Data Overload Problem: Data Normalization Strategies Are Expensive

Christian Wiens Director of Marketing

Christian Wiens is Director of Marketing at MixMode. He has 10+ years of experience as a cybersecurity professional. He has his BA from The University of California, Berkeley and resides in Austin, TX.

The following is an excerpt from our recently published whitepaper, “The Data Overload Problem in Cybersecurity.” In this whitepaper, we dive into the data overload problem plaguing the cybersecurity industry and uncover how organizations can greatly reduce or even completely eliminate many of these challenges by adopting an AI-driven solution to analyze network behavior in the context of current data while meeting compliance and regulatory requirements.

Data Normalization Is Expensive

Financial institutions spend five to ten million dollars each year managing data. A recent Computer Services Inc (CSI) study reveals that most banks expect to spend up to 40 percent of their budgets on regulatory compliance cybersecurity, often adopting expensive data normalization strategies.

Normalizing data so it can be used in the course of business or for security platform compatibility is costly, in part because of third-party costing processes. As data is accessed, enterprises accumulate additional fees at each stage of the process:

Storage
Extraction
Normalization
Consolidation with other extracted data
Standardization for querying
Reporting and analytic optimization

Much of the baked-in cost of making data useful to an organization lies with extraction and normalization costs that may not even be necessary. For example, an enterprise’s investment into data management and normalization can include:

Millions of dollars for warehousing structured application data via Snowflake
Millions of dollars to store machine generated data via cloud based databases
Long term data storage to meet regulatory compliance obligations
Associated energy and network transportation costs

Not only is data handling expensive, in many cases the investment doesn’t even pay off. Despite all the cost and oversight commitments, too much enterprise data can remain inaccessible, unusable, and valueless because it is unsearchable.

Many enterprises put all of their data into a kind of hot storage, where it is available on demand. However, most companies don’t need to access or query all their data. Because it is so expensive, most enterprises do not invest in normalization, which means the data may be sitting in storage, but that it is unsearchable. In effect, the company can’t fully understand, leverage or query their own data.