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Five steps to bringing analytics to life

big-data [1]In today’s digital world, the use of analytics has been recognized as a critical part of any decision-making process in businesses. The explosion in the amount of transactional and non-transactional data that organizations have access to has made the need for new tools and technologies vital for organizational success.

As such, finance professionals need to develop knowledge about analytics to understand and embrace the potential value of Big Data. A recent report by the Chartered Institute of Management Accountants and Infosys articulates how analytics can be integrated into the accounts receivable (AR) process to generate business-relevant insights that can help to improver decision-making.

Leveraging analytics within AR can provide organizations with a better understand of their exposure through segmentation of their AR portfolio. This could lead to more effective collections and dispute strategies, thus improving cash flow and reducing non-payment. Analytics could also provide insight into customer behavior that can result in bolder market-entry strategies with less risk to the organization.

As organizations move from descriptive analytics, which merely describe what happened, to more forward-looking predictive analytics and prescriptive analytics, more significant business benefits will begin to be realized. Business leaders need to have a clear analytics strategy that articulates key priorities and pathways from insights to business outcomes. This will lead to actionable insights. Without this, any analytics initiatives will fail to deliver the anticipated insights.

How to maximize analytics

A successful analytics framework needs to ensure that insights are action-focused and clearly identify the root causes of an issue. To maximize the value of analytics investments, organizations need to:

Practical steps for implementation include:

  1. Understand and articulate the central problem. Take time to understand what the real issue is.
  2. Develop a model that explains the organizational processes and what factors drive performance. This will help to determine which data are important and which data are missing.
  3. Capture the relevant data across the organizations. The necessary data may reside in different databases (e.g., HR, marketing, sales, or production) and would need to be integrated before any analysis can be done.
  4. Apply analytical methods. A range of methods can be used, including simple cross tabulations, regressions, stochastic process modelling, factor analysis, cluster analysis, and experimental design. It is important to understand that strengths and weaknesses of each method. Applying the right method to the right questions is critical to producing valid findings.
  5. Present analytical findings to stakeholders. For management to be able to translate analytical findings into action, they need to understand the information they receive. The results need to be specific and relevant to stakeholders. They need to be presented in a way that is consistent with management philosophy and language.

Your ShindelRock professional can assist you in this important process.