Remember the good ol’ days when software had to be downloaded and accessed on-premises? Well, the advent of SaaS changed all that and made life easier for everybody involved. Scalable, easy to install, and most importantly, cheap, Software as a Service (SaaS) has a host of benefits, including the ability to optimize the individual business […]
Remember the good ol’ days when software had to be downloaded and accessed on-premises? Well, the advent of SaaS changed all that and made life easier for everybody involved. Scalable, easy to install, and most importantly, cheap, Software as a Service (SaaS) has a host of benefits, including the ability to optimize the individual business functions efficiently so that departments can now procure and use the desired systems. However, everything has a good side and a bad side, and this applies to SaaS as well. Everywhere you look now, there’s data present and every system possesses its own dataset that is stored in multiple formats. So, it is getting harder to combine and rely on data.
Fusing one or more dissimilar dataset into a trusted, unified dataset is difficult, not to mention time-consuming. But it is not impossible. You just have to watch out for these five challenges and figure out ways to avoid them. Find more details below:
Believe it or not, but the removal of duplicate data takes a long time and consumes a lot of your valuable business resources. However, this process is a must unless you want to risk the onset of inaccuracy in your consolidated dataset. For example, without duplicate data removal, you might be dealing with contacts or accounts that have not been consolidated into specific records.
You need a two-pronged approach if you’re going to tackle the duplicate data problem. First, you must begin the de-duplication process within a certain silo to prevent applications from having more duplicate data inside them. Once that’s done and you’re ready to merge datasets, you have to connect similar records throughout all the systems in your organization. If you require duplicate cleanup work within a certain application, then you must load the non-duplicate data and flag any duplicates you find for cleanup within their systems of origin.
A big advantage of SaaS systems is how several business processes and users contribute to a shared database to power the application. However, an unintended consequence of this method is how different apps end up with different data on the same clients. If your system shows records of a customer having two separate accounts, your analysis encounters some severe obstacles. Even a single update is capable of spangling various databases, tables, and even rows, with conflicts. And resolving these kinds of conflicts “by hand” is not only difficult but impractical as well.
Thankfully, there are two approaches – both automated – that can help you resolve conflicts existing in your data, viz. Last Modified and System of Record (SOR). While the latter focuses on the ranking of the system to find out which one is the winner in case of a conflict between two types of data, Last Modified involves using the most recently updated information across different systems for a specific field. It is possible to use a single approach to avoid any future data conflicts or a mixture of both, depending on the circumstances.
While conflicts jeopardize the accuracy of your company data, inconsistent formats cause the values to conflict with one another. What this means is, even if the data is not wrong, one system might format the dates as YYYY-MM-DD and the other might use the DD-MM-YYYY format. So, even though both the details are technically correct, querying the same information can prove a hassle. From Booleans to states, phone numbers to capitalization, when you’re applying a certain standard to your data, you can update the formats for a countless number of fields.
The solution here is to standardize all your data into a single format and establish consistency. This will help improve the speed of the comparison processes as the databases will no longer have to verify the different formats against one another at a specific time.
Creating rules about which formats are going to be treated as the canonical standard for every type of entity helps make sense of the acronyms, abbreviations, order matching, and casing. Thanks to the removal of inconsistencies, improvements in data quality become noticeable, analysis becomes more reliable and querying speeds up.
The relative objects tend to differ considerably when a SaaS solution is built and deployed in isolation. Related objects encompass a vast range of data associated with a specific contact, such as their opportunities, account, support tickets, departmental activities, and so on. However, a lot of the related data gets lost during data extraction, thereby causing problems with the completeness of consolidated datasets.
The best solution is to compare records on common identifiers between non-identifying and identifying fields. For matching a Contact record, for example, you must begin with an email address, since this common identifier offers the greatest probability for a singular match across different systems. There are multi-level de-duplicating keys that incorporate extra supporting data like company, address, and name. No matter what sort of common identifiers you use, related objects should always be mapped so you’re able to achieve a complete standard data schema for powering your analytics.
Data constantly gets updated, which signifies that the consolidated data sets manufactured by your business might become obsolete if a part of the source data changes. It is difficult to keep data constantly updated. When connected data sources are no longer in sync, the data used to feed business intelligence tools, like dashboards, start outputting less reliable reports.
It’s tedious to query siloed systems for the latest data every time the data inputs are changed. It is better if you spend your time analyzing different datasets, finding insights, and sharing recommendations with others in your company. Use automated pipelines to join the dots between the data in apps and your central database to analyze, bridge the gaps between analytics and apps.
In such situations, data is going to be updated in almost real-time, anywhere from five minutes to 24 hours. This unified and consolidated data not only saves data prep time but also provides trusted data resources. This ensures that every customer record in your company is available in a centralized, trusted source, but at the same time allows separate SaaS apps to perform vital business functions.
Concluding Remarks
So, there you have it – the unintended consequences of SaaS and how to successfully overcome them. Thanks to these handy tips and tricks, you won’t have to worry about accessing your software online or mishandling of data.
