Studio Moderna

Branka Virtič

If a company wants to get on a bus to go on a data journey, it needs to be prepared for a long ride with many stops in between. And the truth is that slowly every company will be forced to embark on a trip to the data world not to risk being left behind by technologically advanced competitors. As for every trip, the company needs to have a well-designed roadmap, define where its possibilities are to explore and make the journey meaningful along the way. This can be achieved by setting short-term milestones and concentrating on the journey instead of a destination.

I have been working in the field of data for more than a decade and I can say that there are no shortcuts. Normally data analytics follows three sequenced stages in the aspect of data maturity in the company. In the beginning, after the company gathers relevant data, it can start by analysing the past, doing so business gets some knowledge on crucial factors, measures, and business outcomes. After this is achieved, it can level up and start understanding the present by monitoring these key indicators.

Finally, once the first two stages are covered, the company can start by predicting the future. Nowadays society is facing many challenges, for example, geopolitical issues, and global disease outbreaks, things that the majority of us never experienced before. If certain stakeholders were still now relying on their common sense and making decisions solely based on their experience, they were left in the dark when suddenly unpredictable happened.

This is when machines come into play since they need substantially less time to recalculate the outcomes while considering new factors or changed conditions. The data world went through some rapid changes mostly in the last couple of years, mainly due to digitalization and an increase in the amount of data. Retail and E-commerce are on top of the list if looking by industry vertical when talking about data science platform market share.

“If a company is not already on a journey to employ a data-driven strategy, it should at least start with data collection, as this process is not retrospective.”

Data science and predictive analytics platforms facilitate the usage of advanced techniques for data modelling and data wrangling with a drag-and-drop user interface and machine learning capabilities. I would not say that these platforms are replacing data team members, instead, they represent another piece of software that helps data specialists tackle several projects in parallel while giving them an opportunity to dedicate more time to projects that require greater domain knowledge. When it comes to leveraging data, marketing and sales are business lines to start with.

Everything revolves around the customers and if a company wants to put customers in the centre (adopt a so-called customer-centric approach), it needs to understand them, and know their value (historical and future) to better adjust communication strategy and allocate marketing costs accordingly. The latter is relevant, especially for the acquisition of new customers. By analysing customers' behaviour and their preferences, the company can promote loyalty more efficiently and decrease the churn ratio.

Data analytics can have added value in customer profiling, channel selection, establishing trigger points, content personalization, media mix modelling and many more. Customers are becoming increasingly demanding, they are aware of other choices on the market, and they always look for a better offer or better story. If a company is not already on a journey to employ a data-driven strategy, it should at least start with data collection, as this process is not retrospective.