How to overcome the 5 key challenges of using Big Data and Analytics


Peter Chisambara ACMA CGMA

Today, Big Data is the cornerstone of business strategy. Companies are no longer relying only on information generated from their ERP systems to drive strategic, financial and operational decisions, rather, they have to make use of data from other sources such as CRM systems, social media, and website clicks.

As a result, executives are looking for data-driven insights from CFOs and their teams to support decision-making and propel their businesses forward. This has led to calls on Finance to be the organization’s “Analytics Powerhouse” and drive the data analytics agenda across the business.

There is increased pressure on finance to transition from the traditional historical reporting role into a more forward-looking function, with increased analytical abilities to predict the future and help improve business performance. This transition can only happen if finance embraces data analytics and masters the art of predicting and influencing performance.

Properly analysed and applied, Big Data and analytics can help decision makers identify and predict trends, understand customer behaviours, manage risks intelligently, improve pricing decisions, optimize supply chains, and identify new sources of profitability.

Companies that are successfully transforming their finance organizations have mastered the opportunities presented by Big Data and made analytics an important part of their DNA.

On the other hand, some finance organizations are still struggling to reap positive returns from their data analytics investments, and some of the reasons for the negative returns are:

  1. Lack of Coherent Data Strategy

In order to benefit from Big Data and analytics investments, organizations should have a coherent data strategy. Implementing data analytics is more than technology and gathering as much data as you can and then try to generate insights from the data collected.

First, establish the need for the data and then identify the right type and sources of data to use. What do you want to achieve and what are your objectives?

Data collection, storage and analysis is a costly exercise. That is why is important that you clearly establish the data needs, otherwise you will end up wasting resources and efforts collecting data that you have got no use for at all.

  1. Using the Wrong Data

I am sure you have heard of the saying “Garbage In, Garbage Out”. The same applies when it comes to Big Data and analytics investments. Analytics has changed alongside Big Data: from descriptive analytics, through to diagnostic analytics, to predictive analytics and now prescriptive analytics.

Each of the above analytical tools serves a different purpose and for the analysis to be trustworthy, data used must be reliable and accurate. One of the challenges facing many Finance teams is that they lack an understanding of which type of data to use for a specific analysis.

Collecting data just for the sake of collecting is their priority.

If the wrong data is analysed at the input level, no matter how crisp the analysis is, the insights generated will always be wrong leading to poor decision making.

To solve this problem, Finance should develop an understanding of what data is currently available to the organization. This helps evaluate any additional data needs. Furthermore, the function should be able to locate where in the organization the data resides. This also helps address any data-security related issues and if the data is easily accessible to everyone and prone to tampering.

  1. Lack of Data Integration

To successfully benefit from Big Data and enable more informed analysis, finance people must be able to combine multiple, disparate datasets, regardless of location or format. Unfortunately, this is a big challenge for many finance organizations.

This is mostly because of the disconnection between business and IT. Finance speaks the business language, forms a hypothesis or wishes to perform “what if” scenarios and simulations with the data in order to derive decision insights. On the other hand, IT speaks the technical language, and writes the reports.

The problem comes in when Finance and IT fail to understand each other, to such an extent that when IT writes the reports, they fail to aggregate all the data sources leading to incomplete analysis.

Lack of coordination between departments regarding information sharing is also a cause of data disintegration. As business partners, finance teams should learn to collaborate with supply chain, marketing, sales and operational teams. However, some business managers might refuse to share information with finance for reasons known to them.

To address this lack of information sharing, executives should change the corporate culture and increase cross-team co-operation and integrated decision making.

Lack of data integration is also a result of legacy systems incapable of communicating with other systems within the business. For instance, a budgeting and forecasting software failing to communicate with the supply chain management system, leading to inadequate forecasts.

  1. Lack of leadership talent and skills

Investing in Big Data and analytics requires a new breed of talent and skills. As finance organizations become more data-driven in their approach, they also need to restructure their teams.

Instead of going for the traditional accountants, CFOs must acquire people with data analytics skills. For instance, data cleansing and normalization is a highly technical and time-consuming task.

A finance professional who traditionally trained as an accountant and never upskilled will struggle to view and combine, for instance, social media data with existing data sources for analysis. Even though this individual is lacking in technical skills, he or she might be strong in business partnering.

An individual who is strong in business can complement one who is strong in technical skills. Both attributes are necessary for achieving breakthrough performance.

Thus, it is critical for the CFO that the right team with different strengths and backgrounds is in place. Although technology is an enabler, people make sense of data and give it meaning.

  1. Aiming too high for success

Embracing data analytics and transforming finance into an analytics powerhouse is a journey. Unfortunately, many finance organizations aim too high without first developing an understanding of how to use analytics to improve business performance.

Starting with a pilot project will help you plan quick wins, take lessons and move forward.

It is therefore advisable to start small and celebrate small wins before embarking on a full-on implementation across the enterprise.

How you use data will determine whether your organization moves forward or backwards.


Peter Chisambara is the Founder and CEO of ERPM Insights, an independent consulting firm based in South Africa specializing in Enterprise Performance Management (EPM). ERPM Insights provides strategic performance management consulting services to organizations of all types and sizes in South Africa, and across the globe. We help them implement strategy successfully, measure, monitor, and improve business performance. Visit


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