But is it?
If data is the currency your business is trading in, how is that working out for you? Can anyone in your company do anything meaningful with your data? Can you extract insights that you can act on? Is your data providing value in your daily operations? If not, you’re not alone.
The truth is, the ability to both collect and extract actionable insights has never been greater; yet, most companies are spending far more time and thought only on the first part of the equation. This results in a have/have-not divide: If data is a currency, then it’s a currency right now that can only be spent by a select handful of people.
Given the state of things, I’d say data is more like the new snake oil than crude oil. But we can change that.
Data isn’t the new currency of business. Or crude oil, or specialty pizza. At times it does resemble an all-you-can-eat buffet, as companies gobble up every bit a data they can until they get data-bloated and have to take a nap.
If data is the new anything, it’s the new problem that businesses must deal with. They need to figure out how to effectively manage and leverage data — and which data to focus on.
Imagine walking into the Library of Congress and finding it in complete disarray, like a mini-tornado touched down. You know how much valuable information is there, you know exactly what you are looking for, but you’ll never find it among the chaos. That’s a much different experience than showing up pre-tornado and with a friendly librarian to guide you around.
So, how do transmute data lead to insight gold? Here’s a simple 5 Step Data to Insights Methodology we came up with to help. It’s an easy to follow framework I find useful for moving from a data-capture to an insight-action mindset.
- Understand Data is a Problem
- Before data can be an opportunity, it is a problem to be solved. Death by data-overflow is an issue confronting most organizations. The costs of storing data will continue to rise, threatening to pull from resources devoted to forward-looking projects. Companies continue to spend money storing data they don’t need. Today there is still greater fear of not having enough data than having too much. But think about the level of visibility you have into the data you’re harboring right now. Could you maximize value from it today? Or is most of it tossed blindly into a data dumpster and would require significant costs in time and money to sift through, manage, and secure? Before thinking about the benefits data can provide, figure out the pain points it causes today.
2. Choose Your Outcomes
- Decide what you want to accomplish with your data. Map out your company’s data journey, with data outcomes as the destination. Think about the problems your data currently causes and mark those along the different paths on your map, figuring out workarounds or forcing yourself to come up with solutions. Do you ford the river or caulk the wagon and float it? A plan requires structure, but it also needs flexibility. Know going in that your journey won’t be a straight line. Business priorities shift; whole industries shift. No matter how many times things change, always have an outcome in mind.
3. Onboard Data Strategically
- Now that you have an idea of your desired outcomes, you can be strategic about collecting data. Think about three things (1) what data matters, (2) what data may matter, and (3) what data is useless for solving your specific use-cases. Don’t waste resources and money on data you don’t need. Organize the data you know you need from the start. Prioritize and structure it so you can extract insights right away. For data you may need, find somewhere inexpensive to store it and onboard it methodically from there. For the data you know you likely will never need but just can’t let go of, dump it in the cheapest place you can find. When you do this, you will notice that your highest priority data and least important data generally stand out. Those are the easiest to figure out. The data in the maybe pile takes the most time and effort to sort through and organize. Don’t get discouraged by the process. Be disciplined.
4. Design for Transparency
- Everything is moving to the cloud, and every business is becoming an IT company. With this, the benefits and concerns of the cloud are coming to the boardroom. There is a growing realization that business units can no longer operate in silos. That cross-departmental collaboration is possible. Knowledge, sharing, and insights are the future. But for too long the IT department and technicians have remained cut off from the business side of most organizations, and there hasn’t been an easy bridge to let them communicate on the same level. Designing for transparency is giving everyone the tools they need to explore and discover insights relevant to their job. It’s stakeholders, executives, IT technicians, and security teams being able to sit at the same table and have productive discussions because they all share a common language. It’s the foundation of an insight-driven organization built on data. Understanding and accessing data can no longer remain under one department’s control. The world of data is too big for that and every unit of business needs to be able to acquire and share insights amongst each other.
5. Evolve Data into Insights
- Data on its own is useless. It is our job to make it useful. Machine learning and AI make discovering insights easier. But discovering an insight, recognizing its potential, and transforming that into action requires something special: a mix of human intuition, experience, and creativity. Plenty of platforms make it easy to create flashy visuals out of data, but few allow people to interact with data directly and explore the meaning behind the data. Even fewer tools help a company craft and tell the story written in between the lines of data. This part is not a technical process. It’s giving people the tools, support, and community to understand and engage with data.
Insights are what drive organizations forward. You can hoard the entirety of the world’s data; but until you have a system that creates meaning from it that people can act upon, it is a burden.
Data is both a problem and a solution. The solution only appears when you have the capabilities that can organize data of value and extract insights from it that are directly related to the use cases you aim to solve.