October 17, 2023
Knocking down data silos and delivering the same set of uniform, consistent data has been on the minds of enterprise leaders for years. Consistent and uniform data is critical for business decision making, analytics and AI strategies. Yet, data silos continue to exist, and for most companies there are few signs that these silos are nearing extinction.
Why? Unifying all data silos into a single, uniform data repository is time-consuming and expensive.
Using Master Data Management to Silo-Bust
The primary vehicle for unifying data and bringing all data silos under singular management is master data management (MDM) software. What MDM does is create “golden records” that are master “single versions of the truth” data.
To be admitted into an MDM master data repository, each data element must be thoroughly prepared. This means that inconsistent data, multiple names for data elements, incomplete data and junk data must be eliminated. There are data preparation tools and automation technology that can aid in this data preparation process. However, in many cases it is end-user subject matter experts and IT who must “hand clean” the data. This process is tedious and time-consuming. When the board and C-level management look at this effort, they see staff time and money that is being poured into a background data infrastructure project that doesn’t benefit the company’s bottom line. Also, once the MDM is up and running, on an MDM subscriber basis, an MDM license can cost enterprises between $3.25-$9.00 per device per month, according to a 2022 study. If you have 10,000 different devices in your enterprise that use the MDM, your monthly bill can run as high as $90,000.
Who uses MDM? It's large enterprises that are experiencing such significant pain and risk of erroneously informed business decisions. They are the major users, and they simply can’t afford to operate without investing in MDM. They want to unify their data and make the same set of consistent and high-quality data available to anyone, anywhere across the organization.
Because of the time, expense, and effort required to create an MDM, small- and mid-sized companies, along with large enterprises that that haven’t reached insurmountable data silo pain points, opt to silo bust without the help of MDM.
Silo-Busting Without MDM
An alternate approach to using MDM for silo busting is to sign on with a vendor that can offer an end-to-end system for all (or at least most) company operations.
ERP (enterprise resource planning) systems are the most obvious path for an enterprise-wide data unification effort; with CRM (customer relationship management) being a second, but more constricted option.
Here’s how these systems work:
You’re a mid-sized enterprise, and you have a financial software vendor that supplies accounts payable, accounts receivable, the general ledger, risk management, cost management, budgeting, reporting, and other financial systems. On the manufacturing side, the company uses a manufacturing software vendor that supplies material requirements planning (MRP), master scheduling, workflow management, etc. Engineering has its own computer-aided design (CAD) and product development systems. Purchasing has a system. Sales has a system. Marketing has a system. HR has a system.
Each of these functional software ecosystems operates independently of each other, so the result is that the company has many data silos. There are data exchanges between systems, but in many cases the software doesn’t integrate well on its own, so IT ends up developing custom system interfaces. The company faces the fact that it must operate with data silos and inconsistent data.
To cure the data silo conundrum, that company decides to install a corporate-wide ERP system that includes software for all the main company functions such as finance, manufacturing, purchasing, and engineering. The “catch” is that the ERP system might not include functions such as sales, marketing, and customer support. However, a CRM system can be brought in to create a 360-degree, data-consistent view of a customer for sales, marketing, and service/support.
In a phased migration that proceeds from department to department, the company methodically migrates off its plethora of different systems and data silos, and onto more unified data systems where everyone will see and use the same data.
The Net of it All
Over the past decade, the siren call to IT has been to eliminate all data silos and to bring all data into a single version of the truth so that confusion among departments and delays in decision making can be eradicated.
But is this realistic? There are some corporate systems where it may be beneficial to retain them in standalone mode. Examples might be the company’s legal system, or its future product development work, and intellectual property team.
There are other cases where there just isn’t a sufficiently compelling reason that can justify the time, effort and cost it would take to break down data silos. Examples might be a small company that doesn’t require an end-to-end ERP solution.
In still other cases, companies might decide that they need to eradicate some data silos, but not all. An example of this might be a company that is experiencing such significant pain from failing to integrate customer information from customer-facing functions that it is losing customers. In this case, a company is likely to consider a CRM system, which can bring together all the information on each customer from sales, service and marketing. That can unify that data and make it available to employees who interact with customers.
The bottom line for many companies is that they might never entirely eliminate all data silos; however, they cannot afford to ignore these enclaves of data, either.
For CIOs who are charged with stewarding data, a reasonable approach is to do the following:
Identify those areas of the company where the pain and business risk from data silos is so great that the silos must be eliminated
Identify those areas in the company where data can, and even should, be siloed
Ensure that they maintain an accurate count of how many data silos are “out there,” and who uses them.
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