Predictive Analytics: Improving Accuracy with Big Data

In this ever metamorphizing technological world driven unanimously by consumer-centric strategies, change is the only constant. Today it has become imperative for companies to be substantially agile and sophisticated; enough to keep up with the pace of changing demands such as a market trend or change in consumer needs. This lays the foundation for predictive analytics.

What is Predictive Analytics?

As the name suggests, predictive analytics is the category of data analytics that involves predicting future outcomes by taking into account historical data sets and past behavior. It leverages techniques like data mining, machine learning, artificial intelligence, and statistics to extract data points that can efficiently forecast trends and behaviors into the future. In other words, predictive analytics uses historical and transactional data patterns to identify future risks and opportunities for businesses.

According to GlobeNewswire; Global predictive analytics market is expected to reach 22.2 Billion USD by 2027, growing at a CAGR of 19%.

Essence of predictive analytics is evident from the fact that it allows organizations to focus on day to day critical tasks while automating forecasting.

Why is Predictive Analytics important?

While the complete utilization of vast amounts of data is still enigmatic, enterprises have already started capitalizing on data analytics to remain competitive and agile among one another. Advanced machine learning predictive models have taken a step further by enabling companies to design business prototypes through simulating responses based on past data.

Big data in amalgamation with predictive analytics has not only enables businesses to take strategic business decisions but also articulate a roadmap for an agile and astute business. You no longer need to depend on a ‘crystal ball’ to leverage a vast trove of data for future decisions.

Firstly, the urge to remain competitive through cutting edge products and services prompts companies to look out for strategies to solve long term persistent problems through data-driven predictive models. For instance, manufacturers rely on predictive analytics to effectively maintain equipment by anticipating failures, forecasting requirements, and reducing operating costs.

Secondly, impulse to enhance customer retention rate and promote cross-selling opportunities has driven companies to invest in technologies which can effectively predict ROI and performance of marketing campaigns. This helps organizations to speculate new campaign success through past campaign metrics such as the revenue generated, churn rate, conversion rate, etc.

Thirdly, operational efficiency by effectively managing resources and inventory is another aspect where companies are relying on predictive analytics. For instance, predictive models are important for airlines to regulate a feasible ticket price.

Fourthly, ability to identify prospective customers has increased demand for predictive analytics tools among companies. These tools can effectively analyse structured and unstructured customer data, and eventually predict customer expectations. This in turn helps companies increase business opportunities.

Fifthly, risk anticipation and mitigation open another area of reliance on predictive analysis wherein companies can use predictive models to the creditworthiness of a potential investment. For example; banks use predictive models to calculate credit scores in assessing the creditworthiness of a buyer.

Sixthly, customer relationship management is an integral aspect of any organization and predictive analytics play an important role by analysing customer behaviour as well as existing inventory data. Companies can now leverage customer data through social media, transactions, web browsing, etc. to analyse their spending patterns and interests, and eventually convert this information into meaningful insights.

Finally, predictive analytics finds its importance in supply chain management by rendering the process an accurate, reliable, and low-cost structure. Companies are now applying predictive analysis at every step of supply chain management to facilitate effective execution and prevent possible ripples.

Predictive Models and Analysis Applied to Business

Credibility of predictive analysis in accurately anticipating future forecasts and making the right decisions is evident from the above-mentioned reasons. An efficacious predictive analytics solution is always supported by various models and algorithms based on an extensive range of applications. It is therefore imperative for businesses to meticulously choose the best predictive analytics model for a successful predictive analytics solution.

Choosing a predictive model depends on various factors like the learning set or historical time period to get data from, variables having probable relation with predicted events, algorithms to be applied to the model, and output of the model.

Given below are top 5 predictive models adapted by companies in various businesses and how they are used for predictive analysis:

1. Classification Model:

These models work by assigning different data points into specific targets or classes, based on their unique features. Classification models can be binary models wherein the target variable is binary (True or False), or multiple models (with nominal target variable). These are the simplest and widely used predictive models because of their ability to provide broader analysis for quick and nimble decisive actions.

