Analytics Competence Center, the first step toward a Data Driven Company

The rapid spread of a true analytical culture has prompted many players in the financial sector to equip themselves with a so-called Analytics Competence Center with a view to internalizing and focusing the knowledge and best practices present in the facility on Advanced Analytics.

Definition

The Analytics Competence Center (ACC) is defined in the specialized literature as an interdisciplinary team that clearly and continuously defines roles, objectives, processes, and responsibilities with the aim of fostering a true analytic culture flanked by identified best practices.

In addition to having a strategic planning role, the Analytics Competence Center is also responsible for the actual grounding of projects and the application of a fact-based approach in the company's decision-making process.

Analytics: some clarity

Before analyzing the components of an Analytics Competence Center in detail, it is necessary to shed some light on the various meanings that the term "analytics" holds in everyday use.

Analytics can in fact mean:

• A particular business intelligence technique (predictive analytics)
• The business strategy underlying the use of analytics (analytics in fraud)
• Applications in analytics, i.e., a set of multiple solutions for a particular business process (credit management or sales forecasting)
• The entire Analytics universe that includes its hardware, software, human and process components.

In terms of the scope covered, Analytics is understood here in its broadest sense, with all its components the people, the processes and the platform.

People

People involved in an ACC may find themselves playing three roles: producers, consumers and facilitators.

Analysts who define and carry out all analysis activities on the data are considered producers.

Instead, all those business units that "consume" Analytics products and also manage performance using Analytics are considered consumers.

Finally, there is the category of facilitators, mainly consisting of IT-type figures, who facilitate the performance of information management tasks necessary for the smooth running of Analytics projects and decision making.

Processes

The processes within a Competence Center can be divided into three macro categories:

• Decision-making processes-They need to be as automated and cross-silos as possible. Historically, in fact, many analytics processes have remained separate from decision-making processes related to business applications.

Program management, that is, the coordinated management of a portfolio of projects, still does not enjoy defined and well-documented processes, especially in Analytics.

• Analytical processes--need careful definition of goals, metrics for a project's success, standards for design, and definition of the elements that define the type of analysis and data to be used.
• Information governance processes-Information governance processes are thus defined as all those processes that act as a bridge between the actual analysis and their business value, enabling interdisciplinary teams to tap into an up-to-date, available, quality mass of data.

Platform

An extremely widespread issue has to do with the diversity of IT and business intentions: while IT struggles to spread awareness about the usefulness of an integrated platform, business often reasons on the basis of temporary needs, lacking strategic vision.

The adoption of a platform-type solution cannot be separated from its integrability into the information systems of the business: this is what is known as Seamless integration, for which IT must become an ambassador within the company, implementing the solution with a focus on the needs of the business.

Implementation strategies

The doctrine mainly distinguishes two types of Competence Centers:

Efficiency driven - They are characterized by the absence of a dedicated budget act as service providers for the business units. They are usually reporting to the business units from which they receive proposals for use case development.

Innovation driven - They enjoy broad autonomy in terms of budget and therefore do not require funding from the business units to formulate new use cases. They are characterized by peer-to-peer coordination and a strategic approach, with the specific goal of fostering innovation and the development of an analytical culture.

Conclusions

To date, a company that wants to invest in its data assets with a long-term view should set out to establish an Analytics Competence Center.

This solution is one of the fastest and most effective ways in creating a Data Driven Company that knows how to properly value its data and invest in innovation from a strategic perspective.

In the current financial scenario also, the blending of the platform concept and the use of analytics could be the ideal driving force for a player looking for a particularly specialized innovation solution with a significant return on investment.