Whether you own a business or work for one where your decisions impact the bottom line, you likely already know that you need to use data and analytics to make better business decisions. However, knowing that you need to make data-driven decisions and building the systems that will get you there is not the same thing. Implementing any kind of data analytics at your business may seem like a daunting task — and while it can be challenging, it’s doable!
Three important ways you can narrow your focus and build your company’s first analytics are: Have a clear vision, make a plan (start small!), and implement that plan one step at a time. Breaking up the process into manageable chunks can help prevent analysis paralysis and take you from zero to insights faster. Remember: The best analytics initiative is one that exists in the real world — not on a sheet of paper!
Let’s start by creating a vision for how you want to use data to improve decisions!
Define a clear vision for the outcomes you want to achieve
If you don’t have analytics in place at your business, your head is probably swimming with all the possibilities for your deployment and all the decisions you want to make using data. Maybe you have complex dashboards in mind or are grappling with the immense scale of your datasets — wherever you’re at in your analytics journey, take a deep breath, then take a step back.
Whether you’re starting from scratch, retooling existing analytics, or seeking to add more use cases, think small. Start with one decision or process where you want to implement insights from data, then work backward to define your vision. Ask a sequence of questions like these:
- What decision are you trying to improve with insights from your data?
- What KPI (key performance indicator) will help you make a better decision?
- What metrics roll up to that KPI?
- Where does the data behind those metrics live?
Now that you’ve drawn a line from the business decision back to your data, it’s time to align on business requirements. If you’re the decision maker for the specific process you’re trying to improve, you likely already know your business needs and where you can insert insights into your process. If not, you’ll need to sit down with the business stakeholders in question and do some user interviews.
Once you see how insights will influence a decision and understand how best to surface those insights (a dashboard, a widget, a single indicator, etc.), you’re ready to start building.
Make a plan that considers people, processes, technology
When we talk about making a plan for analytics success, we talk about people, processes, and technology. Understanding how these three elements work together is key to making a successful plan.
People are the most important resource that a business has (in our view, data is a close second). Without the right people in the right places, nothing gets done. People at different levels of the organization will have different roles to fulfill to implement analytics successfully: Executives are in charge of creating a clear analytics vision, business stakeholders are daily analytics users and provide input when building them, and the tech team does the heavy lifting to put together the analytics that eventually lands in front of end-users.
We spoke before about understanding the business processes where you’re seeking to implement analytics. By the planning phase, you should have already connected those outcomes with business activities. You should also have a handle on how data is going to flow through individual systems into whatever aggregated middle stage makes sense for your needs (a database, data warehouse, data lake, etc.).
From there, the data is transformed and presented in a manner that can be easily analyzed. Ultimately, our goal is to create a deliverable that helps a business user make a decision. This could be a data visualization, a mini dashboard, or even a single-stat indicator, like a red or green signal that tells the business user when a KPI is within a certain range, and a specific action is required.
With your processes laid out, it’s time to pick the right analytics software. This is a crucial decision, as these tools can act as a bridge between datasets, store and transform data, and create visualizations. Choose software that automates processes and minimizes the drain on your human capital. Time really is money when it comes to developing data analytics. Whatever technology you use, cost, performance, reliability, and scalability should all be key considerations.
No matter the behavior you’re trying to drive with analytics, the deliverable used to drive that behavior needs to be simple and instructive, helping the user make smarter decisions quickly and easily. The days of huge, unwieldy dashboards are over — more is most definitely not more. Having too many KPIs and widgets, and visualization on one dashboard just overwhelms users with more noise and decreases the chances of adoption.
Speaking of which, be generous with your adoption timeline: There’s going to be a lag between when people get the analytics and when those analytics start driving meaningful change at your organization. That’s another reason why starting small is so important — you can more quickly build and roll out a small, single-KPI solution, then start seeing if it drives impact. Building out a million widgets or one big complex dashboard and giving them to the whole company makes it harder to figure out who’s using analytics, which decisions are being influenced, and what (if anything) is different as a result!
This all leads us to implementation!
Execute the plan
By this point, you understand the business process you wish to influence with data and the people who will rely on those analytics. You’ve got your data sources mapped out and a plan using different technologies to create analytics that will give your end users the insights they need to make better decisions.
Now it’s time to execute the plan.
Start by picking a workflow style (agile or waterfall are the most popular), then build in regular reviews to ensure that tasks are being completed according to the project timeline and in line with technical and business requirements. (Basically, are you building what you said you’d build, and is it going to work how you said it’d work?) Also, make sure your analytics build is properly staffed — once you’ve got your end-users primed to get their hands on analytics in their workflows, waiting too long will all but ensure your deployment lands with a thud.
Once it’s time to roll out those precious analytics, you’ll need yet another timeline. You can’t expect every user to embrace analytics to the same degree at the same pace. Training and check-ins will be vital during the early phases of adoption. User acceptance testing pre-rollout can only tell you so much. Expect widespread usage to surface changes you’ll need to make to ensure end-users will actually use your analytics.
This is yet another reason why starting small is so crucial for any analytics program: Once you start putting analytics in your users’ hands, you can also start seeing who is using them and the positive outcomes from their usage. That’s all fuel for the next round of development as you start rolling out analytics to other parts of the organization. If you’ve done things well during your first deployment, word of mouth can be your best friend, building buzz across the company.
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Be bold with your analytics efforts
If you’re trying to drive growth at your company in the modern business environment, then data and analytics will be part of your journey. Now’s definitely the time to get started — and starting small is your best bet. Create a clear vision for how you’re going to use analytics to improve your business, put together a thoughtful plan, and execute it a little at a time. Smaller, successive rollouts with lots of hand-holding will give you the best chance at driving adoption and seeing results, which paves the way for future analytics success.
By: John Loury, President, CAUSE+EFFECT Strategy
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