Business Analystin tehtävä on ponnahduslauta huippu-uralle digitalisoituvassa maailmassa.

Organisaatiot ovat saavuttaneet keskimäärin vain 20% digitalisaation potentiaalista. (mckinsey.com)

Teknologinen kehitys kiihtyy, mutta kyky soveltaa laahaa pahasti perässä.

Organisaatiot kaipaavat kipeästi liiketoiminnan kehittäjiä, joilla on kyky soveltaa digitalisaation mahdollisuuksia liiketoimintaan: liiketoimintamalleihin, prosesseihin, myyntiin ja markkinointiin, tuote- ja palvelukehitykseen jne.

Business Analyst on liiketoiminnan tulkki ja siten keskeisessä roolissa digitalisaation koko potentiaalin saavuttamisessa.

Parhaat tulkit ovat tulevaisuuden liiketoimintajohtajia ja yritystensä arvostetuimpia asiantuntijoita.

Robotisaation, tekoälyn ja älykkään automaation myötä digitalisaation potentiaali kasvaa entisestään – samoin kuin myös haasteet.

Business Analystin rooli on entistä tärkeämpi, kun liiketoiminnan ja IT:n lisäksi tulkkausta tarvitsevat data-, analytiikka- ja AI-ammattilaiset.

Kysy lisää yrityskohtaisista toteutuksista.

Business Analyst -koulutus on mahdollista hankkia tarpeidenne mukaan räätälöitynä kokonaisuutena.

Ota yhteyttä

Business Analystin keskeiset taidot:

  1. Liiketoiminnan tulkkina Business Analystin tärkein ominaisuus (soft skill) on kyky edistää yhteistyötä eri osaajaryhmien välillä.
  2. Ylivoimaisesti tärkein kova taito (hard skill) on liiketoimintaongelmien ja -vaatimusten määrittäminen. Ongelmanratkaisu lähtee aina ongelman määrittämisestä. Digitaalisessa kehityksessä määrittäminen tarkoittaa ongelman mallintamista siten, että se voidaan teknologian, datan ja analytiikan avulla ratkaista.

Nämä kaksi taitoa kietoutuvat tiukasti yhteen. Kehitystiimin on tehtävä tiiviisti yhteistyötä yhteistaämaalia kohden. Yhteisen maalin määrittäminen vaatii liiketoimintaongelman määrittämistä niin, että kaikki ymmärtävät sen samalla tavoin.

Tämän merkitystä ei voi liikaa korostaa. Hankkeet jotka epäonnistuvat, epäonnistuvat tyypillisesti siihen, että kehitystiimillä ei ole yhteistä maalia.

Business Analystin työn uudet haasteet digitalisaatiossa.

Digitalisaation myötä data on noussut keskeiseen rooliin liiketoiminnan kehittämisessä.

Business Analystin pitää ymmärtää sitä jalostusketjua, miten data muuttuu mm. automaatioksi, robotiikaksi ja tekoälyksi.

Ymmärryksen lisäksi Business Analyst tarvitsee myös uusia työkaluja liiketoimintaongelmien mallintamiseen ja yhteistyön fasilitointiin. Business Analystin työkalulaatikkoon kuuluu tyypillisesti mm. käyttötapauskuvaukset ja prosessimallinnus. Käsitemallinnus on välttämätön lisä työkalulaatikkoon silloin kun datan rooli liiketoimintaongelmassa on merkittävä.

Hyvin tehty käsitemalli kuvaa sekä liiketoimintaongelmaa että sitä vastaavaa analyyttistä ongelmaa. Käsitemallia tarkentamalla täsmentyvät myös datavaatimukset.

Käsitemallinnus on parhaimmillaan täydellinen väline liiketoimintaongelman määrittämiseen yhtenäisesti liiketoiminnalle, IT:lle ja analyytikoille. Näin varmistetaan, että kaikki ratkovat oikeaa ja samaa liiketoimintaongelmaa.

Käsitemallinnus on kivijalka, jonka päälle voi rakentaa keskeisiä Business Analystin taitoja ongelman määrittämisestä, hankkeiden johtamiseen sekä yhteistyön fasilitointiin.

