Big Data Analytics and Data Science: Tools to Manage Communication Between Management and Staff

Information, in a broad sense, is systematic, processed and organised data. It gives context to other data and allows effective decision making. For instance, a single consumer’s sale at a restaurant, once identified, becomes data that becomes information when the company is able to identify which dish is the best or least popular. The same is true of complex organisations. For example an airline’s order system can be accessed by a wide range of people, including employees, customers and suppliers, to allow them to make informed decisions about the next flight.

Data has a tremendous value within organisations. Without data, management would be unable to analyse the effectiveness and efficiency of their operations, processes and systems. However, information does not necessarily exist in the form of data collected by human eyes, in the form of reports provided to management, staff and customers. Organisations must utilise information technology in order to extract information from the real world, whether it is stored on computers, telecommunications networks, or mobile devices. Doing so requires a combination of different disciplines such as information science, computer science and business administration, as well as knowledge and expertise from those disciplines.

In comparison, data science refers to applications and tools that help businesses understand how to gather and process large volumes of data, usually for the purpose of improving internal and external functions and procedures. Business analytics tools, on the other hand, refer to software tools that enable business owners to gather information and manage it effectively. Some of the examples include business intelligence tools, business metrics tools and business intelligence applications. Whilst business analytics tools to facilitate the management of large quantities of data and improve business efficiency and effectiveness, they are not the complete solution.

In order for information and data to become truly useful, it needs to be managed, analysed and used appropriately. Unfortunately, managing information and data often involve not just one but many different activities. For example, collecting and evaluating information may be done manually at different stages of an organisation’s development, such as when employees first join an organisation or when a product has been launched. Manual collection of information rarely produces effective results, whereas the use of business analytics tools may reduce the time required to collect accurate data, thus increasing efficiency. However, the optimisation of information can only be achieved through the collective efforts of those involved in the collection, analysis and management of data – this is where organisations must find strategies to address issues arising from data misuse.

As a result, one way of addressing the issues of information misuse is to create a culture of information governance – a process where individuals, groups and organisations learn to identify, define and manage their own knowledge, keeping it under check against the threats posed by the misuse of information. In order for such a culture to be sustainable, it must be consciously created and developed. One way of creating such a culture is through the implementation of policies and guidelines which define what information is relevant, who is responsible for maintaining it, how it should be utilised and in what circumstances it should be shared or published. Such policies and guidelines may not be instant, universal solutions to the problem of data misuse. Rather, they will need to be implemented consistently in order to be effectively implemented throughout an organisation so that it becomes a natural part of everyday life.

Another way of addressing the issue of information misuse is through the use of sophisticated analytics. This includes using big data analytics, such as the commercial tools of KDD (Kerry Doyle, et al), in order to detect patterns and anomalies, as well as more fundamental types of analytics, such as data science and machine learning. Data science, for example, can help to analyse large amounts of structured or unstructured data in order to discover patterns and relationships. Machine learning techniques such as reinforcement training can be used to teach computers how to anticipate and solve problems, something that was earlier only possible in the hands of highly trained humans. In both these cases, it is important to remember that big data and analytics will not replace managers, but will complement their efforts, allowing them to proactively monitor and control the extent of their organisation’s involvement in managing information and its distribution.