As a company grows, the number of business applications that are used to run the organization grows as well. The more systems that are implemented, the more fractured the data becomes, consequently making it more difficult to report on business critical metrics and gain insights into factors that will help the organization grow, thrive, and expand.
On average, a company has 5-10 business applications that contain crucial business information. As most of the business information pertains to customer interactions, gaining a full picture of the customer’s journey requires pulling data from marketing automation tools, CRMs, e-commerce & financial systems, support tickets, and possibly more. No single system can create a clear 360 degree customer view. There are analytics tools that can read the aggregated data from across an organization’s business application ecosystem only after they are synchronized into a single database, and only then can they allow you to effectively analyze the data.
Using an analytics tool is only the last step in making sense of your dispersed data.
The four considerations described here are those which distinguish companies applying best practices when it pertains to data management. An organization that applies the best practices with data management will have a competitive edge over their peers.
With one system with many users the risk of duplicate entries is real. Once two systems are are integrated and automated processes are involved, the risk of creating duplicates increases exponentially. Duplicates are costly in many ways, from simply making it difficult to find or log the proper data to paying exorbitant storage fees because duplicates got out of hand.
Merge rules can be both internal to a system, running in the background, or for integrated systems, merge rules can be applied as the data is flowing from one place to another. Typically, merge rules rely on data that would be unique to a specific customer (e.g. email), however often times people have multiple emails or in a B2C environment members of the same household might have email addresses with no commonalities. For this reason merge rules that can be setup to review several criteria concurrently will be a very powerful tool in reducing duplicates.
Integrating two or more systems requires careful consideration of how the data will flow. Data flow is not trivial when involving more than two systems as data from one system may not necessarily be required in other systems (eg CRM activities into an E-commerce platform). As well, when integrating several systems, organizations need to consider when the data needs to be in the adjoining systems. For example new prospect data may need to be in a CRM database within five minutes, however financial data may only need to be synchronized once a day.
When your organization has more than one system that needs to be interconnected to optimize the the flow of data, a consideration would be finding a partner that will allow your organization to build business application ecosystem. This would be an ecosystem where multiple SaaS products can connect independently to a central hub and the data would flow from one system to any other system, or from one to several others.
Different departments talk different languages and so do their softwares. When integrating two or more business applications, a serious consideration is mapping the data. When mapping data from system to system it is important to understand the data type and format of both the source and target fields, as well as whether that data flows uni- or bi-directionally. For example, if a mapping goes from an alpha-numeric to a straight numeric field, or the phone number fields in two systems do not have a common format, at the very least the movement of that piece of data will stop, at worst the entire synchronization between the two systems will crash.
Data management between systems ensures that the interconnected systems communicate well together. Facilitating the communication between systems typically involves data formatting, transformation rules, translation tables, and filters that control when and under what conditions the data should move and flow between the two systems. As well, data management will reduce if not eliminate the volume of errors that can come up when synchronizing multiple systems.
The last consideration, is possibly the most important when connecting business applications is customization. Typically CRMs, ERPs, and marketing automation tools allow for some level of customization both on the creation of new objects and creation of new fields below either native fields or custom objects. Most companies take advantage of this functionality and create both custom objects and custom fields so the business application can more accurately match the way the business runs.
This customization however can lead to issues when trying to connect to another system. Often times it is complicated to connect custom objects with their custom object counterpart or native objects in another system. For this reason it is important to, at least on a rudimentary level, to determine which if any of the custom objects will be mapped from one system to any other system. Once you know how much flexibility your organization will require when creating a synchronized business application ecosystem, then you can quickly determine which data application management options would best suit your organization.
Unless your organization has one business application platform that manages all aspects of your business, your organization will be required to integrate multiple systems. Without the proper care and management of the flow and management of the data, an installed integration platform can cripple a company’s ability to report. Without the ability to create reliable business reports, management will not be able to make the right decisions that will help the organization grow and expanded into lucrative territories.
A data management solution must be capable of reducing duplicates, controlling movement of data, and transforming data so that it can be properly placed and stored in the accepted format of the target system.
Companies that follow proper data management rules have a competitive edge within their marketplace.
Vertify is a universal data management application that connects with over 80 different SaaS products using endpoints. Once connected to Vertify, organizations gain API developer level control of their connected systems without writing code in an easy to use straightforward drag and drop interface. Once the endpoints are connected organizations can leverage Vertify’s powerful data management toolset.
Mark Shalinsky, PhD, has spent his life living in data. As an academic he wrestled with managing huge data files trying to understand the correlation between blood and neuronal activity. In the private sector Mark worked in sales operations managing and synchronizing large datasets in an effort to identify sales and marketing sweet spots.