Building an Analytical Organization

Companies that are able to unlock actionable insights from a steady stream of data are more secure and confident when it comes to remaining competitive.

For many organizations, though, building a framework for infusing analytics into every corner of the operation can be an uphill climb. Where do you start? What skills do you need? What are you trying to achieve with an increased reliance on analytics?

Like every new journey, becoming an analytical organization requires a roadmap to follow. This roadmap needs to be about more than technology, it needs to be about culture as well.

In this article, we’ll be breaking down the steps your organization can take to greatly improve not just your analytics capabilities but also the way you organize and manage your data as well. 

It all starts with …

1. Get everyone onboard

Not every member of your organization may understand how—or why—you need to expand your analytics capabilities. For many C-suite executives, for example, the expense of transforming operations may be too big a pill to swallow—at least at first.

As you begin your push toward expanding your analytics capabilities, it’s important to establish business value. You also need to create cross-functional teams to ensure every corner of the organization is aware of what you’re trying to achieve.

Once there is agreement across your organization, the next step is to identify which analytics projects you want to tackle first. That means compiling a list of business problems, working backwards to understand the data that will be required to address those problems, and identifying the resources you will need to expend.

Most importantly, it’s critical that your organization doesn’t try to do too much at once. This is often the main culprit when companies try to expand or improve their capabilities. Your goal should be to start small, then build upon what you’ve learned.

2. Get your data in order

Analytics is only as good as the data it crunches. To get usable data for analytics, you need to ensure it’s properly stored, cleaned, and governed.

Cloud providers have made the first component relatively inexpensive and practical due to their usage-pricing and ability to scale. Cleaning and governance, however, involve the use of automation—routing certain data here, obscuring private information there—in order to make data widely accessible.

Remember, your goal should be to empower departments throughout your organization to use analytics. This “democratization of data” requires:

• Knowing where your data is coming from
• Understanding what data you have
• Identifying any potential privacy or regulatory issues in your data
• Properly storing data with governance in place to ensure only those that need specific data are able to access it

Another area to pay attention to is gaps in your data that need to be filled, either by your own teams or through a third party.

3. Get the process optimized

There are four stages in the analytics process: ingest, prepare, analyze, and act.

Ingesting data involves data creation, transmission, and ingestion. Preparing data is how data is integrated into your operations and staged for discovery. Analysis involves the cleaning and organization of data and running models against data sets. Finally, acting is where findings are fine-tuned and visualized for use. 

In order to keep your analytics initiatives from becoming mired in any of these steps, the entire process needs to be optimized to ensure none of the steps create roadblocks. 

This involves widespread agreement on how data is brought in, made available, analyzed, and reported on. The last thing you want is for some segments of your organization to deviate from how other segments handle analytics. That’s a recipe for chaos.

4. Get to know the common mistakes (and how to avoid them)

We’ve already talked about the most common mistake organizations make in building out their analytics capabilities. Beyond trying to do too much too quickly, there are some other potential pitfalls to avoid.

One is failing to align data science with business needs. Data scientists are a creative bunch but without guidance on what your organization is trying to achieve, you run the risk of models created in isolation never reaching the end zone.

Another is not picking the right problem to kick off with. You want a project that will quickly deliver results—before you start to accrue technical debt. Also, the more you can use existing infrastructure and your traditional BI tools for your first analytics projects, the stronger a foundation you will have to build upon.

In closing

Expanding your organization’s analytics capabilities will allow you to leverage data to make smarter decisions throughout your operations.

It can also position you to adopt more advanced technologies like predictive analytics, artificial intelligence, and machine learning.

Before you invest time and resources, however, you need to ensure everyone is on the same page and all your data is in a place where it can be utilized widely by your teams.

To learn more about analytics, or for expertise in expanding your organization’s capabilities, reach out to us today.