People executing the operations of organizations often wish that some of their daily decision-making tasks could be automated away. Oddly, the data engineers who can build such automation pipelines often find themselves wishing that the automation infrastructure itself could be automated.
You know the drill: managing server timeouts, setting up databases, setting permissions, and writing integration code. It claims a massive chunk of productivity. We know how it feels. We've been there, done that.
But there is good news. Solutions now exist that can take this load off of your shoulders — and operational analytics are vital to making it all work.
What Is Operational Analytics?
Operational analytics is a segment of analytics that was born from a realization that various delays in decision-making could be — and should be — avoided.
Many algorithms are designed to "intelligently" adapt to situations and take a limited set of decisions on behalf of humans. Operational analytics basically uses both data at rest and real-time data to create adaptive, responsive, and scalable decision pipelines at the operations level.
What Are the Benefits of Operational Analytics?
At its core, operational analytics serves organizations by making them leaner and more agile. There are a wide range of ways that this can play out in the real world, and companies can achieve different benefits depending upon their use cases, needs, and breadth of implementation.
Overall, there is no shortage of potential impact, and the following six benefits are commonly seen by organizations of all sizes.
1. Quicker Decisions
Domain experts define the parameters and bounds within which variations in data can be analyzed and used for decisions. These decisions are mapped to sets of actions that can be actuated in a matter of seconds — without human intervention. This greatly reduces the time lost between obtaining insights and transforming them to actions, resulting in substantial savings each year.
2. Customer Retention
Data at the operational level typically consists of granular information. Over time, this builds a deep contextual understanding of what the best action could be for any particular situation — and the improved customer experience naturally leads to repeat sales.
3. Embedding Expertise
Operational expertise gets encoded in the analytical algorithm, with results stored in databases. This alleviates some of the productivity losses that could result when a domain expert leaves the organization, making it easier to track down the exact decision making logic used.
4. Straight-Through Processing
Data pipelines don't have to be just for data. A myriad of business processes can be fully automated end-to-end. If a team is repeating a task with and regular occurrence, it's almost a guarantee that it could be broken apart, turned into code, and made into an operational analytics effort. Once implemented in code, your decision making processes become more aligned with service-oriented architectures and adaptive control systems.
5. Leveraging Existing Knowledge
It's almost a given at this point that you have some data sitting around in a legacy. data warehouse that isn't being used. Why not find a way to utilize that data to amplify your business decisions? Operational analytics combines the best of legacy decision making systems and real-time predictive analytics, with a way to truly drive ROI from all of the data your business has on hand.
Not sure how to effectively use the data your business already has? Operational analytics systems let you automatically run experiments to test user responses to various situations. You don't have to have all of the answers up front, but investing in a platform that facilitates experimentation can give you a better picture of what can be improved.
Designing Operational Analytics the Right Way
While the benefits are clearly great, it should also be noted that there are situations when complex, high-impact decisions require human intervention even at the operations level. Such decisions just cannot be entrusted to an algorithm.
That isn't so much a downside as it just a reality. Moreover, the design element of operational analytics can also have a major impact on what this tool can bring to the table.
Depending on the use case, the method of designing an operational analytics pipeline will vary. However, in all cases, it is important to design it for scalability from day one.
There are at least six focus areas that companies need to prioritize.
First, organizations must identify decisions and key performance indicators (KPIs) for various use cases and visualize them as models. The model design captures the actors, entities, the flow of information, and actions. This can be done using the Decision Model and Notation method, or by building out decision diagrams for business processes in your organization.
2. Rule Creation
Next, business rules should be created based on the policies, regulations, and the know-how of daily operations. Such rules and algorithms can be created in a partnership between domain experts and data team members. The final output should be an algorithm, based in SQL or Python (or any language of choice) that cleans, integrate, and returns the data in the expected format.
3. Model Validation
With an algorithm in place, you can use existing data or simulated data to observe how well the rules you defined result in an agreeable decision making process. Techniques like sensitivity analysis, regression, and cross-validation can all be used to validate the model. Additional tests need to be conducted under various scenarios to check for edge cases and the impact on operations.
Once verified, the algorithms should then be integrated with live sources of data like data warehouses or APIs, and run on a frequent basis. The data being input to the algorithm should be constantly updated so the decision support algorithms can be refined over time.
Algorithms will generally output some numeric value that indicates how closely it does, or doesn't, match a situation. This score usually accounts for business rules, regulations, best practices, and information about risks and opportunities. Based on the scores, a corresponding action should be auto-selected and executed.
6. Dashboards for Efficacy
Instead of building dashboards that help inform one off decisions, you can now build dashboards that look at the efficiency of your decision making algorithm. Is the algorithm resulting the right decisions? Are your KPIs continuing to perform better than before the algorithm implementation?
This updated approach to dashboarding helps ensure that as time passes, human capital isn't used to make mundane decisions, but is used to figure out how to make existing automated decisions even more impactful given new datasets and new knowledge.
7. Algorithm Evolution
The algorithms need to remain relevant as the operating environment changes. Although there are times when the algorithms may need to be refreshed manually, they should be designed to evolve by storing and "learning" from large datasets of scenarios. This method helps achieve repeatability and also helps run experiments on the datasets.
How Operational Analytics Improves Efficiency
When you automate operations, it will free up experts who previously were relegated to mundane tasks. This allows them to focus on higher-level work that actually drives more value and essentially gives the company extra personnel resources to allocate elsewhere.
No matter the use case or vertical, this is one of the best reasons to embrace operational analytics: efficiency. Work is streamlined and your best people can start helping you innovate and improve rather than just keeping the lights on.
How does this play out in the real world? Explore some of our operational analytics use cases or read on to see some of the ways that different industries can benefit.
Customer preferences of brands, price ranges, color choices, design choices, purchase frequencies, and item returns can help cross-sell products. Operational analytics can also identify demand and place requests with vendors and delivery personnel. Home-deliveries can be better scheduled based on the customer's preferences.
Sensors in vehicles and aircraft can predict or report issues and automatically connect with an authorized service center to schedule a slot. The central operations server can be notified of this and the server can automatically allocate other vehicles for the route.
Trends captured based on seasons, time of the day, and special events can help predict demand and automatically send notifications. This can help hire more delivery personnel on demand and also request partner branches to cater to excess orders. Vendors can also automatically be notified to replenish supplies.
The availability of blood units or donated organs can be checked automatically. Transport can be scheduled to bring it to the hospital on time. Consulting physician's availability and pay can be determined based on patient influx and emergency care requirements. Allocation of beds, requests to pharmacies for stock replenishment based on treatments and operations conducted can also be automated.
Historical energy load profiles, information on current events, and weather patterns can help automatically forecast and manage electricity load.
Getting the Most Out of Operational Analytics
Experienced engineers know how complex it is to implement, monitor, scale and duplicate such automated systems.
Even the best organizations with deep expertise can use some help.
Shipyard was built to iron out exactly these type of deployment and maintenance headaches. Along with the deep knowledge gained by working with so many enterprises, we offer a resilient, containerized orchestration environment that's intuitive to use.
Not only can we help you get operational analytics right to start bringing benefits to your business, but we can also take away some of the operational burden so that your engineers can focus on core tasks and start driving ROI from your data.
Want to learn more? Get started with our free 14-day trial.
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