Transforming Data Into Information

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Data and information are crucial in the undercurrent of a Successful Service Pattern approach. In this light, this paper tends to evaluate the variances between data and information. The importance of extracting information from data is also relevant and hence, will be a focus in the development of this paper. This essay will also further justify why it is significant to make a distinction between data and information.
Service pattern approach is fundamental in the conventional wisdom because it tends to bring an up-close interrelation between business and IT. Currently, numerous business enterprises have evolved their operations to a service pattern approach. In this case, businesses opt to re-design their operations in any means possible towards the more suitable service pattern approach. Adopting the service pattern approach is critical and in the process, businesses realise its numerous issues. Most of these issues include technology, procedures and infrastructure. This paper aims to comprehensively analyse the most vital question – What strategies should be embraced to evolve its data in lock-step? This question is essential in an enterprise that strategically intends to evolve towards Service pattern approach.
Data is a critical component in any modern business enterprise. However, the data question is not given due concerns and thus considered as a second thought. This paper examines deeper and investigate the details on what data interoperability involves. There have been a lot of actions in the subject of incomparability in service pattern approach.
However, these actions tend to emphasise on interoperability at the structure/platform and programming language level. In this case, the scope of interoperability is bounded to only the development-time features of’ the Service pattern approach. However, the comprehensive value-add of interoperability is only gained when two services in a Service pattern approach can effectively communicate with each other. It is also realised when exchange of data is ultimately fruitful and in a productive manner. Essentially, the data passed by the service provider to the consumer becomes of value. This is realised only when the data is expendable and if the used data is easily processed into information by the consumer.
Data is considered raw when a service consumer requests data from a service provider. These services are sent across in a way that the consumer still needs to manipulate and process the data. This process ensures that data is converted into consumable information. Raw Data is also referred to as the contents of enterprise databases and other items within the enterprise where data is stored in businesses. The data is raw since it has not been processed and hence not effective in terms of usage. The data is efficient when it has fully passed the processing procedures, namely raw data, processed information, business knowledge and business intelligence.
Raw Data can be described as the enterprises’ database content storage. The data is termed as raw because it has not undergone the normal processing and not is a form that cannot be sufficiently consumed.
Processed information can be termed as data which has evolved from its most elementary state into something more significant to the enterprise. Usually, software applications use the raw data and present it in more significant business entities and their inter-relationships.
Business Knowledge is a crucial stage of data evolution. Here, there is a deeper and more inclusive understanding of the active background where the business functions have been captured. Business knowledge can be understood as a process-information, but only at an advanced level of evolution with overloaded semantic sense and cross-references.
The Business Intelligence period of evolution is when data has evolved to a more advanced state. This stage also depicts an actionable representation to a decision-maker. This is done through the right decision making and minimisation of resultant risks,  effective resource utility and advanced business operations efficiency.
All enterprises have data in each of the above stages of information evolution. In this case, it is critically essential to consider the strategies of data evolution into useful information, especially when considering a transformational action like adopting a service pattern approach.
Making informed decision is vital in business enterprises. In this light, emphasis has to be made in order to ensure that there is a transfer of raw data into information. A great problem usually arises when information is needed by multiple stakeholders in an enterprise.
At this point, the information is essential in guiding the decision making by stakeholders. In this regard, understanding the information by all parties is significantly important in organisations. Understanding the information is also essential to consumers. In this case, all consumers need to have their own perception of the information. The business enterprise should also understand how consumers perceive the raw data and significantly how information should be evolved to present the actionable intelligence.
At first glimpse, this problem is easily resolved by adopting a universal canonical information model (metadata) and positioning it across the enterprise. However, this is similar to forcing everybody in the world to speak English and is not tolerable from a practical. ethnic. political and maybe even a legal point of view. These necessities allows a certain amount or boundary in enabling single groups of consumers to consume the data by way of converting it to suit their precise desires. In fact, this scope can be introduced as a design pattern or a best exercise.
Looking at a global model, there is a high possibility of each consumer encountering transformational agreements. These transformational agreements permit for adjustments of the corporate and universal definition of data to the field specific data definition. When moving from functional to genuine data, the transformation agreements can amend the data so as it is simply consumable within the resident area of the consumer. Therefore, each consumer is authorised to be accountable for transforming the mutual stream of data into a circumstantial definition for its own consumption. In addition, it should be noted that once a consumer defines their own set of transformational agreements, there are articles which can be re-used by succeeding interested parties.
The chief purpose of Service pattern approach is to bring a closer alignment of business and IT. Primarily, this approach is to be realistic in its nature. Therefore, the critical transfer of information between enterprise sources and consumers is required. The information provision is also crucial in the development and success of the business. The Service pattern approach also demonstrates immense significance in business and thus lots of investments have to be considered. Service pattern approach architects and enterprise managers would do well to explore in detail this very important issue.
Wori is a US-based Systems Analyst.