By Lance Rubin, group member since June 2017.


Over the past few months I have been asked this question a lot during webinars, workshops and directly by clients, so I decided to write this post to help more people understand these evolving business capabilities.
What is the difference between Data Analytics (DA) and Financial Modeling (FM) ?
I know some might think one is better or more important, especially for those looking to up-skill, but the reality is they are quite different.
There is of course a lot more hype around Big Data and Data Analytics because it has enabled such significant changes to our lives, which is a good thing. Breaking traditional business models (no pun intended) and enabling new ones.
As a financial modeler, I might be a little biased if I had to choose, but the fact remains they are absolutely complimentary and definitely not interchangeable for one another (as many people seem to think).
Advancing one without the other is like a chef buying the best ingredients and then not following the recipe or using the wrong cooking methods. You need to understand your problem first then find the solution, not simply implement the solution and hope it solves your problems.
Just because everyone else is doing it doesn’t mean you should right now without considering the problem deeply first.  Understand your why?
Why is it important? Do I care?
If you want your business to survive for the next decade you should care. Already many traditional businesses are failing for lack of progressing technology in this domain. It’s very important to understand what you get when developing core skills/systems and processes in these 2 areas.
The business world has become highly competitive and new entrants are disrupting all sorts of long standing operators by using the latest technology that enable DA and FM and assist them in running leaner and more profitable business models. Uber uses data analytics which helps them stay ahead of every other taxi company in the world.
In my experience businesses are developing one without the other thinking that it alone will provide all the necessary insight and powerful decision-making capability.
Don’t even get me started with artificial intelligence (AI), internet of things (IOT) and other emerging concepts which again are complimentary but let’s just stick to DA and FM for now shall we.
If you don’t have DA and FM capabilities as a start, jumping straight to IOT and AI is a little pointless. Especially when IOT generates a lot of data. Again, what is the problem not the trend being set by others. You risk wasting a lot of money going down the wrong rabbit hole.
Depending on the phase of your business you might prioritise one over the other, but if you don’t have either or only looking to develop one of the 2 your business is at a significant disadvantage to competitors that have systems and capabilities across both.


When I refer to DA I mean all forms of big data and business intelligence which enable a business to visualise, slice and dice and interrogate large amounts of data and/or write specific rules relating to that data. This can also be referred to as predictive analytics (not to be confused with financial modeling and cashflow forecasting).
Some examples of the systems used in this context are Microsoft’s Power BI, Tableau, Qlikview, Domo etc.
When it comes to Financial Modeling there is currently really only 1 system (Excel) that is sophisticated and flexible enough to cater for all the different dimensions of a 3-way financial modeling (Income Statement, Balance Sheet and Cash Flow) integrated with complex business logic and accounting.
There are of course powerful add-ins to Excel like Modano that turn a simple spreadsheet into a powerful content management system.
Excel is still the most versatile application due to its flexibility and ease of use and most importantly the widely spread users in business for even basic decision making.
For financial modelers like myself and many other Finance professionals it’s still the Swiss Army knife for business, but not great for large amounts of data and certainly there are aspects that you might miss without such tools. It is certainly worth taking a closer look, but understand what you get when doing so.


Data Analytics (DA)
Benefit – Greater Hindsight and Insight and elements of predicative capability.

  • Enables a business to gain insight on what’s happening in that moment (especially if connected live like your Uber App) or hindsight based on what’s occurred historically.
  • Answering questions like how many loaves of brown bread are sold (and at what time) across all supermarkets in the country?
  • Which branches and bankers across the country have provided discretionary discounts on home loan/mortgage interest rates to their customers and why?

Shortcoming – Lacks Strategic Financial and Cash Flow Foresight
Using the above examples typically DA systems, processes and staff with those skills will not be able to provide financial based foresight of a decision if it were made relating to the above.
For example, based on the above:

  • Understanding the potential financial impacts (profitability, cash flow and business valuation) and scenarios relating to changes in bread production. These changes can impact cash flow on the delivery, transport and staff costs associated with volume changes and sales of brown bread across all stores if pricing and volume changes were made at particular times.
  • Understanding the financial impact of interest rate changes, capital allocation, return on equity and potential share price changes of mortgage discounts is equally important. If these discounts were only given for particular customers meeting certain credit criteria and incentives for bankers were aligned to these measures (not just volume as it is in most cases) perhaps the return to shareholders wouldn’t be as significant.
  • There is a lack of supply of DA skills across all sectors which has seen many Big 4 accounting firms build large data analytics teams and capabilities to cope with this higher demand and short supply.

Financial Modeling (FM)
Benefit – Foresight and strategic alignment to cash flows and business value drivers

  • By understanding the value chains of business logic from the entry and exist of financial information in a model it is possible to connect the past performance to the strategic vision and hypothesis on key management decisions. This will enable the key decision makers in the business to be across all the key financial aspects which could be impacted by their decision. Navigating the Titanic through icebergs looking out the back is not a good strategy, perhaps looking forward might avoid some nasty issues.
  • The impact of producing and therefore selling more brown bread is not as simple as it sounds depending on the business model ie producing onsite and holding sufficient inventory, delivering by a 3rd party and if volumes are to increase significantly staffing levels may need to adjust at certain times all impacting on costs and therefore cashflow and profits. By having a financial model which already has the business logic and assumptions driving all these dimensions it is possible to sensitise and hypothesis a decision like this. Deciding then to change the business model can also be considered depending on management’s strategic priorities.
  • Similarly, the impacts and elasticity of mortgage pricing can be robustly tested in terms of the net interest margin, capital and risk returns on the balance sheet can be determined using a financial model. Securitising the mortgage book and stress testing mortgage defaults can also be considered.

Shortcoming – Data processing limitations

  • Whilst Excel is great for FM it cannot process large amounts of data whether historical or live in production. Many people don’t even know the existence of the XLSB (Binary) file format for Excel. This format is useful when making larger spreadsheets run more efficiently and reduce their size, but it’s never enough in today’s oversupply of large amounts of data. The next time your Excel file hangs a lot or crashes try XLSB, but it might be time to consider other options.
  • There is also a lack of talented financial modelers. The process of forecasting and planning has existed for many decades and accountants in Finance teams have been doing this process for a long time, however it hasn’t evolved. It certainly hasn’t evolved to the level of the investment banker or other professional financial modeling firms including the Big 4.


There is now an even greater focus on higher value add services provided by Finance teams and finance professionals including CFOs, business partners or members of an FP&A team.
It is important to consider both Data Analytics and Financial Modeling as part of the tools in your team’s toolkit, however make sure you understand what the problem is first and priorities accordingly.
Maybe in the future we will see the merging of these 2 disciplines, but for the moment they are very specialised to problems that need solving.
Originally published on ModelCitizn
Source : Generation CFO LI Group