Figure below shows BIRD’s classification model based on decision tree algorithm predicting priority level of incidents for an IT helpdesk. The model takes into account factors like urgency, duration of the incident, severity of the incident, etc.

Figure 1: Classification Model based on Decision Tree Algorithm

2. Forecast Model:

These models are used to generate estimated numeric value for new data by considering inputs from historical data. Forecast models are the widely used predictive models, because of their ability to consider multiple input parameters and are used in handling metric value predictions.

For example; A restaurant owner can predict the number of customers by considering factors like nearby events, weather forecasts, disease outcomes, etc.

Figure below shows BIRD’s forecast model which had predicted number of insurance claims for next months with an accuracy of about 65%. The screenshot also shows the possible trends on the number of claims.

Figure 2: Forecasting Model predicting number of insurance claims

3. Time Series Model:

A time series model uses different data points taken from previous year data to develop a numeric metric which can predict possible future trends for a stipulated time period. These models are applicable in cases where it is required to monitor a particular variable transition over time.

For example, a hospital might use a time series model to study change in number of patients over past few months. A business owner would use the same to study sales for past few quarters.

Compared to other models, a time series model can successfully forecast progress of a variable for multiple regions or projects at the same time.

4. Clustering Model

A Clustering model works by sorting data points into different groups based on common attributes. This is useful for applications which require creating tailored strategies without compromising on time and effort.

For instance, an e-commerce company would rely on a clustering model to promptly separate customers into groups with similar characteristics and eventually devise large scale strategies.

Given below is a figure showing an Insurance claims data divided into different clusters with similar attributes. The model is based on K-means clustering and also shows statistics and frequency of different factors within each cluster.

Figure 3: Clustering Model on insurance claim data

5. Outlier Model

Unlike the other forms of predictive models which work with the entire set of historical data, an outlier model considers anomalous data points within the dataset. It is defined to identify anomalous or unusual data different from the norm. The unique data is either identified individually or in conjunction with different numbers and categories.

One of the most vital use of this model is in fraud detection, especially by the retail and finance sector to identify fraudulent transactions including information such as money lost, purchase history, time and location of purchase, etc.

Predictive Analytics and Big Data

Proliferation of various data sources and smart devices has resulted in an exponential growth of data over the past few years. With advent of advanced technologies, it is now possible to store and analyse the vast prevailing troves of data.

The term ‘big data’ is now a commonly used term describing the juxtaposition of structured, semi-structured, and unstructured data from disparate sources. However, this staggering volume of collected data is of no use unless and until there is a strategy to exploit the information and extract new insights. This is where the role of predictive analytics comes into big data scenario.

Predictive analytics can be termed as the pragmatic consequence of big data and business intelligence, providing the functionality to extract intelligence from the data sets. It forms the crux of a business intelligence project working on the insights extracted from big data analytics.

Predictive analytics uses historical data along with the gained insights to forecast future events. It is effective in transitioning big data from past view to future perspective view.

Best practices for better predictive modelling results

Even though the credibility of predictive modeling for a successful data-driven business is undoubtful, achieving the desired results can be intimidating at times. In fact, even after implementing all the effective strategies and algorithms, you might end up with an inaccurate model.

Two of the most common elements of imprecision in predictive modelling are bias and variance, with both being indirectly proportional to each other. While biasing implies your model is not capturing relevant signal from the data, variance means the model is too flexible picking up random signals and patterns in addition to the main data points.

Given below are seven apt and succinct methods which you can consider applying to overcome above mentioned problems and improve accuracy of your predictive model:

1. Add more data:

Affording to append your existing datasets with more data is always a good idea to achieve better and accurate results. Even though it not practically possible to increase the size of training data, one can simply ask for more data and thereby bring variance down, without affecting bias.

2. Deal with missing and outlier values

One of the primary reasons of a biased model is the presence of missing and outlier values in the training data. Sophisticated methods to deal and treat outlier/missing values include:

3. Do feature engineering

This is the most important element of your entire modelling exercise involving extraction of more information from the existing data. It is therefore important to choose your variables carefully and with precision. Feature engineering can be done either by transforming existing variables to normalized value and/or deriving new variables from the existing ones.

