Algorithm Steps: How To Build Your Own Algorithm

In modern business, an algorithm is a key factor in automating tasks and solving complex problems efficiently. While most of us know the term, creating one and outlining the steps involved can be confusing. In this article, we’ll introduce the more technical aspects of algorithms to you and how you can build them for your business’s specific needs.

Introduction to algorithms

An algorithm is a set of instructions designed to perform a variety of tasks, from data analysis to decision-making. By breaking down a problem into smaller, manageable steps, you can create a systematic approach to solving it. 

Algorithms are invaluable in uncovering hidden patterns that can significantly impact overall business performance. To be more specific, understanding how to build and implement algorithms is useful for professionals who need to optimize processes, solve complex problems, or automate repetitive tasks. 

For example, in a business setting, an algorithm could be used to streamline data processing, allowing analysts to focus on interpreting results rather than manual calculations.

For business analysts, algorithms can automate the processing of large datasets to identify patterns and trends. Marketers, on the other hand, can use algorithms to personalize customer experiences by analyzing behavior and preferences. 

Algorithms must also consider time complexity, which measures how the runtime increases with the size of the input data. Efficient algorithms minimize time complexity, running quickly even with large datasets.

Is an algorithm a step-by-step sequence?

Yes, an algorithm is a step-by-step sequence that follows a logical order, leading from the problem’s inputs to the final output. With this flow, the algorithm makes sure the task is performed accurately and efficiently. By breaking down complex problems into smaller, manageable steps, algorithms provide a structured approach to problem-solving.

Each step in an algorithm must be carefully designed to minimize time complexity and maximize performance. This approach allows the algorithm to handle different inputs efficiently, providing correct and reliable outputs.

For instance, you can use an algorithm to check and optimize ad spend for various marketing campaigns. A marketing team might use an algorithm and visualize data through dashboards  to manage and analyze large datasets from multiple sources, such as Google Ads, Facebook Ads, and website analytics. The problem is to maximize the return on investment (ROI) for each marketing dollar spent.

The algorithm would first take various inputs, such as historical ad spend, conversion rates, and customer behavior data. You can even conduct an interview or survey of your marketing team for their perspective.

It then processes these inputs step by step, applying machine learning models to predict the expected performance of different campaign strategies. The final output is a set of recommendations on how to allocate budget across different channels to achieve the best possible outcomes.

To track the effectiveness of the algorithm, you can use a counter to monitor how often the predicted outcomes align with the actual results. 

Another example would be in the finance industry, where algorithms are used for predicting maximum stock prices or future sales revenue. A financial analyst might face the problem of forecasting the next quarter’s revenue for a company, using a variety of economic indicators, past sales data, and market trends as inputs.

The algorithm breaks down the forecasting process into smaller steps. It first normalizes and preprocesses the data to ensure consistency. Then, it applies a time-series analysis model, such as ARIMA, to identify patterns and trends in the historical data. This structured approach helps financial analysts understand how different factors influence the forecasted revenue, making it easier to identify potential risks and opportunities.

How to build a working algorithm in 9 steps

The steps to building a working algorithm start with defining the problem it’ll solve. Ultimately, the final product should help drive productivity, uncover insights, and give your business a competitive edge.

1. Know the algorithm’s goal
2. Collect relevant data
3. Preprocess the data
4. Select the algorithm type
5. Design the algorithm structure
6. Implement the algorithm
7. Test the algorithm
8. Fine-tune the algorithm
9. Deploy and monitor the algorithm

Step 1: Know the algorithm’s goal

Step 1 in creating an algorithm involves defining what you’ll use it for. The problem statement sets the foundation for the next steps and paves the way for a focused and effective system.

Your algorithm’s goal and statement should be clear and specific to address the particular problem and questions you want to solve or the task you want to accomplish. 

For example, LiveData wanted to provide custom mobile dashboards for their clients without developing their own software from scratch. Using Klipfolio, they developed their own algorithm to deliver financial information and core business activities to their clients without compromising quality and speed.

Another goal for creating an algorithm could be to detect anomalies in your marketing data, alerting you to unusual patterns that need attention. Each goal directs the algorithm's structure, inputs, and expected outputs, making the subsequent steps more manageable and purposeful.

Defining the algorithm’s object also impacts the time complexity and performance. A well-defined goal focuses only on relevant data and processes to reduce unnecessary steps and generate timely and accurate results for your algorithm.

Step 2: Collect relevant data

In step 2, you must collect relevant data for your algorithm by knowing the information you need and where to find it. Typical data sources include spreadsheets, SQL databases, and cloud data.

