Data Science for Startups — Formula to Boost Your Startup!
In this article, we will discuss data science technology for startups. We will see how startups can use data pipelining and build their own data platform in order to harness the power of data. Then, we will look at some of the features of Data Science in startups.
Before starting this article you must check — What is Data Science and how data scientists work?
Benefits of Data Science in Startups
Data is becoming increasingly popular across various industries. From healthcare to manufacturing, all industries are using data science extensively. However, many industries which are already large scale in size break down production stages in building scalable predictive models.
For startups, many data scientists have to build the architecture from scratch, implying that while larger industries consume a colossal amount of data, they may not have to build products from scratch due to their previous experiences and abundance of services.
Some of the key responsibilities that a Data Scientist has to fulfill in a startup are -
- Extracting Data.
- Building Data Pipelines.
- Developing Metrics for Measuring Health of Product.
- Visualizing key data findings.
- Predicting the future with existing models.
- Building Data Products for Startups.
- Testing and Validating to improve performance.
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1. Data Extraction and Tracking
The first step towards building any data product is to collect the data. In order to collect the data, you must first envision the user base and the number of event logs that will access your application. For example, your startup specializes in building video games. Now, you have to realize the user base in three stages of your application usage. First, you need to realize how many users will install your application. Secondly, you must know the number of active sessions that the users are currently using. And thirdly, you ought to know about the microservices used by the users and how much they are spending.
Therefore, you need to collect data based on the above mentioned three parameters in order to analyze the number of users that will be using the product. Furthermore, you will need the domain-specific attributes to gauge the number of users who will use your product and how they will use the product. You will also be able to analyze the dropout rate of users and make your product better in order to reduce that rate.
In order to carry out this procedure, you should embed your application with trackers. The best measure to carry this out is through writing tracking specifications in order to identify attributes and take appropriate steps to implement events. The tracking events are essential on the client side as they send data to the server which is for analysis and for the development of your data products. Early stage startups usually suffer from data starvation. Therefore, in order to make products better, embedding event trackers in your product is the best approach towards collecting data at a dynamic pace.
2. Building Data Pipelines
After collecting data, you need to analyze, process and output the results in real time to the users. Data processing is the most important part that startups must cater to. Therefore, a data pipeline is responsible for processing the collected data and help the data scientists to analyze the data. The data pipeline is connected to a database which can be a Hadoop platform or an SQL platform. This is the end destination of a data pipeline where all the data processing takes place.
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Following are the properties of a data pipeline -
Minimal Latency and Real-Time Delivery — The data pipeline should allow the data scientists to access and process the data in real time. By real-time, we mean within minutes or even seconds. This is an important property that ensures the data products operating in real-time.
Versatile Querying — By versatile querying, we mean that the queries should be able to support lengthy batch queries as well as smaller interactive queries. This allows data scientists to analyze and understand data without any delay.
Scalable Pipelines — Since a startup has to deal with a colossal amount of data, the pipeline should be able to withstand all of this data.
Stable Updates of Pipeline — You should be able to make changes and updates to the pipeline without causing data loss or compromising with the data pipeline.
Generating Alerts — In case if there is an error in the notification of the error and there is no reception of data, then the pipeline should generate alerts.
Pipeline Testing — You should test the components of your pipeline in order to assess their scalability and data processing capabilities. However, the test events should not end up in your database and should be done without the knowledge of the end users.
Data Types for Building Data Pipelines
Also, in order to build the pipeline, the startup must recognize the type of data they will deal with and then deploy a suitable pipeline to process it accordingly. A startup can deal with the following types of data -
Raw Data — Raw data do not have any schema applied to them and are not present in any designated format. Usually, the tracking events sent are in the form of raw data and appropriate schemas are applied to them in the later stages of the pipeline.
Processed Data — With the implementation of schemas over the raw data, it becomes processed data. It is encoded in specified formats and is stored in a different location in the data pipeline.
Cooked Data — Cooked Data is basically a summary of the processed data. For example, a user event can contain multiple attributes based on his usage of the data product. This can be treated as an input to cooked data that can be used to summarize the daily usage of the product.
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3. Analyzing Health of the Product
Analyzing the metrics of data is the most important part of a data scientists role. These key metrics are pertaining to the measurement of the health of the data product. Turning raw data into cooked data that summarizes the health of your product is an essential step for data scientists in a startup. The main idea is to report the company on the performance of the data product through the identification of key metrics. There are several tools that allow us to transform raw data into cooked data. Some of the key measurements for analyzing the performance of the data-product are -
3.1 KPIs
KPIs or key performance indicators are used for measuring the performance of startups and data products. Generally speaking, it measures the health of the startup. It captures engagement, retention, growth in order to determine the usefulness of the changes applied to the product or the startup. Often, many startups use data scientists for all the roles that require data. This involves data engineering and standalone analysis. However, with the implementation of reproducible reporting events and dashboards that track product performance, we can reduce the task of supporting manual processes. This will also reduce unnecessary burdens on the data scientist, making him focus on the important roles of his job.
3.2 R for Generating Reports
R is the most popular programming language for data science. As discussed above, manual reporting should be transformed into reproducible reporting. While R is used widely in data science for creating plots and building web-applications, it is also used for automated report generation. This is an essential step that cuts down on manual reporting and reduces the burden on data scientists. Some of the useful approaches towards building reports with R is using R to directly create the base plots, generating reports with R Markdown and using Shiny to create interactive visualizations.
