Business intelligence tools have been the standard for organizations looking to remain ahead of the competition for the past few decades. With the expanding pace of digital changes in business, most analysts are increasingly asking, “What more can we do with data to assist business decisions?” Thankfully, there is predictive analytics.
Adopting data analytics solutions is a significant milestone in the development and success of any business. Predictive analytics is a widely used data analytics strategy that improves your company decisions by observing patterns in previous occurrences. As predictive analytics methodology predicts outcomes based on data, it proves to be more accurate than any result achieved through gut feelings or being influenced by anecdotal experiences.
While working on a predictive analytics project, the primary concern of any data scientist is to get reliable and unbiased results from the predictive analytics models. And that is only possible when common mistakes while implementing predictive analytics are avoided.
There’s so much that could go wrong in a predictive analytics project. But, it doesn’t have to. In this guide, our data scientists share some common pitfalls to avoid and tips on running your predictive analytics project successfully.
How to Run a Successful Predictive Analytics Project
Below are some of the expected points that you might consider when running a predictive analytics project:
1. Define your project
The first step is to find a problem that can be solved using predictive analytics. Recognize the end user for your project and identify their problems, goals, and the solutions they expect.
To make your job easier, create a document that specifies your project’s inputs and deliverables, and then double-check your resource and format criteria related to the description of your predictive analytics project. Doing this will ease your task and help you better understand what is expected from the project implementation.
While this may appear to be an easy task, many organizations struggle to spot compelling problems that need predictive analytics. Below are some of the common issues which you can address by implementing predictive analytics:
- Revenue Forecasting
- Predicting customer churn
- Fraud detection and prevention
- Improvements in marketing campaigns
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2. Identify your dataset
Gathering data is the most crucial stage of any data science project. Predictive analytics is all about analyzing the vast amount of data into trends. This collected data helps you with future predictions and remain ahead of the competition.
Note that the data you collected will work as the data lake for your project. It includes all the information you collect, ranging from structured to unstructured format, including tables, charts, and social media graphics. It is a collection of raw data, and it needs to reside in a database that is compatible with your chosen analytics tool.
Your predictive analytics model should be based on the data and demand patterns unique to your business offerings. Also, many organizations train their model solely on the generic data available on the internet. Doing so does not forecast precise results to suit your business case.
3. Analyze your data
Before moving further, it is crucial to ensure that your data collection is compatible with your predictive analytics tool. Once you gather the raw data, you can dissect and refine it until you retrieve the precisely needed information for your predictive analytics project.
Consider working with your past events, successes, and failures to identify the truth behind your insights and implement it into your new dataset to predict the future.
Do not spend too much time filtering and cleansing your data, as it may delay your project timeline.
4. Choose the right team
One of the most important decisions you’ll make is to choose the right team for your data analytics project. Predictive analytics is a field that has vast potential, but it takes a skilled data analytics partner to execute it accurately. It’s essential to have people with diverse skill sets working for you for the success of your business.
To succeed in your predictive analytics project, choose a professional team that has experience building intelligent self-learning systems. Having the right team is the heart of your project, and it becomes challenging to create a strategy or set up the right goal without them.
5. Involve others in your plan
Now that you have defined the problem to solve and gathered the data that can help you reach your goal state, it is an excellent time to involve the stakeholders and executives of your organization in the plan.
As stakeholders are the critical aspects of your predictive analytics project, they can help you with the cross-functional data you will require while setting up your project and also help to promote the initiatives to others.
Gather a diverse group of people from various positions and departments of your organization to ensure that you receive well-rounded input on the solution. Don’t forget to consult the people involved in maintaining the IT operations of your predictive analytics project.
6. Consider statistical implementation
Even though machine learning predictive analytics largely depends on data analysis, implementing statistical models works as a cherry on the cake. Consumer behavior analysis and fraud identification are often carried out using statistical models by testing and validating the assumptions.
It is wise to prepare to have your ideas tested by evidence and understand that the obvious logical conclusions are not always confirmed by reality. Believe in your calculations, and at the same time, keep an open mind.
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7. Prepare your model
The next step is to choose a predictive analytics model that best suits the requirements of your predictive analytics project.
With growing data-powered technologies around the market, many analytical services offer a wide range of predictive analytics tools based on different methods and mechanisms. Make sure you choose the right tool which empowers your predictive analytics project and is compatible with your data.
8. Close the gap between insights & actions
No data is worth anything if you cannot utilize it properly. The insights provided by the predictive analytics model are often not transparent or relevant to the person responsible for implementing those insights. In such a scenario, the insights are not utilized fully.
