Chapter 3

 

Ch 3 - Data and Knowledge Management 

3.1 Managing date

With so much data scattered, multi-sourced and with specific requirements; effective storage and organisational systems are key to managing large amounts of data. 

For efficient and effective data management, data must be in a central location, current, secure, regulated, and high quality.

An approach organisations use to manage information is 'data governance', implemented by 'master data management' to help maintain accurate and consistent systems across the organisation. 

3.2 The database approach

Adapting to a database approach can:

minimise: redundancy, isolation, inconsistency

maximise: security, integrity, independence

Database Management systems (DBMS) - provides all important organisational, storing and accessing systems.

Because databases can contain large amounts of data, they are arranged in a hierarchy- to make them more understandable and useful.

So think of it like a tree:  

For example, in a university database, one branch (field) might be a specific student. That branch would contain the name, and connecting to that branch are other branches, such as the student major, student number, address, etc. 

3.3 Database management systems 

The most common structure for a DBMS is the relational database model. This model is made up of numerous tables. Each table contains records and attributes.

Advantages - easy to compare info and items are easily retrievable. 

Disadvantage - larger scale models can contain many intertwined tables, which becomes complicated and slower to search and access.

Query languages - allow users to request information from a database.

- SQL uses keywords to make searches  

- QBE use grids or templates to make searches

Normalisation  - breaks down and streamlines complex groups of data, minimising data redundancy and maximising data integrity, all to improve the processing performance of databases.

3.4 Data warehouses and data marts

Data warehouses and data marts:

- integrates data systems 

- uses historical data to make predictions

- restricts users from making changes

- is multidimensional 

A data warehouse environment includes: 

- sources systems 

- data integration

- storing the data

- metadata

- data quality 

- governance

- users

The organisational, storage, and processing structure of data warehouses and marts, provide organisations with an advantage when it comes to decisional response times and flexibility regarding opportunities and market changes. 

3.5 Knowledge management 

Knowledge management - systematically ensures knowledge is shared and dispersed across an organisation. Successful organisations recognise knowledge as a key asset that needs to be harnessed and applied in an environment that allows knowledge to grow.

 

Knowledge can come in the form of:

1. Explicit  

     - knowledge that can be physically documented in some form and can be distributed and 

       applied in a strategic process. 

e.g. policies, reports, strategies, goals, etc.

2. Tacit 

     - knowledge that is based on personal, informal, and unstructured learning. It is difficult

       to document and transfer.

e.g. experiences, insights, skill sets, organisational culture, etc.

Organisations that use knowledge management effectively can expect greater changes in product efficiency, customer service and employee loyalty. But I believe it's only when businesses recognise the true influence and importance of having diverse areas of knowledge on operations that they can strategise to the best of their abilities and flourish because of it.

Gray, H., Issa, T., Pye, G., Troshani, I., Rainer, R. K., Prince, B., & Hugh, J. W. (2015). Management information systems. Wiley.

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Quote of the week:  

"Data analytics is the future, and the future is NOW! Every mouse click, keyboard button press, swipe or tap is used to shape business decisions. Everything is about data these days. Data is information, and information is power." - Radi Hilaneh