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7 Steps To Creating A Successful Analytics Strategy

December 29, 2023

Mile Zivkovic

Want to become more data-driven but don’t know where to start? Here are seven practical steps to create a data analytics strategy.

Data is the currency of the future, no matter what industry you’re in. Businesses that rely on data have better decision-making, more powerful initiatives and they hit their goals more easily. But if you’ve never collected, analyzed or interpreted data, getting started can feel like a big step.

You don’t have to do it all at once. Instead of jumping straight into a data strategy, create a plan first. Here are the exact steps you should take to create a data analytics strategy for your team or business.

Defining data analytics objectives

The first step of a successful data analytics operation is determining your goals. If you just collect data for the sake of it, you won’t be able to use it to make better business decisions. Also, you won’t have a tangible goal you can use to measure your success.

First, define what your overarching goal with data is. For example:

  • Reporting on internal KPIs and metrics
  • Reporting on a specific project
  • Building out a reporting engine for a software product
  • Tracking the return on investment on a long-term project
  • Using customer data points to refine your product

With the most important step of the way, the second issue arises: who is your data analytics strategy for? Depending on the goal you choose, your strategy will be geared towards different audiences.

For example, reporting on internal KPIs will have managers and executive-level leaders as your target audience. On the other hand, if you’re creating a reporting engine, the audience can be very complex.

If your product serves multiple audiences (such as C-level functions, as well as individual contributors), you’re going to need a more layered approach to reporting.

Once you know that, you’ll determine what type of information you need to make better decisions and speed up progress. 

Setting up a team

Besides analytics tools, you’re probably going to need a data expert or two on your team to launch your analytics strategy. Depending on what you’re trying to accomplish and how in-depth you want to analyze data, you’re going to need one or several people on board.

For typical analytics projects, you’ll likely involve roles such as:

  • Data analyst
  • Data scientist
  • Business intelligence (BI) analyst
  • Data engineer
  • Database administrator (DBA)
  • Data quality analyst
  • Data governance analyst
  • Data architect
  • Chief data officer

Although these roles are essential for technical data projects, more and more business people get involved in analytics strategy today as ‘citizen data scientists’. Equipped with more intuitive BI solutions and artificial intelligence, you no longer need deep knowledge of data science to be able to make sense of data.

data analytics team roles
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In corporate teams, for example, leadership or business roles like sales or marketing are involved in the analytics strategy much earlier on. The reason is that they know best which insights are needed to make better decisions. 

On the other hand, in software teams, it’s the product manager and customer success experts who often lead the project to build out reporting features for their clients, because they know which data their customers need.

Depending on the tech stack you use (more on that in a second), and the type of data you need, you can determine the need for one or more of these roles.

You can then determine whether meeting your business objectives around data can be done with your in-house team or if you should hire more people. Alternatively, you can outsource your work to an external partner to meet your data needs.

Creating a budget, planning and timeline

Create a roadmap of when you want to complete your successful analytics strategy, as well as which resources you’ll need for the job. This involves:

  • The budget (how much money you want to spend and what your expected ROI on these activities is)
  • The timeline (when you want to collect, analyze, visualize, and present data)
  • The final presentation for the finished dashboard

Creating a budget and a plan allows you to spot issues before they arise. In case you need additional budget or manpower, having a plan in place makes for a convincing argument in your favor.

Choosing the tech stack you want to work with

The tools you choose can make your analytics strategy a breeze or turn your life into a living hell. If you have an analytics team, you should be able to determine the tools you need quite easily. 

Depending on your goals, you’ll have different choices, but here is what you should consider for a typical data analytics setup for existing and new data.

A data collection tool or setup. For most businesses, the data that powers their business intelligence comes from various sources. You want a tool that connects to a variety of sources or has an API that is versatile and allows you to easily plug data.

Choose your data collection tool based on the data sources you need for your dashboards.

Data modeling or ETL tool. Once collected, your data is still a tangled mess that is of no use for data discovery, exploration and analysis. Data modeling and ETL tools take your data, transform it, clean it and prepare it for storage, exploration and analysis.

Data warehouse or data lake tool. Your analytics initiatives are bound to produce a lot of data. Over time, you’re going to have to find ways to store it somewhere that is safe and fast. A data warehouse or a data lake allows you to store large volumes of data. More importantly, they have connections with data exploration, and visualization tools, allowing you rapid access to that data.

