Big data is a miracle worker. Right?! Data offers us so many competitive advantages—time savings, customer insights, increased efficiencies. It helps us better target our customers, personalize our marketing efforts, and create new products our customers really want. So, why is it that of 95 percent of Fortune 1000 entrepreneurs who have undertaken big data projects, less than 50 percent of them have seen an actual benefit? Turns out there are some serious obstacles to reaching data-driven goals.
If you’ve been moving toward data-driven decision-making and data-driven culture within your company, you likely already know this. The sheer magnitude of data created in a given day is enough to overwhelm even the savviest business professional. Indeed, it’s said that the amount of information in computers doubles every two years. That would be fine if the data was properly tagged, organized, accessible, and purged on an ongoing basis. But most of the data is actually unstructured—pictures, information, and “stuff” that is difficult to organize, let alone create value from. That’s why it’s not enough to have data-driven goals. It’s essential to address data challenges while you’re at it.
Data-Driven Goals: Day-to-Day Management
First and foremost, if you’re going to start implementing data-driven decision-making at your company, you need to launch a clear and cohesive program to keep that data in check. As we have learned over and over, more data is not always a good thing. It can overload your system, it goes out of date quickly, and it presents a huge security risk. In other words: the less data you need to keep safe and secure, the safer your company will be. And, the less data you’ll need to update / organize / access / and store on an ongoing basis. Your data management program should involve things like deduplication, compression, tiering, central accessibility, and of course purging like crazy any data that isn’t 100 percent relevant to the task at hand.
One step of data-to-day data management includes hiring the right team members—data analytics, engineers, etc.—who can keep the data organized and useful. Another step, and equally as important, is to ensure that you are using AI to help you manage and make use of the data itself. With so much data in place today, there is no way you can make sense of it without AI. If you aren’t willing to implement AI systems into your data management, you may as well skip big data altogether.
Data-Driven Goals: Real-Time Use
The thing about data is that it is outdated as soon as it is created. That’s why there is so much emphasis now on real-time data processing. It doesn’t matter how much data you pull if you only look at it once a week, month, or quarter. The only way to use data meaningfully today is in real-time. This underscores the point above regarding using AI to process your data quickly. If you don’t know where to start, consider AI journey mapping. It will help you ensure that you have the infrastructure, people, and data in place to start using AI meaningfully to hit your data-driven goals.
Data-Driven Goals: Use Diverse Sources
Is your data lopsided? Unless you are pulling it from a wide range of sources, it probably is. Imagine if all you knew about your customers, for instance, was the happy stuff they posted on social media. Or, if the only thing you knew about your customers was what Alexa happened to overhear and feed to your data system. You’d never get insights into the issues they’re facing and the opportunity areas in their lives where you can proactively reach out to help. (And, you’d never know if the data you were looking at was accurate.) That’s why pulling data from numerous sources will help round out the information you know about your customers. It will help you ensure that your insights aren’t biased, and it will give you a deeper understanding of them, as well.
Data-Driven Goals: Secure That Data Pile
How do you keep your data safe? How do you keep it from being swampy, dirty, and disorganized? How do you know that the data you’re compiling is worth the effort and cost of securing it? Part of making data-backed decisions means making decisions on whether the data you’re pulling is useful, and whether there is an ROI to keep as safe as it needs to be. If your data isn’t safe from corruption, it is worthless. And if it isn’t safe from hackers, it could also cost you your entire brand and reputation. Security will always impact the success of your data-driven goals.
Data-Driven Goals: Culture Matters
At the end of the data, it doesn’t matter how much data you gather if no one—including your executives—are willing to use it to make important company decisions. Culture is essential in moving any project in digital transformation forward. Employees need to believe in the ability of data to provide useful insights, and they need to be willing to take a leap when data directs them into new territory. If your company is not building a data-driven culture in lock step with your data-driven projects, you will never see success in meeting data-driven goals.
Yes, big data can be a game-changer. It can provide mind-blowing insights, save countless hours of human work, shave countless dollars from your bottom line. But legacy-era thinking isn’t just going to move out of the way to allow data-driven work styles to take over. You need to make a conscious effort to welcome data into your company, and that means clearing a path for it to do what it does: transform.
The original version of this article was first published on Forbes.