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ENTERPRISE DATA & BUSINESS ANALYTICS: A HOLISTIC APPROACH TO INSIGHTS-DRIVEN
INNOVATION


A QUICK GUIDE TO TRANSFORMING DARK DATA INTO ACTIONABLE INTELLIGENCE

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Tags: Enterprise Data Data Driven Enterprise data mangement predictive analytics
data mining basics prescriptive analytics

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Elizabeth Mixson
10/30/2020
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Every minute of every day organizations of all types accumulate almost
unfathomable amounts of data. For example, GPS systems collect data on where we
are going, by which route and how long it takes. Major ecommerce sites keep
track of what product's we're browsing, clicking on and purchasing. Social media
sites track what people are viewing, sharing and commenting on.

Behind the scenes companies are also collecting huge amounts of data ranging
from day-to-day financial transactions to changes in their workforce to
industrial performance data. The totality of all of the digital information any
given organization collects is known as enterprise data.

Centralized data that is shared by many users throughout the organization,
enterprise data tells the story of your business, past, present and future.
However, data within itself is not valuable. In order to harness the limitless
power of enterprise data, it must be transformed into insights.

Business Analytics (BA) is the application of statistical methods and
technologies on raw, historical data to derive actionable “insights” from it.
This “business intelligence” can then be used to guide decision making, enhance
strategic planning, develop evidence-based solutions to complex problems and
even predict future outcomes.

BA typically fall into one of three categories:

 * Descriptive analytics – which describe what happened
 * Predictive analytics - which provides insight into what could happen based on
   historical outcomes
 * Prescriptive Analytics - which are the combination of descriptive and
   predictive analytics using artificial intelligence (AI) and machine learning
   (ML) technology, to convey what will happen, when it will happen, and why it
   will happen.


While data analytics refers to the science of identifying meaningful patterns in
data, enterprise data analytics (EDA) is an umbrella term that encompasses the
strategies, processes, and tools surrounding the discipline. In other words, it
refers both to the overarching strategy and the IT infrastructure involved in
managing the end-to-end data analysis process, from data acquisition to
visualization.

 


WHAT IS DATA MINING?

Data Mining refers to the process of identifying patterns, anomalies,
relationships and trends within large sets of data and using these insights to
predict future outcomes. By combining statistical and artificial intelligence
(such as neural networks and machine learning) tools with database management,
businesses are able to analyze large data sets.

For example, Air France KLM used data mining techniques to integrate info from
trip searches, bookings, and flight operations with web, social media, call
center, and airport lounge interactions. Using these insights, they were able to
create a holistic, 360 view of customer behavior which they used to create
personalized travel experiences.

Though data analysis and data mining are interrelated, they are not exactly the
same things. Very generally speaking, data mining seeks to identify previously
unknown relationships in data whereas data analysis seeks to deliver more
focused insights that enhance decision making or help solve specific business
challenges.



*INFOGRAPHIC SOURCED FROM "DATA MINING VS DATA ANALYSIS,"
HTTPS://WWW.EDUCBA.COM/DATA-MINING-VS-DATA-ANALYSIS/ 

 


WHAT IS DATA MONETIZATION?

Data monetization is the process of using data to increase revenue or drive
quantifiable economic profit. Though the term “data monetization” can refer to
the literal sale of company data to third parties (known as direct data
monetization), it also pertains to the internal use of data-driven insights to
optimize process, develop new products or drive innovation (indirect data
monetization).

One example of direct data monetization is the sale of location data from a ride
share app to local retailers who in turn use this information to develop
targeted, geo-specific marketing campaigns.

An example of indirect data monetization is the use of past purchase and
browsing data by e-retailers (i.e. Amazon) to provide customers with
personalized product recommendations. Or if a credit card company used internal
fraud data to build an AI powered tool that essentially automated the fraud
detection and response process, thereby increasing operational efficiency,
curtailing fraud related financial losses and boosting consumer confidence.

Though, as of 2020, only about 17% of companies have truly monetized data, this
discipline is certainly poised to grow. However, according to the 2019 Business
Application Research Center (BARC) Data Monetization Survey, the number one
inhibitor data monetization is poor data quality.

 



VIDEO SOURCED FROM "HOW DO COMPANIES MONETIZE THEIR DATA?",
HTTPS://BI-SURVEY.COM/DATA-MONETIZATION

 

For many organizations, establishing a robust enterprise data management and
analytics approach is the first step in understanding and capitalizing on the
value of its own, internal data.



READ NEXT: Tesla. Automaker or Data Company?

 


TYPES OF ENTERPRISE DATA

Transactional data – Operational data generated from day-to-day business
transactions such as purchases, orders, payroll, invoices, etc. Think number of
products sold to customers, number of employees, money owed, extc. Transactional
data is typically created by Enterprise Resource Planning (ERP), Employee
Management Systems (EMS) and Customer Relationship Management (CRM) systems.

