The fact that technology is playing an increasingly significant role in executing many traditional finance tasks while at the same time generating greater insights that drive business performance is irrefutable.
However, even though many organizations are significantly investing in advanced data and analytics technologies, majority of these investments are reportedly yielding disappointing returns.
This is often because they focus mostly on the implementation of new tools, and less on the processes and people. For instance, there is a widespread misconception that data analytics is a technology issue, and also about having the most data.
As a result, many organizations are ultimately spending large amounts of money on new technologies capable of mining and managing large datasets.
There is relatively little focus on generating actionable insights out of the data, and building a data-driven culture, which is the people aspect of analysis.
Data analytics is not purely a technology issue. Instead, it is a strategic business imperative that entails leveraging new technologies to analyze the data at your disposal, gain greater insight about your business, and guide effective strategy execution.
Furthermore, having the right data is more important than having the most data. Your organization might have the most data, but if you’re not analyzing that data and asking better questions to help you make better decisions, it counts for nothing.
Reconsider your approach to people, processes and technology
The success of any new technology greatly depends on the skills of the people using it. Additionally, you need to convince people to use the new technology or system, otherwise it will end up being another worthless investment.
Given digital transformation is here to stay, for any technology transformation to be a success, it’s imperative to have the right balance of people, IT and digital skills.
It’s not an issue of technology versus humans. It’s about striking the right balance between technology and people, with each other doing what they do best. In other words, establishing the right combination of people and technology.
For example, advanced data analytics technologies are, by far, better than humans at analyzing complex data streams. They can easily identify trends and patterns in real-time.
But, generating useful insights from the data all depends on human ability to ask the right key performance questions, and identify specific questions that existing tools and techniques are currently not able to answer.
Rather than hastily acquiring new data and tools, start by reviewing current data analytics tools, systems and related applications. This helps identify existing capabilities including tangible opportunities for improvement.
It’s not about how much the business has invested or is willing to invest in data analytics capabilities; it’s about how the business is leveraging existing tools, data and insights it currently has to drive business growth, and where necessary, blending traditional BI tools with new emerging data analytics tools.
That’s why it’s critical to build a clear understanding of which new technologies will be most beneficial to your business.
Technology alone will not fix broken or outdated processes. Many businesses are spending significant amounts of money on new tools, only to find out later that it’s not the existing tool that is at fault but rather the process itself.
Take data management process as an example, mostly at larger, more complex organizations. Achieving a single view of the truth is repeatedly challenging. This is often because data is often confined in silos, inconsistently defined or locked behind access controls.
A centralized data governance model is the missing link. There are too many fragmented ERP systems. Data is spread across the business, and it’s difficult to identify all data projects and how they are systematized across the business.
In such cases, even if you acquire the right data analytics tools, the fact that you have a weak data governance model as well as a non-integrated systems infrastructure can act as a stumbling block to generating reliable and useful decision-support insights.
To fix the faltering data management process, you need to establish a data governance model that is flexible across the business and provides a centralized view of enterprise data – that is, the data the organization owns and how it is being managed and used across the business.
This is key to breaking down internal silos, achieving a single view of the truth, and building trust in your data to enable effective analytics and decision making.
Is your organization spending more time collecting and organizing data than analyzing it?
Have you put in place robust data governance and ownership processes required to achieve a single version of the truth?
Are you attracting and retaining the right data analytics talent and skills necessary to drive data exploration, experimentation and decision making?