Data Governance

Data Governance

The key component of an enterprise information management strategy, data governance is the decision-making and an oversight procedure to create and maintain the quality and integrity of an enterprise data.

Data governance Institute state that  “Data Governance is a system of decision rights and accountability for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.”



Reason for use of data governance

Uncover adulterated data from enterprise which is not just that of hackers but also by staffs and contractors having right of usage to sensitive areas, whilst the necessary safeguards are not sufficient.

Two examples of significant data slip in 2014

One of the largest bank in the state JP Morgan Chase, had a huge data breach which affected 76 million households and about 7 million small businesses. Hackers got hold of personal information of customer names, phone numbers, email addresses and addresses.

The other organisation is the world`s largest home improvement, Home Depot, it was confirmed that whopping 56 million credit and debit cards were affected by a data breach, and also 53 million email addresses.

The Irish media also reported various number of data governance issues in connection with government departments and private companies.

Data Protection Commissioner Annual Report for 2014, reports a record number of data breach notifications in 2014 (2,264).

A thriving data governance system must including the following:

  • Custodians and owners of data assets of an organisation must be clearly stated or define.
  • Responsibilities of the different section of data and the outline of level of responsibilities in relation to Accuracy, accessibility, consistency and completeness must be clearly written.
  • There must be written organisation policy regarding storing, archive, back up of data and protection from possible mishaps.
  • Controls and audit procedures must be put in place to ensure ongoing compliance’s with organisation and government regulations.
  • Rules protecting data access and levels of accessibility by personnel

Data governance is a vital component of any organisation as it addresses data quality and security requirements regardless of where the data is captured, utilized or stored. Its importance is being understood globally as evidenced by Rand worldwide survey in 2013 on data governance, which found that 96% of respondents shows that data governance is very  important within the organisation.

Customer demands, regulatory compliance, competitive pressures and mergers and acquisitions are motivating organizations to improve data quality and access to information. Leading organizations now realize that data governance is critical to their future viability actionable and intelligent data which gives an organisation a competitive advantage.


References – Data Governance Benefits for Today and the Long Term (10 Jun 2013) – data governance

The data governance – New Regulations Bring Advancements in Data Governance

Data Governance online: retrieved 18/08/2015

Gwen Thomas (2014) Defining Data Governance; retrieved 12/08/2015


Master Data Management


Master Data Management

Master data is the high-value, core “entity” data used to support critical business processes across the enterprise

master data management

Master data management (MDM) is the collection of models, procedures, and channels for the right identification, meaning and management of data component within a business organisation.

According to Gartner (2013) Master data is consistent and uniform in set of identifiers and spread in association to the core entities of the enterprise including customers, anticipated clients, suppliers, sites, ranking and chart of accounts.

Information and Data Management (IDM) can be seen as a set of related disciplines that aims to manage the data quality positively, from the incubation to end

Benefit of Master Data management (MDM)

MDM strategy and solution of an organisation can be an added value to the business achievement by:

Organizations that employ an MDM strategy and solution can achieve measurable business value by:

  • A trusted image of data for the improvement of reports and better decision making
  • Customer relations, customer acquisition and customer retention will improve by the ability to access a single view of the customers.
  • Support Data integrity and improve data visibility to improve regulatory report compliance and decrease risk.
  • Increases operation efficiency within quality master data, result in decrease manual process and errors.
  • MDMs put all resources together, that is, the people, methods, and system using the information data and having a standardise views of the organisation data.
  • MDMs vision is to make available an in depth and reliable meaning of all organisation data
  • It careful method of data management and decision making, give it a grounded framework for business governance


References & Resources

Master Data Management; retrieved 13/07/2015


Database Approach

Organisation needs vital information to be successful in an agile world,this information is needed to gain a competitive advantage against it opponents. different levels in an organisation requires different data information’s.

Types of Database

Organisation choice of database will be totally dependent on the type of business engaged in ,the usage and the swiftly avail abilities of information gathered.


A sales retail organisation such as dealing in products and services will require an up to date information on sale,stocks,payments and supply purchases showing day to day operations.every transactions must be on record correctly and promptly.a database that is built mainly to help a business day to day operation is known as an operational database(on-line transaction processing(OLTP),TRANSACTION OR PRODUCTION DATABASE).

Data Governance Framework

decision diagram



Naturally,analytically databases consist of two key fundamental:a data warehouse and an online analytically processing(OLAP) front end.

Data warehouse

The data warehouse is a technical database which stores data in a configured optimized decision aid.


Try R

“R is a tool for statistics and data modeling. The R programming language is elegant, versatile, and has a highly expressive syntax designed around working with data. R is more than that, though — it also includes extremely powerful graphics capabilities” ( use in the manupulation of data and the presentation to making it compelling.





Fusion Table


1   Importing Data

The Raw Data used for the project was downloaded through the Irish population census 2011,CSO website.

2    Preparing files for analysis with the use of Excel

  •    The raw data from CSO was downloaded in to Excel sheet and filtered.
  •    The original data was inserted into 4 different columns and it was sorted to just 2 columns i.e. province and the total of each of the province     The raw data also contain North and South Tipperary population figures, these two were merge together into one single value for Tipperary and Dublin total number was used
  •        Instead of the areas under Dublin as shown in the raw data.