For instance, consider a sentiment analysis application in which the predictive analytics model has identified your customer being unsatisfied with your customer support team. How will you make this information helpful?
The information is helpful to those who work with the customer support team. The customer support team will resolve the issue and improve the brand image for future customers.
Therefore, while developing your predictive analytics model, it is crucial to identify who needs to know about your predictive analytics solutions and what they might want to do with them.
9. Deploy your project
Once you are done with the data analysis and statistical analysis, it is time to calibrate the model and interpret the results on daily routines. Remember, you don’t have to serve the numbers and statistics that show what is best for the organization unless those numbers translate into meaningful actions.
Instead of directly publishing your product to the market, it is recommended to create a prototype product and pass it to your executives and stakeholders for the beta test. There might be chances that your first few versions won’t be quite right, and it may take a few more iterations to create something that’s both useful and valuable.
10. Perform regular iteration
As the market trends change so fast, it does not take enough time for previous expectations to become old news. In such conditions, you should stay aware of the new predictive analytics features available and continuously improve your application into a more recent, better product.
It’s a good idea to regularly examine and monitor your product and test it with the new data set to ensure it hasn’t lost its importance.
13 Mistakes to Avoid in Implementing Predictive Analytics
Even though implementing predictive analytics solutions enables managers to make informed decisions, there is no perfect predictive model. The data scientists are always searching for unbiased results that can be used for their business purposes. The only way to ensure this is to be aware of and avoid potential inaccuracies and errors.
Let us discuss some common mistakes to avoid when building predictive analytics project for your business:
1. Uncertain hypothesis
Just like any other activity where you don’t know what to achieve, you usually end up wasting your time for nothing. Similarly, before beginning with your predictive analytics project, it is wise to understand your goal and have all the necessary sources that you need to achieve those goals.
2. Uncleaned and imbalanced data
Data imbalance is a critical component of any predictive analytics puzzle, and it’s something that you can’t measure in a traditional accuracy evaluation. Remember that your predictive analytics model is only as good as the data you have. If the information is outdated, scattered, or incomplete, do not expect to get reliable results out of it.
As a solution, make sure your data is clean, organized, and ready to get processed before implementing the model. You can use tools like pivot tables to quickly analyze your dataset and avoid duplicate records, errors, and biased models, which can mislead you towards false predictions.
3. Working with a closed mind
Too frequently, data scientists work with what they’ve been given and don’t spend enough time thinking about more creative elements from the underlying data that might improve models in ways that an upgraded algorithm can’t. You can significantly improve the results of your predictive analytics projects by creating some unique features and characteristics that can better explain your data patterns.
4. Not differentiating between causation and correlation
While analyzing the solutions of any data analytics model, it is a widespread mistake to define the correlation between two or more variables. It is easy to assume that one of them caused the other, but that’s not the case every time.
Mixing causing correlation is like finding the correlation in the statement– “everyone who ate the fruit died,” as this statement cannot be universally true. There are hundreds of such fake correlations that exist, and hence, do not jump to conclusions before identifying the actual causation of your results.
5. Over/Underfitting data
Over or underfitting the predictive analytics solution is a common mistake that any data scientist makes while developing their model. Overfitting your data refers to creating a complicated data model that fits your limited set of data. On the other hand, underfitting your data refers to the missing parameter, which can provide a transparent and impartial outcome.
To avoid this common mistake, devise a data analytics model that fits your set of data efficiently. Use external tools like OpenRefine and IBM InfoSphere to cleanse your dataset and provide yourself with transparent outcomes from your project.
6. Sampling bias
It is often noticed that many prospective data analysts fall victim to sample bias. It happens when the analyst tries to identify the results by inputting just a sample of data. For example, analyzing and predicting the results by running a Twitter Ads campaign for just a couple of days. This cherry-picking nature of data analytics can lead to false outcomes.
Moreover, many business sectors experience a drastic change in their sales depending on the seasonality. For instance, e-commerce sales go spaced out during festivals and holidays. Ignoring this sales prediction by considering the seasonality change can be a costly mistake.
Remember that various elements such as time duration, tools, etc., play a vital role in your outcomes. Consider every aspect of your metrics and acquire as large and feasible an image as possible.
7. Data dredging
Data analysts often test the new hypothesis with the same old dataset to save significant time and effort. Doing this will always lead to biased correlations with the results of the previous theory.
Do not repeat this mistake. Testing new hypotheses with a new dataset will always provide you with a clear better picture of your predictive analytics project. For example, you wish to identify the e-commerce sales depending on the sales data of years 2019 and 2020.
