We have made an effort to keep this website jargon-free but realise some
words, phrases and acronyms may need further explanation
Transforming data into insights
Data on its own is meaningless. It needs to be organised into information and transformed into insight before it can be put into context and interpreted into actionable insight for strategic and value adding decision making.
Analytics leverages tools and techniques in areas like computer science, statistics and mathematics to develop complex algorithms and methodologies which when combined with dynamic visualisation tools can communicate powerful insights. A business analytics programme aims to provide a quantitative process for arriving at optimal decisions and performing knowledge discovery. Analytics frequently involves data mining, process mining, statistical analysis, predictive analysis, predictive modelling, business process modelling, scenario modelling, optimisation and prescriptive analytics.
Historically data analysis and Business Intelligence (BI) have been the focus but as data has grown bigger, become cheaper and more accessible, competition more fierce and margins tighter, the wider science of analytics and its advanced concepts have begun to grow in popularity, use and value. Competitive advantage will be attained by those organisations that succeed in being ‘data driven’ (although a key challenge will always be data quality & veracity).
A sizeable subject
A recently coined but now widely used phrase, ‘big data’ is used to describe a collection of data sets that have grown so large and complex that they become awkward or impossible to work with using current databases and solutions such as relational databases and desktop statistical and visualisation packages. This creates the need to use new technologies like massively parallel processing (MPP).
Describing the data as ‘big’ is not only down to volume of data but also the variety, velocity, veracity and overall complexity. As their data gets ‘bigger’, organisations are facing serious challenges with capture, storage, cleansing and linking this data for normal day to day activity let alone for applying analytics to produce insight. In fact many organisations are rapidly realising that they have IT infrastructures that simply do not support business needs moving forward.
In addition to a lack of hardware and software to support analytics there is also a lack of people. A number of reports have highlighted a significant lack of strong analytics talent as well as ‘data savvy’ management that is not likely to catch up with the big data growth trend anytime soon.
Improving your organisation
Until fairly recently Business Intelligence (BI) was used for measuring and monitoring performance and had been the primary form of ‘analytics’ used by organisations. Most software solutions in the market aimed to analyse and compare historical trend data and then visualise this using tools like dashboards and performance metrics. Insight was primarily a view of what had happened in the past and sometimes an indicator as to why.
The term was coined in 1958 by IBM researcher Hans Peter Luhn who defined BI as "the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal”. In 1989, Howard Dresner (later a Gartner Group analyst) proposed BI as an umbrella term to describe "concepts and methods to improve business decision making by using fact-based support systems.
The fuel of analytics
‘Data’ is the plural of the Latin word ‘Datum’ meaning ‘something given’. Data can be items like numbers, alphabets, symbols or images which are in a raw and unorganised form and can be both qualitative and quantitative.
Data is limitless. Some is stored by machines, some by our human minds and some is floating around the universe not stored at all.
In the context of computer science and data processing ‘data’ is often used to refer to information which leads to knowledge or insight. But at its lowest level of abstraction, data has no meaning.
Informed Predictions and Calculations
Moving beyond data analysis and Business Intelligence (BI) that looks at trends in the past, data science involves the more advanced and exploratory part of analytics that moves from asking why things occur in a data set into predicting what will happen in the future.
A successful team of ‘data scientists’ will incorporate skills in areas of econometrics, data engineering, scientific method, mathematics, statistics, advanced computing, industry and functional expertise and visualisation to solve complex data problems using techniques like data mining, machine learning and natural language processing (NLP).