Enterprise Resource Planning vs Data Analytics… this is a general comparison, so the evaluation is open to interpretation based on how you define each of these tools – but I think for business leaders even a high-level understanding of the difference can be helpful.
I have found the biggest three differences between the two are the following:
· Past/Current vs. Future
· Structured vs. Customization
· GAAP vs KPIs
When designed correctly, they complement each other and deliver insight with results. ERP’s help you operate better. Data analytics helps you make better decisions. Better decisions and better operations equal greater profits.
Past/Current vs. Future
Accounting is historical, it reports on the past. Enterprise Resource Programs (ERP) includes accounting and operational elements which are in a current timeframe. Data Analytics can be any combination of past, present or future. By not limiting the time structure, data analytics can create full timeframe analysis allowing the user to forecast, execute and see results.
Structured vs. Customization
Because ERPs are designed to execute processes (issue purchase orders, accept inventories etc.) it inherently requires a designed structure. Users need to know that a process change in one part will result in a predictable outcome within the system – so a fixed structure is critically important.
Data analytics by contrast is highly custom. The ability to pull data from different sources across the enterprise and even from sources outside the organization is incredibly valuable and requires a great deal of flexibility and creativity by the designer.
GAAP vs KPIs
Because ERPs are intrinsically tied to accounting, it is crucial that GAAP is followed. GAAP is specifically designed to ensure that the system reports a profit (or loss) according to accounting rules which does not always align with operational needs or best performance measures. Because the entire concept of Key Performance Indicators is to provide measures of performance – they need to be independent of GAAP rules or constraints. That is not to imply that a KPI can never be GAAP constrained - % of revenue, COGS, and profit margin are all GAAP constrained KPIs. But a KPI needs to measure what is crucial to the viewer. A good example can be in cash flow forecasting – which is an analytical process that requires a series of assumptions where GAAP rules would provide little or no purpose.
We live in a data-driven world
Most companies use several applications to manage operations including accounting, ERP, CRM and sales channels. They are designed to support functional and operational needs and can offer reporting based on their role in the processes. We know that as complexity increases, so does your data. Today even smaller companies can be managing over 50,000,000 data elements each month from these different sources. Companies that prioritize the importance of data analytics as core to their strategy have a tremendous advantage over those who do not.
Add more software if you have too much data
It may be counter-intuitive that adding more software – and data – to your organization actually reduces complexity. But that is the precise purpose of data analytics. Data analytics pulls, processes and organizes your data. The purpose of data analytics is not to drive processes but rather to enhance decision making.
With the complexity of your business growing, enhancing the decision-making capabilities of your leadership team through smart data analytics is often the best strategy you can implement.