Prescriptive analytics models utilize predictive analytics models that have been trained on past experience data. These predictions are then applied within the context of the company’s business rules, allowing optimal actions to be recommended. The final decision of which recommended action to follow can be automated through integration into machinery or dynamic control of workflow systems; such decisions are usually high frequency and low impact in nature such as providing customers with real-time customized price discounts. Alternatively, the recommendations can simply be provided to support human decision makers and are often higher impact and lower frequency types of decisions; examples of such decisions may be making large changes to a company’s operations, for example building a new distribution hub for a company with a suitably complex supply chain.
BusinessOptics has been built to be easily integrated with other systems, including web services and existing enterprise systems. A variety of adapters exist to connect to different systems and services. However, should there not be direct support for the system you data resides in, it is very easy to use the BusinessOptics REST API to automatically update data in BusinessOptics which can then be used in your models and dashboards. This allows you to make sure that the decisions you take are based on the most up-to-date information available. It also allows you to easily compose BusinessOptics with other cloud systems. The simplicity of the REST API also makes it easy to use third party data integration tools.
In the unsecured lending environment clients who service their debt in line with their contractual agreements cost a business very little after the sales process is complete. In contrast, lenders spend significant amounts of money and employee time on call centre interactions, administrative and legal processes for the (typically smaller) group of non-performing borrowers. Predictive credit risk analytics can help target the most effective course of action to take with a particular borrower, given their demographic details and payment history.
Traditional supply chain management techniques struggle to take into account real time events affecting the delivery of goods. However, in order to remain competitive, companies and their supply chains need to be more flexible and adaptable to those realtime events. Examples of such events include sudden changes in route availability (e.g. due to accidents or traffic congestion), short-notice requirements from customers and mechanical problems with delivery vehicles amongst many others.
In the previous blog post we had a look at What Analytics is, today we will be exploring the three dominant types of Analytics: descriptive, predictive and prescriptive.
As prescriptive modelling becomes more important and broadly deployed in all aspects of an enterprise’s business processes the need for collaboration between teams of modellers has become increasingly more important.
The base of any collaborative modelling system is the ability to understand and ‘read’ your colleagues’ models. This can be extremely difficult when working in a statistical (or general purpose) programming language such as R or SAS. Even seasoned programmers can struggle to understand the purpose of large amounts of code. This problem is exacerbated in spreadsheets where the underlying logic is hidden from the user until they click on the cell. BusinessOptics however is inherently visual in nature and abstracts away all the underlying computational mechanisms and complexities. This results in models that are very close to the domain problem as well as easily visually navigated. This makes understanding the workings of a BusinessOptics model much simpler, especially in cases where there are complex relationships between many variables and through time.
In its day-to-day operations an unsecured lender devotes the majority of its efforts to operational concerns such as:
marketing to new clients,
evaluating loan applications,
managing non- and slow-payers,
Each of the above operational tasks involves an interaction with a client through a call centre or other correspondence. The business succeeds or fails according to the aggregation of the outcomes of these individual interactions. It is therefore critical that each of these interactions is aligned to an effective strategy which is coordinated by management.
The BusinessOptics Sales and Customer Analytics solution is continuously increasing the scope of its application throughout organisations by:
rolling out additional predictive analytics models that apply to various business functions; and
increasing the functionality within existing models to more closely reflect specific business problems.
In addition, technological enhancements to the platform are enabling users to be more efficient and increase the speed at which they can extract value.
We are glad to announce that the solution has once again reached new heights and the latest release of the Sales and Customer Analytics solution is now available.
Demand forecasting and management is a vital part of supply chain management. Over and underestimating demand both result in unnecessary costs being incurred which can be extremely detrimental to businesses’ bottom lines. Big data prescriptive analytics can help companies dramatically improve their demand forecasting accuracy thereby reducing costs and improving customer satisfaction.
According to Gartner “analytics” is used to describe statistical and mathematical data analysis that clusters, segments, scores and predicts what scenarios are most likely to happen.
Bernard Marr refers to analytics as the capability to collect and use data to generate insights that can influence fact-based decision making.
BusinessOptics believes analytics is the use of data combined with computational models of reality both past and future in order to support decision making.