8 Common Data Issues Preventing Tech Visibility

May 16, 2024

Data is the lifeblood of decision-making and strategic planning throughout every department in enterprises today. And data about the technology used in your organization is no different. But what happens when that data is messy, unorganized, incomplete, or outdated? Frustration, inefficiencies, and lost opportunities happen. 

In our experience, this is common. Whether the data issues are due to manual work, lack of a single source of truth, inconsistent methods and processes, or mergers and acquisitions, the results are the same. When we cleanse and normalize our clients vendor, product, and broader tech data, we typically see: 

  • A 30% discrepancy rate between their enterprise data repositories
  • A data error / inconsistent value rate of 25%
  • An unclassified data rate of up to 50%

It’s hard to make good tech decisions with this level of cloudiness. Entrio can seamlessly merge multiple data sources and accurately classify all technology automatically. Through this cleansing, normalization, and categorization process, we typically identify:

  • An opportunity to reduce data records by 40% 
  • A 5-10% rate of unnecessarily redundant technology
  • A contract consolidation opportunity of 15%

So let’s dive into the common data issues that prevent technology stack visibility and why they need to be addressed continuously for an optimized tech stack. 

1. Siloed Data

Data silos occur when information is isolated within different departments or systems, creating barriers to accessing the full picture. Between sourcing systems, EA tools, TPRM tools, finance platforms, and manual spreadsheets, there’s a lot of different lenses to help navigate the technology landscape but without an authoritative source of truth, you’ll just be treading water. This fragmented approach slows down decision-making, reduces collaboration, and limits your ability to innovate and adapt. A technology catalog should be able to give you visibility into all software, open source, market data, hardware, and IT professional services used across your organization. 

2. Discrepancies Between Siloes

Trying to merge data silos can be a frustrating experience. Different departments, systems, or companies you’re merging with or acquiring may use varied data sources, fields, and data values. Even if data silos are somewhat manageable, discrepancies between them can lead to major problems. It takes considerable effort to merge or maintain all records individually. But failure to do so leads to doubt about the accuracy or relevance of the data. This lack of alignment causes unnecessary confusion and can undermine your strategic objectives. 

3. Only Having Vendor-Level Data

Relying solely on vendor-level data is not enough. Indicating you’re engaged with Atlassian or Pegasystems leaves a lot of gaps to be filled. Is it Jira, Confluence, Bitbucket or all three? Product-level information can likely be found in unstructured formats in engagement descriptions or from invoices and contracts but it is difficult to extract manually and include in a catalog. Without individual products and solutions, it's difficult to conduct a comprehensive analysis. You can’t promote reuse, identify accurate spend, spot opportunities or risks, or optimize your tech stack. 

4. Uncategorized or Poorly Categorized Technology

How your vendors, products, and broader tech solutions are categorized is a foundational element for tech stack visibility and decision-making. And many organizations are relying on catalogs with too many unclassified or incorrectly classified solutions. Lack of a granular taxonomy makes it challenging to accurately map, assess, and search your tech stack, promote reuse, compare different solutions, and consolidate redundant technology. Lack of a consistent taxonomy across data sources or lack of a consistent application of that taxonomy also poses problems. If similar solutions are grouped differently across datasets, you may end up making decisions based on incomplete or skewed comparisons. The taxonomy should automatically be able to map to your unique financial services functions, shared corporate functions, and cover a wide range of technology solutions. It should also be considered a living organism that constantly evolves with the market. The importance of the right taxonomy cannot be overstated. 

5. Obsolete Vendor and Product Names

Technology evolves rapidly, and vendors frequently change product names, rebrand their offerings, merge with or get acquired by others. If your data doesn’t reflect these rapid changes, that outdated or irrelevant information hinders your ability to keep up with the latest market developments, consolidate contracts, and see the bigger picture. 

6. Inaccurate Vendor and Product Hierarchies

Inaccurate data on the relationships between vendors and their products puts you at a disadvantage. Properly structuring data in an accurate hierarchy is essential to recognize contract consolidation opportunities and assess certain risks.

7. Typos

Many market leading catalogs require a lot of manual data entry. Any time manual effort is involved, it can lead to errors, such as typos. While these may seem like small issues, they can cause big problems. Inaccurate spelling or numbering can result in misplaced, misidentified, or duplicated records. This can impede your ability to search and retrieve data accurately, causing delays and potentially extra work.

8. Duplicate or Irrelevant Entries

Duplicate entries and irrelevant records not only inflate data volume, making it harder to manage, but also create ambiguity. Many systems that are being used today cannot link multiple engagements, statuses, and owners in one record. This requires you to maintain multiple instances, which is inefficient and prone to error. When duplicates are caused by any of the other reasons above, you need to decide which is the right record to keep and figure out how to merge the information. Feigning ignorance of the duplicates will not lead to bliss. 

Stop One-Off Data Normalization Projects 

Messy data can significantly impact visibility into your tech stack, making it essential to clean, organize, and maintain your data sources. Addressing these common issues, ensures your data is reliable, accurate, and useful for making informed decisions. 

But manually cleaning and updating data is time consuming and a never ending effort. There are likely still many errors left over. Plus a one-time clean up will not suffice in the fast paced tech environment we’re in today. 

Normalize your Tech Data Continuously 

Entrio’s normalization and classification engines automatically cleanse, normalize, categorize, and enrich all of your tech data, so you don’t have to. Our platform addresses each of the data issues outlined above in days, and keeps the data clean and up to date continuously. With our Live Solutions Catalog, all the tech data you need is in one centralized place so you can maintain full visibility of your tech stack and take the necessary actions to optimize it.