There’s also the issue of a single source of truth. Essentially, with data spread out over different systems and often duplicated, you don’t know which numbers are accurate. This means that instead of making data-driven decisions, you are involved in guesswork. The problem is that you can be wrong and erroneous numbers can cause more problems than just going with assumptions based on your gut.
That’s without taking into account human error and missing data, which can skew the results. So, not only will your organization have to deal with inefficiency, but even potentially erroneous decisions.
Silos also cause trust issues and will negatively affect collaboration between departments and teams. This is definitely the last thing you need, because it will hurt employee engagement, while also affecting productivity and more.
Costs will also add up because of redundancies in your IT infrastructure and applications. Plus, maintenance will be a nightmare, lowering the efficiency of your IT staff.
All this, in the end, will translate into operational inefficiencies, reduced employee engagement, and a poor customer experience, which will lead to high employee and customer churn and significant lost revenues.
If a customer’s data is, for example, spread across two or three different silos, it’s virtually impossible to put together an effective customer profile. Without that profile, you can’t personalize the experience. You can’t even really streamline the experience across multiple channels, which is essential nowadays.
So, if you really want a chance to compete, then you will have to eliminate technology silos.
How to Eliminate Silos
Eliminating silos has to start with a culture change. Management needs to encourage the sharing of information across departments. Everyone needs to know that only by working together will the organization succeed. If departments and teams are competing against each other, it will only hurt the organization, which will then hurt the individuals.
Next, you need to deal with the actual technology issue. The most effective approach would be to overhaul the entire system. However, it poses a number of challenges and is a bit unrealistic.
Firstly, you have to consider the cost. Starting from scratch will involve a significant investment of money, time, and labor. Pus, it would also require you to basically stop operations under the new system has been tested, implemented, the data migrated, staff trained, and so on.
There’s also the matter of migrating your legacy data, which also comes with a slew of challenges. You need to decide what data to keep, then you have to validate it, decide on the best transfer method so it lines up with the new system, verify integrity after the transfer, etc.
The most reasonable and efficient solution would be to use a custom-built integration which can aggregate all your data into a single source of truth. You can then expand the custom-built platform to slowly replace your existing out-of-the-box software with tailor-made solutions. This can be done based on an IT roadmap, resource availability or product lifecycles.