Reduce data noise and optimize costs of handling data using predictive analytics

Knowledge is power

In the era of digital transformation, the phrase “knowledge is power” takes on a new dimension. The power of knowledge is no longer confined to the possession of information, but extends to the ability to use this information effectively for strategic decision-making. This is where predictive analytics comes into play. It not only helps businesses navigate the data deluge, but also transforms raw data into actionable insights.

Predictive analytics enables businesses to leverage their data to anticipate customer needs, optimize operations, and drive strategic decision-making. It empowers organizations to move from reactive to proactive operations, thereby gaining a competitive edge in the market.

Moreover, predictive analytics democratizes data, making it accessible and understandable by all stakeholders in an organization. This fosters a data-driven culture where every decision is backed by data, reducing biases and improving overall business outcomes.

In the ever-evolving realm of contemporary business, a multitude of systems and solutions has emerged, tailored to address the diverse and complex requirements of organizations.

However, this abundance of systems often leads to the generation of massive volumes of data. The sheer magnitude of information produced is overwhelming for businesses, posing challenges in terms of analysis and the extraction of meaningful insights. Knowledge is a vital asset in today’s world, but it needs to be built on reliable information extracted from data and IT systems. A decision-making process supported by data has become the digital holy grail.

Currently, 402.74 million terabytes of data are created every day (“created” includes data that is newly generated, captured, copied and consumed).[1] Not all IT enterprise data needs to be managed with the same frequency or picked up for analysis.

The information overload may lead to decreased productivity, and an increased likelihood of overlooking genuinely important alerts. To foster a more efficient and focused work environment, businesses need to implement intelligent filtering mechanisms and prioritize notifications, ensuring that employees receive only the pertinent information necessary for decision-making and problem resolution.

Operational costs of out-of-control data management

Building an entire team of specialists to sift through data can significantly escalate operational costs for a business. According to the Komprise 2023 State of Unstructured Data Management Report, the majority of enterprise IT organizations are spending over 30% of their budget on data storage, backups, and disaster recovery.[The recruitment, onboarding, and continuous training of data experts requires substantial financial investment. Moreover, maintaining a specialized workforce demands ongoing salaries, benefits, and infrastructure, further adding to the financial burden.

In the context of data analysis, operational costs extend beyond the obvious financial expenditures. They also encompass the time and resources invested in data management. As businesses grapple with increasingly complex data ecosystems, the operational costs associated with data analysis can skyrocket.

One significant cost factor is the continuous evolution of data technologies. Keeping up with these advancements requires ongoing training and upskilling of the data team. Furthermore, as data volumes grow, so does the need for robust and scalable infrastructure. This not only includes the physical hardware, but also the software solutions for data storage, processing, and analysis.

Another often overlooked cost is the opportunity cost. When specialists spend their time sifting through data, they are diverted from other strategic tasks that could potentially bring more value to the business. Different studies estimate that HR teams using manual data handling spend between 15% and 50% of their time managing information by hand.[3] This is where predictive analytics can make a significant difference. By automating the data analysis process, predictive analytics frees up specialists to focus on strategic decision-making and innovation.

Lastly, there’s the cost of errors. Manual data analysis is prone to them, which can lead to misguided decisions and strategies. These mistakes can be costly for businesses, both financially and in terms of reputation. Errors in data processing can cost organizations an average of $12.9 million each year.[4] Predictive analytics, with its automated and accurate analysis, can significantly reduce the risk of such errors.

In conclusion, while the operational costs of data analysis are multifaceted and substantial, the adoption of predictive analytics can help businesses optimize these costs effectively. By automating data analysis, reducing errors, and freeing up specialists for strategic tasks, predictive analytics can transform the cost center of data analysis into a strategic asset for the business.

Sustainability versus manual data handling

Manual systems can quickly become outdated, requiring periodic upgrades or replacements that generate electronic waste and further resource consumption.

Major companies often mass produce single-use data that is generated and discarded quickly in manual processes, which impacts a company’s sustainability by increasing waste and resource consumption.