 

Koulututusohjelma Business Analysteille

Koulutusohjelma on suunniteltu Business Analysteille sekä muille liiketoiminnan kehitykseen osallistuville asiantuntijoille ja esimiehille – erityisesti heille, jotka toimivat liiketoiminnan ja teknologian rajapinnassa.

Koulutusohjelman tarkoituksena on täydentää Business Analystin osaamista niillä taidoilla, mitä datavetoinen liiketoiminnan kehittäminen vaatii. Koulutuskokonaisuus keskittyy näihin uusiin taitoihin ja Business Analystin perustaitoja käydään niiltä osin kuin ne kytkeytyvät näihin uusiin taitoihin. Kokemus Business Analystin tehtävistä ei kuitenkaan ole esitietovaatimuksena näille koulutuksile. Koulutukset soveltuvat kaikille liiketoiminnan kehityksen parissa työskenteleville junioriasiantuntijoista johtajiin.

 

SKILLS FOR DATA DRIVEN BUSINESS DEVELOPMENT

LANGUAGE: Finnish or English
DURATION:
2 days

Basic skill in data driven business development.

    • Conceptual modeling background
      • why conceptual modeling is essential
      • the relationship of business and data
    • Practical guidelines
      • tools (CASE tools, whiteboard), Ellie
      • conducting modeling sessions with subject matter experts
      • engaging business and technology experts alike
    • The role of conceptual modeling in Information Architecture

LANGUAGE: Finnish or English
DURATION:
1 day

Facilitating collaboration in online environment to overcome its limitations compared to face-to-face collaboration.

    • success factors and common pitfalls of data and analytics projects
      • why do most data and analytics projects fail
      • why do they fail right from the start
    • why communication and collaboration are the key in successful projects
      • data and analytics are absract – the difference of collaborating on tangible things vs. abstract things
      • the role of verbal, non-verbal, written and visual communication
    • working on an online environment
      • how does it affect when people are deprived of the means of nonverbal communication (in the context of data and anlytics initiatives)
      • how to compensate with other means of communication
    • tools for online collaboration
      • process modeling, use case diagrams, conceptual modeling
      • fishbone diagram
      • conceptual modeling as a lingua franca for business, technology and analytics tribes
      • Ellie conceptual modeling tool

 

LANGUAGE: Finnish or English
DURATION:
2 days

Learning SQL to retrieve information is an effective way to develop one’s data literacy.

  • SQL is the basic workhorse tool for all data professionals.
    • The majority of relevant business information resides in relational databases
    • The relationship of SQL and relational databases
    • Despite advances in analytics tools, SQL is still widely referred as the most important skill of data scientists. Why is it so?
  • SQL is powerful tool to develop data literacy for business (non-data) professionals
    • Understanding the connection between business and the data that describes it
  • Learning to retrieve information from databases with SQL
    • SQL structure, statements and queries
    • Query exercises on the workstation
    • Groupings, functions
    • JOINs
    • subqueries
  • Creating database tables
    • Creating tables
    • Referential integrity
LANGUAGE: Finnish or English
DURATION:
2 days

Learn to collaborate with data scientists and AI developers by understanding basics of Python.

    • Background
      • Understanding the basics of programming greatly helps in working with software developers
      • Python is possibly the most English-like programming language, and thus beginner-friendly.
      • Python is the leading programming language for data science and AI development.
    • Day 1
      • History and versions of Python
      • Basic concepts
      • Running Python n a Microsoft Azure environment
      • Data types and data structures
      • Functions
      • Randomness
      • Object-oriented programming, Functional programming
    • Day 2
      • Data Analysis Libraries: Numpy, Pandas
      • Reading and writing from Excel
      • Preparation
      • Predicting
      • Visualization
      • Machine learning

APPLYING SKILLS

LANGUAGE: Finnish or English
DURATION:
1 day

Modeling driven approach for business analysis, requirements specification, and collaboration .

