Becoming a data-driven organization in a fast-moving world - SAS italySAS Italy
SAS italy - Per diventare un’organizzazione data-driven è fondamentale mantenere la Governance del “fiume” di dati a disposizione. Una gestione necessaria che deve strutturarsi attraverso 3 fattori chiave: Persone, Processi e Tecnologie. Scopri nella presentazione di Oliver Penel, SAS EMEA Data Management Business Director, le raccomandazioni, i focus e le giuste domande che le aziende devono porsi per non rischiare di farsi travolgere dall’ondata di informazioni a loro disposizione.
Data Analytics: An On-Ramp to a Better Understanding of Your BusinessSkoda Minotti
Data analytics is a hot topic in business today. But is it right for your business? What does it do for you, and most importantly, how do you get started? This executive overview explores the business implications of data analytics, while leaving the technicalities to the side.
Becoming a data-driven organization in a fast-moving world - SAS italySAS Italy
SAS italy - Per diventare un’organizzazione data-driven è fondamentale mantenere la Governance del “fiume” di dati a disposizione. Una gestione necessaria che deve strutturarsi attraverso 3 fattori chiave: Persone, Processi e Tecnologie. Scopri nella presentazione di Oliver Penel, SAS EMEA Data Management Business Director, le raccomandazioni, i focus e le giuste domande che le aziende devono porsi per non rischiare di farsi travolgere dall’ondata di informazioni a loro disposizione.
Data Analytics: An On-Ramp to a Better Understanding of Your BusinessSkoda Minotti
Data analytics is a hot topic in business today. But is it right for your business? What does it do for you, and most importantly, how do you get started? This executive overview explores the business implications of data analytics, while leaving the technicalities to the side.
Data security, quality and process transparency are areas that are posing risks for organisations in the age of Big and Small Data. In this presentation I define the problem and present some solutions to bridge the Data Governance chasm.
Data-Ed Online Webinar: Data Governance StrategiesDATAVERSITY
The data governance function exercises authority and control over the management of your mission critical assets and guides how all other data management functions are performed. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. This webinar provides you with an understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity that often surrounds initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy.
Takeaways:
Understanding why data governance can be tricky for most organizations
Steps for improving data governance within your organization
Guiding principles & lessons learned
Understanding foundational data governance concepts based on the DAMA DMBOK
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
In this PPT, We describing the important things about Data Management and Data Governance. The data governance approach provides the right practices and processes that help an enterprise manage its data flows.
Enterprise Data World Webinar: A Strategic Approach to Data Quality DATAVERSITY
We will also explore how to apply the 12 Directives, through a set of tactics to help you assess organizational readiness for data quality strategy. The purpose of such an assessment is to surface priorities for strategic action and to formulate a long-term approach to an organization’s data quality improvement.
A 3 day examination preparation course including live sitting of examinations for students who wish to attain the DAMA Certified Data Management Professional qualification (CDMP)
chris.bradley@dmadvisors.co.uk
How data scientists add value to your business organizationJanBask Training
Mostly data scientists are being trained in computer science, math and statistics. The expertise of data scientists can be used in data visualization, data mining, and the information management.
https://www.janbasktraining.com/data-science
Are you ready for Big Data? This assessment review from Data Management Advisors will provide pragmatic recommendations & actionable transition steps to help you achieve your Big Data goals & deliver actionable insights.
info@dmadvisors.co.uk
Startup and Growth companies that have unique and compelling product ideas still need to find a strategic pathway towards building that vision into a final product. Designing and building features is just part of the puzzle and fast iterations are only helpful if you are gaining real and useful learning from those releases. Data strategy ensures that each product feature released is backed by data to measure its impact and effectiveness.
Data-Ed Webinar: Data Warehouse StrategiesDATAVERSITY
Integrating data across systems has been a perpetual challenge. Unfortunately, the current technology-focused solutions have not helped IT to improve its dismal project success statistics. Data warehouses, BI implementations, and general analytical efforts achieve the same levels of success as other IT projects – approximately 1/3rd are considered successes when measured against price, schedule, or functionality objectives. The first step is determining the appropriate analysis approach to the data system integration challenge. The second step is understanding the strengths and weaknesses of various approaches. Turns out that proper analysis at this stage makes actual technology selection far more accurate. Only when these are accomplished can proper matching between problem and capabilities be achieved as the third step and true business value be delivered. This webinar will illustrate that good systems development more often depends on at least three data management disciplines in order to provide a solid foundation.
Takeaways:
•Data system integration challenge analysis
•Understanding of a range of data system-integration technologies including Problem space (BI, Analytics, Big Data), Data (Warehousing, Vault, Cube) and alternative approaches (Virtualization, Linked Data, Portals, Meta-models)
•Understanding foundational data warehousing & BI concepts based on the Data Management Body of Knowledge (DMBOK)
•How to utilize data warehousing & BI in support of business strategy
Leveraging the best of traditional modelling with the latest big data, data profiling & semantic web techniques to accelerate delivery & value realisation
This introduction to data governance presentation covers the inter-related DM foundational disciplines (Data Integration / DWH, Business Intelligence and Data Governance). Some of the pitfalls and success factors for data governance.
• IM Foundational Disciplines
• Cross-functional Workflow Exchange
• Key Objectives of the Data Governance Framework
• Components of a Data Governance Framework
• Key Roles in Data Governance
• Data Governance Committee (DGC)
• 4 Data Governance Policy Areas
• 3 Challenges to Implementing Data Governance
• Data Governance Success Factors
Chief Data & Analytics Officer Fall Boston - PresentationSrinivasan Sankar
Data Asset Catalog & Metadata Management - Is It a Fad or Is It the Future?
Many have dubbed metadata as “the new black,” but is this accurate?
How to leverage metadata management to streamline data governance and ensure transparency
Improving data quality and ensuring consistency and accuracy of data across various reporting systems
Looking at the flip side: what are the additional training requirements and value-added for the business?
Building an External Reputation - Nov 2017Martha Horler
Session at the You and Improved! AUA event in November 2017, that covered how to network without 'networking' and build links by finding your area of expertise.
Data security, quality and process transparency are areas that are posing risks for organisations in the age of Big and Small Data. In this presentation I define the problem and present some solutions to bridge the Data Governance chasm.
Data-Ed Online Webinar: Data Governance StrategiesDATAVERSITY
The data governance function exercises authority and control over the management of your mission critical assets and guides how all other data management functions are performed. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. This webinar provides you with an understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity that often surrounds initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy.
Takeaways:
Understanding why data governance can be tricky for most organizations
Steps for improving data governance within your organization
Guiding principles & lessons learned
Understanding foundational data governance concepts based on the DAMA DMBOK
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
In this PPT, We describing the important things about Data Management and Data Governance. The data governance approach provides the right practices and processes that help an enterprise manage its data flows.
Enterprise Data World Webinar: A Strategic Approach to Data Quality DATAVERSITY
We will also explore how to apply the 12 Directives, through a set of tactics to help you assess organizational readiness for data quality strategy. The purpose of such an assessment is to surface priorities for strategic action and to formulate a long-term approach to an organization’s data quality improvement.
A 3 day examination preparation course including live sitting of examinations for students who wish to attain the DAMA Certified Data Management Professional qualification (CDMP)
chris.bradley@dmadvisors.co.uk
How data scientists add value to your business organizationJanBask Training
Mostly data scientists are being trained in computer science, math and statistics. The expertise of data scientists can be used in data visualization, data mining, and the information management.
https://www.janbasktraining.com/data-science
Are you ready for Big Data? This assessment review from Data Management Advisors will provide pragmatic recommendations & actionable transition steps to help you achieve your Big Data goals & deliver actionable insights.
info@dmadvisors.co.uk
Startup and Growth companies that have unique and compelling product ideas still need to find a strategic pathway towards building that vision into a final product. Designing and building features is just part of the puzzle and fast iterations are only helpful if you are gaining real and useful learning from those releases. Data strategy ensures that each product feature released is backed by data to measure its impact and effectiveness.
Data-Ed Webinar: Data Warehouse StrategiesDATAVERSITY
Integrating data across systems has been a perpetual challenge. Unfortunately, the current technology-focused solutions have not helped IT to improve its dismal project success statistics. Data warehouses, BI implementations, and general analytical efforts achieve the same levels of success as other IT projects – approximately 1/3rd are considered successes when measured against price, schedule, or functionality objectives. The first step is determining the appropriate analysis approach to the data system integration challenge. The second step is understanding the strengths and weaknesses of various approaches. Turns out that proper analysis at this stage makes actual technology selection far more accurate. Only when these are accomplished can proper matching between problem and capabilities be achieved as the third step and true business value be delivered. This webinar will illustrate that good systems development more often depends on at least three data management disciplines in order to provide a solid foundation.
Takeaways:
•Data system integration challenge analysis
•Understanding of a range of data system-integration technologies including Problem space (BI, Analytics, Big Data), Data (Warehousing, Vault, Cube) and alternative approaches (Virtualization, Linked Data, Portals, Meta-models)
•Understanding foundational data warehousing & BI concepts based on the Data Management Body of Knowledge (DMBOK)
•How to utilize data warehousing & BI in support of business strategy
Leveraging the best of traditional modelling with the latest big data, data profiling & semantic web techniques to accelerate delivery & value realisation
This introduction to data governance presentation covers the inter-related DM foundational disciplines (Data Integration / DWH, Business Intelligence and Data Governance). Some of the pitfalls and success factors for data governance.
• IM Foundational Disciplines
• Cross-functional Workflow Exchange
• Key Objectives of the Data Governance Framework
• Components of a Data Governance Framework
• Key Roles in Data Governance
• Data Governance Committee (DGC)
• 4 Data Governance Policy Areas
• 3 Challenges to Implementing Data Governance
• Data Governance Success Factors
Chief Data & Analytics Officer Fall Boston - PresentationSrinivasan Sankar
Data Asset Catalog & Metadata Management - Is It a Fad or Is It the Future?
Many have dubbed metadata as “the new black,” but is this accurate?
How to leverage metadata management to streamline data governance and ensure transparency
Improving data quality and ensuring consistency and accuracy of data across various reporting systems
Looking at the flip side: what are the additional training requirements and value-added for the business?
Building an External Reputation - Nov 2017Martha Horler
Session at the You and Improved! AUA event in November 2017, that covered how to network without 'networking' and build links by finding your area of expertise.
6 PART 1 • OVERVIEW OF STRATEGIC MANAGEMENT Strategic mana.docxevonnehoggarth79783
6 PART 1 • OVERVIEW OF STRATEGIC MANAGEMENT
Strategic management can be defined as the art and science o f formulating, implementing,
and evaluating cross-functional decisions that enable an organization to achieve its objec-
tives. As this definition implies, strategic management focuses on integrating management,
m a r k e t i n g , finance/accounting, production/operations, research and development, and
information systems to achieve organizational success. The term strategic management in
this text is used synonymously w i t h the term strategic plcmning. The latter term is more
often used i n the business w o r l d , whereas the former is often used in academia. Sometimes
the term strategic management is used to refer to strategy formulation, implementation, and
evaluation, w i t h strategic planning refemng only to strategy formulation. The purpose o f
strategic management is to exploit and create new and different opportunities for tomorrow;
long-range planning, in contrast, tries to optimize for tomoiTOW the trends o f today.
The term strategic planning originated in the 1950s and was very popular between the
mid-1960s and the mid-1970s. D u r i n g these years, strategic planning was w i d e l y believed
to be the answer for all problems. A t the time, much o f coiporate A m e r i c a was "obsessed"
w i t h strategic planning. F o l l o w i n g that "boom," however, strategic planning was cast aside
d u r i n g the 1980s as various p l a n n i n g models d i d not y i e l d higher returns. The 1990s,
however, brought the revival o f strategic pl a nni ng, and the process is w i d e l y practiced
today in the business w o r l d .
A strategic plan is, in essence, a company's game plan. Just as a football team needs a
good game plan to have a chance for success, a company must have a good strategic plan
to compete successfully. Profit margins a m o n g firms in most industries have been so
reduced by the global economic recession that there is httle room for eiTor in the overall
strategic plan. A strategic plan results f r o m tough managerial choices among numerous
good alternatives, and i t signals c o m m i t m e n t to specific markets, policies, procedures, and
operations i n l i e u o f other, "less desirable" courses o f action.
The term strategic management is used at many colleges and universities as the subti-
tle for the capstone course i n business administration. This course integrates material f r o m
all business courses. The Strategic Management C l u b O nl i ne at wwvv.strategyclub.com
offers many benefits for business p o l i c y and strategic management students. Professor
Hansen at Stetson University provides a strategic management slide show for this entire
text (www.stetson.edu/~rhansen/strategy).
Stages of Strategic Management
The strategic-management process consists o f three stages: strategy formulation, strategy
implementation, and strategy evaluation. Strategy formulation.
An organization's success depends on the performance of its people. ADEPTCentral are human performance experts who increase the productivity of teams, boost operational effectiveness, and drive adoption of new technologies and protocols. We use proven science and established methodologies that are proven effective across industries and verticals.
