Big data lecture
Introduction to topic:
 Big data is a term used to describe this exponential growth, availability and use of
information
 ‘Big data’ is being generated by everything around us. Every digital process and
social media exchange produce it.
 The volume of data that organisations can gather continues to grow, with new data
constantly streaming in social media, transactions and other different channels.
 Data comes from two areas - what you can capture and what you can purchase
 i.e. Experian buys their data from various sources for example, government.
 Decreasing storage costs make it easier to hold the data; but it is now important for
organisations to understand which data is relevant and how to create value from it.
 Google Chief Executive Eric Schmidt suggested that the world creates five exabytes
of data every two days – which is roughly the same amount created between the
dawn of civilisation and 2003.
 “According to a recent survey carried out by German market research institute for
the Guardian, 86% of marketers consider that big data will change the function of
marketing, and a further 62% say that it has already fundamentally changed their
role.”
Value to Marketing:
This section looks at how big data can be useful to businesses and marketers.
Through the combination of big data along with integrated marketing strategies, a
substantial impact can be caused in following areas:
 Customer engagement. Big data can deliver insight into not just who an
organisation’s customers are, but where they are, what they want, how they want to
be contacted and when.
 Customer retention and loyalty. Big data can help discover what influences
customer loyalty and what keeps them coming back again and again.
 Marketing optimisation/performance. With big data, organisations can determine
the optimal marketing spend across multiple channels, as well as continuously
optimise marketing programs through testing, measurement and analysis.
Five ways marketing directors can use big data article:
Data structures:
1. Structured
 Data that can be processed, stored, and retrieved in a fixed format.
 Refers to highly organised information that can be readily/seamlessly stored and
accessed from a database by simple search engine algorithms.
 For instance, the employee table in a company database will be structured as the
employee details, their job positions, their salaries, etc., will be present in an
organised manner.
2. Unstructured
 Unstructured data refers to the data that lacks any specific form or structure
whatsoever.
 This makes it very difficult and time-consuming to process and analyse unstructured
data.
 Email is an example of unstructured data.
3. Semi-structured
 Semi-structured data pertains to the data containing both the formats mentioned
above, that is, structured and unstructured data.
 To be precise, it refers to the data that has not been classified under a particular
database, yet it contains vital information or tags that segregate individual elements
within the data.
Data/ Information Sources - Proliferation:
So many different ways for marketers to capture data.
Diagrams below are the various methods of gaining this data/information.
Modern sources -
Sources
Traditional sources -
Modelling
Appending/
Linking
Integration
Data &
Integration
De-Duping/
Filtering
Cleansing
Warehousing/
Storing
Profiling
Points to note:
 Access to rich customer data enables marketers to provide a personalised
experience for customers, with relevant advertising and recommendations,
increasing loyalty and brand engagement as well as driving sales.
 Making connections between things such as purchase history and physical location
means that precisely targeted real-time marketing becomes possible, though
execution today falls short of what marketers want to achieve.
 Gathering and storing a huge volume of data for its own sake is a mistake. Having
clear objectives – and data that matches those objectives – is the key to success.
 The privacy issues diminish if customers understand the value, they get from sharing
data. The industry needs to do a better job of communicating these benefits.
Controlling Big Data:
 Volume: Organizations collect data from a variety of sources, including business
transactions, smart (IoT) devices, industrial equipment, videos, social media and
more. In the past, storing it would have been a problem – but cheaper storage on
platforms like data lakes and Hadoop have eased the burden.
 Velocity: With the growth in the Internet of Things, data streams in to businesses at
an unprecedented speed and must be handled in a timely manner. RFID tags,
sensors and smart meters are driving the need to deal with these torrents of data in
near-real time.
 Variety: Data comes in all types of formats – from structured, numeric data in
traditional databases to unstructured text documents, emails, videos, audios, stock
ticker data and financial transactions.
- Laney (2001)
Big Data Tactics:
 Reward Scheme’s.
“Sign up today for a 10% discount”
 Forms/ Surveys/ Polls/ Account Set Up’s
 Purchase data.
 Digital interactions; Logins, purchases, repurchases, QR code scans, mobile
payments, signing in/ up, check in’s, Proximity Marketing
IoT/ IIoT + Digital = More Data:
 IoT is an abbreviation for ‘Internet of Things’
 Defined as the network of interconnected things/devices.
 Also defined as ‘an architectural framework which allows integration and data
exchange between the physical world and computer systems over existing network
infrastructure’
 Embedded with sensors, software, network connectivity and necessary electronics
that enables them to collect and exchange data making them responsive.
 By 2020 it is expected that 26 billion devices will be part of the IoT.
 Components which create the Internet of Things (IoT):
1. Hardware
 Making physical objects responsive and giving them capability to retrieve data and
respond to instructions.
