Databases Reference
In-Depth Information
Table 1.2 Data Output
Source
Data
Metric
Weather
Latitude/longitude
Temperature
Forecast
Time zone
Date and hour
Average daily temperature
Average snowfall/day
Maximum temperature
Minimum temperature
Customer
sentiment
Sentiment: happy,
disappointed, frustrated
Tone
Channel
Influence
Followers
Posts
Total number of posts
Average number of posts
Average repost
Total positive
Total negative
Total followers
Amplification
Competition
Competitor name
Product/service
Channel
Posts
Authors
Total number of posts
Total number of authors
Average post/channel
Average post/author
Average compare/product
Average compare/author
Contracts
Type
Date range
Liabilities
Total number of contracts
Total type of contracts
Contracts/date range—expiry
Contract/type of liability
Location
Address
Date and time
Staff friendliness
Cleanliness
Quality of service
Number of visits
Service time
Wait time
Quality of service
Cleanliness
and customer) and outside-in (i.e., your customer or market's view of you) viewpoints in the form of
data and its associated analytics and visualizations. This extends to including data such as contracts,
compliance reporting, Excel spreadsheets, safety reports, surveys and feedback, and other data sets.
The next section discusses examples of additional data and metrics associated with the data that form
portions of the Big Data needed by an organization.
Additional data types
Let us proceed further and assume that all the data has been extracted and transformed from various
sources. The output for each of them will look as outlined in
Table 1.2
.
When all the data is integrated with the data existing in the current business intelligence plat-
forms, the fast-food company can get better insights into the following subject areas:
●
Customers
●
Markets
●
Products