Single-use data refers to data that is collected and used for a single purpose or event, often without being stored, analyzed, or reused for other purposes. This practice can have significant implications for a company’s sustainability. It leads to higher paper usage, greater energy consumption for data entry and storage, and an increased environmental footprint due to the frequent disposal and replacement of outdated materials.

Without a strategy for reusing data, companies may find themselves repeatedly collecting similar data for different purposes, leading to inefficiencies and increased costs.

By addressing these inefficiencies and missed opportunities associated with single-use data, companies can enhance their sustainability efforts, reduce their environmental impact, and gain a competitive advantage in their industry.

A case for predictive analytics for your IT

From a predictive analytics standpoint, implementing such a system provides tangible benefits for businesses, including a significant reduction in the impact on operations by proactively addressing potential issues before they escalate. By leveraging insights from connections between performance alerts, including monitoring and observability system data such as:

• Metric data stream
• Events
• Logs

As well as service management data, including configuration management system data such as

• CI asset/relationships
• Topology information
• Resolver group information

Service management system data such as change tickets and knowledge management information for known issues the predictive analytics platform empowers the delivery team to address technical issues proactively, minimizing downtime and enhancing overall service health. 

For example, warning alerts coming from multiple infrastructure nodes would typically result in tickets for the different resolver teams responsible for each technology. But by referencing a service management CMDB (Configuration Management Database) and service topology trees, predictive analytics can quickly correlate multiple alerts to create a probable root cause ticket for the relevant resolver team, reducing time for service restoration or preventing service interruption altogether.

Conversely, time series alert data, including system resource utilization thresholds such as CPU utilization, may be analyzed for recurring breach patterns. Such cyclical alerts can be correlated with service management information such as a planned maintenance window for nightly backups, which could prevent false positive tickets and decrease work for resolvers.

Predictive analytics can effectively reduce the number of Priority 1 (P1) tickets by analyzing historical data, patterns, and trends to identify potential issues before they escalate to critical levels. By employing machine learning algorithms and statistical models, predictive analytics anticipates potential incidents based on past occurrences, enabling proactive intervention.

In essence, predictive analytics acts as a preemptive tool, helping organizations stay ahead of critical incidents and maintain a more stable and efficient IT environment.

From our experience, for clients struggling with conducting root cause analysis, reactive instead of proactive problem identification, and a low success rate in detecting patterns in problem analysis, we were able to provide tangible outcomes by implementing predictive intelligence (PI).

Based on a survey conducted on the problem management teams of certain projects, we can point out:

• 100% of users implementing the PI solution
• 20% time saved comparing to manual work
• For 80% of the users, the PI dashboard enhanced the analysis process, improving overall problem handling.

Other benefits include:

• Identification of patterns that evade manual detection, enabling valuable insights to enhance the operational efficiency of the problem management process
• Enhanced ability to identify major incidents in advance, avoiding potential disruptions
• Enabled data-driven decisions for proactive problem identification
• Reduced manual efforts, saving valuable time and resources for higher-value tasks.
  

Summary

Harnessing the power of data is crucial for strategic decision-making. Predictive analytics elevates this capability by transforming vast amounts of raw data into actionable insights. It enables businesses to anticipate customer needs, optimize operations, and proactively address potential issues. This shift from reactive to proactive operations provides a significant competitive advantage.

Predictive analytics not only democratizes data access, making it understandable for all stakeholders, but also fosters a culture of informed decision-making. This reduces biases and enhances overall business outcomes. By automating data analysis, predictive analytics lowers operational costs, minimizes errors, and frees up specialists for strategic initiatives. It supports sustainability by reducing reliance on manual data handling, leading to lower operational costs and waste.

Furthermore, predictive analytics enhances IT operations by proactively addressing potential issues, correlating alerts, and preventing disruptions. This transformation turns data analysis from a cost center into a strategic asset, driving efficiency, accuracy, and sustainability in modern business environments.

Feel free to contact us if you’d like to get familiar with predictive analytics solutions for your business.

[1] https://explodingtopics.com/blog/data-generated-per-day

[2] https://www.komprise.com/glossary_terms/data-storage-costs
[3] https://explodingtopics.com/blog/data-generated-per-day
[4] https://www.komprise.com/glossary_terms/data-storage-costs