Should we be using Excel for Analytics?

Simon Stewart
Feb 11, 2016
3 min read

Microsoft Excel remains a key reporting tool in most organisations, regardless of their level of investment in enterprise software packages. Speaking to business-users, the key takeaway is that the familiarity, flexibility and usability of Excel are essential features which enable them to meet the business need for immediate insight delivered at low cost.

Speak to IT or one of the numerous BI houses proposing dedicated analytics toolsets and you'll hear a different story. Excel should be avoided due to its lack of scalability, reliance on manual data extractions and the opaque nature of calculations hidden in formulae and macros created by non-technical business users.

So who's right? And how do organisations know if it is time to move from Excel to a dedicated analytics package?

To answer this question it helps to borrow the concept of technical debt from software development:

Technical debt is the consequence of producing a "quick and dirty" solution that gets the job done without being the most elegant or efficient option available.

The reason a development team may consciously choose to take on technical debt is in order to release software to users so that they can begin realising the value of the software today, rather than waiting for the elegant solution that will take additional months of development effort.

Developers try to quantify the cost of delay when prioritising their work backlog, asking:

"What is the cost of delaying an elegant solution versus the benefit of releasing a quick and dirty solution now?"

If the cost of delay is greater than the benefit of releasing the quick and dirty solution, then the team will release the software and patch it later.

What has this got to do with Excel and Enterprise Analytics?

Excel is ubiquitous in organisations for the reasons outlined above: the business know how to use it and they can get results fast. But building an information architecture on spreadsheets creates a technical debt that must be serviced with payments in the form of manual data processing, lack of transparency and data security risks. As processes become embedded in the organisation, overreliance on spreadsheets leads to overreliance on the resources who built them, creating single points of failure and limiting the ability of managers to move resources around the organisation. Further, it creates a data culture in which analyst time is spent on the extraction of data and transformation into information, rather than the generation of insight and recommendation of action.

Despite these pitfalls, organisations have opted for the technical debt of Excel-based architectures due to the long lead times associated with traditional Enterprise BI projects. Where the cost of delay is high, businesses will opt to take on the technical debt, rather than forgo the opportunity to generate insight in Excel while they wait for an enterprise platform to be delivered. And this is often the right thing to do.

"If you only quantify one thing, quantify the cost of delay."

- Don Reinertsen

However, with the release of new data and analytics technologies that support greater agility in implementation, the time it takes to realise value from a dedicated platform has shortened. Cloud-based technologies that support flexible architectures reduce the need for "big requirements up front", enabling implementers to show solutions to their customers and use stakeholder feedback as an input into design.

The emergence of these technologies should prompt organisations to evaluate their existing technical debt and the ongoing technical deficit that the continuing creation of new spreadsheets represents. As the costs to realise value from dedicated platforms falls relative to the cost of the technical debt incurred with Excel-based solutions, the case for delay weakens. Similarly, as data sets expand and regulations tighten, the cost of holding technical debt and risk of doing nothing increase.

So what should I do about it?

The first step should be to consider the level of technical debt within your organisation and try to evaluate the cost of delaying the implementation of a dedicated analytics solution. Secondly, consider the potential value to your organisation of a dedicated solution with powerful analysis and visualisation capabilities. Finally, review the marketplace for toolsets and look for an implementation partner that is able to help you evaluate the value of these toolset in relation to the objectives of your organisation.

For more information about how Centrotec can help your organisation understand its technical debt, the cost of delay and the range of solution and implementation options available for a dedicated analytics platform, please get in touch.