• Spreadsheets: Many businesses store data in Excel or Google Sheets, which are great for small to medium-sized datasets such as lists and easy to manage.
• SQL Databases: SQL databases are the usual option for larger datasets since they allow for efficient querying and data management.
• Cloud Data: You can integrate data for your algorithm from cloud services like Google Analytics, Salesforce, or AWS for real-time updates and scalability.

Klipfolio’s data connectors let you import data from various sources. This way, you can gather all relevant data in one place to create your algorithm easily. 

As you import data and hop from a number of sources, remember to focus on inputs directly related to the problem your algorithm aims to solve. Efficient data collection minimizes complexity, as irrelevant data can increase processing time and lead to less accurate outputs.

Step 3: Preprocess the data

For step 3 of creating an algorithm, you must reprocess data to make sure it’s accurate. The techniques involve removing duplicates and correcting errors, like missing values, which can skew your results and reduce the accuracy of your algorithm.

• Imputation: Replacing missing values with the mean, median, or mode
• Deletion: Removing rows or columns with missing values if they are not crucial
• Normalization: Adjusting the scale of your data so that it fits within a specific range, usually the numbers 0 to 1
• Encoding categorical variables: Converting text categories into numerical values
• Feature engineering: Creating new features or modifying existing ones to represent the problem better
• Aggregation: Summarizing data to a higher level, such as converting minute-by-minute data to hourly averages

Our data feeds let you handle missing values, remove duplicates, and standardize your data. Meanwhile, our visual query builder allows you to create new data structures and perform all these complex transformations without writing code.

Step 4: Select the algorithm type

Choosing the right type of algorithm can help you solve your specific problem efficiently. Different algorithms are designed for different types of tasks, and selecting the best one can significantly affect your results.

• ARIMA (AutoRegressive Integrated Moving Average): Predict future values based on past data, such as sales forecasting or predicting website traffic.
• Anomaly detection: Practical uses involve detecting fraud, identifying equipment failures, or spotting unusual trends in marketing campaigns.
• Regression: Predict a continuous output based on input features, like estimating costs, projecting customer lifetime value, or forecasting demand.
• Classification: Sort data into predefined classes, like email spam detection, customer segmentation, or risk assessment.

Cervinodata, a data management and analytics company, used the ARIMA and anomaly detection models to predict their clients’ cost per click (CPC) on ads. By combining these models, they effectively leveraged historical data to anticipate changes while identifying spikes in CPC that might indicate potential issues or opportunities in their clients' campaigns. 

This approach helped them provide more accurate insights and better campaign management.

Klipfolio offers advanced functions and formulas that make it easier to apply any of these algorithms to your data. You just have to choose the right ones for your system, and you’re good to go.

Step 5: Design the algorithm structure

Designing your algorithm’s structure involves outlining the steps and flow so it functions correctly and efficiently. A clear structure helps visualize the process and identify potential issues before implementation.

You can use flowcharts to represent the steps of the algorithm, using symbols like arrows and boxes to show the flow of operations. These tools help you understand the sequence of steps and decision points.

On the other hand, you can also use pseudocode, which is a simplified version of code written in plain language. It outlines the algorithm's logic without getting into the syntax of a specific programming language. Pseudocode is useful for planning and communicating the algorithm’s structure.

If you’re worried about creating flowcharts and pseudocode for your algorithm, PowerMetrics’ visual query builder lets you drag and drop data sources, apply transformations, and define data flow operations with no complexity. 

Step 6: Implement the algorithm

Implementing the algorithm involves writing the code that will execute the steps you have designed. Here, your algorithm transforms from a theoretical plan into a functional tool that can process data and solve problems.

Python is a popular language for implementing algorithms due to its simplicity and powerful libraries. Meanwhile, R is widely used for statistical analysis, and SQL lets you query and manage data stored in relational databases.

But we don’t want to spend so much time creating code from scratch. Instead, you can choose from our wide range of pre-built functions and formulas to manipulate data, perform statistical analyses, and create custom metrics without the need for complex programming.

Once your algorithm is implemented, Klipfolio allows you to automate and schedule its execution. You can run it at regular intervals to process new data continuously and update your dashboards, turning it into a well-oiled machine.

Step 7: Test the algorithm

Test your algorithm through cross-validation or A/B testing. Cross-validation is a technique where you divide data into several subsets. You train the algorithm on some subsets and test the remaining ones, repeating the process multiple times to evaluate its performance.

For instance, if you have 100 data points, you can split them into 5 sets of 20. Train the algorithm on 4 sets and test it on the 5th. Repeat this process 5 times, each time with a different set as the test set.

On the other hand, A/B testing involves comparing the algorithm's performance against a baseline or another algorithm. It helps in determining if the new algorithm performs better than the existing one.

