Operational Intelligence: Main Steps to Achieve OI

Achieving sustainable success rests upon an ability to make fact-based decisions. A company should utilize operational intelligence (OI) insights to improve its daily operations and monitor processes to solve any issues that might arise proactively. Utilizing OI tools facilitates tracking server metrics and performing pipeline and equipment monitoring using smart sensors. OI practices involve collecting large volumes of data and analyzing them to unveil trends, discover barriers hindering growth, and empower employees by providing them with the information they need to make result-yielding decisions. In this article, we will focus on the significance of OI and explore the ways of achieving it.

Operational Intelligence Explained

OI is the analysis of data collected via an IT infrastructure. It reflects how streamlined processes are and allows businesses to identify the areas for improvement.

OI tools are designed to perform various tasks besides analyzing data in real time and streamlining decision-making. They collect information from many sources, including Internet of Things (IoT) devices, apps, and smart sensors. Deploying such solutions enables firms to analyze IT operations and visualize data.

Cloud-based platforms with OI tools leverage the power of machine learning (ML) to detect issues and anticipate problems. Using such services is instrumental in increasing transparency, streamlining workflows, and boosting the efficiency of business processes.

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Main Operational Intelligence Processes

Resource optimization relies on data analytics, requiring businesses to deploy efficient OI systems capable of retrieving data from services, networks, devices, and databases. Their capabilities depend on technology. Gaining valuable business intelligence (BI) insights requires scrutinizing huge datasets and extracting information about problems. It allows a venture to fix them efficiently. OI is based on the following processes:

Data collection: Achieving OI requires server and network monitoring. Advanced systems collect, process, and visualize data in less than a minute, enabling business owners to stay updated on current affairs.
Analysis: OI tools reduce the risk of data silos and enable large organizations to run many apps simultaneously without any issues. OI systems consider them separate data sources and check whether they function without disruptions. If any issue arises, it could be investigated separately. It ensures that large datasets will be broken down into multiple parts to facilitate their detailed analysis using AI-driven tools.
Visualization: Using convenient dashboards, analysts display data in different ways to make it easier to understand.

Advanced OI solutions leverage modern technologies to extract valuable info and process it without delays. Process automation is necessary to minimize errors and achieve maximum uptime.

Why Focus on Operational Intelligence?

Efficient business process management (BPM) necessitates visualizing data trends and performing other tasks to enhance OI. Enterprises deploy OI solutions to perform the following tasks:

Real time operations monitoring: OI systems analyze info collected by manufacturing machine sensors, sales bots, and other systems to prevent crashes and delays.
Present information in a comprehensible format: Users access visualized data via a centralized dashboard. It can be customized depending on specific needs.
Configure real-time alerts: OI solutions alert users when a situation deteriorates. Enterprises indicate the conditions that should be met for OI tools to send notifications.
Analyze the situation within an industry: Companies configure dashboards depending on the needs of the industry. It allows employees to see the most relevant data and make informed decisions.
Generate reports: Such documents enable staff members to gain a deeper understanding of the current environment and discover new ways of dealing with pressing issues.
Expand the scope of operations: As it becomes more challenging to store and process data as the number of clients increases, OI technologies enable businesses to scale up using cloud-native infrastructure. Leveraging artificial intelligence (AI), companies process large amounts of data and accurately predict outcomes caused by strategy adjustments.
Perform automatic remediation tasks: When an OI solution detects an issue, it can send an alert or try to fix it using pre-configured scripts.

Prioritizing OI enables enterprises to increase the efficiency of business processes and save costs by making them more streamlined.