Figure below illustrates how BIRD enables feature engineering on a simple drag and drop of variables. You can either select all the fields or particular fields, and even add new field using the ‘create custom field’.

Figure 4: Feature Engineering in BIRD BI tool

4. Work on Feature Selection:

This practice is applicable when you have more features compared to the data points, identifying the attributes which demonstrate relation between the variables and the target variable. Common methods include selecting features with higher impact, visualizing relation between variables, and using statistical parameters.

5. Optimize parameters for algorithms

Most of the machine learning models are driven by parameters which play an important role in the outcome of the training processes. It is important to decipher meaning of each parameter and how it impacts the model.

Figure below shows BIRD’s Create Model screen wherein users are allowed to optimize parameter values for a selected model type.

Figure 5: Parameter tuning in BIRD

6. Use ensemble methods

This is an efficient approach combining various learning sets (individual models) to get a final stabilized and accurate predictive model. This is achieved by the following:

  • Bootstrap Aggregation or Bagging: It involves using several versions of the same model on different training set samples.
  • Boosting: It consists of training different models in a succession with each learning from its predecessor.

7. Cross Validation:

Unlike the above 6 methods which aim to improve accuracy of the model, this practice is suitable to ensure efficient performance of the model. It involves testing the finalized model on an unused and untrained sample of dataset.

Even though the above-mentioned best practices are effective to smartly outrun your peers in the competitive market, their credibility can be achieved only when you have mastered each step. Only then it is possible to achieve the desired robust predictive model.

How Predictive Analytics transforms data into future insights?

In this data driven world and hyper competitive market, unleashing the potential of data is imperative for organizations to stay ahead in the competition. The humongous amount of data is of no use unless you have derived insights from it. Predictive analytics allows brands to go beyond ‘whats’ and ‘whys’ of events, to comprehend future happenings and get prescient insights.

While understanding the in-depth mechanism involved in predictive analytics can be complicated due to the complex algorithms involved, this article tries to articulate a high-level view of converting data into future insights using predictive analytics.

Figure 6: Predictive analytics stages

Once the predictive model is successfully simulated and operated, the user is presented with a dashboard displaying insights providing a sneak peek into the future. Out of the plethora of business intelligence vendors offering augmented analytics solution, BIRD is one of the most propitious options.

BIRD’s smart analytics platform provides users with accurate, authentic, and prescient insights in the simplest and most effortless way.

The artificial intelligence and machine learning-powered augmented analytics engine not only lets even the naive non-technical user ‘drive’ but also get access to the most unanticipated analysis. The figure below shows one such example of an augmented analytics dashboard showing derived future insights from raw data, using a clustering algorithm.

Figure 7: BIRD’s augmented analytics storyboard showing future insights

Common Misconceptions of Predictive Analytics

Predictive analytics has undoubtedly established a strong dominance for organizations in the competitive marketplace. Companies can proactively take decisions like reducing costs, improving services, increasing customer base, improving customer retention, etc.

However, despite the countless advantages, few myths and misconceptions are clouding an organization’s judgement to invest in predictive analytics. In this article, we are addressing five common misconceptions and try to debunk these myths so that your plan to adopt this powerful technology is not impeded.

Misconception #1: You need a PHD or Math’s degree to build predictive models

A generally accepted belief has been prevalent, since a long time, that implementing predictive analytics solution would require huge amount of highly educated and skilled human capital.

On the contrary, you actually need only some working knowledge of statistics and data to effectively build a predictive model and glean insights from the model. In fact, there are software solutions like BIRD which effectively guide you towards building an efficient predictive model.

Misconception #2: Human judgement would be replaced by predictive models

The fact that predictive analysis is majorly powered by artificial intelligence, makes it prone to misconception that human judgement or intuition will be replaced or completely dismissed in the entire process.

