Top 10 Big Data Predictive Analytics Tools | 2019
The term big data is used to refer to data sets or their combinations whose size (volume), rate of growth (velocity) and complexity(variability) make them difficult to be captured, managed and analyzed using conventional tools and technologies. There is no specific volume above which data is considered to be big but most analysts refer to data sets from 30-50 terabytes to multiple petabytes as big data. It would take such a lot of time for big data to be handled using conventional tools such as visualization packages and relational databases, that the data would not be useful. Big data predictive analysis comes to rescue us from this situation; whereby you have a lot of data but your conventional tools cannot utilize it.
Where does big data come from?
Technological advancements have led to generation and use of many devices. These devices and the people who use them(us) are continuously generating data. User activity, whether it is streaming a video, making app purchases, playing a game generates data about user needs and preferences. Even when we are not using our devices, the network does not stop generating location data and other data to keep services running. According to a reliable study, it is estimated that by 2018, each smartphone will be generating 2gB of data each month. The big data technology is expected to grow at a 40% compound annual growth rate.
How does predictive analytics work?
Predictive analytics uses algorithms to determine patterns in big data that might be used to predict similar future outcomes. For example, a model can be determined to predict which customers are more likely to churn. To do this, the firm, say a telecommunications firm will use customer data such as calls made, number of texts sent, the average bill and all other useful variables. A model is then formulated to predict which customers are likely to change mobile carriers. If carriers can be used to predict reasons why customers churn, the firm can take preemptive actions to avoid the undesirable outcome. Big data predictive analysis is not a one-time practice. It should be performed on every new data to ensure that models are effective and to fulfill customers’ evolving desires.
How useful is big data predictive analysis?
Firms use big data to make a wide range of decisions, such as:
- Recommending the most competitive offers to subscribers
- Communicating with users about their usage
- Configuring the network in order to ensure delivery of more reliable services
- Designing more competitive prices, packages and offers
- Monitoring QoE to correct potential problems
These decisions and activities ensure improved user experience, creation of smarter networks, increased loyalty and extended network functionality that facilitates progress.
Ensuring the success of predictive analysis
For businesses to be able to optimize success of big data predictive analysis, they must do the following things:
1. Set business goals: Any successful predictive analytics project must have clearly stated business goals. The goal might be to prevent life-threatening hospital admittance, to upsell to existing customers or to achieve more generic business goals like increasing revenue. Predictive analysis enables us to discover correlations that can be used to suggest strategy improvements.
2. Understand data from different sources: Business’ valuable data comes from many sources. Firms use internal data from their various departments and external data from the government, social media and other public sources. Data analysts can use advanced data visualization tools to explore data from the sources and determine whether such data is relevant for predictive analysis. It is common practice among data analysts to collect all available data and let the algorithms to choose the most relevant.
3. Data preparation: Raw data is not suitable for predictive analytics. Extensive preprocessing of data is required before running the data analysis algorithms. Activities involved here include adding calculated aggregate fields, eliminating extraneous variables or information and combining data from multiple sources.
4. Create the predictive model: Predictive analysis modeling tools are used to run algorithms against data. There are hundreds of algorithms that the data analyst can use to find predictive models. The data analyst runs the algorithms on a subset of the data(known as training data). The remaining subset of the data is called “test data”, which is used to evaluate the model.
5. Evaluate the model: The model is run against the test data set in order to evaluate its predictive power. If the model is found to be effective than a random selection of the outcome, it is considered for comparison with other models. Analysts continue to run other algorithms on models until the most predictive model is found. In some cases, they may not find any because the data is not enough, or is too random to uncover a model for the desired outcome.
6. Monitor the model: The effectiveness of the predictive model should be continually monitored. As many business analysts caution, “Past results don’t guarantee future outcomes”. Firms should continue with the predictive analytics process in order to stay on top of business goals.
Every ambitious business should embrace the important role big data predictive analysis plays – ensuring optimal growth and performance by supporting a data-based decision making process.
Alteryx offers an end-to-end self service data analytics software that empowers data analysts and scientists alike. Alteryx can perform various statistical functions while boasting a user friendly interface, and empowers users to unlock greater insights in hours, not weeks. Learn about Alteryx here!
IBM Predictive Analytics employs advanced analytics capabilities spanning ad-hoc statistical analysis, predictive modeling, data mining, text analytics, optimization, real-time scoring and machine learning.
Anticipate the likelihood of future outcomes and steer your business in the right direction with predictive algorithms and machine learning.
BOARD BI Software offers the best tools to incorporate all the predictive analytics into daily business operations and the decision-making process. Predictive analytics tools of BOARD allows to drive better decision-making with more meaningful and predictive insights.
ADVIZOR Solutions provides business analysis software & consulting services. Make better, faster decisions from your data with visual, interactive software!
Happy employees lead to happy customers. Zendesk Explore helps companies provide great service through our predictive analytics tools.
Spotfire is so much more than a fancy chart maker. It’s a user friendly analytics tool that makes advanced features easy and consumable for the masses.
With Adobe analytics predictive marketing, marketers can easily analyze volumes of data, uncover the most impactful insights, and predict which campaigns will yield the best results.
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Experience predictive analytics and prescriptive analytics for the real-world. Emcien’s machine learning software solves the problems preventing businesses from creating value with predictive analytics. Get software that programs itself, loves your dirty data, and provides explainable remedies with every prediction.