2. Software
 Enabling the data collection, storage, processing, manipulating and instructing.
3. Communication Infrastructure
 Most important of all, is the communication infrastructure which consists of
protocols and technologies which enable two physical objects to exchange data.
Challenges with collecting Big Data:
 Need for talent
 Data scientists and big data experts are among the most highly coveted —and highly
paid — workers in the IT field.
 Hiring or training staff can increase costs considerably, and the process of acquiring
big data skills can take considerable time.
 Data quality
 Before use of big data for analytics efforts, data scientists and analysts need to
ensure that the information they are using is accurate, relevant and in the proper
format for analysis.
 That slows the reporting process but if enterprises don't address data quality issues,
they may find that the insights generated by their analytics are worthless.
 Need for cultural change
 Many of the organizations that are utilizing big data analytics want to use analytics
to create a data-driven culture throughout the company.
 NewVantage survey found that, 98.6% of executives said that their firms were in the
process of creating this new type of corporate culture. However, only 32.4%
reported success.
 Compliance
 Much of the information included in companies' big data stores is sensitive or
personal
 Means the firm may need to ensure that they are meeting industry standards or
government requirements when handling and storing the data.
 Syncsort survey found that data governance, including compliance, was the 3rd most
significant barrier to working with big data.
 Hardware needs
 Storage space to house the data, networking bandwidth to transfer it to and from
analytics systems, and compute resources to perform those analytics are all
expensive to purchase and maintain.
 Rapid change
 Another potential drawback to big data analytics is that the technology is changing
rapidly.
 Syncsort respondents ranked this disadvantage of big data 4th among all the
potential challenges they faced.
 Costs
 Many of today's big data tools rely on open source technology,
 Which reduces software costs, but enterprises still face expenses related to staffing,
hardware, maintenance and related services.
 Difficulty integrating legacy systems
 Integrating all those disparate data sources and moving data where it needs to be,
also adds to the time and expense of working with big data.
Benefits of collecting Big Data:
 Better decision-making
 Analytics can give business decision-makers the data-driven insights they need to
help their companies compete and grow.
 Increased productivity
 Modern big data tools are allowing analysts to analyse more data, more quickly,
which increases their personal productivity.
 Reduce costs
 Big data analytics were helping companies decrease their expenses.
 Improved customer service
 Social media, customer relationship management (CRM) systems and other points of
customer contact give organisations a wealth of information about their customers.
 It is only natural that they would use this data to better serve those customers.
 Fraud detection
 One of the big advantages of big data analytic systems, that rely on machine
learning, is that they are excellent at detecting patterns and anomalies.
 Increased revenue
 When organizations use big data to improve their decision-making and improve their
customer service, increased revenue is often the natural result.
 Syncsort survey, more than half of respondents (54.7%) said they were using big
data tools to increase revenue and accelerate growth, based on better insights.
 Greater innovation
 Innovation is another common benefit of big data as well as glean insights that their
competitors don't have, they may be able to get out ahead of the rest of the market
with new products and services.
 NewVantage survey found that 11.6% of executives are investing in analytics
primarily as a means to innovate.
- Datamation (2018)
Note - Survey statistics is not sourced from Datamation (2018).
In practice (Helps with):
 Real time segmentation
 Segmentation of audience based on calculated metrics or behaviours that update in
real-time as customers act.
 Filtering options that can aid a business find a specific audience include = Purchase
frequently, High lifetime value, etc.
 As customers interact with the business, CRM systems can real time segment which
drives content and leads to personalisation/Automation.
 Personalisation/Automation
‘Automation’ = technology that manages marketing processes and multifunctional
campaigns, across multiple channels, automatically.
 Businesses can target customers with automated messages across email, web, social,
and text i.e. emails with birthday discounts as software has saved customer’s
information.
 Analysis/Reporting
 Planning & Forecasting
Driving disruption within the market:
 Using data to gain competitive advantage within your market, causing friction with
similar business (this is what is meant by disruption).
 Big Data can help organisations understand the dynamic of the competitive
landscape; what competitors are doing, whilst impacting chosen organisation.
Real life application - Boots:
Note - IoT improves the way that businesses manufacture and distribute products.
 Boots was one of the first organisations to launch a loyalty scheme.
 While customers are rewarded, the company is capturing Big Data including:
 Purchase History
 Purchase Value
 Products
 Email address
 Services
For exam (if you pick this topic):
 Refer to McKinsey’s 7 S framework
Big data has changed the skills that digital marketers need.
No longer about understanding customers, it’s about being able to manipulate data and
insight and respond accordingly.
Using different systems - marketers needs different systems to work with.
Changed the structure of teams due to marketing departments and functions having a
greater presence in their CRM (Customer relationship management) team.
More people working data than campaigns due to the automation that big data provides
within marketing.
Big data and technological capabilities have enabled automation of campaigns.