So, you’re wise enough to appreciate the power of data, and as you generate more of it from a growing range of sources, you’ve had the foresight to gather it in one place, using the likes of data warehouses and data lakes. That’s one hurdle jumped. Before you can make sense of it all — before analyzing, visualizing, and interpreting it — you’ll need to weave all your data together. But why is it so important, and how can you achieve it successfully?
Why you’ve got to get your data together
Data is only good when it’s organized and used properly. Otherwise, it’s just a morass of tangled information that slows or even disables the process of extracting great insights. Weaving it together is tricky because different datasets can hold conflicting information in incompatible formats, unnecessary duplication or discrepancies that could compromise the quality of your data.
As our head of BI says, “Anyone can make something difficult. The real challenge is making something simple.” And it’s essential that you base your decisions on reliable and robust datasets. Without properly uniting your data sources, your analysis and conclusions will rest on shaky foundations.
“Anyone can make something difficult. The real challenge is making something simple”
Build a master data strategy
Every organization has tons of data, but most fail to put a master data strategy in place. This strategy is key to giving your organization the flexibility to draw insights from a wide array of data sources.
Even companies using repositories like Snowflake don’t always have a master data strategy. Creating this plan might require changing existing processes or introducing new ones. You might meet resistance from a lot of stakeholders, but a data model on its own doesn't provide your business with any value. What’s critical is being able to maintain consistency of your data flow and to scale up as it grows. The solution is to use a BI and analytics platform that handles any data from any source, with the capability to connect and merge it all.
Know your ingredients: Why good data is like great pasta sauce?
Overcoming stakeholder resistance could require a leap of imagination. So, explain it like this: data is like pasta sauce. To make the best, you can’t just mix a bunch of random ingredients. You should identify the best ingredients and bring them together to create a balanced flavor. Take care about where they come from. Are you selecting the best San Marzano tomatoes for your base? Is your basil garden-fresh or from the supermarket? Selecting your ingredients influences your results, and when you care about the ingredients, and truly understand how your end product is made, then you’ll create the tastiest sauce, and you can be confident you’ve got the most from your ingredients.
It’s the same with data. Your users and stakeholders need to appreciate that a strong data model is the foundation of great BI. If it were explained more simply and more practically, like this, you would have many more business stakeholders and leaders of companies who would buy into the process.
“Identifying, appreciating, and harmonizing data will result in the best insights for your business.”
When we've been fortunate enough to explain this in layman's terms to different leaders, it’s noticeable how enthusiastic they get. Suddenly, they want to roll up their sleeves and be a part of this process, and that's how adoption occurs. Once you’ve got buy-in from senior executives, then the process is simpler, because adoption is driven from the top down, and it flows throughout the business much more easily.
It’s not the data that’s most important, it’s what people do with it
It’s easy to get bogged down in talking about datasets and models and quality, and forget that the most important thing about data isn’t the data itself, but what people do with it, what conclusions they draw, what insights they gain and what action they take for their business. Even in the most data-driven companies, people, and their decisions, remain the most significant drivers of growth.
Yes, reporting is all about data science, but it’s also about how to use data to affect business decisions. Reporting on its own doesn’t really provide a lot of business value. Neither do visualizations. It’s people who interpret visualizations and make decisions.
“Even in the most data driven companies, people, and their decisions, remain the most significant drivers of growth.”
Accordingly, over the last five years we’ve seen a shift into focusing on adoption instead of simply talking about clever new features and charts. We’ve moved towards actionable, embedded BI and analytics, and making it as intuitive and seamless as possible for the end user, to optimize usage and maximize the benefits that business can gain.
Empowering people with sound data models
Applying robust and comprehensive data models that are built soundly and to scale is vital to empowering people to adopt analytics and ultimately make better business decisions. The best analytics and BI partner will focus on users, not just the data science.
Think of it like this. Sisense is a sportscar. Impressive, yes, but it does no good sitting in my garage if I’ve not been taught — empowered — how to drive it. If nobody’s helped you learn to know when to shift gears to maximize acceleration, then all of this wonderful technology goes to waste.
Let the users decide
That’s why what’s vital is empowering users — executives and departmental heads — outside of IT and data analytics.
The critical driver of adoption must be the ability to convince people that data analytics is for everybody, that it specifically meets your requirements, so you can be sure you’ll get value from it and together with your provider, you can build a data model that scales with you. And a significant part of this is ensuring that all your data sources can be seamlessly weaved together.
And that's where it does take some expertise and some skill: to bring all these things together, make them sing and work and derive value from them. It’s a big challenge but a really fun, collaborative and rewarding process.