The frightful fall season of Halloween is upon us. This means spooky things are on the rise. It’s right to be wary of things that bump at night or in the day when looking at the tangle of legacy data systems running within your organization. Is your company running its operations with a pieced-together, Frankenstein-patched-up amalgamation of data systems? Are you continually adopting new data technologies individually without a strategic overall plan for architecture?
Maybe it’s high time to consider if your organization has its own ‘scary’ aspects of using overlapping and underperforming technology processes.
Integrating Applications
A recent connectivity study reveals that the number of business technology applications used within companies now exceeds 1,000. However, integration between these apps is in short supply. Less than 30% of these are integrated. This lack of integration creates data silos and productivity bottlenecks and leads to rising costs, duplicated work efforts, and disconnected user experiences across the various applications.
Some examples of this type of dysfunctional app integration are seen in these situations:
- Multiple instances of core ERP systems or a mix of ERP alongside departmental apps, cloud services, and on-premise databases. This mix lacks cohesion, causes headaches, and severely hinders visibility.
- Holes or limitations in data flows that lead to manual steps and temporary reconciliations. Data quality issues emerge.
- Heavy dependence on point-to-point interfaces, ETL, and custom middleware to move data around. This is akin to data spaghetti that costs time and effort.
- Folders holding non-current data spreadsheets can get lost in servers.
- Vendor lock-in where migrating off a legacy system is too difficult or costly. This includes IBP/EAM/inventory management or custom-built/homegrown systems. When these are locked in, it prevents a company from moving and being agile.
The big problem with these systems is that the various components rarely ‘speak’ to one another, causing a lack of unified purpose. Plus, each system can house a different data set for the same materials. Companies with multiple ERP systems will end up with “dirty data” due to various integration challenges. Errors and inconsistencies in data mapping and transformation may lead to duplicate records, incorrect data formats, and out-of-sync data.
Inconsistencies arise, too, with a lack of master data management, making way for misnamed customers, duplicate identifiers, and so forth. This could lead to data being manually re-entered, and typos and errors live there too. This lack of strategic tech integration is a significant reason organizations unwittingly stitched together a “Frankenstein stack.”
Overcoming a ‘Frankenstein stack’
A Frankenstein stack is characterized by multiple legacy systems stitched together over time, with add-ons, individual integrations, and tech workarounds. These moves are often instituted without a properly thought-out robust data architecture plan in mind.
A Frankenstein stack exhibits a lack of design thinking or strategic direction. It’s not built for the long-term needs of the business. Instead, it evolves reactively as new requirements and technologies emerge.
Creating a Frankenstein stack often occurs in tandem with organizational resistance to a complete overhaul of systems. Instead of moving forward with the necessary overhaul, the systems are not properly consolidated or replaced, creating complexity and technical debt. This is often fueled by misaligned incentives between departments and ultimately results in various stakeholders becoming more siloed over time.
Unfortunately, while companies may find shortcuts to put out one data fire or another, the technical debt accumulates over time. And companies can eventually be held hostage by this antiquated tech stack in these situations. If you do not choose to modernize your operations with solutions that can make sense of existing data, then your company may end up with cobbled-together, problematic data architectures.
Using an AI solution
The answer for your situation lies in giving your Frankenstein a brain with purpose-built artificial intelligence applied in data systems architecture. Purpose-built solutions that harness artificial intelligence can plug into existing tech stacks and help give companies a much better sense of their data. Executives will know where the data is stored in the ‘Frankenstein stack’ or will locate the discrepancies between the various data sets.
With this type of data harmonization technology, manufacturers no longer need to wait for the perfect data set to get started with optimization. It’s time to shed the old ‘data cleanse’ mentality and start with AI tools. AI can pull insights from the current data to help managers run their businesses better. Ultimately, organizations will have more intelligent and more accurate data decisions to power their operations and begin driving outcomes that are configured and sustainable for the company.
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