Today, data – both structured and unstructured, is seen as the most valuable business asset to solve problems and improve productivity. An article in Forbes says every company today is a data company! However, we often get questions from our clients, whether data can offer insights when there is no complete data available. The short answer is YES; data can provide insights even when the complete data set is NOT available! Here is a one example to illustrate this.

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In this CFO Talk guests Lance Rubin and Giles Male cover what CFOs need to know about modeling.

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SurePayd, a company driving the customer experience and mining customer intelligence from accounts receivable, asked me to answer some questions for their community about the CFO role.

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In his presentation, Analytics Through the Lens of the CFO, to a class at The Analytics Academy Steve describes how the convergence of data analytics, accounting and finance is creating excitement in the CFO suite.

The CFO’s historical role as chief fact officer coupled with their more recent responsibilities as chief future officer have put data analytics in the CFO’s bullseye.

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While many companies have embarked on data analytics initiatives, only a few have been successful. Studies have shown that over 70% of data analytics programs fail to realize their full potential and over 80% of the digital transformation initiatives fail. While there are many reasons that affect successful deployment of data analytics, one fundamental reason is lack of good quality data. However, many business enterprises realize this and invest considerable time and effort in data cleansing and remediation; technically known as data engineering. It is estimated that about 60 to 70% of the effort in data analytics is on data engineering. Given that data quality is an essential requirement for analytics, there are 5 key reasons on why data analytics is heavy on data engineering.

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