What Every Enterprise Must Know About Predictive Analytics

1 min read

Predictive analytics software solutions analyze large amounts of data, construct predictive models by leveraging statistical algorithms, and incorporate machine learning (ML) into business processes. Today, these solutions are seeing increasing popularity in enterprises across industries.

Keys Features of Predictive Analytics

Openness

A successful predictive analytics implementation must natively integrate with key open-source ML frameworks such as Python, R, and TensorFlow. The more open a predictive analytics platform is, the higher the efficiency and productivity of an organization. Open predictive analytics solutions allow for models to be scripted in diverse languages. An open solution can also be deployed in any environment, such as on-premises in a data center, in a private or public cloud, or even on a hybrid landscape.

Security

Security is an important part of predictive analytics. Today, malicious cyberattacks are becoming increasingly common, and predictive analytics solutions must come with robust security features that address regulatory demands. An ideal predictive analytics platform must include features such as anonymization and real-time masking at the database level.

Speed

Predictive analytics solutions process data at immense speeds, and whether these solutions are useful or not heavily depends on how this processing speed is leveraged. ML models with an in-memory database can score live data on a real-time basis—a feature that is necessary for effectively embedding AI into large-scale business operations. Correctly-deployed high-speed predictive analytics solutions allow users to combine advanced analytics processes such as text, streaming, and spatial analysis with ML algorithms.

Apart from high processing speeds, predictive analytics platforms should provide quick deployments and easy maintenance models for developers and operations teams. To achieve this, a strong portfolio of function libraries, APIs, and core ML models should be indexed and categorized for developers according to ML tasks such as regression, classification, forecasting, segmentation, link analysis, and recommendation. Once this is achieved, developers will be able to train the models efficiently by using specific datasets based on their needs.

Challenges with Predictive Analytics

Lack of Expertise

A business with IT personnel who do not have a thorough understanding of Python, R, and statistical modeling may not be able to leverage predictive analytics to its fullest capacity. To overcome this shortcoming, such businesses can either add data scientists to their staff roster, invite them in a consulting capacity, or hire a third-party managed services provider.

Absence of Actionable Insights

Predictive analytics solutions generally forecast business trends and are unable to generate concrete actionable insights for end users. Businesses can overcome this hurdle by adopting another tool to go with their predictive analytics solutions, but this might lead to workflow interruptions.

Disruptive Adoption Process

Predictive analytics is typically leveraged through standalone solutions that require businesses to discard their primary applications, which may have worked great in the past. Without correct infrastructure and training to complement the adoption of predictive analytics, a business might face big problems due to incorrect implementation and use.

Takeaway

While implementing predictive analytics technology may seem risky and expensive at first, modernization has several benefits in the long term, such as delivery of cutting-edge business tools and improved cybersecurity. Predictive analytics platforms enable businesses to seize opportunities that drive value for stakeholders and customers alike. If an enterprise sees a genuine business need for predictive analytics in its daily operations, it should not hesitate to begin implementation as early as possible.

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