1. Telling Stories with Data
Gogula G. Aryalingam

2. Speaker Profile Software Architect at Navantis
MCT, MCSE (BI and Data Platform)
Former SQL Server MVP
Subject Matter Expert for developing SQL Server certification exams
Author of a Chapter on the SQL Server MVP Deep Dives II book

3. Data is All Around…
Telling Stories with Data

4. Data is all around Look around your organization…
You’ll find data all over…
…in various places
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Telling Stories with Data

5. Data is all around Look at the world… ? You’ll find data all over…
…in various places ?
Exchange Rates
Other
Stock Market
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Telling Stories with Data

6. A need to make sense of this data…
Telling Stories with Data

7. Before… traditional We used Business Intelligence… ^
Heterogeneous data sources
Data Marts/Data Warehouse
Reports/Visualizations/Analysis
Typically, a business intelligence solution looked like what is depicted in this slide.
Raw data (i.e. data that is in its native structure, eg: transactional systems, spreadsheets etc.) are extracted from their source systems, brought together, transformed and loaded (a.k.a. ETL or Extract, Transform and Load) into a special type of database called data warehouses or data marts. These are essentially relational databases that are structured differently from a traditional OLTP structure. This structure which is usually designed using the dimensional modelling technique is optimal for reading.
A data mart is essentially similar to a data warehouse, except that it contains data from a single department or silo of an organization, whereas a data warehouse contains information from across the organization.
Data from the data warehouse/data mart is then pulled into a special type of database called OLAP databases, which have structures called cubes instead of tables. A cube has multiple dimensions, much unlike tables which have only two dimensions (rows and columns). A cube can have 2, 3, 4 or more dimensions; making it a very fast database for reading large amounts of data for reporting and analysis.
Finally, the visualizations. From enterprise reports that are 10 pages long that no one would read to nifty dashboards that show the state of the business at-a-glance can be created from the data that is collected.
Traditional BI (or the traditional way of doing BI, a.k.a. Corporate BI because business intelligence was usually used by corporates in a large scale) mostly stored data in a data warehouse. This data was periodically updated as new data came into the source systems. The updates were usually performed on a weekly, or nightly basis; and as time went by, and when technologies became better and cheaper, the updates were performed on an hourly and sometimes every minute.
These systems usually provided hindsight into the data and in certain cases some insight as well.
Still, traditional BI involved high costs for enterprise scale software and hardware. It was specialist-built or built by IT, and in a lot of cases did not exactly cater to what the business users wanted.
These BI systems were usually used by top-level management who mostly looked at the very high level picture of the business (dashboards) and some business users who did some analysis on the data (reports, interactive reports, scorecards).
And in most cases these systems were not used at all.
Most BI projects take a quite a long time to complete. Some take as much as 2-3 years, whereas a few others take almost 5 years… A lot are abandoned part way through. Most of the time it is because of the ambitious nature of trying to build the system for the entire organization, and due to no proper understanding between the technical folk building the system and the business folk who are the stakeholders, and during this (long) time things happen: people move out, new ones come in, requirements change, technologies get better (and others go obsolete) – visions are lost.
Changes take long to be incorporated etc. etc.
Extract, Transform & Load
OLAP/Cubes
Source:
Telling Stories with Data

8. And in a lot of cases, not used at all…
Top Management
CxO
Some business users
Typically used by
And in a lot of cases, not used at all…
Traditional BI systems were usually used by the top management of an organization, including CxOs and some business users. These users mostly required high-level information such as dashboards and scorecards, and graphical reports. Decisions were usually taken based on information presented through these media. Rarely was any data discovery done – Since systems were too complicated for them to use. Certain business users though, took it upon themselves to perform further analysis and then presenting their findings to the top personnel.
From:
Telling Stories with Data

9. Microsoft Tools for Enterprise BI
SQL Server
Integration Services
Analysis Services
Multidimensional
Tabular
Reporting Services
SharePoint PerformancePoint Services
Telling Stories with Data

10. Distributed storage and processing
Social Media
Distributed storage
and processing
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Telling Stories with Data
Telling Stories with Data

11. Then it was not enough… Data changed Requirements changed
More people wanted to join in the fun…
…on their own terms
Telling Stories with Data

12. A shift from Business Intelligence to Data Discovery
Image source:
Telling Stories with Data

13. Self-Service – The way to go…
Insights through Data Discovery
Mashup data from various sources
Within the organization (without/with less IT involvement)
Outside the organization
Share findings, tell stories and take action
Telling Stories with Data

14. Tools for the Data Discovery Age…
Power BI
Excel + Add-ons
Azure Machine Learning
Azure SQL Warehouse
HDInsight
Telling Stories with Data

15. Demo
Telling Stories with Data

16. Thank you!
Telling Stories with Data

17. Each complete feedback form gives you a chance at the raffle
We want your feedback
Each complete feedback form gives you a chance at the raffle
Telling Stories with Data

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Telling Stories with Data