In reality, predictive modeling aims to enhance or augment human expertise in unleashing the potential of data analysis. In fact, human involvement is always mandatory to comprehend data-driven reports and carefully select datasets.

Misconception #3: You don’t get anything new from predictive models

While you might be lucky in few occasions to get your intuitions right, most of the times predictive models will turn the tables, bringing new, unexpected insights to light. Predictive models can actually reverse or confirm your notion about existing data variables, actually with robust anecdotal evidence.

Misconception #4: You must start small with limited set of features

To start small in your predictive analytics project means picking up a limited set of existing business problems, using a sample representative of input data points, and/or applying the models to a small number of instances. However, none of these would prove beneficial, rather would restrict the capability to effectively segment data and extract prominent features.

Misconception #5: The job is done once predictions are successfully converted to actions

Do not forget the fact that predictive models are built on dynamic data sets and environmental assumptions which might change at any moment. Henceforth, it is mandatory to constantly revaluate and update the models at frequent intervals. This is to make sure the models do not degrade and lose their predictive prowess.

Thanks to the availability of multiple efficient tools in the market, predictive analytics has now transitioned from being the sole prerogative of data scientists to mainstream operations. Therefore, it is imperative for organizations to be aware of the above mentioned and many other misconceptions before banking on any predictive analytics solution.

Example of Predictive Analytics

Since its inception, predictive analytics has successfully leveraged advanced technologies like data mining, statistics, artificial intelligence, deep learning, and machine learning to help enterprises reach their business goals. Predictive analytics is implemented across every industry in a multitude of uses such as optimizing marketing campaigns, detecting fraudulent transactions, mitigating risks, improving operations, etc.

Given below are two examples of predictive analytics in different sectors:

Healthcare: In this highly uncertain pandemic era, predictive analytics has emerged as the viable solution to ensure seamless delivery of patient care services.

One such example has been a study published in Mayo Clinic Proceedings, which used web-based predictive analytics to ascertain COVID-19 hotspots by identifying the correlation between Google search trends and the number of daily COVID-19 cases in each state across the USA.

Mohamad Bydon, a Mayo Clinic neurosurgeon and principal investigator at Mayo’s Neuro-Informatics Laboratory, said: “The Neuro-Informatics team is focused on analytics for neural diseases and neuroscience. However, when the novel coronavirus emerged, my team and I directed resources toward better understanding and tracking the spread of the pandemic”.

“Looking at Google Trends data, we found that we were able to identify predictors of hot spots, using keywords, that would emerge over a six-week timeline.”

Retail: The most ubiquitous influence of predictive analytics can be found in the retail sector, especially in forecasting sales and improving customer relations. For example; an online retailer recently banked on BIRD’s predictive analytics engine to model relation between sales and other factors.

Below given storyboard illustrates the aforementioned example showing how sales can be influenced by various factors like quantity, profit, discount, state, etc.

Figure 8: Sales forecasting smart insights storyboard

Above given storyboard shows the following information:

Firstly, value of sales with respect to different parameters in both graphical and narrative visualization.

Secondly, comparison of actual and predictive values of sales for each factor, showing the outliers.

Thirdly, expected sales value along with the simulation model wherein user can control the output by changing different parameter values.

Predictive Analytics Trends

Predictive analytics powered by artificial intelligence and machine learning (its subset) have undoubtedly disrupted almost every industry even before the Corona virus pandemic stuck the world in 2020. COVID-19 pandemic indeed had, and is still having overt effects on businesses.

Now that most of the year 2020 has withered away in fighting the outbreak, the next year is surely going to see self-learning algorithms and smart analytics helping us in continuing this fight.

Given below are five predictive analytics trends to expect in the coming years which will definitely reshape our business strategies and priorities.

Figure 9: Predictive analytics trends for 2021

A plethora of predictive analytics software is emerging as a powerful business tool in the market, ensuring automated analysis and operations. You can now focus on strategies to improve business and revenue, while your artificial intelligence-powered solution will help augment accurate forecasts.

And yes! you can surely count on BIRD as the most efficacious predictive analytics tool.