The word innovation is a broad term and can mean many things. Innovation can be coming up with new products or services. It also can mean challenging the status quo and creating new ideas for products or services. Take, for example, Uber, a company that truly changed the ride-hailing game. They didn’t necessarily invent the ridesharing business model because taxis and cabs had already been around for many years. Instead, Uber came up with a new idea for ridesharing. A different way of providing the same service to customers, and look how successful they’ve become. Let’s try applying the same way of thinking to your organization’s analytics strategy.

Align Strategy with the People Who Actually Decide

A quick search tells you that many experts believe aligning your analytics strategy with your business strategy is the smart thing to do, and we can’t argue with that. In fact, it’s great advice, but what would happen if while aligning your analytics strategy with your goals, you also linked it with how your business actually operates and who makes decisions? Instead of just focusing on the overall goals, you also focus on your people. The people who actually make decisions in your organization.

Recent benchmarks show that only about 37% of organizations report having created a data-driven or data-and-AI-driven organization. Closely aligning your analytics strategy with your operating structure is one step toward creating a data-driven culture that is woven into every fabric from the bottom up. Doing so will empower your staff to maximize data-driven decision making everywhere throughout your organization, not just at the C-Suite level. Culture, people, and organizational structure remain the top barrier for the large majority of companies trying to become data-driven.

Let Data Flow Through the Real Decision Paths

It’s still the responsibility of the CEO or C-Suite executive to keep the strategy razor-focused so that people aren’t creating their own focus or metrics to chase after. The executive still needs to define what success is for the organization and identify the Key Performance Indicators (KPIs) to measure. They then need to communicate those KPIs to the other management or levels in the organization and set up processes on who is monitoring what, and who is going to make adjustments based on their level’s KPIs.

Kevin Larrivee, an Executive Analytics Consultant with BT Partners says “Linking your analytics strategy with your organization chart allows data to be shared throughout the organization so stakeholders can make the data-driven decisions in real-time.” It gives your “boots on the ground” people the ability to improve business on their level immediately. It’s like turning a cruise ship. The captain, or CEO, ultimately turns the wheel, but there are still a lot of things that must happen from the master’s cabin down to the ship’s deck before the captain moves the ship in the direction he or she wants.

Build a Future-Ready Operating Model

As previously discussed, when implementing a data analytics strategy, we recommend an organized approach with defined metrics and a structured rollout. Once that happens, you then need to link it to how the business operates. The reporting structure usually outlines the different KPIs needed at the different levels, which are all focused on decision-making. This makes it easy to lay out the type of decisions your staff can make with the data. Getting the operating model right is increasingly recognized as critical for turning analytics investments into actual business results.

A great example of this was given in our how senior-living organizations can leverage actionable dashboards. Instead of a human coming in for weekly report meetings, it’s a live dashboard they can view at any time. The analytics strategy would help each level, or each department, to create a dashboard that shows the key parts for each level. Like the executive (shows high-level KPIs), the manager (shows information that influences the high-level KPIs), and the customer service representative (shows detailed tactical KPIs they can act on at their level).

Give Everyone a Role and Improve Results

Jacob Specht, VP of Executive Analytics at BT Partners explains, “Everyone has a part to play when it comes to data driven decision making. It’s not just for the top of the company. Everybody can use the data to make more informed decisions.” When people at every level can act on relevant insight, the organization moves faster and more efficiently. When you look at an HVAC client of BT Partners, the CEO needs insight, but when the technician has more insight, they’re able to also make decisions throughout their day that can directly impact sales and profitability.

For example, picture an HVAC technician at a customer’s home for a heating or cooling service call. While they’re at the customer’s home, they could check the records and discover that item XYZ was last serviced five years ago and is due for a replacement. The tech can initiate this new service call, while they’re already at the house, and replace the equipment during this trip if the customer agrees. This saves a second service call to the customer’s house, cuts down on costs, and bumps revenue.

So, you’ve decided to try linking your analytics strategy with how your business operates. Great! Now, you need to figure out what specific data points you need, and what are the drivers to meet these points. Start by looking at your operating structure and deciding on what KPIs are key to each position, and which ones also impact the decision making in other roles. Some KPIs standalone, but we recommend focusing on KPIs that affect other departments and levels in the organization as well. For example, look for KPIs that cause a domino effect. Once you have the KPIs decided on, you need to clarify what is needed to reach these targets and determine how to measure these metrics so people can easily find out if they’re on target or lagging. Doing so will give everyone higher visibility and a more connected and efficient decision-making process. Additionally, when looking in the rearview, you are in a way better position to correct course or realign priorities if needed. It’s a win-win situation.

Don’t forget to consider the flip side as well. Look at the data that’s going into the system. Is it dirty data? That’s a biggie because dirty data can throw all your decisions off, essentially making them useless. So, how do you know if you have dirty data? Try looking at your analytics strategy and the organizational structure to determine if you need cleaner or more data so that people can make better, more informed decisions. Is there duplicate, outdated, missing, or inaccurate data? Are you wasting resources, losing productivity, or lacking communication because of the data you own? These are key signs of dirty data. Implementations can often shine a spotlight on dirty data, so if you have one planned, make sure to use it as an opportunity to try changing a process and capturing different metrics that may possibly improve your staff’s data driven decision making.

We always encourage our clients to embrace innovation. Whether that’s with implementing new analytics technology or expanding on traditional processes and thinking outside the box, like shaping your analytics strategy alongside how the business actually operates. If you’re interested in knowing more about how you can create a successful data and analytics roadmap for your organization that drives a data-driven culture, talk to our experts at BT Partners.