Data exploration tools. These are key tools that allow you to filter, sort and search through your data. This is where you would use data sampling and descriptive analytics to make sense of the clean, structured data you got from the previous steps.

For example, if you have a sales tool, this is where you would combine and analyze data from different channels. You’d then come up with a definitive answer on what is driving sales, from which channel, and how much individual products contribute to overall sales.

Traditionally, some data exploration tools come as default choices, such as Tableau or PowerBI. However, as the barrier to entry gets lower for businesses, many of them consider these tools to be overly complicated and difficult to use. 

Business intelligence/data visualization tool. Before data analysis, you need to visualize all your data in a way that is easy to understand. Think charts and graphs instead of long strings of numbers.

spaceflow analytics dashboard
An example of a visualization dashboard in Luzmo, for our customer Spaceflow

The most important consideration at this step is choosing a tool that is easy to use. Think something that allows you to go from tables to visualizations in just a few steps. Moreover, the visualization tool should have just that: the right number and kind of visualizations.

Choosing your Key Performance Indicators

A good analytics strategy relies on metrics and KPIs. Whether it’s business leaders, managers or your customers, everyone needs some form of metrics to determine if they’re making progress or moving backwards.

For example, let’s say you have a SaaS product for call center management. With your product, your customers call people or get calls in their customer service departments.

To prove the business value of your product, you can add an embedded analytics dashboard to it. The call center reps using your product get data access to metrics such as:

  • First call resolution
  • The average speed of answer
  • Customer satisfaction score
  • Cost per call
  • Average handle time
  • First response time
  • And others

Depending on the business needs, you’re going to choose the metrics that are the most relevant to your goals. The customer choosing these metrics prioritizes handling customer problems quickly and achieving great customer satisfaction scores.

If you’re creating an in-house analytics strategy, make sure to consult with key decision-makers on the metrics you choose and track. On the other hand, if you’re creating dashboards for your customers (and their end-customers), give them the freedom to choose their own metrics.

You can provide some help in the form of dashboard templates with relevant metrics. Use customer feedback from support tickets, customer interviews or feature suggestions to build these dashboards, and guide your customers to finding the right insights. But in the end, every business strategy is unique and they should be able to choose their own performance metrics.

Ultimately, you could even create an embedded dashboard editor and allow the end customer to build their own analytics strategy setup in your product. 

Building an MVP

The minimum viable product for your analytics strategy will depend on your team, business goals and the processes used. However, most businesses choose dashboards of some type because they allow them to easily view large volumes of data in one place.

a minimum viable product in dashboards
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For example, an MVP for SaaS products is going to be an embedded dashboard. As a part of your product, it can show customers what your product does and help them make data-driven decisions.

If we take a look at our previous example of call center software, the dashboard will have effective data on the calls you receive and make. For example, you’ll be able to spot the best sales rep and customer support managers in your entire organization.

The important thing about an MVP is that it’s completed and that you have a rough first version. It may not look pretty, but it has the right data that you can use to solve business problems.

Testing and iterating

Once your target audience sees the MVP, they can give you feedback. You’ll want to learn:

  • Whether they understand the data
  • Whether all the relevant data is presented
  • Whether the dashboards need additional explanations and tool tips
  • Whether the dashboard tells a clear story or has inconsistencies
  • Whether all the relevant data is included for business users reading the dashboard

In most cases, you’ll go back to the drawing board after the initial round of feedback.

Once your target audience drills down into data, they can find underlying patterns and draw conclusions. Also, they can tell you whether the dashboards meet their expectations in terms of functionality and detail.

You can then refine your existing dashboards and create new ones for different use cases and stakeholders. The important thing is that once your data is connected and you’re using a good tool for data visualization, you can make adjustments in real-time. No dashboard design is set in stone.

In fact, you can even create multiple dashboards with the same data, but at different levels of detail, for different stakeholders or business units.

Wrapping up

A successful analytics strategy requires thoughtful planning and considerable investments in time and money. But once you start adding up your organization’s data to one place, you’ll see the return on investment for your business and your customers.

On both accounts, it helps you create a data-driven culture where decisions are based on evidence and not assumptions. And thanks to new technologies such as artificial intelligence and tools like Luzmo, creating an analytics strategy is easier than ever before.

Book a demo with us today and we’ll show you how!

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