Given the technology advancement we reached, data is being created in massive amounts through all company activities and it is easily accessible. This gives us the opportunity to understand data and enables better Data Analytics utilisation.
Previously industries and companies didn’t have access to all this data. We could barely get data in a country, let alone consolidate across regions. There were times and I am talking late 2000s when silos between departments was a result of not being able to link systems. Now, Data Analytics maturity made it easy to have access to data in a more centralised form using the cloud. It also provided accessible tools to link departmental data together regardless of systems.
There are many examples of cross-functional Data Analytics projects:
- Forecast Models: that includes a wide variety of things like products and revenue streams and covers factors such as volume seasonality, geography, demographic patterns and more.
- Pricing Optimisation: Looking at how a company can set its prices and linking that to demand elasticity, market preferences, discounts based on volumes and so on.
- Supply Chain Optimisation: This is really important especially when companies have different facilities around the world, taking into account logistics costs, time and customer demand is essential. Through analytics teams can identify factory capacity by country, plan how to take advantage of underutilised capacity. They can also move production from/to countries depending on different risks.
Those are just few examples of Data Analytics projects and how companies are utilising it so they are able to work cross-functionally. What do you think? What has your cross-functional experience been? As always, looking forward to hearing your thoughts and exchanging experience!