3.3 ETLs for Data Transformation
ETL stands for Extract, Transform and Load. The main role of ETL is to transform raw data into processed data and processed data into cooked data. This cooked data is present in the form of aggregated data. One of the key components of a pipeline is the raw events table. The ETL processors can be set up to transform raw data into processed data. We can also create cooked data from processed data using ETLs. We can schedule the collection of ETLs to run on the data pipeline. There are various tools that can assist in monitoring and managing complex data.
4. Exploratory Data Analysis for your Data Product
After setting up your data pipeline, the next step is to explore the data and gain insights about improving your product. With Exploratory Data Analysis or EDA, you can understand the shape of your data, find relationships between data features and gain insights about the data.
Some of the methods of analyzing the data are -
4.1 Summary Statistics
With Summary Statistics, we can better understand the dataset. Summary statistics involves mean, median, mode, variance, quartiles etc. It gives out a quick overview of the dataset.
4.2 Data Plotting
Data plotting and visualization is the method of providing a graphical overview of the data. You can perform data plotting through line charts, histograms, bar-plots, pie charts. Also applying log-transforms to data not present in normally distributed forms can provide with better results when plotting the data.
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4.3 Analyzing the Correlation of Labels
Using correlation analysis of several features, we can find which features are correlated within the dataset. In correlation analysis, we compare each feature of the dataset with the goal of finding a correlation between a single feature.
4.4 Identifying Important features
The most important step towards data analysis is to identify the important features. The features are selected based on their influence on other features. It is an important metric for deciding important features that are correlation heavy. For examples, two features are strongly correlated with a third feature. However, in order to find one important feature, we make use of feature analysis. We weigh several features and find the strongest one.
5. Developing Predictive Models
Data Science requires Machine Learning. Machine Learning is used to make predictions and make a classification of the data. Predictive modeling is useful for forecasting the behavior of the user. It helps startups to tailor their products based on how the user will utilize their product, that is, predicting user behavior. For example, if your startup makes use of a recommendation system, then you can develop a predictive model to recommend movies and films to the user based on their watch history. Predictive Modeling is of two types -
5.1 Supervised Learning
Supervised Learning is the development of a prediction model based on labeled data. The two supervised learning techniques are regression and classification. While regression is about predicting continuous values, classification is about categorizing values in classes. Classification deals with the likelihood of the outcome of a variable.
5.2 Unsupervised Learning
While Supervised Learning is about labeled data, Unsupervised Learning is applied where data is not explicitly arranged in labels. Clustering and Segmentation are two of the most popular unsupervised learning techniques.
As a startup, if you want to apply the above two machine learning, you must know about the eager model and the lazy model. In an eager model, the rulesets are formed dynamically during the training time. This applies to searching for coefficients in linear regression models. On the contrary, the lazy approach generates rulesets during training time. Lazy approaches are generally used for building real-time application systems where the model is updated with changes in data.
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The performance of the model is evaluated based on the type of problem — that is, a regression problem or a classification problem. Mean Absolute Errors and Root Mean Squared Errors are generally used in regression problems. On the contrary, ROC, AUC, precision, recall, sensitivity is used in classification.
Some of the tools that you can use for developing prediction models are Weka, BigML, R and Scikit-learn (Python).
6. Developing Data Science Products
Data Scientists can contribute to their startups by building data products that can be used to improve their products. In order to do so, Data Scientists need to move from model training to model deployment. There are various tools that can help you to develop new data products. Many times, handing over a report of the data or specification of the model will not ensure the operational issues of the model. Therefore, manifesting the data specifications in a real-life product will help to address various issues and prove beneficial to the data science team of the startup.
You can use Google DataFlow to put your models in production. Using this tool, you can work much closer with the data science team. With this, you can build and manage a scalable model. You can also set up several staging environments to test parts of the data pipeline prior to its deployment. Data Scientists can also use Predictive Model Markup Language and Google’s Cloud Workflow to scale large models.
Therefore, using a combination of PMML and Cloud Workflow, you can use predictive models which grasp the Dataflow services to reduce requirements for infrastructure maintenance. Basically, there are two types of data deployments — Batch Deployment and Live Deployment. In a batch deployment, the results obtained from the model when applied to large collection of records is saved for later use. Whereas, in Live Deployment,the results are obtained in real-time interface with the users.
7. Experimentations to make better products
When introducing changes to the products, it is analyzed if the results are beneficial to the startup or not. It is gauged if the product is well received by the customers or the contrary. One of the most popular and common forms of experimentation is the A/B Testing. A/B testing is used to draw statistical conclusions while applying hypothesis testing to compare the two versions of the variables. However, A/B testing offers a limitation when we cannot control the users that are part of the two groups.
Google Play provides a staged rollout feature that allows you to release your application to a sample of users. Using this, you can manage two different versions of your application simultaneously. However, you can come across biases while using the staged rollout feature. There are also other methodologies like time-series and bootstrapping that you can run along with staged rollout to have comprehensive experimentation of your product.
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Summary
We conclude that data science required for startups has to help them make their products better. Since data is the lifeline of the startups, a data scientist must do his best to increase the quality of the product. Furthermore, you must build data pipelines to assist in faster processing of the data. Analyzing the health of the product is an important aspect of the startup’s progress. Exploring the summary statistics and plotting the data can also help startups to take data-driven decisions. Furthermore, the development of predictive models to analyze and forecast the performance of data products is a substantial part of boosting a startup’s growth. Finally, we take a brief look at the experimentation techniques that a data scientist at a startup must adapt in order to improve the product.
Hope you enjoyed reading this Data Science for startups article. Please share your views on it through comments.
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