 3   The sorted data and kml file was then uploaded into fusion table through Google  drive.

 4 Visualizing

  • used the data to create map instantly and then filter for more selective visualizations


 5   used the data to create 2011 counties story in the data acquired

  • Achieved by Application of colors on selected data by making intensity map for the counties by the use of KML polygons.

6   Publish the visualization on



Data Quality and Data Governance

The use of Quality data within a  database system in an organisation is an important aspect a business must consider carefully if accurate business decisions must be reached.

Data Quality examines various stages which must include the following:

  • Accuracy
  • Relevances
  • Completeness
  • Timeliness

data qulity pic

Example of Data Governance: CBIG’s data governance experts:Improve Data Quality, Manage Risk, Maintain Compliance


Data is the building blocks of vital information which is used to process data and it is essential that this data must be meaniful,the acquired information must be on point i.e accurate,relevant to the business and timely.this are the critarial of a sucessful database in arriving at making a good business decision.


References & Resources

Online;CBIG’s data governance experts:Improve Data Quality, Manage Risk, Maintain Compliance: retrieved 23rd July 2015.


Database Management System(DBMS)

“in a world where scientists have sequenced the three billion based pairs that make up human DNA,we take for granted the benefits brought to us by relational databases” Carlos Coronel(2013)

Making vital decisions to taking advantage of current opportunitie presented by the digital world,from which raw facts can be derived from, known as data.

Example of Raw Facts:

CCAFS Big Facts on coffee production in Latin America (facts not from the Working Paper) by Cecilia Schubert, CCAFS Communications Officer with theme Data & Tools and Linking Knowledge with Action.



Raw facts can be effectively and efficiently managed when stored in a database which can be stored,access and adjusted swiftly when required.

Having a good understanding of the features and benefits of filing systems is vital due to the fact that, this is usually the source of complex data management limitations.

data management pic



References & Resources

Text book

Stephen Morris, Keeley Crockett , Peter Rob & Carlos Coronel (2013) ;Database Principles: Fundamentals of Design, Implementations and Management

Online;A look at how a changing climate will hit South and Central America: retrieved 23th July 2015.

Big Data Analytic

Big Data Analytics is high-volume

Let first start by understanding what a data is and then proceed to what really are Big Data Analytic and the importance of Big Data Analytics to individual business and organisation.

A Data can be referred to as a collection of facts, like numbers, words, measurements, observations, facts, or even just explanatory of things.

  • Qualitative data is descriptive information in nature (it describe)
  • Quantitative data, this is numerical information in nature (numbers)


While Big Data Analytics is defined “ as the process of examining large data sets containing a variety of data types — i.e., big data — to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. The analytical findings can lead to more effective marketing, new revenue opportunities, better customer service, improved operational efficiency, competitive advantages over rival organizations and other business benefits” Margaret Rouse(2015).

Importance of Big Data Analytics

Big data analytics helps organizations to analyse a mix of structured, semi-structured and unstructured data in a bid to detect valuable business information and insights which will help in the growth of the organisation and gain competitive advantage. This help in the examination of large data sets which contain a mixture of data types.

The Big Data Analytics help in the following ways:

  • It reveals hidden patterns
  • Unknown correlations and reveals common misconceptions
  • Current Marketing trends
  • Preferences of Customers’
  • Reveals Growth and sustainability.
  • Provide valuable business information.
  • Opens doors to new revenue opportunities
  • Improves customer relationship and services
  • Effective and efficient operational service.

Why Businesses should be interested in Big Data Analytics

According to the research firm IDC, “the worldwide big data technology and services market will grow at a 27% compounded annual rate, to exceed $32 billion by 2017”.

The emergent of the social networks, Blogging and apps is revealing a lot of information that was not readily available before. Organisations are learning to use this fundamental new source of data to generate more revenue.

Businesses needs to show more interest in big data because it can help reveal more patterns and interesting variances than smaller data sets, with the possibilities to shows new observation into the customer’s behaviour, patterns or perception of customers  and much more.                                                                     Never the less, to gain business value from data’s obtained; new technologies & tools are required, which can managing and analyse non-traditional data and work together with the traditional enterprise data.

big data sourceDom Nicastro (2013): Data Week’s 2013 Award Winners for Analytics and Big Data Strategies


References & Resources


Tiffani Crawford (2013) Big Data Analytics Project Management

Inhi Cho Suh (2014) Vice President of Big Data, IBM: Five Ways Companies Can Compete Using Big Data and Analytic:



Management Information System (MIS)

Welcome to DBS Data management project.

IT issues are part of an organisation internal competitive advantage.the achievement of top management decisions mostly depends on data and information available.

This blog is about the developmental,policies and procedure of data management which entails managing information life cycle needed in an organisation in an effective manner.

Management Information System (MIS)

Management information systems involved user management in considering the information they used and how they used it.IS professionals had to find new techniques of information analysis such as data modelling entity analysis to find ways of organising and delivering information for effective management

MIS (Management Information Systems)” is the hardware and software systems within an enterprise that provide the information that management needs to run an enterprise” (Margaret Rouse, 2014).




MIS is used widely in different contexts as a decision support systems, resource and human capital management applications, project management and database management applications. MIS covers systems which are critical to the success and survival of the business



John Ward and Joe Peppard (2011) Strategic Planning for Information Systems,3rd edition.

Margaret Rouse (2013); online: