower-performing leaders, the high performers tend to catalyze the analytics prosperity across their organizations. High-performer executives are most often ranked as senior-management whose involvement decides the most to their analytics success.

Such leaders not only ensure senior-management involvement in data and analytics activities but also contribute in designing effective data architecture and technology infrastructure to support it. Securing internal leadership for analytics projects who can provide business functions with access to support for both data and analytics is crucial. Moreover, analytics leaders are in charge of creating flexibility in existing processes to take advantage of new insights and also track their business impact. Attracting and retaining appropriate talent who can translate their vision into expected outcomes and constructing an effective strategy for investing at scale for analytics initiatives are among some quintessential attributes of such leadership. Regardless of their methodology, every analytics leader possesses three key characteristics: management know-how, the ability to identify potential value and holistic views of their organizations.

Today, leaders invest in both traditional and advanced analytics tools to earmark significantly more money than their less-advanced peers to implement artificial intelligence, machine learning, and predictive analytics. They understand the importance of a strong technology foundation and address the requirement for comprehensive and accurate data analytics. Subsequently, leaders spend considerably more on modern foundational infrastructure technologies, including intelligent networks to propel humongous sets of data and insights.

They tend to maintain a balance between both corporate and departmental analytics initiatives. Today, analytics leaders realize that organizations apply analytics differently according to the operation or department, therefore they support the adoption of the "hub and spoke" model. Centralized, "hub" activities focus on collecting and drilling into enterprise-wide information. Moreover, centralized architectures lessen the risk of data fragmentation and inconsistent results that can lead to misunderstandings among decision-makers. Further, the aim of "spoke" initiatives is to build insights with maximum impact for individual departments and locations.

According to Gartner, as an analytics leader, they must also champion workforce data literacy as an enabler of digital business and treat information as a second language