Big Data Analytics
What is big data?
Although there isn’t an authoritative definition of what data size constitutes big data, it is generally
considered to include systems which process at least 1 terabyte of data per hour, or datasets that
traditional database management tools find difficult to process & analyse. Examples of big data include
search engine, traffic, loyalty card & patient data.
What big data does KCC have?
It can be divided into two categories, individual big datasets which are “big” enough in their own right and
aggregated datasets which are combination of different types of data on the same customer, asset or
transaction and become big data as a result.
Individual big datasets include sensor data (i.e. energy & parking meters, telecare sensors, traffic
signals) and software logs (usage & performance). Big data produced by KCC customers but not
captured within its systems include user-generated content (text, multimedia & location) on social
Aggregated datasets include a combination of two or more of the following: transactional data (i.e.
revenue & benefits), master data (i.e. customer & asset information), reference data (i.e. location),
metadata, analytical data (i.e. ERP or Mosaic), multimedia, email & voice data.
What are big data analytics?
This describes the analysis of different datasets on a common type of information to identify patterns, such
as health conditions, weather forecasts, or customer insight.
Why are organisations investigating this area?
The growth of big data has come about through an increase in the
1. Need for organisations to manage large sets of data through greater processing power, data
storage capacity and the cloud, the
2. Ability for users to generate content through social networks
3. Needs for organisations to track products & people on the move through sensors or RIFD
What is the business or customer need?
Big data analytics can be used to identify and uncover patterns in a variety of ways, of which these are the
principal five that are relevant to local government:
Streamline transactions by avoiding to ask a person the same information many times
Profile customers to target interventions (channel shift, troubled families to health advice)
Analyse a location to understand its dynamics (traffic congestion/commuting patterns)
Investigate social media to identify local concerns (public disorder, natural disasters)
Aggregate sensor data to track performance (energy consumption or telecare)
KCC is analysing data on the first two areas, although research would need to be undertaken with the
relevant leads in these areas to identify whether they have or need access to big data to carry out an
analysis. More research would be needed to identify what teams are analysing “big data” sets, although the
Traffic Management System can be considered as big data as will the NHS patient outcome reporting data
that may transfer over to KCC responsibilities.
What tasks do big data analytics involve?
Big data analytics involves the following tasks: store & manage the data, process & query data and patterns
of data, integrate different types of data and make statistically analyses.
What are the constraints on KCC in considering the use of big data analytics?
Information asset audits to identify high value data (and from that, big data)
Interoperability between systems to combine data
Data management, analytics modelling & business analysis skills to interpret the patterns
Information governance to assure the appropriate use of different datasets
Awareness of customer consent policies on data being analysed
Data quality to understand the reliability of data being used
What is the maturity in use of big data analytics in the public sector?
There is a strong experience of big data analytics in NHS, moderate but growing in DWP & DFT and in the
new Government Digital Service. The key factor in becoming more mature in this area is less in the choice
of big data analytics solutions to use and more the data analysis skills to be able to derive value from it.
What is the impact of big data on information architecture & governance?
As well as assuring the timeliness, quality & accuracy of data, governance of big data also requires
managing its volume, velocity, and its value, particularly given the nature of big data is that it is constantly
growing, being generated in real time and frequently formed of combined datasets.
To be able to make use of big data analytics, there is a need to integrate the analytics solutions to existing
database management & business intelligence systems, as well as end user applications.
What could be the potential next steps to investigate the potential of big data?
Understand if & in which there is a need to analyse data that is too complex or big to be currently
done within KCC’s existing database management systems
Identify existing KCC systems that can manage, store, process and/or analyse big data
Understand impact on information architecture & governance
Identify if relevant skills & resources exist to make use of big data analytics tools
Identify & engage related services/organisations who have successfully used big data analytics
solutions to understand lessons learned and their route map