The reality for many organizations, whether a smaller nonprofit, a large international advocacy organization or a government agency, is that their data has been living in separate silos for a while. Tools and channels that house data often do not make it easy to navigate. For many organizations, different departments have different data storage requirements and they end up with their data stuck in different platforms. Does this sound familiar?
Many of the tools organizations work closely with were built by different teams with different business models. Because of this, they aren’t incentivized to talk to each other, which makes it difficult to see the full picture and make decisions quickly. Digging into each analytics platform individually to pull your information to try to tell a cohesive story can be frustrating. Some common challenges amongst organizations include:
- Not knowing where to start.
- Not sure how to compare or connect data sources.
- Not having the time.
- Too many tools to manage.
- Too many manual processes to get what you need.
No matter your biggest challenge, the ticket to solving those issues is figuring out how to break down these silos and make your data analysis easier. While there isn’t a magic tool that does all of the work for you at once, we have some steps to start breaking down your data silos and begin making data-informed decisions.
Step 1: Take inventory of your data
You should begin by taking stock of what data exists across your organization, who owns what, and who has access to what. When we talk about breaking down silos, it’s not just between platforms, but also between departments or teams within your organization. While it’s important to emphasize transparency and understanding data across departments, it is still very important to maintain clear lines of ownership for each data source. There should still only be one team responsible for maintaining or updating any given data source.
The next piece of taking an inventory of your data is to understand its integrity. Start looking at how clean your data is or how trustworthy it is. Are there any major gaps where data was entered into a system incorrectly? If so, it’s best to figure that out now and correct it before getting too far into the weeds of building any sophisticated data warehousing or reporting systems.
Step 2: Draft questions
This next step doesn’t necessarily have to happen linearly, you can start this while you are taking inventory of your data. You want to begin by drafting a list of questions that folks at different levels across your organization might like to ask of your data, or maybe have already started asking. Think of this as developing your “backlog” or “wishlist” that will help you keep your eyes on the prize as you start to build a system that can more easily pull the data you need out of the tools you have and store it in a usable format.
You will want to start writing your questions as early as possible to make sure that you have the data you need to answer them while you are taking an inventory of all the sources you can tap into. Here are a few sample questions you might consider adding to your list:
- How fast are we growing?
- What are our largest sources of growth?
- What services do our audiences engage with most?
- What does X process cost us every month?
- Does our website traffic spike when X event happens?
If there is a certain theme or central topic that a lot of questions continue to bubble up around, like costs of operational tasks or ROI, but you don’t have access to any financial data, that’s going to present a problem. At this point, you’ll either need to find out who does have access to that financial data and the appropriate steps to gain access, or if there is no collection point for financial data at the moment in your organization it might be time to start brainstorming on how to build one in.
Step 3: Map your stack
This is the part where we get to the real nuts and bolts of how to get all the data you need working together for you when you need it and in the format you need it in.
When we talk about mapping out your data management “stack,” we mean outlining the tools you will use to control the flow of raw data from all of those disparate silos to organized reports that will help you be more data-informed and all the steps that are required in between.
To know what goes into your data management stack of tools, the full data management approach looks like this:
- Standardize and consolidate data collection
- Develop a sustainable, scalable, and secure data storage solution
- Enable self-service reporting and analysis
Standardize and consolidate data collection
When you look at all of your different tools and platforms, many of them do not spit out data in similar formats, so rarely are you even looking at similar metrics. Start with standardizing and collecting all of the raw data that you need to answer the questions that you hope to ask of your data. Using those guiding questions you are hoping to answer, you’ll first work to first pull the relevant data points out of the data sources you have, that’s what’s referred to as extracting.
More often than not, you will need to do a little cleanup, which could include shifting the format of a date, deleting some weird characters, or performing some mathematical functions, which is referred to as transformations, before you load it into an organized data warehouse. All together this process is called Extract, Transform, Load (ETL).
Develop a sustainable, scalable, and secure data storage solution
This is where your data warehouse comes into play. You want to make sure you’re building a sustainable and scalable storage solution for your data, not just creating something that you’ll have to reorganize and sort through in a few years.
During the ETL process, it’s important to establish a logical data model that will make it easy for you to pull the data you need. Think of it like building a Costco, you want to make sure you organize the aisles in a way that makes sense for the people who are going to be shopping there.
Enable self-service reporting and analysis
Your next step is making your data accessible for the folks at your organization looking to make data-informed decisions. The key piece to unlocking that well-organized data and making it accessible is plugging your data warehouse into a business intelligence or data visualization platform like Tableau or Microsoft Power BI.
Once you have made that connection, you’ll be able to build out reports and dashboards to help you and your organization answer the questions you started drafting as you were bringing all of the data sources together at the beginning of the process.
Step 4: Take an iterative approach to analyzing and asking questions of your data
Consistent iteration is key to success in the process of breaking down your data silos. It is important to continually review your questions and prioritize what you’re trying to answer or looking to research so you can allocate both human and technical resources accordingly. You will likely need to do some additional data collection and manipulation to get to a point where you can analyze and distill the answers and insights you’re looking for. These insights should help your team evaluate and prioritize the next question to tackle.
While it may start as a point of frustration, breaking down data silos is a critical step in elevating your organization’s overall data maturity. The more you can increase awareness of all the data you have, bring it together, monitor it on a regular basis, and use it drive key decisions, the closer you will be to embedding data-informed decision-making in the culture of your organization.
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