Operational Intelligence: Main Steps to Achieve OI

How to Implement Operational Intelligence Practices

On the way to process optimization, enterprises should follow specific guidelines to ensure their transformation is successful. Organizations that aim to facilitate decision making by processing large datasets need to take several steps to benefit from OI:

Set clear goals: Starting from choosing the areas where OI can help a firm achieve maximum improvement is the key to making new practices impactful. Examining the existing pain points is instrumental to minimizing risks and getting the most from OI tools.
Build a team of experts: Every manager should know how to solve specific issues a company may face when moving toward OI. A CTO can focus on removing the barriers that do not allow for enhancing network uptime.
Analyze the available information: It won’t suffice to gather raw data, as a company needs to process it to extract valuable insights to discover the right solutions for implementing its development strategy. Professional audit enables a firm to understand what information is created within the system and whether it is stored securely.
Improve the data: OI systems may not be ready to process raw data unless the latter is prepared for further use. It is necessary to collect more information, enhance its quality, or discover more recent data to avoid errors affecting the strategy’s implementation. Employees must learn how to collect the most recent data and ensure its reliability. Installing new sensors or transforming data feeds is an excellent solution as well.
Use KPI metrics: This step will enable a firm to identify the problems that should be fixed. Enterprises seek to achieve multiple goals, including minimizing wait times, boosting sales, or achieving better uptime. A choice of metrics depends on the specific result a firm is interested in.
Focus on small goals: Instead of prioritizing broad objectives, an enterprise should divide each task into smaller ones and see how minor improvements can bring about the desired changes in the system. Discovering the reason behind app crashes may lead to other improvements besides reduced downtime. Progress toward small goals is easier to track.

Efficient integration of insights from multiple sources requires training a team entrusted with the task and transforming a company’s corporate culture. Using new tools requires having specific skills to avoid common issues. Seamless orchestration requires integrating data and processes into a scalable system that enables a firm to improve its performance.

Challenges on the Way to Achieving Operational Intelligence

On the way to OI implementation, companies face obstacles hindering the adoption of efficient practices. There are three main barriers every firm must consider:

Resistance to change: Even the most experienced employees may be reluctant to make the innovative methods an integral part of their workflows if they do not have an extensive technical background. The corporate culture also plays a pivotal role in expediting the adoption. Besides, organizations with many complex processes may be reluctant to re-organize them. Solving this problem requires improving digital literacy and conducting training sessions to demonstrate the advantages of OI.
High data quality requirements: It may be arduous to prepare large datasets and check whether they contain relevant and reliable information that can help companies achieve OI. Analyzing huge volumes of data quickly is difficult unless a company finds the right balance between quality and speed.
Other priorities: Every organization has its unique needs and goals. Some of them possess a limited amount of resources, while others focus on specific tasks to meet increased demand and win clients’ loyalty. Implementing OI systems may not be their top goal, especially if top managers fail to recognize the advantages process optimization can yield.

Raising awareness of the potential increases in profitability is crucial to expedite the adoption. Customizing OI systems enables ventures to achieve their strategic goals and discover how to make processes more efficient.

Main Use Cases of OI Solutions

Enhancing OI is necessary for organizations from different industries. Let’s consider the most outstanding examples of deploying operational intelligence solutions across economic sectors.

Finance: Banks, trading platforms, marketplaces, and other organizations utilize OI to receive timely notifications about changing currency rates and prices. Algorithm-based tools allow them to process data, analyze behavior patterns, and prevent fraud.
Human resources: Managers utilize OI to make internal processes efficient, reduce workload, and analyze performance.
IT: Companies utilize OI to facilitate infrastructure maintenance and avoid failures disrupting their workflows.
eCommerce: OI is invaluable for those who need to learn more about product demand or track expiration dates.
Logistics: Using OI, firms track delivery times, avoid understocking, and ensure the uninterrupted functioning of supply chains.
Manufacturing: OI facilitates keeping production lines in a functional state. Utilizing such tools streamlines maintenance processes.
Marketing: Professionals use OI to make advertising campaigns efficient and target audience segments with high precision.

Process mining is one of the stages of achieving AI. It is different from BI, as the latter allows employees to perform more advanced analysis and utilize contextual data to increase client satisfaction. In contrast, process mining prioritizes discovering information that can help enterprises optimize specific business processes and eliminate bottlenecks.

Operational intelligence is the future of data analytics, as it allows firms to optimize operations by monitoring and analyzing data. OI is expected to evolve due to the adoption of AI, IoT, and blockchain technologies, as it will allow organizations to process information efficiently and improve its traceability. Embracing the best OI practices early will give ventures a cutting edge and allow them to achieve sustainable growth.