Product Analytics
What is Data Consolidation and Why is it a Good Investment?
Last updateD on
October 4, 2022
Data is the lifeblood that runs through your organization. It powers automated workflows, gives customer service reps the full story every time the phone rings, drives every upgrade planned for a product, informs decision-making leaders on what to focus next, and an endless list of etceteras.
Wouldn’t it be amazing to have all your data in one place? Yes.
Can you? Well…. It’s complicated.
The idea of data fragmentation is not new. But basically, the excessive proliferation of data collected, analyzed, and stored by an organization has the potential to reach such an amount that data ends up sitting idle, unproductive, unknown, or worse: unprotected.
In a practical sense, having too much data will not only prove unmanageable but also end up being an unnecessary burden, driving storage costs up, decreasing teams’ productivity, and slowing your overall ability to make much-needed data-driven decisions.
As product analytics are fed by data, they are just as affected by mass data fragmentation. Similar to the exponential growth in data, there are dozens upon dozens of tools, platforms, and solutions that can be used when it comes to knowing a customer, understanding their behavior, diagnosing application usability — or lack thereof -, and report it all in a fast, efficient, and comprehensive manner.
If you go back and forth between several of these tools when doing your job, you might find some of the below problems and notice that they’re leading to your organization unnecessarily burning cash.
Data is stored. However, not all data is useful at all times. In fact, almost as much as 80% of data is not actually critical for operations. So what happens with it? Well, as you probably guessed by now, nothing. Many times data ends up being stored endlessly in different files, in different geographic locations or cloud servers, and processed by different data centers. Needless to say, your organization is most likely paying for each of those services.
So, aside from the obvious split of bills, having scattered information hinders your ability to collect, centralize, and analyze it properly. Unifying the place and the way that you store data helps you make a better choice of the vendors you will use to analyze it and thus, the type of insights you will get from it. If your data is all over the place, so will be your data insights, and, in the long run, so will be your product and your business.
Plus, the tighter control you have on your data, the more you can ensure data privacy will not be something to worry about.
According to Hubspot, organizations may have data duplication rates ranging from 10% to 30%. For the record, this duplication came to be because there’s a lack of control and quality processes for that data.
Similarly, when you use several analytics solutions that either analyze similar data sets or have similar output insights, you will have duplication. Not only will this take more storage space (which, as said above, you’re probably paying for anyway) but you may also end up paying two or more different vendors for the same data.
You might be thinking “I’ll never do that” but what happens when you are paying for a particular tool and then your co-worker is paying for another one with almost identical outcomes?
Unsurprisingly, when data is duplicated and siloed away, it prevents team collaboration from blooming. Without seeing similar information, teams can’t really align behind consensual goals. If the whole point of product analytics is to offer useful insights, wouldn’t you want everyone to benefit from them?
When data insights are consolidated into a single space, every team can join the conversation. That way, you can make sure that new, innovative ideas come up. For example, if the development team runs an analysis of an app crashing for some users, the marketing team might want to come up with communications to mitigate the impact with notifications, while the sales team can refocus the selling points away from the issue.
Collaboration is also way easier when all the insights your data gives you are in one place. Getting a centralized dashboard to look at in order to find direction not only gives a quick snapshot of how things are going but, more importantly, puts work in context for everyone.
Following the example above, if the marketing team doesn’t understand why their push notification campaigns failed to reach a determined target amongst certain users, they can use only a couple of clicks to find out that the app crashed for some of the targeted users — without even having to ask the developers. It also makes the effects of the crash visible to the developers, helping them understand more of the customer journey.
Plus, having a one-stop location for analytics simplifies costs, because there’s only one analytics bill, less time onboarding new hires, and more productivity (from the time team members might involuntarily waste moving through different tools, arguing over which data sets are correct, and trying to make time to learn entirely new data sets that they don’t normally use).
And so, everything we listed above will come down to data-driven decisions being made without the right data: resources will be allocated incorrectly, budgets will be inaccurate, reports will be unrealistic, and work hours will be wasted.
Merging a complete overview of the customer journey, its visibility by all teams, and the ability to quickly report on different teams’ KPIs, all from one place, will finally make the team leaders’ decisions truly data-powered.
Every bite of data processed by a product analytics platform must be useful for all teams, relevant to your KPIs, and cost-efficient. Determine what data is important to you and how to track it in no time over a call with us or look at how you could get started today with product analytics that enables innovation for product, engineering, data, and marketing teams.
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