Data foundation for analytics excellence

1. Predictive Analytics & Business Insights – 2015 , Chicago
Mudit Mangal
Project Lead, Data Analytics, Supply Chain
Sears Holdings Corporation
06/11/2015

2. Agenda
— WHAT IS HAPPENING
— WHAT IS DATA ANALYTICS AND ITS CHALLENGES
— WHY DATA FOUNDATION
— HOW TO APPROACH
— WHY DATA GOVERNANCE
— WHAT ARE SECURITY ISSUES WITH DATA
— USE CASE
— WHAT IS IN FUTURE
— QUSESTIONS

3. New Data Frontiers
— Fueled by growing demand for anytime anywhere access to
information, technology is disrupting all areas of enterprise, driving
myriad opportunities and challenges.
— Enormous opportunities exist for enterprises to take advantage of
connected devices enabled by the “Internet of Things” to capture vast
amounts of information.
— Digital transformation is changing business models –pricing
strategies, processes, relationship between businesses and customers.
— Declining PC usage and increasing mobile device adoption is
driving a “mobile first” world.
— However, the evolution of the digital enterprise also presents
significant challenges, including new competition, changing
customer engagement and business models, unprecedented
transparency, privacy concerns and cybersecurity threats.

4. Digital Frontiers

5. Understanding Analytics

6. 360-degree view
360-degree view of all the data is important to know what’s happening in
a marketplace—the combination of structured information, human
interactions, and machine-to-machine data.

7. 360-degree view in practice
— In IT operations management, a 360-degree view allows to see logs,
performance ,it also lets you see what the test team said to the app dev
team, what the customers said to the help desk, and what the app
support team said to the help desk.
— In security management, a 360-degree view lets you see security alerts
from the network, applications, and infrastructure, while human
interaction data allows you to see security threats in emails.
— In retail, a 360-degree view allows you to analyze sales in stores and
online, as well as understand consumers’ expectations ,social
sentiment regarding the store. What do people think about your
service compared to that of your competitors?

8. By its definition : “Data that was previously ignored because of
technology limitations” examples includes unstructured data that
companies have struggled to analyze in the past, documents, social
data, customer surveys, web logs, and a lot of ‘dark’ structured data.
Dark Data

9. Data Torturing
Are you still torturing your data to get what you want?

10. Data Foundation
— Data is the foundation of all information solutions, BI and analytical
decisions and choosing the right technology is important.
— The data foundation encompasses the integration of data from
multiple, disparate sources into a trusted, understandable form for
use in analytics and making data as an enterprise asset.
— The escalating volume, variety, and velocity of information that is
being generated today present with many critical challenges.
— However, this overabundance of information can be an important asset
to those organizations that choose to capitalize on it.

11. Components of data foundation

12. Benefits of robust data foundation
A robust data foundation provides an organization with tremendous
benefits in terms of efficiency and effectiveness in decision making.
— One-stop shopping for data: Most significant uses of time in
decision making is getting the data in usable format. A robust data
foundation changes the 80 percent time spent on gathering to 80
percent time spent on analyzing the data.
— Single version of the truth: Getting different answers to same
questions is frustrating experience for decision makers. A robust data
foundation provide single version of truth on which everyone can rely.
— Drives common understanding across the enterprise: One of the
key objectives of data foundation is to integrate data from disparate
sources. A robust data foundation provides the structure and
enforcement of these, resulting everyone in organization working on
same page.

13. Consequences of the lack of a robust
data foundation
— Multiple answers to same questions
— Making less optimal business decisions
— Wasted time finding, collecting, summarizing data for use in analytics

14. Getting Started: Building Out
— Analyzing data often requires a transformational approach to many
critical IT processes.
— Ask yourself what data points do I need, how I am going to get them,
and what am I going to do with them once I have them ?
— Try building a Distributed platform that is small, low cost, fluent in all
forms of data and analytics. E.g. data in motion.
— Next, identify a low impact use case for implementation.
— Your application should be a good candidate for Distributed Data
Computing.
— If so, a successful POC will be assured.

15. From Data Lakes To Data Swamps
“By its definition, a data lake accepts any data, without governance.
Without metadata and a mechanism to maintain it, the data lake risks
turning into a data swamp and leads to hardest problem of data quality.”

16. Big Data without Governance
— Dumping data into Big Data Lake without repeatable processes and
data governance will create messy, uncontrollable data environment.
— Insights harvested from ungoverned data lake is not reliable and
trustworthy, so cannot make business decisions confidently.
— In an industry where data is the most valuable asset, data integrity is
essential. If the data is compromised, it can have vast consequences.
— Data must be physically safe. Whether data is stored internally or
within the cloud, Disaster recovery, security and other actions must be
taken to ensure the physical integrity of data.
— Humans make mistakes. Maintaining data integrity is difficult when
humans enter free-form text into software systems.

17. Governance Disciplines

18. Evolving Data Governance

19. Security Risk for Big Data
— As cyber threats continue to multiply, it is becoming harder to
safeguard data, intellectual property, and personal information.
— Greater use of the internet, smartphones and tablets in combination
with bring-your-own-device policies has made organizations’ data
more accessible and vulnerable.
— More data implies higher risk of exposure.
— New data types may give rise to new security breach scenarios.

20. Data Lake Security Solutions

21. Evolving Data Security
— Apache Knox : Perimeter/ Network Security
— Apache Ranger : Data Protection, Authorization, Audit tracking
— Apache Sentry : Authorization

22. Retail Use Case
Let’s face it: when it comes to giving business users the information they
need, retail is as tough. With multiple stores, myriad, ever-changing
products and constant transactions, every day is a new challenge.
— Most retailers already have systems in place to provide business users
with information. The question is, how to do better?
— Web-based retail analytic applications extend existing business
reporting and analytic solutions. Helps in understanding Customers.
— Sears has a very intensive big data program to drive customer loyalty,
Sears is doing amazing things with technology and Competes On Big
Data.

23. Predicting Future

24. Future of Data
What kind of data will be Big Data in the future?
— Structured data - This is the data companies store today: sales
transactions, maintenance details. Since Big Data technology allows us
to store more data and analyze it much faster, there will be increase in
amount of details stored ,in the time period for which data is kept.
— Human interaction data - Unstructured data refers to the data of
human interactions: emails, phone conversations, video, pictures,
documents, social media interactions on Twitter, Facebook and other
communities. This type of data represents 90 percent of useful data.
— Machine to machine data - By 2020 there will have been a massive
increase in the number of connected smart devices. Cooktops,
shopping carts, home thermostats, cars, bicycles, and refrigerators will
run applications that connect to the emerging Internet of Things.
These devices will generate huge amounts of data. Collecting and
analyzing this data will lead to new insights.

25. Conclusion
— Reporting and Analytics can be transformational for an organization.
However, having the proper data foundation that provides trusted,
well-integrated and well-managed data is essential to realize the
desired reporting and analytical capabilities.
— Mapping out a strategy and plan to establish the data foundation is
time well spent and will provide a return many times over.

26. Questions?
www.linkedin.com/in/muditmangal