Don’t leave your technology implementations to chance.
At least 50% of technology implementations fail - the same odds as a coin flip - costing IT businesses more than $100 billion per year. The effectiveness of your IT budgets deserves better than a coin flip. Contact ADEPTCentral today to find out how to increase your chances of success.
Five Trends in Analytics - How to Take Advantage Today - StampedeCon 2013StampedeCon
At the StampedeCon 2013 Big Data conference in St. Louis, ohn Lucker, Partner and Principal at Deloitte Consulting, discussed Five Trends in Analytics - How to Take Advantage Today. Lucker will discuss the latest advancements in the world of analytics and offer strategies for tapping into their potential. The topic areas include visualization and design, mobile analytics and strategy analytics.
Hiring Top Healthcare Analytics Talent: Five Best PracticesHealth Catalyst
COVID-19 has escalated healthcare’s decision-making demands, reinforcing the industry’s need for highly skilled analytics team members. As a result, health systems face mounting pressure to hire the best-suited analytics talent in a timely manner and with minimal burden on existing team members.
Five proven inclusive strategies will help hiring managers efficiently build an analytics team that can adapt to healthcare’s shifting environment and also fit within an organization’s culture:
Open positions to remote employees and conduct interviews via video conferencing.
Insert “tollgates” into the hiring process.
Use scenario-based role play to assess many competencies concurrently.
Assess cultural fit.
Follow up with and provide feedback to all candidates.
Driving A Data-Centric Culture: The Leadership ChallengePlatfora
Embracing data as a corporate asset—and a source of competitive advantage—is not just a “good idea” that companies should consider. Such adoption will help determine the winners and losers across multiple markets and industries in the future.
In the last couple of years, corporate focus has shifted: first, from investing in the right technology and tools; then to acquiring the right talent and skills; and now to building the right organizational culture that can realize the business value of powerful big-data analytic tools.
Most organizations today are still focused on putting in place the right technology and talent, but others have evolved further and are working toward fostering a data-centric corporate culture.
Data Scienceis an interdisciplinary field about processes and systems to extractknowledgeor insights fromdata, which is a continuation of some of the data analysis fields such as statistics,data mining, andpredictive analytics, similar toKnowledge Discovery in Databases(KDD).
مهم است بدانید اولین قدم به سمت، تغییر داده و سیستمها به شکل دیجیتال آن است
ذخیره داده بر سرورها، ابر یا ابزارهاي
ذخیره محلی براي خلق یک سیستمی که کاربرد دیجیتالی آن داده - منابع ارزشمندي براي کسب وکار، توسعهدهندگان و کارآفرینان-
فراهم میکند حیاتی است....
مشتري بخش كاملي از زنجيره تأمين است.
شامل جابجايي كالا از تأمين كنندگان به سازندگان و توزيع كنندگان است. همچنين شامل جابجايي اطلاعات، پول و محصول در هر دو جهت است.
دقيق تر خواهد بود اگر از اصلاح ”شبكه تأمين“ يا ”وب تأمين“ استفاده شود.
يك زنجيره تأمين عموما شامل تأمين كنندگان، سازندگان، توزيع كنندگان، خرده فروشان و مشتريان است.
البته در برخي از موارد تمامي مراحل وجود ندارد.
از 1950 که رايانه در تحليل و ذخيره سازي داده ها مورد استفاده و بهره برداری قرار گرفت، تا سال 1970 حجم داده ها در پايگاه داده ها دو برابر گردید. با پیشرفت فن آوري اطلاعات هر سال 1990 هر دو سال يکبار حجم داده ها، دو برابر شد. (tan94)
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
2. Dr. Saeed Rouhani
What is Strategy?
H a r v a r d B u s i n e s s R e v i e w
A strategy is a plan of action designed to achieve a specific
goal.
2
3. Dr. Saeed Rouhani
What is Data?
facts and statistics collected together for reference or
analysis.
Philosophy
things known or assumed as facts, making the basis of
reasoning or calculation.
3
4. Dr. Saeed Rouhani
We have twin
D a t a S t r a t e g y
• Data Strategy describes a “set of choices and
decisions that together, chart a high-level course of
action to achieve high-level goals.” This includes
business plans to use information to a competitive
advantage and support enterprise goals.
D a t a D r i v e n S t r a t e g y
• A data strategy enables data-driven decision-making
using technology and applications that help a
business achieve its strategic imperatives. Data
strategy must be driven by the business not by the
technology that services the business.
