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Data-Driven Business Transformation
Doing Analytics & Being Analytical – Find the difference
The moments of rest, a runner takes between the sprints, denotes the downtime that he utilizes to prepare for his next step. To rest, catch the breath and rethink the strategy and approach towards the competition. Similarly, while organizations and individuals get distressed when there is a slowdown, this is actually the right time to prepare for the first up-turn when the markets open up. While the majority of individuals and organizations think of the slowdown as a time to wait and watch, there are a few who use this time to emerge stronger than ever.
The key enabler is the ability to leverage data and get relevant business insights through actionable analytics (also, it helps Insurance company to optimize business performance). Here it is important to realize not only the importance of analytics but also to align analytics with your business decision-making process.
So to say the gap between doing analytics and being analytical can be filled by aligning the analytics with business decision making. Aligning analytics is not a destination or a goal a company should aspire to, rather, it is a continuous process of internalizing and integrating analytics in a company’s business decision-making process.
Steps to Align Analytics with Business Decision Making Process
Communication: Successful consumption of analytics is a collaborative endeavour. The first step in this process is to take your analytics intent beyond your core team and sell it to a wider group of decision-makers – the prospective daily consumers of analytics in your organization. The current economic scenario gives you a compelling storyline and helps you create a convincing platform to evangelize analytics in your organization.
- Implement: When asked what key technology or business challenges organizations have faced or expect to face in implementing business analytics, respondents always state data integration with multiple source systems, data quality, integration with the rest of their enterprise applications, ability to handle complex queries, and administration and security. However, strong leadership also plays a key role in implementing the wider analytics adoption in organizations. The initial focus of implementation should be on getting all the right aspects in place to create the basic human and technology infrastructures to solve the underlying problem.
- Measure: Analytics can be measured by applying analytics on them. However, the benefits need not always be as high as expected. Sometimes, changes are due to the maturing of a culture, of objective debates, arguments and viewpoints driven by data and not otherwise. It is important to consider here that overestimating the impact of analytics cannot be encouraged compared to the human involvement associated with it. A successful business decision is a healthy combination of business experience and analytics - both are equally important for successful analytics consumption.
- Align Incentives: If an organization wants the successful consumption of analytics, it needs to create more structured decision-making processes driven by data and analysis. This implementation will also bring in new stakeholders in your employees’ decisions as well as higher levels of inaccuracy. Sometimes a general tendency of designation bias exists, and employees do not want to come out of their comfort zone. The organizations then need to create strong incentives to overcome these barriers.
- Develop Cognitive Repairs: Our everyday decision-making is influenced by numerous prejudices intrinsic to human nature. With data and analysis organizations can challenge these biases and drive everyone out of their comfort zone. This results in undesirable conflicts and dysfunctional behaviors. Creation of counterintuitive business insights based on data and then going and proving it right for all to see is by far the most effective to both expose biases and create repairs.
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