    • Data as the lifeblood of organization as its business
      • Organization’s Business Logic and Information Architecture are two sides of the same mirror
      • How precise is the mirror’s reflection determines how well the business logic can be enforced
    • Conceptual modeling as the mirror between Business Logic and Information Architecture
      • Hovi Data Framework for business driven modeling
      • Aligning business, analytics and IT professionals
    • Business driven approach for Data Management
      • Best practices from the largest private and public organizations in Finland and globally

LANGUAGE: Finnish or English
DURATION:
1 day

How to eat an elephant – solving complex business problems by breaking it down to edible bites.

    • Organization is a system
      • What is a system – ”a group of interacting or interrelated entities that form a unified whole”? (source: Wikipedia)
      • All organizations are systems, but only leading data-driven organizations are run as such
      • Why does this give an competitive edge to companies such as Amazon
    • Complex business problems are usually complex due to their many dependencies
      • how to break down a problem to smaller problems without disregarding important dependencies
      • Systems thinking skills and tools
    • Lean thinking to problem solving
      • Solve only problems that are solvable, i.e. small and understandable enough
      • Consider the big picture only at the level of detail that is understandable
    • The relationship between Systems Thinking and Information Arhcitectryure

 

LANGUAGE: Finnish or English
DURATION:
1 day

Learn to apply AI to solve business problems without in-depth AI skills.

    • Background – breakthroughs in analytics, machine learning and AI
      • Why all data is potentially valuable and usable for AI
      • The machine learning and AI revolution in brief
      • Machine learning terms and operation explained.
    • Understanding AI intuitively
      • AI algorithms are not intelligent, but they are powerful – understanding what happens under the hood
      • Deep Learning algorithm explained.
    • The ongoing change of how businesses are run, enabled by the development of AI
      • The change in operations and management
      • use cases across industries
    • How to spot opportunities around me – in my own business

UNDERSTANDING TECHNOLOGY THAT SUPPORTS DATA DRIVEN BUSINESS DEVELOPMENT

LANGUAGE: Finnish or English
DURATION:
1 day

Understanding the technology that supports data driven business development

    • Data Platforms – why are they needed
      • Why do organizations build dedicated data platforms
      • Terminology, concepts and types of data warehousing and platforms
      • Can a modern organization do without a data platform?
    • Various data platform architectures such
      • EDW (Enterprise DW), Data Vault and Datamart architectures
      • Big Data tehcnology and architectures
    • Data Platform projects
      • How do Data Platform projects relate to Business Development projects
      • Design methods for data platforms – e.g. star schema and EDW design
    • Data Platform strategy and Business Strategy
      • how shall business strategy guide the development of data platforms
      • change of organizational culture

ADVANCED

LANGUAGE:  English
DURATION:
2 days

Description

This highly participative workshop introduces proven techniques for discovering, documenting, and verifying application requirements. In three-tier architecture terms, it covers both the Presentation Services (User Interface) and Business Services (Logic and Rules) layers.
The workshop uses an “outward-looking” form of use cases to define external (Presentation Services) requirements – that is, how a user wishes to interact with a system. To define internal (Business Services) requirements – the validation, rules, and data updates performed “behind” the user interface – a variety of techniques are covered, including event analysis, state transition diagramming, and service specification. Important synergies between these techniques are demonstrated, as well as making use of the analysts’s other main techniques – data modeling and process modeling.

This unique class bridges the gap between two common extremes. At one end are simplistic, easily understood prototyping or list-based approaches that are too imprecise and incomplete for all but the simplest applications. At the other extreme are techniques that are so complex they are indecipherable to most users and analysts, and thus produce results that are just as undependable.

 

Objectives:

On workshop completion, participants will be able to:

  • Use a variety of techniques to identify a system’s use cases and business services.
  • Discover and document “external” application requirements, especially UI behavior
  • Discover and document “internal” application requirements, particularly logic and rules
  • Understand how use cases and services fit with process models and data models
  • Create and apply a set of use case scenarios that exercise and demonstrate the use cases

Prerequisites:

None, although some understanding of multi-tier information systems concepts, and data modeling in particular, will be helpful.