4
5. Dr. Saeed Rouhani
A BUSINESS STRATEGY is a medium to long term business
plan which details the aims & objectives of a business
and how it means to achieve them .
5
A DATA STRATEGY is a medium to long term plan for the
improvement, management & exploitation of data
across a business, and how it is to be achieved.
6. Dr. Saeed RouhaniDr. Saeed Rouhani
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut
gravida eros erat. Proin a tellus sed risus lobortis sagittis eu quis
est. Duis ut aliquam nisi. Suspendisse vehicula mi diam, sit amet
lacinia massa sodales ac. Fusce condimentum egestas nunc a
maximus. Quisque et orci purus. Proin dolor mi, ultrices sit amet
ipsum placerat, congue mattis turpis. Donec vestibulum eros
eget mauris dignissim, ut ultricies dolor viverra. Phasellus
efficitur ante nec sem convallis, in ornare est accumsan. Lorem
ipsum dolor sit amet, consectetur adipiscing elit. Ut gravida eros
erat. Proin a tellus sed risus lobortis sagittis eu quis est.
6
7. Dr. Saeed RouhaniDr. Saeed Rouhani
Business & Data
Strategy –the
Interdependency
Businesses Develop a Data Strategy to:
• Manage torrents of data that are critical to a
company’s success.
• Think of the future and trends and how to
best manage them.
• Drive innovation and establish a data culture.
• Support the re-imaging of decision making in
an organization – at all levels.
• To develop a sustainable competitive
advantage given the volume, depth and
accessibility of digital data.
7
8. Dr. Saeed Rouhani
THE AGE OF
ANALYTICS
C o m p e t i n g i n a d a t a - d r i v e n w o r l d :
D a t a a n d a n a l y t i c s a r e c h a n g i n g t h e b a s i s o f
c o m p e t i t i o n .
8
10. Dr. Saeed Rouhani
MAPPING VALUE IN
DATA ECOSYSTEMS
But how much is all this data worth? Data’s
value comes down to how unique it is and
how it will be used, and by whom.
Understanding the value in all these small
bits of information that need to be
gathered, sifted, and analyzed is a tricky
proposition, particularly since
organizations cannot nail down the value
of data until they are able to clearly specify
its uses, either immediate or potential.
10
11. Dr. Saeed RouhaniDr. Saeed Rouhani
The Data-Driven Planning
Framework
Strategic planning that focuses on
and effectively uses all of these
types of data is "data-driven
strategic planning."
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut
gravida eros erat. Proin a tellus sed risus lobortis sagittis eu quis
est. Duis ut aliquam nisi. Suspendisse vehicula mi diam, sit amet
lacinia massa sodales ac. Fusce condimentum egestas nunc a
maximus. Quisque et orci purus. Proin dolor mi, ultrices sit amet
ipsum placerat, congue mattis turpis. Donec vestibulum eros
eget mauris dignissim, ut ultricies dolor viverra. Phasellus
efficitur ante nec sem convallis, in ornare est accumsan. Lorem
ipsum dolor sit amet, consectetur adipiscing elit. Ut gravida eros
erat. Proin a tellus sed risus lobortis sagittis eu quis est.
11
12. Dr. Saeed RouhaniDr. Saeed Rouhani
NOT ALL DATA ARE
CREATED EQUAL—AND
THEIR VALUE DEPENDS
ON THEIR UNIQUENESS
AND END USES
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut
gravida eros erat. Proin a tellus sed risus lobortis sagittis eu quis
est. Duis ut aliquam nisi. Suspendisse vehicula mi diam, sit amet
lacinia massa sodales ac. Fusce condimentum egestas nunc a
maximus. Quisque et orci purus. Proin dolor mi, ultrices sit amet
ipsum placerat, congue mattis turpis. Donec vestibulum eros
eget mauris dignissim, ut ultricies dolor viverra. Phasellus
efficitur ante nec sem convallis, in ornare est accumsan. Lorem
ipsum dolor sit amet, consectetur adipiscing elit. Ut gravida eros
erat. Proin a tellus sed risus lobortis sagittis eu quis est.
12
There are many types of uses for data; several examples are
described below.
• Internal cost and revenue optimization
• Marketing and advertising
• Market intelligence
• Market-making
• Training data for artificial intelligence
13. Dr. Saeed RouhaniDr. Saeed Rouhani
Analytical Dashboards
D a t a s t r a t e g y g i f t s f o r
d a t a d r i v e n s t r a t e g y .
13