Analytics and data are changing every facet of our world. In The State of Analytics and BI, we expand on our original research, keeping you ahead of the curve on the world of analytics, data, and business intelligence.
If you’re a larger company or an enterprise, odds are you want to increase analytics usage organization-wide. The COVID-19 pandemic has forced companies of all kinds to shift tactics and in some cases pivot their entire business models. However you’ve been weathering the storm, you’ve got data, you’ve got talented people who can use it, and you’ve got big decisions to make.
This article covers the hurdles present when attempting to implement analytics and BI in a large company as well as lessons larger companies can learn from smaller ones. Explore strategies for spreading analytics through your organization, including gradually rolling out data initiatives across the company, racking up small wins (and failing fast), and tackling smaller-scale, value-demonstrating analytics projects until organizational consensus has been built.
Big companies, big challenges
According to our recent State of BI & Analytics survey of over 500 companies, small organizations lead large ones when it comes to widespread use of analytics. These companies use analytics across every department: Specifically, 68% of small businesses are using analytics in Operations, 56% in Finance, 50% in Sales, and 45% in Product.
Oftentimes, a large company’s biggest challenge to widespread analytics usage is its own size. There’s so much data being collected (though often not shared) and different departments may already even have analytics tools in place. Plus, buying software in a big organization is often still a major undertaking.
According to a Forbes Insights/Cisco survey, 70% of leaders said that strong cooperation between IT teams and business teams was a key requisite for analytics success. Creating that strong collaboration in a big company isn’t easy, but it’s worth it:
“In the enterprise, success comes when you centralize the platform and empower every team to leverage it,” says Charles Holive, Sisense Managing Director of Data Monetization and Strategy Consulting. Keep these factors to keep in mind as we explore how big companies can expand analytics and BI usage in-house and organization-wide.
Start with achievable projects
Do not approach analytics and BI at your large company as if this is a single, straightforward process. Once you figure out how to get your hands on some analytics and BI software or find a friendly team/department that already has it and convince them to let you in on it too, you can start the first part of the process: finding small wins, failing fast, and doubling down on successes.
Experimenting boldly with analytics and collaborating with other creative builders is essential, especially when it comes to creating analytic apps and monetizing your data. You have to be willing to try different ideas, knowing they won’t all succeed.
“You can’t predict which analytics initiative will have the highest return-on-investment,” Charles advises. “Hence you need a centralized platform and organization to facilitate innovation across all groups.”
Armed with an analytics platform and some worthwhile datasets, you’re ready to get to work. A good tactic is to start by identifying a single business question or a few closely-related ones.
Luma Health, a small software company that delivers a patient engagement platform in the healthcare sector, saw customer usage patterns changing rapidly with the onset of COVID-19. By analyzing how doctors were pivoting their messaging to address the latest best-practices, Luma Health was able to rapidly update the messaging offerings within their product to stay relevant during an unprecedented period in healthcare.
The lesson here is to stay focused on how you can deliver value for your users, internal or external: ask simple questions, take copious notes, and look for projects you can pull off quickly and iterate on.
Double down on small wins
Successfully innovating with data and analytics requires a combination of the right software, the right data, and the right builders from within your own team or other departments. This is where smaller businesses often have an edge over larger ones: It’s easier to bring together these elements in a smaller setting where talking to a data expert might mean yelling across the room or walking to a different part of the office, vs a big company where your data team might be on a different floor, in a different building, or even farther away.
Partnering with your data team can help you get the most out of early wins by building versatile data models that can be used for multiple applications. The professional services team at Trax used to spend an incredible amount of time and resources customizing dashboards for each customer, despite the fact that many of their customers had very similar needs. Using a standardized data model allowed the team to tackle their customers’ other high priority needs quickly.
According to Trax’s Director of Sales Engineering, Doron Mutsafi, “70% of the data model can be reused for every customer. This is a big win for taking the pressure off our professional services team.”
Whatever your early experiments are with data, building a data model that can work for multiple teams will lay the foundation for long-term success.
Smaller organizations are often able to roll out analytics more easily because they can more quickly create consensus among key stakeholders and vital change agents. The COVID-19 crisis has caused every business to turn to analytics to chart a course towards success in the “new normal.” According to our State of BI and Analytics survey, small and medium-sized businesses are most focused on using analytics to efficiency, supporting customers, and predicting changes.
Is your team or department focused on these challenges? Whatever your current analytics and BI goals, finding others within your company who want the same answers is a vital part of spreading analytics and BI adoption. The upheaval caused by COVID-19 means that a lot of people who might not have been interested in analytics before may be willing to listen to you now.
However, be prepared for setbacks. Experimenting with analytics is not a linear process. You are going to have situations where a new team picks up the tools and data that have been successful for others in your company and they don’t get good results at all! That’s why starter projects should be small and able to quickly show you if you’re on the right path or need to start over/change something significantly. Fail fast, start over if necessary, and keep building!
Startups move quickly because they can, but also because they have to. Build something fast, ship it, sell it, and hopefully keep the lights on another quarter. This means that when people have new ideas, even weird and outlandish ones, they often get a chance to build them and see what happens. Not so at large companies. Corporate inertia and making “safe bets” can mean that starting up an innovation center, even with all the resources (data, tech, and people) at your disposal, isn’t always easy at a large enterprise.
If you really want to change the way things work at your company using analytics and data, you have an uphill battle ahead of you, but it is winnable! Focusing on small projects and building your network of in-house data-and-analytics evangelists may sound like a slow approach (and on many levels it is). But it’s also the foundation that will allow you to build a world-class analytics program and start experimenting in bold new ways.
Once more teams throughout your enterprise have started using data and analytics in their daily roles, it’s only a matter of time before those same teams (and others) start looking for external use cases, delighting customers, and bringing in new revenue streams. Then you’ll really be ready to build boldly.
Jack Cieslak is a 10-year veteran of the tech world. He’s written for Amazon, CB Insights, and others, on topics ranging from ecommerce and VC investments to crazy product launches and top-secret startup projects.