If you have a current algorithm predicting sales, run the new algorithm alongside it for a period of time and compare the numbers to see which one performs better.

Our real-time data updates and interactive dashboards let you monitor and refine your algorithm regularly without the complexity. You can compare the performance of different versions or approaches at a glance, enabling you to iterate and improve your model quickly. 

Step 8: Fine-tune the algorithm

Use the insights you get from testing the algorithm to fine-tune it. This step involves adjusting various parameters and evaluating the model to achieve the best possible outcomes.

There’s hyperparameter tuning, where you tweak the settings that control the algorithm’s learning process. For instance, in an ARIMA model, hyperparameters include p (lag order), d (degree of differencing), and q (order of moving average). Adjusting these can help the model better fit your data.

You can also use model evaluation metrics for your algorithm, such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared for regression models; precision, recall, and F1 score for classification models.

Let’s say your algorithm predicts sales, you might use MAE to measure the average magnitude of errors in the predictions. This metric provides a clear, interpretable measure of the model's accuracy. 

Additionally, it’s less sensitive to outliers compared to other error metrics like Mean Squared Error (MSE), making it the best choice when your sales data may contain some extreme or unusual values.

Step 9: Deploy and monitor the algorithm

Once you've fine-tuned the algorithm, it's time to deploy it to your production environment, whether that's a dashboard, an API, or a scheduled report. Monitor its output and watch for any changes or anomalies that may need further adjustments. Review the algorithm's accuracy, precision, and other key metrics to keep it relevant to your business goals.

Cervinodata’s CPC prediction algorithm accurately predicted certain trends but also encountered challenges with unexpected spikes and drops. For instance, it successfully predicted a steady rise in CPC from September 5th to September 9th, aligning with the actual observed data.

However, the algorithm also encountered challenges, particularly when an unexpected spike occurred around September 25th. The actual CPC experienced a sharp increase that was not fully anticipated by the algorithm, highlighting the importance of continuous monitoring and adjustment.

Solve your problems with the right algorithm steps

Building an algorithm for your business involves knowing your goals, gathering the right data from a list of sources, and cleaning it up. In the development stage, you have to choose the best algorithm type and design how it will work.

Additionally, understanding the data and potential variables involved will help you build an algorithm that addresses the problem properly. The more precise your inputs and problem definition, the better your algorithm will perform, offering an efficient and reliable solution that meets your business needs.

Creating an algorithm doesn’t have to be scary. With Klipfolio’s visual query builder, you won’t need help from your DevOps to create, test, and monitor your algorithm. With our low-code platform, you can just drag and drop data sources, apply transformations, and define the data flow with just a few clicks and zero tech skills.

FAQs

What are the basics of an algorithm?

The basics of an algorithm revolve around creating a clear and step-by-step sequence of instructions that can provide a solution to a problem or perform a task. Algorithms take specific inputs, process them according to predefined rules, and then produce outputs.

Time complexity also plays an essential role in determining its efficiency. It measures how the algorithm's runtime increases as the input data grows. Efficient algorithms are designed to minimize time complexity so that, even with large datasets, they perform quickly and effectively.

Whether you are dealing with simple or complex problems, understanding the basics of algorithms is important for anyone looking to solve business challenges efficiently.

What are the 5 guiding principles of an algorithm?

Clarity, efficiency, scalability, correctness, and readability are the five guiding principles of an algorithm. Each step in the algorithm should be clearly defined and easy to understand so users can follow the logic without confusion. 

A good algorithm should also handle increasing amounts of data or more complex tasks without a significant drop in performance. Moreover, it must produce the correct output for all possible inputs to solve the problem it was designed to address reliably. 

As your system grows, the inputs and data structures will likely expand, so your algorithm must be robust enough to maintain efficiency. A scalable algorithm adapts to larger numbers of inputs and more complex processing tasks without becoming bogged down or inefficient.

Lastly, the algorithm should be easy for others to read and understand. A readable algorithm uses clear code, logical variable names, and well-organized steps to make the process easy to follow. 

How do I create the right algorithm?

Finding the right algorithm depends on the specific problem you are trying to solve, the type of data you have, and the desired outcome. 

First, define the problem and understand the inputs and outputs required. Next, list factors such as the data structure, time complexity, and the need for accuracy or speed.

For example, if you need to forecast future trends based on historical data, algorithms like ARIMA might be suitable. On the other hand, if your goal is to classify data into categories, a classification algorithm such as decision trees or support vector machines could be the best choice.

Consider the complexity of the problem and how the algorithm will scale with more data. Simpler algorithms might be more efficient for smaller datasets, while more complex algorithms may be necessary for large-scale problems.