How to Make Your Analytics Journey More Data-Driven
October 31, 2022
Your future as a successful business depends on being more data-driven. A successful analytics journey transforms an organization from using its data to understand what happened in the past, to using real-time data to help users decide on the best course of action at the moment. To complete this journey, your culture, your vision, and your way of thinking about data will likely need a facelift.
According to PricewaterhouseCoopers (PwC), 86% of C-Suite executives believe culture is critical to their organizations’ success. You’ll have to kick a few old habits, such as relying on your limited supply of data engineers and data scientists for everything users need and finding ways to enable others to self-serve. Bottlenecks lead to missed opportunities to increase revenue, reduce costs, improve customer experience, operate more efficiently, and more.
How real-time data analytics helps create a data-driven culture:
- Self-service gives users insights faster so businesses can realize the value of data faster.
- Enterprise-wide collaborative iteration engages talent at all levels across an organization to improve decision-making.
- Analytics embedded within day-to-day tools and applications deliver data in the right context.
- Inclusion of employees in decision-making helps attract and retain talent.
Your future of data analytics hinges on your data-driven vision (what you hope real-time analytics will deliver). You’ll never be able to collect and analyze all data and you shouldn’t even try. Always start with business goals in mind, then work back from there to determine what data is needed to achieve them.
For example, if your objective is to improve customer experience, you need to zero in on data that will help you build a 360-degree view of your customers so that you know what times are right to engage with them. Then, you can provide meaningful actions and experiences that build their loyalty for the long run. Supply chain resiliency should be your focus. You’ll benefit from data on supply and demand, inventory, transportation, warehouse operations, labor utilization and more.
Data Product Thinking
Many data practitioners make the mistake of focusing first on making data available to the organization, and then figuring out how to make it align with various stakeholders’ needs. This is like having a hammer and looking for nails. The data sets you give users are often not what they were expecting, frustrating your data consumers. Instead, you should operate the other way around: first understand your stakeholders’ needs, then work to identify and deliver data that meets those needs.
This is similar to how software product managers think, applied to the world of data. You can apply “Product” thinking that first understands user needs and then designs functionality to address them throughout your analytics journey. Just like a product that needs new feature development to keep its customers happy, you can identify and address gaps in your user’s data experience. Understanding your users’ needs to be a fundamental real-time data analytics design rule.
Here are a few pointers:
Know Your Users. With product data thinking, users are your customers. You probably already know the needs of your traditional data engineers and data scientists very well. But what about your line-of-business users? These are important stakeholders who deal with solving business challenges daily. Provide them with easy access to data that is relevant to their business specialties. Examples include financial analysis, sales and marketing operations, supply chain and distribution, customer service and customer success, healthcare and spend management, and fraud and risk management.
Know Their Pain. Persistent problems with a product or service can cause inconveniences to customer’s data analytics software, access and user experience and needless friction for usability. Think of data meaning and relevance as product benefits to satisfy user needs. Present your data product on time and in the right context.
Know How They Measure Success. You will need to prioritize data that will help users meet their goals in the same way you prioritize features that customers find most value in. This often depends on how users measure their performance. Are users trying to improve customer satisfaction (CSAT) and Net Promoter Score (NPS) data? How are they measuring operational excellence? What financial metrics are important to them?
Want More Data-Driven Guidance?
Your future in data analytics becomes more promising the more your data journey becomes more data- driven. Download our eBook, How to Maximize Business Value with Real-Time Data Analytics, to learn how your business can start this journey.
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