Main Data - As defined by Gartner, main data "is the consistent and uniform set
of identifiers and extended attributes that describes the core entities of the
enterprise including customers, prospects, citizens, suppliers, sites,
hierarchies and chart of accounts.” In other words, key business information
that supports business transactions. Generally, master data does not change and
does not need to be created with every transaction. Main data is usually low
quality and disparate, generated, stored accessed across multiple systems
throughout the enterprise.

Strategic data – Complex data related to business growth and competitiveness.
Can be used to make business decisions. For example, the change in purchase
volumes over time, stock market trends, customer retention, time to hire, and so
on. Though this data can be pulled from multiple systems, it’s typically stored
in Online Analytical Processing (OLAP) repositories, such as data warehouses and
data lakes.

Data warehouses and data lakes represent 2 different approaches to storing data.
Data lakes are, essentially, giant pools of unstructured and structured data
from various company data sources. Data warehouses are repositories for
structured, filtered historical data that fit a relational database schema.
Though this is changing, in the past, data lakes were really only used by
engineers and data scientists while data warehouses were the more user friendly
option for business leaders.

However, in a business environment where everyone needs to think like a data
scientist, organizations are developing new ways to give everyone access to the
advanced, strategic enterprise data analytics, like deep learning and real-time
analytics, data lakes enable.

 


THE FUNDAMENTALS OF ENTERPRISE DATA MANAGEMENT

As mentioned before, organizations of all kinds generate massive amounts of data
on a daily basis. However, only a fraction of it is actually usable. According
to one recent global study, on average companies only actually utilize about 45%
of the data they produce. Why? Often because they lack the robust enterprise
data management processes, tools and activities necessary to ensure data
accuracy, quality, security, availability, and good governance.

Enterprise data management (EDM) refers not only to the process of inventorying
and governing enterprise data, but as the folks at Tableau put it, EDM also
“means making sure your people have the accurate and timely data they need, and
that they follow your standards for storing quality data in a standardized,
secure, and governed place.”

By ensuring enterprise data is controlled, integrated and usable, EDM serves as
the foundation for big data and data monetization.

Key processes, practices, and activities that comprise EDM include:

Data Integration – At its simplest, the process of combining data from multiple
sources into one central location to provide a single, unified view of the data.
The goal of data integration is to ensure highly accurate, timely and consistent
data is easily accessible by employees no matter what function they work in or
where they may be located. Not only does it save time by eliminating the need to
manually look up different data in different systems, by combining different
types of data, it also enables more complex, strategic data analysis. Without
data integration, accurate analytics would be impossible to achieve.

Main data management (MDM) - The core process used to manage, centralize,
organize, categorize, localize, synchronize and enrich master data according to
a succinct set of business rules. Similar to data integration, the goal of MDM
is to provide a single, trusted view of main data across the enterprise.

Data governance (DG) – A set of rules, standards and policies that control or
manage the availability, usability, integrity and security of enterprise data.
DG frameworks are comprehensive, outlining the people, processes and technology
required to ensure data is properly and uniformly handled across the entire
enterprise. One of the major benefits of data governance is that it enables the
democratization of data. In other words, by breaking down siloes and driving
collaborative data practices, it makes it possible for non-data scientist to
access and utilize accurate, timely and relevant data.

Data quality management– Data quality management refers to the processes,
methods, and technologies put in place to ensure enterprise data meets specific
business requirements. In other words, that data is high quality and usable.
This can be done in a variety of ways. Data cleansing, for example, the process
of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or
incomplete data within a dataset. Another DQM strategy, data enrichment,
involves enhancing, refining, and improving raw data, typically by combining it
with data from outside sources.

Data stewardship – A key component of data governance and master data
management, data stewardship is the person responsible for making sure data
usage, management and security policies are adhered to. They essentially serve
as a liaison between the analytics team and the business.

Data warehouse – a type of data management system that serves as a repository
for large amounts of historical data pulled from multiple systems. Not only do
these platforms centralize and consolidate data, they also include analytical
capabilities as well.

ETL/ELT – Standing for extract, transform and load, ETL is one approach to data
integration. Often used to build data warehouses, data is extracted from a
source system, converted into a format that can be analyzed, and stored into a
data warehouse or other system.



 

______________________________

 

Where are you on your enterprise analytics journey?

We at ADA our thrilled to announce out latest survey on the current state &
future outlook of enterprise data & analytics. We invite you to take 2 minutes
to complete our latest survey.

 



powered by



Create your own user feedback survey

Can't view our the embedded survey above? Access it here:
https://www.surveymonkey.com/r/JXF5H82  

 


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