Volatility, uncertainty, complexity, and ambiguity can best describe the macro environment governing business decisions today. In this volatile environment, forward thinking organizations are recognizing data as a strategic asset and leveraging it to strengthen their competitive advantage. Data Analytics supports strategic decision making by providing data driven insights about products, customers, competitors and any aspect of the business environment.
Analytics today is practiced in most organizations on a need basis. While most organizations are still pondering on making investments in data analytics and business intelligence, they need to understand that the process of integrating analytics into the organizational framework includes much more than investing in tools and the right people.
The cornerstone of this framework is the data-driven culture, which is a key element for successful integration of analytics into the organizational framework. The process of integration begins with a resolve to be data driven. In order to be data-driven, big data analysis and advanced analytics must be recognized as an organizational function at the corporate level. Projects and assignments undertaken must be evaluated from an analytical perspective, where the amount, quality, implication of the data generated and how data at hand can be put to use are of primary importance. At the corporate level, senior leaders must be advocates of big data analytics, they can influence and direct teams and functional departments to use data for generating actionable insights. Every function and department in an organization must have a data analytics personnel integrated into the team to evaluate and ensure that valuable data is being generated at every stage of the business process. While generating valuable data and analyzing it is the key, presenting the results in a form which speaks the language of its audience is also crucial. This can be achieved using advanced visualization techniques. Thus building a data driven culture means looking at all critical aspects of business with the help of big data, apart from the normal use of business experience.
Resources are a function of the right people, and the right tools. Having qualified and adequate number of data scientists, data mining experts, data specialists, data visualizers and project managers is a prerequisite to successful implementation of analytics across teams and functions. With the right tools and people, successful integration further depends on centralization and decentralization of resources. While decentralization makes it easier to collaborate with the business units within the organization, centralization allows all data scientists to be present at a single unit allowing easier access to data, and taking a holistic view of business imperatives. Data Analytics is both an “art” and a “science”. One therefore needs to be careful and give a balanced importance to both Tools and People. In a lot of organisations (typically technology centric organisations), there is far greater significance given to tools and software, compared to Analytics professionals, which results in sub-optimal results.
The third aspect of the framework is process. Process focuses on the storage, quality and consistency of data, along with creation of sound data and analytical models. Process also refers to the usage and application of data driven strategies. Data must be stored in a manner that is easy to access and aligns with business strategy that can create value. Frameworks, processes, and incentives must be in place to support analytical decision making. The process of developing data and analytical capabilities and using the analytic insights in strategic decision making is not a simple task and very few companies have been successful in their attempts. Leaders build up their analytic capabilities by investing in four things: data-savvy people, quality data, state of the art tools and processes and incentives that support analytical decision making. A good framework for evaluating the analytical processes is to review if the processes in hand can effectively function to build and deploy analytical/predictive models and at the same time measure the business impact of these models.
The final part of the framework is the governance model. The primary objective of the analytics governance model should be to ensure smooth functioning of the analytical teams along with adaption of Analytical processes embedded in the business processes. Ensuring successful integration of the analytical function with other departmental function can mean availability of data and smooth deployment of analytical models. A typical governance committee would have a steering committee composed of senior members from the top management, and relevant and experienced members of the IT function. The governance committee would also be responsible in making long term decisions, and ensure transparency, and accountability in the analytical function.
Sign up to get the latest perspectives on analytics, insights, and AI.