To correctly train your model in such a scenario, you can separate the datasets into two groups, i.e., training and testing. Later, consider the sales data for 2019 as the training data and test the predictions against the data of the year 2020.
Suppose the findings are too good to be true while working with a predictive analytics project. In that case, it’s worth investing additional time on your validations and maybe seeking a second opinion to double-check your work. Doing this will provide you with two different results, and hence you can measure the accuracy of your outcomes for a well-informed decision.
8. False-positive and false-negatives
While working with a predictive analytics project, statistics is the game’s hero. Most of the time, data scientists fail to identify the errors present in the statistics and ultimately end up with the wrong prediction.
Identifying false positives and negatives from analytics is the most crucial task in dealing with data science projects. False-positive indicates the condition where the statistics suggest the results which are not valid. On the other hand, false negatives are reciprocal, i.e., the statistics incorrectly fail to reveal the presence of the results present in the data.
To avoid this common mistake while dealing with your predictive analytics project, pay extra attention to your statistical hypothesis testing. You can use many online tools to filter your dataset and identify the errors that are pretty standard to notice but can impact your results.
9. Ignoring the possibilities
Always remember that every action has its equal opposite reaction, and at the same time, every reaction has its level of uncertainty. Data scientists often assume that the results are 100% reliable, and if the company takes action A, it will achieve goal B.
However, in reality, it is not that easy. There is always more than just one possibility of results while working with a predictive analytics project. As the model fetches data according to their need and requirements, you cannot ignore the possibility of more than one outcome.
Make sure you always plan your scenarios and company decisions considering more than one possibility and use probability theory to ensure accuracy in results.
10. Using primitive tools
Modern predictive algorithms forecast the outcomes from the data but cannot explain the “why” behind the results. For instance, why will promoting X product generate more revenue than product Y? What product factors should we consider promoting the most?
The primary issue is that marketers expect to anticipate the future based on current data and fail to employ cutting-edge techniques and technology. As a result, the number of characteristics defining the future is relatively low, which doesn’t provide you with deeper insights.
11. Neglecting data visualization in data analytics solutions
Data visualization plays an essential role while dealing with data analytics solutions. Even though many visualization tools like Tableau and Plotly are available online, data scientists are often so busy with technical issues that they tend to forget about transparently presenting their results.
Avoid making the same mistake. Ensure that your results are correctly visualized and prepared in an attractive way to be presented to the company’s stakeholders. If you provide the stakeholders with just numbers, you cannot expect them to understand and invest in your project.
12. Expecting machines to understand business
Predictive analytics is an emerging field that can transform how we do business. However, it may not be as easy as simply plugging in data and expecting machines to understand what is happening in your industry.
Often data analysts fail to understand that machines don’t have human intuition and biases. The machine’s predictability is only as good as the data you feed it. Creating a successful project requires more than just collecting the data, training the machine, and letting it loose. You also need to consider the nuances and exceptions of your business and factor them into the model. You should also set up KPIs to measure the project’s success before deploying the project.
13. Not considering the real-time scoring environment
It is often noticed that data scientists get off the rails for developing the perfect model. They are focused on developing the ideal model which can solve all their business needs; however, the same model is rather difficult to apply in real-world circumstances.
Developing a feasible model considering all real-time environment situations is a great way to avoid getting stuck in perfection. Do not make your predictive analytics project so complex that you cannot integrate the model into the operational system.
Predictive analytics is the most sophisticated analytics technique, allowing you to map out the number of alternatives for making better judgments and withstanding competition, ultimately helping your firm achieve sky-high success.
Project risks and potential mitigation measures have been forecasted for years based on experience, knowledge, and various risk methodologies. Predictive analytics projects enable risk assessment by leveraging data and intelligence in a way that goes beyond personal ability.
Companies nowadays are inundated with data from transactional databases, equipment log files, pictures, video, sensors, and other data sources.
Despite having in-house data analytics and business intelligence teams, many businesses fail to identify mistakes in their predictive analytics project and derive results as planned. Sometimes, it gets difficult to spot our own mistakes. Onboarding a different and experienced pair of eyes can help you objectively identify the shortcomings and have a smoother implementation of predictive analytics solutions.
The data scientists at Maruti Techlabs can help you successfully leverage realistic and actionable insights for your business growth. Our machine learning services take simple data analytics a step ahead by building advanced analytical models. Using machine learning techniques like deep learning frameworks and neural networks, we help our clients gain an edge over their competitors.
Translate your actionable insights into a blueprint of ambitious and effective growth. Simply drop us a note here, and we’ll get in touch with you!