Being data-driven can boost every aspect of business operations. The difference between the companies that embrace this culture and companies that don’t is increasing at such an exponential rate, that heading towards the data model has become a simple matter of survival.
Nevertheless, turning a business into a data-driven powerhouse requires a careful transition. Companies have to make sure that data offers valuable insight and try not to collect mass data without a clear purpose. This will be safer when it comes to avoiding employee frustration, providing real value to customers, and complying with data protection laws.
So, how can we make sure about gathering valuable data? Why is it that important? How is that you can achieve an outstanding data strategy that will make the difference?
Data-driven is the new customer-centric
When you try to achieve a certain level of personalization at scale, being data-driven becomes a fundamental key. It’s data the element that will allow you to deliver fully personalized products and services. Keen data analysis, regular testing, and cross-functional dev teams will make possible a continuous improvement cycle. Cloud-powered analytics machine that uses AI will optimize your app experience for customers. A data-led business will make a priority of understanding customers better, a new way of being customer-centric.
When it comes to product-service hybrids, for instance, being data-driven (or customer-centric) is the best way to generate additional revenue, build long-term loyalty with customers, and keep a competitive edge. Whilst this isn’t the only type of business that can benefit from being data-driven, it presents a clear advantage – the feedback loop.
By offering continued services on products, companies create access to a wealth of valuable data that can help them to discover things such as how often their product is being used, what functions are obsolete, or how many times a particular service is shared. Through a data-driven strategy, organizations have the opportunity to create a feedback loop that allows them to continuously refine and improve both their product and services.
Setting valuable data variables
However, a usual doubt comes alive when companies start working towards this culture. How can we make sure we are collecting what’s valuable and responding to the right data? As this data is the one that will help organizations draw the right conclusions, we don’t want to be misleading. And if most executives feel overwhelmed by the volume of available data when making decisions, it’s because it’s hard to differentiate crucial information from the irrelevant. It’s a lot, and everything is mixed up:
- From in-app usage
- Download rates
- Customer conversation records
- Internal operations
- Partner integrations
Most companies struggle with a surfeit, rather than a shortage. And here’s where a data strategy becomes crucial. It helps to define which areas of business operations need vital insight and choose the variable and tools required to discover and analyze it.
Your data strategy must be tailored to your business needs but there are steps or areas that are common to everyone.
8 core data variables steps
1 – Data sources: No matter if it’s a consumer app or a piece of machinery on a factory line, the first stage is to ensure you’re collecting data from useful sources.
2 – Data integration and analytics architectures: How will you ensure your data gets from the source to a point for analysis? The right architecture will be your ally here, removing any siloed information.
3 – Data privacy and governance policies: What steps are you taking to protect and manage this data flow, whether internal or external? Are these processes recorded?
4 – Data visualization: Dashboards and graphic representations can help the company make sense of the raw data at hand in a fast and efficient manner.
5 – Machine learning and data science: AI and machine learning are available to automate systems, process data, recognize unseen patterns, and analyze rich data such as images.
6 – Aided decision-making systems: Will you use this information to help forecast and predict outcomes? Plan ahead to know how this data will aid your overall decisions.
7 – Data monetization: What monetization model are you going to use (if any) and how data adds value to improve rentability?
8 – Big data and data lake strategies: Are you in a position to run an analysis of unstructured data? If so this can offer unseen insight, particularly for large scale operations such as production lines.
Achieving a real data-driven culture
Setting a plan for strategy revision can’t be ignored. Although many companies were ahead of the curve and already implemented BI platforms, this doesn’t mean they can rest on their laurels for long. That strategy must be updated with the latest tech trends to remain effective.
Data strategy is now an integral and pivotal part of business strategy. However, as you may have gathered from the complexity and number of areas a data strategy can focus on, it’s important to handle it with an agile framework. Transparency, continuous improvement, and collaboration from all areas (yes, all Scrum-based values) will be your friends to achieve a data-driven culture and become a truly data-driven organization.