 

Target Audience:

Business analysts, systems analysts, and developers responsible for defining application requirements, or documenting legacy/custom/packaged application behavior in a structured way. Also, technical resources (programmers, UI designers, DBAs) interested in requirements definition, project leaders needing to understand current analysis techniques, and content experts with a significant role to play in specifying requirements.

Course Outline / Topics:

  • Application requirements definition – goals, issues, and approaches that work in real life
  • Use cases and services (”application logic”) – terms, concepts, and interrelationships
  • Discovering use cases and services at the right granularity – a multi-pronged method
  • Documenting use cases with progressive detail and precision – a phased approach
  • Documenting “out of context” use cases – dealing with recurring and reusable elements
  • Discovering process scenarios and use case scenarios – making the use cases real
  • Developing use case and use case scenario dialogues – refining use cases and requirements
  • Service specification – invocation, validation, rules, and updates
  • State transition analysis – relating events, entity states, and business rules
  • Wrap-up – summary, “what’s next?,” and resources

 

LANGUAGE:  English
DURATION:
2 days

Description

Whether a new application is purchased or custom-developed, it’s almost certain that improved or redesigned business processes will be involved.  This workshop will give business analysts a solid exposure to the modeling and analysis of a process workflow, the key phases and techniques, and the issues that must be addressed.  With initiatives like enterprise application implementation and e-commerce driving the redesign of business processes, these skills can make a real difference to a project’s success.

The workshop complements the techniques covered in our Data Modeling and Requirements Modeling workshops, and integrates proven analysis techniques with developments from fields such as business process management and quality management.  First, participants will learn the key factors to consider when dealing with business processes, and then how to specify the scope and goals of a business process, model the current workflow, assess it, and apply three critical process redesign techniques.

Key principles are illustrated throughout with workshop exercises and discussions. Business professionals with responsibility for improving their processes and business analysts needing solid techniques will both benefit from this workshop.

 

Objectives:

On workshop completion, participants will be able to:

  • Describe the key factors that differentiate process and functional approaches
  • Employ a variety of techniques to keep stakeholders involved, and promote “process orientation”
  • Identify a “true” business process, and specify its boundaries and goals
  • Model process workflow at progressive levels of detail using Swimlane Diagrams
  • Stop process modeling at the appropriate point, and move on to other techniques or phases
  • Conduct a structured assessment of a business process
  • Develop a process redesign while avoiding common (and serious!) pitfalls

Prerequisites:

None. However, business analysts who expect to do extensive workflow modeling will find that some understanding of information systems concepts may be helpful in establishing context.

 

Target Audience:

Business analysts who are responsible for requirements specification or are involved in business process re-design or improvement; business managers and content experts who will participate in process re-design or process-oriented application development efforts.

Course Outline / Topics:

  • Thinking in process terms – concepts, terminology, principles, and techniques
  • A three-phase approach to completing a process-oriented project
  • Framing the process – discovering a business process, and clarifying its purpose and scope
  • Initial assessment of the ”as-is” process and goal-setting for the “to-be” process
  • Modeling process workflow – practical tips and techniques for using swimlane diagrams
  • Controlling detail – three levels of workflow model (handoff, milestone, and task)
  • Applying workflow modeling to the as-is process – facilitating a workflow session
  • Final assessment of the as-is process – a framework for assessment, relation to redesign
  • Characterizing the to-be process – generating creative improvements and assessing them
  • Creating the new workflow – turning the to-be characteristics into a workflow model
  • When to stop – making the transition to use cases and application requirements
  • Wrap-up – summary, tips, and resources

 

LANGUAGE:  English
DURATION:
2 days

Communication, Consistency, and Complexity

Overview:

After gaining some practical experience, data modelers encounter situations such as the enforcement of complex business rules, handling recurring patterns, dealing with existing databases or packaged applications, and other issues not covered in introductory data modelling classes.  This intense, participative workshop provides approaches for many advanced data modelling situations, as well as techniques for improving communication between data modelers, business analysts, designer/developers, and subject matter experts.

