Clojure is particularly good at data processing and concurrent programming, two applications that have become increasingly relevant in computing. Clojure is used for everything from simple web sites to desktop applications to music synthesis systems to cloud-based Twitter analysis engines to high-frequency trading. It’s a powerful tool for building high-leverage abstractions. And its simplicity makes it great for managing the complexity of the real-world.
Prescriptive analytics, an area of business analytics dedicated to finding the best course of action for a given situation, is related to both descriptive and predictive analytics. While descriptive analytics aims to provide insight into what has happened and predictive analytics helps model and forecast what might happen, prescriptive analytics seeks to determine the best solution or outcome among various choices given the known parameters. It can also suggest decision options for how to take advantage of a future opportunity or mitigate a future risk, and illustrate the implications of each decision option. In practice, prescriptive analytics can continually and automatically process new data to improve the accuracy of predictions and provide better decision options.
Specific techniques used in prescriptive analytics include optimisation, simulation, game theory and decision-analysis methods. Advancements in the speed of computing and the development of complex mathematical algorithms applied to the data sets have boosted demand for prescriptive analysis skills.
Prescriptive analytics can be used in two ways:
- Inform decision logic with analytics. Decision logic needs data as an input to make the decision. The veracity and timeliness of data will ensure that the decision logic will operate as expected. It doesn’t matter if the decision logic is that of a person or embedded in an application — in both cases, prescriptive analytics provides the input to the process. Prescriptive analytics can be as simple as aggregate analytics about how much a customer spent on products last month or as sophisticated as a predictive model that predicts the next best offer to a customer. The decision logic may even include an optimisation model to determine how much, if any, discount to offer to the customer.
- Evolve decision logic. Decision logic must evolve to improve or maintain its effectiveness. In some cases, decision logic itself may be flawed or degrade over time. Measuring and analysing the effectiveness or ineffectiveness of enterprises decisions allows developers to refine or redo decision logic to make it even better. It can be as simple as marketing managers reviewing email conversion rates and adjusting the decision logic to target an additional audience. Alternatively, it can be as sophisticated as embedding a machine learning model in the decision logic for an email marketing campaign to automatically adjust what content is sent to target audiences.
Every businesses and organisations face the risk of unexpected, harmful events that can cost the company money or cause it to permanently close. Evaluating and managing risk is an obsession for most businesses; for others it is something to ignore at great peril to their future success. Risk management allows organisations to attempt to prepare for the unexpected by minimising risks and extra costs before they happen.
It is the process of identifying, assessing and controlling threats to an organisation's capital and earnings. These threats, or risks, could stem from a wide variety of sources, including financial uncertainty, legal liabilities, strategic management errors, accidents and natural disasters. IT security threats and data-related risks, and the risk management strategies to alleviate them, have become a top priority for digitised companies. As a result, a risk management plan increasingly includes companies' processes for identifying and controlling threats to its digital assets, including proprietary corporate data, a customer's personally identifiable information (PII) and intellectual property.
By implementing a risk management plan and considering the various potential risks or events before they occur, an organisation can save money and protect their future. This is because a robust risk management plan will help a company establish procedures to avoid potential threats, minimise their impact should they occur and cope with the results. This ability to understand and control risk enables organisations to be more confident in their business decisions. Furthermore, strong corporate governance principles that focus specifically on risk management can help a company reach their goals.
Teradata Corporation provides database and hybrid enterprise cloud analytics-related software, product, and services for various industries comprising communications, financial services, government, healthcare, insurance, manufacturing, media and entertainment, oil and gas, retail, travel and transportation, and utilities. Teradata’s cloud and hardware-based data warehousing, business analytics, and consulting services have been around for many years, including its Vantage platform introduced in 2018. What’s giving it a boost lately has been the boom in big data and advanced data analytics solutions which we have discuss in detail in this MI reports and in past issues.