Business Process Re-engineering (BPR) How to fight Maverick Rebel. A too frequently overlooked cause preventing process optimization. This is one of the causes why too big companies are having issues which must be addressed DATA QUALITY ASSURANCE - DQA a typically overlooked domain.

1. Catch the Rebel
Maverick Reducing
Corporate ROI
Data Complexity
THE EXPERT VIEWPOINT

2. The expert viewpoint
Alexander Kopriwa
Big Data is a strategic facility to outwit the competition. Both big and small
data discovery helps to identify trends, inefficiencies and risk exposures
early. This delivers a true corporate strategic competitive advantage, when
used correctly.
It is extremely important, especially for small and midsized businesses
(SME/SMB), to scrutinize new and emerging technology offerings
discovering competitive threats and new business models to ensure
enterprise survival and sustainability. This requires digital and
technological know-how across all enterprise domains (beyond the IT
department) where subject matter experts and data owners of various
business processes are critical to shaping the future of corporate success.
All organizations seek disruptive, cheaper and more agile utilization of all
assets, including information processing resources. For example,
manufacturing customizations require end-to-end digital processes
cutting time-to-market delays, as well as, gaining needed agility, flexibility
and optimal productivity.
Implementing new strategic business goals often imposes business
process reengineering (BPR) projects, like implementing a new ERP
system such as SAP’s S4HANA (featuring high speed in memory data
bases). Other requirements like Industry 4.0 and Internet of Things (IoT)
architectures may also be of the essence.
How long time available
to analyze and report?
23%
Not enough time
to extract
meaningful
C-Level Dashboard
insights.
More then 77% of
the controller effort
is spent for data
acquisition
and preparation
77%
For these reasons, substantial gains can be
realized by modernizing the tools, and the
methodologies, for use in data preparation.
2
Organizations are awash with too
many and too poorly managed data
assets.
A strong correlation exists between
high quality data from ERP solutions
and other expert applications
providers to empower and optimize
business process efficiency.
However, too frequently, one
serious strategic omission
diminishes above investments ROI.
Organizations have hit on a rebel
maverick inhibiting successful BPR
and reducing overall ROI. This Data
Complexity oversight is known as:
Data Quality Assurance!
How to Implement Data Quality
Assurance (DQA)?
Data capture and business process
systems can be improved (at high
cost), yet over time, the DQA wilts.
Pressured employees tend to
duplicate data records, hampering
analytical process reconciliation and
future optimization opportunities.
Sooner or later, most enterprise
business systems will suffer from
poor DQA, lack of archiving, poor
Meta data, inconsistent and
incompatible formats. Organizations
subsequently must rely on humans
“opinions” to guide processes, as
“we can’t trust the data in the
system” (C-Level comment from a
1.8B USD food and beverage group,
cited as an Asian prime company by
global investment advisory firm).
Digital business processes
transformation reaches far beyond
manufacturing, it affects all
economic sectors, it is integrated
within products and services
(smartphones, automobiles,
banking, insurance etc.). This
generates data from previously
unknown sources and usages, all
capable to improve mass
customization and mass production,
in response to volatile market
demand and fast changing needs.
Copyright © 2016 datonix SpA www.datonix.it

3. 4
The DQA solution uses Datonix data preparation
platform, it is a modern data management governance
framework designed to resolve most analytic challenges.
It improves DQA, including key ERP definitions of
suppliers, customers, SKU (stock-keeping unit), BOM (bill
of materials) and MRP (material requirements planning)
and more.
Management can quickly identify, correct, unify and
validate data to avoid unproductive complexity costs. In
addition Datonix FDG (fractal data guardian) ensures
solid fiduciary audit trails of information assets which
cannot be falsified after validation sign-off.
All periodic procedures can be fully automated to save
time, monthly reporting tasks can be augment with
insightful graphical reporting, all configured to customer
preferences both for desktops, tablets and smart mobile
devices.
In addition users can fully benefit from powerful data
journalism tools like Dynamic-PDF, Dynamic-WordPress,
Dynamic PowerPoint and custom formats, all fully
secured.
Datonix granular and accessible fractal-data-point
management from the FDG, data points at lightening
speed, accelerating data-mining, reporting and
classification tasks.
Datonix uses three simple steps:
· Scan and correct relevant data-points from accounts
and off-book records
· Review, adjust, consolidate all relevant resource usage
consumption
· Publicize and disseminate analytical findings to the
target constituents
The expert viewpoint
Alexander Kopriwa
Using Datonix Data Complexity Analysis to ensure DQA
All organizations periodically should audit and improve their data veracity
(accuracy) before engaging in data driven BPR and strategic business
process transformation projects: Data is a key economic asset and must
be handled with care.
Here reference master data management (MDM) records are critical to the
good functioning of complex ERP and all business systems.
Humans struggle to discern data quality, therefore potentially (erroneous)
suspect data points must be identified, properly readied (structured,
aligned) and analyzed to identify potential DQA issues.
This process requires data adjustment (cleaning). The result is a clean
(unified) data set followed by an Analytic step (i.e. data mining) delivering
reviewed data using advanced classification using AI machine learning
logic by exploiting the “K 4.5” data mining algorithm to discover similar
records.
And to finalize, all of the above is integrated into a data storytelling
scenario aiding C-Level executives to fully understand all hidden issues so
data stewards can apply and correct all DQA inconsistencies.
The above MDM unification process needs to be repeated regularly, as
humans have a tendency to create new duplicate master data when they
cannot find part-numbers, customers or suppliers.
Only when data is fully unified and quality assured, then the analytics
become meaningful for strategic decision making… and BPR efforts can
deliver better ROI with less risk.
Conclusion
Organizations must tackle data complexity, as early as possible, because
hyper decision making agility imposes 100% data accuracy. This is difficult
to achieve with ERP and transactional data base systems fed by humans, it
requires infrastructure like analytical appliances or Cloud processing. Due
to increasing business velocity and rapid change, organizational survival
imposes near automated and periodic data (accuracy) audits to avoid
unknown Data Quality Assurance Risks.
Datonix DQA Solution
Copyright © 2016 datonix SpA www.datonix.it
DATA SOURCE
Active
Dashboard
Operativie
KPI’s Reporting
C-Level Integrative
Reports TBD
DISCLOSE
FIX AND
ADJUSTMENT
DATA MINING
SOURCE
SCAN
CLEANSED
DATA
REVIEWED
DATA
Figure
MDM and Reference
Data Analysis

4. Biography
Alexander Kopriwa, provides advisory services for organizations seeking
competitive and strategic business advantages. Mr. Kopriwa has lived and
worked on five continents and is fluent in five languages. Passionate about
introducing innovative technology to organizations eager to become
market leaders, he is also vice president of international expansion for
Ontonix. A former research director for Gartner Group (and META Group),
he is a proficient handler of global accounts and excels at applying modern
digital methodologies to help clients define disruptive business processes.
Mr. Kopriwa has more than 30 years of experience helping organizations
focus on what matters for expansion and economic survival. Trained in
international economics, IT architectures (Sweden) and change-manage-
ment consulting (Austria), his expansive know-how covers an array of
enterprise constraints in cross-cultural and international environments,
including Mainland China, the United States, Europe and Southeast Asia.
6
The expert viewpoint
Alexander Kopriwa
Copyright © 2016 datonix SpA www.datonix.it