The data-driven organization is no longer a prediction for the future. It’s already here, and companies who have transitioned are reaping the rewards. A report from McKinsey Global estimates data-driven organizations are 19 times more likely to be profitable.
But this is not just about improved finances. Being data-driven can boost every aspect of business operations and the divide between the haves and the have nots is increasing at such an exponential rate that keeping pace with these changes has become a simple matter of survival.
However, turning a business into a data-driven powerhouse requires a careful transition. Not all data offers valuable insight and mass data collection without a clear purpose is a sure-fire way to frustrate your employees, lose the trust of your customers and fall foul of data protection laws.
In this blog, we’ll explore why data is more important than ever, what the essential elements of a data strategy are, and how to avoid a static plan of action.
Product-service hybrids and the feedback loop
The product-service hybrid has become the model of choice for many companies in efforts 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. With this, organizations have the opportunity to create a feedback loop that allows them to continuously refine and improve both their product and services.
For instance, Tandem Bank are keenly aware that there’s no one-size-fits-all banking solution nowadays and people expect fully personalized products and services. A data-driven approach is essential for them to be able to achieve this level of personalization at scale and Tandem prioritize keen data analysis, regular testing and cross-functional dev teams to continue the development of their banking app. In fact, the bank recently launched a new, cloud-powered analytics machine that uses AI to optimize their app experience for the customers. As Noam Zeigerson, Tandem's Chief Data Officer points out: “A challenger bank is a data-led business. At Tandem, we make sure that data management is at the core of our strategy, helping us understand our customers better. Being customer-centric means being data-driven and well informed, in everything we do.”
What data matters? Setting valuable variables
However, within this story lies an extremely important hurdle – how can companies be sure they’re responding to the right data? The data that will help them draw the right conclusions and not lead them down an irrelevant production path?
From in-app usage, download rates, customer conversation records to internal operations and partner integrations, most companies struggle with a surfeit, rather than a shortage, of data to work with. In fact, 94% of executives actually feel overwhelmed by the volume of data available to them when making decisions.
Here’s where a data strategy becomes crucial. This helps to define which areas of business operations need vital insight and define the variable and tools required to discover it.
Your data strategy must be tailored to your business needs but the core elements should cover:
Data sources: This could be a consumer app or a piece of machinery on a factory line, but the first stage ensures you’re collecting data from useful sources. Data sources and data logging should type: entry-hyperlink id: QRK0bu26eBmECzu1y8Any for your AI solutions.
Data integration and analytics architectures: How will you ensure your data gets from the source to a point for analysis? The right architecture ensure this stage takes place seamlessly and removes any siloed information
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?
Data visualization: Dashboards and graphic representations can help the company make sense of the raw data at hand in a fast and efficient manner.
Machine learning and data science: How will you implement the power of AI and machine learning to automate systems, process data, recognize unseen patterns and analyze rich data such as images?
Aided decision making systems: Will you utilize this information to help forecast and predict outcomes? How will this aid your overall decisions?
Data monetization: Do you plan to monetize your data and the insights you find?
Big data and data lake strategies: Are you in position to run analysis of unstructured data? If so this can offer unseen insight, particularly for large scale operations such as production lines.
Keeping your data strategy alive
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, a cookie cutter approach won’t work here. Data strategies must be as agile as the solutions they hope to provide.
This means that 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.
Our next post will explore how to ensure your strategy is comprehensive and up to date, how to put the right architecture and governance in place, the importance of explainable and ethical AI and how to find the experts for the job for the tasks ahead.