The business intelligence lifecycle involves three major components:
You can think of these steps like a necessity and sufficiency clause, seen in logic and mathematics: having a robust data pipeline and a well-organized data warehouse is necessary to effectively use data to make business decisions, but it is not sufficient. To round out the business intelligence lifecycle, you need to invest in data science and analytics.
Here, we’ll cover why data analytics is important for businesses, what question data analysis can help you answer, different types of data analysis, strategies for effective data analysis and more.
The importance of investing in data science and analytics can’t be overstated. Having a data analysis framework that is aligned with a company’s goals and KPIs is invaluable.
Once you’ve put together a data pipeline and decided on a data warehousing solution, you’re ready to move forward with the analytics component of the process. Data pipelines, data infrastructure, warehousing, schemas and organization are the foundational pieces. After that, you can focus on data analytics, which can be divided into two major steps:
Both of these components are important steps of the data analytics roadmap. And ut can be helpful to think of the relationship as a sort of “walk before you run” type of process.
As a business, you need to take stock of what is important to keep an eye on - do you know what success looks like? What are your business’s most important performance indicators? Once you have done that, you can invest more time and dig deeper into your data and build upon your data foundation. No matter where you are as a company in your data analytics journey, you need a bottom-up approach. Think of it like a pyramid, with your data pipeline and data warehouse making up the base. Then you have your basic data analysis, dashboard, and visualization work in the middle and your more advanced analysis at the top of the pyramid.
Data analytics can help support many different teams and business functions, from marketing and sales to finance, product and even business operations.
For example, your marketing team might be interested in visualizing the marketing and sales funnel. Is there a significant drop off at a specific stage of the funnel? How is the marketing team doing driving traffic to your business? Are many customers converting, and what helps them convert? If you are able to pinpoint where in the process your business is succeeding and struggling, you’ll be able to solve problems, identify opportunities and strengthen your funnel.
Similarly, data analytics can be incredibly valuable in trying to quantify your company’s marketing or sales performance - tracking traffic sources, calculating conversion rates, attributing sales to the correct sources, and better understanding your customers are all components of marketing and sales analytics.
Business teams can also utilize data analytics for support with decision making. Maybe your marketing and sales teams are killing it - bringing in tons of new leads and customers with a fantastic conversion rate - but converting customers is only part of the customer lifecycle. Are you converting the right customers, and do you know how much they’re worth to you over time?
Two major key performance indicators during a customer lifecycle include churn and lifetime value. Converting and acquiring new customers adds to your growth rate - but if you are a subscription-based (or similarly-structured) company, the rate at which your subscribers are cancelling (or churn) can have a big impact on your ultimate bottom line. You want to make sure you have a high growth rate and low churn rate.
Related to growth and churn is your customers’ lifetime value (LTV). Customer lifetime value is an incredibly important metric for a number of reasons. While it can be somewhat complex to calculate (correctly and with relative accuracy), once you are able to calculate your customers’ lifetime value effectively, you’re able to make much more informed decisions on how to identify and target the right potential customers (with the highest potential LTV), make sure that your customer acquisition costs do not exceed your customer lifetime value, and project out potential revenue based on your growth rate and your customer LTV.
Lastly, your organization’s product and inventory teams should also rely heavily on data science and analytics. Understanding which products are most popular, which ones help you attract and retain the right customers, which are most (and least) profitable, and what you need to buy more of or discount to move more quickly are critical to ensuring a successful strategy across your entire business.
Saying you’re investing in “data science and analytics” can mean different things to different people. In addition, “data science” and “data analytics” are two terms that are often muddled together and there’s a significant debate on what type of work is “data science” and what type of work is “data analytics.”
In general, data science tends to be more technical than data analytics - a higher level of statistical and programming work typically goes into data science. For example, machine learning, regression analysis, and various statistical modeling work tends to fall into the data science category rather than the data analytics category.
Another important distinction with data analytics is the types of analysis involved in a specific data analytics project. In general, there are four major categories:
Becoming a data-driven organization takes time. While there are a few different ways to approach data analytics, depending on your business and your goals, there are some useful guiding principles that all organizations need to follow.
First, it is important to take a “walk before you run” approach to data science and analytics. Some businesses err by diving too fast into complex data analysis, predictive modeling and trying to connect and parse dozens of data sources at once, rather than prioritizing what to start with. Ultimately, this strategy is likely to fail. As we mentioned before, you need to make sure you have a robust data pipeline in place as well as an organized data warehouse, first - one-off data analysis projects can often take up more time in the end. A data pipeline and data warehouse allows for more reproducible, scalable analytics processes.
With those components squared away, you need to put together a roadmap and start to prioritize what data analytics processes are important to you. Think through the data you have available to you, how you can transform it, what questions you want to answer, and which KPIs you need to focus on. What your specific company goals are, as well as what type of business you run, will determine what analytics and metrics you need to investigate.
For example, if you are an ecommerce company, your top-line metric is obviously revenue - but that can be broken into smaller segments and analyzed in different ways that can help you determine what’s working and what’s not for your business. On one end, how many customers do you have? What’s your conversion rate? How many impressions are you getting? On the other end, what’s your customer lifetime value? Average order value? Do your customers have significant purchasing frequency? How much does it cost you to acquire a customer?
These metrics differ compared to what metrics a software-as-a-service (SaaS) company might be interested in. Since you’re a subscription business, you may be more interested in monthly recurring revenue (MRR). Broken down further, what about the average revenue per account? Or your lead velocity? Other important metrics include your churn rate as well as customer acquisition cost payback period, i.e., how long it takes to “pay back” the cost of acquiring a new customer.
Other effective data analytics techniques include cohort analysis or customer segmentation, which allows you to segment your customers and glean insights on a more granular level. Beyond the metrics, you can also move more into “data science” territory, where you can spend time putting together statistical modeling, predictive metrics, regression analysis, machine learning work, or A/B testing.
On one end of the spectrum, you have out-of-the-box dashboarding tools. In the middle, you have business intelligence tools with some drag-and-drop functionality and some analyst-extension capabilities. And on the other end of the spectrum, you have tools with far more functionality but that require quite a bit more technical skill to operate.
Determining the correct business intelligence tool for your organization depends on the questions you’re trying to answer, what data science and analytics methodologies you are looking to deploy, and whether you have (or want to build out) a data science and analytics team. Turning your organization into a data-driven company can happen in a number of ways, and deciding on the correct business intelligence solution is an important step in that process.
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Effectively utilizing data analytics to help derive insights and make data-driven decisions takes a big commitment and significant buy-in from the entire company. Being a forward-thinking, data-driven organization means you need to invest in a good data foundation - one that starts with a robust data pipeline and an organized data warehouse. Once those components are in place, you need to focus on your guiding data analytics principles:
Data science and analytics is a critical element of any forward-thinking organization, and while there’s no “one size fits all” solution, there are a few guiding principles you should follow:
Plus, further reading about other elements of the business intelligence process:
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