Description:

There are experienced data modelers out there who somehow develop accurate and stable models that are actually used, often in non-typical or high-pressure situations. They get the job done without wasted effort, maintain the involvement and respect of the subject matter experts, and – worst of all! – make it look easy. Others modelers might have great technical skills, but fare poorly, maintaining tense relationships with content experts and developers who “just don’t get it,” and watching in dismay as their models are continually undone by “new” requirements.

What accounts for the difference? Magic? Luck? Better tools? No – it’s having a concrete set of frameworks, methods, techniques, scripts, heuristics, and other tools that they draw on to keep the process moving, with everyone engaged, even when complex, difficult situations are encountered.  And that’s what we’ll cover in this full, but fun, two-day workshop – specific, repeatable techniques that you can use to drive your data modelling skills to the next level.

Three main themes will be explored:

  1. The technical side of data modelling – getting better at modelling difficult, complex situations
  2. Developing and using data models in new ways, and in conjunction with other techniques
  3. The human side of data modelling – improving processes and communication skills

Topics will be covered with a discussion of the issue, a review of techniques, guidelines and examples, a brief workshop exercise, and a group solution and debriefing. The emphasis is on maximizing the delivery of content while keeping everyone engaged – the workshop has recently been extensively redesigned to focus on the topics that data modelling professionals have continually rated as the most concrete and useful.

Instructor – Alec Sharp:

With almost 35 years of consulting experience, Alec has provided hands-on data modelling expertise throughout North America, Asia, Europe, and Australasia –  this workshop is based on real-world experience, not textbook theory. Alec has also delivered hundreds of Data Modelling and Advanced Data Modelling workshops, and top-rated presentations at international conferences, including “The Seven Deadly Sins of Data Modelling,” “Data Modelling – New Uses for New Times,” “The Lost Art of Conceptual Modelling,” “Getting Traction for Data Modelling – Winning Over the Masses,” and “The Human Side of Data Modelling.” Alec is the author of “Workflow Modelling, Second Edition” (Artech House, 2009) which is a consistent best-seller in the field, and is widely used as an MBA text and consulting guide.

 

Target Audience:

Specialist data modelers, data architects, and DBAs who wish to hone their skills. Also business analysts, application developers, and anyone else with substantial data modelling experience who needs additional skills.

  • Historical vs. audit data, and when to show them on a data model
  • “Do you need history?” – how to tell when your client is misleading you
  • Four variations on capturing history in a data model
  • Modelling time – special considerations for recording past, present, and future values
  • Seven questions you should always ask when a date range appears
  • Risk and compliance – why we need “as-of reporting” and how to model data corrections

 

  • Modelling rules on relationships and associations
  • Using multi-way associations to handle complex rules
  • “Use your words” – how assertions, scenarios, and other techniques will improve your modelling
  • Associative entities – circular relationships, shared parentage, and other issues
  • Alternatives for modelling constraints across relationships
  • Advanced normal forms – how to quickly recognize potential 4NF and 5NF issues

 

  • Working with higher-level models
  • Contextual, conceptual, logical models – what they are, who they’re for, when we need them
  • Definitions for each type of model, and common sources of confusion
  • Avoiding the “deep dive into detail” – a three-phase method for data modelling
  • How to start a large project with a contextual data model
  • Guidelines for staying at the conceptual level, and how to tell when you’ve gone too far

 

  • Bridging the “E-R vs. Dimensional” divide – the world’s shortest course on dimensional modelling
  • The perils of dimensional modelling without understanding the underlying E-R model
  • Spotting facts and dimensions – the relationship between dimensional models and E-R models
  • Saving time – building a first-cut dimensional model from an ER model

LANGUAGE:  English
DURATION:
3 days

For practitioners who seek Professional recognition and certification for Information Management including:

  • Business Intelligence & Data Warehouse developers & architects
  • Data Modellers
  • Developers
  • Data Architects
  • Data Analysts
  • Enterprise Architects
  • Solution Architects
  • Application Architects
  • Information Architects
  • Business Analysts
  • Database Administrators
  • Project / Programme Managers
  • IT Consultants
  • Data Governance Managers
  • Data Quality Managers
  • Information Quality Practitioners

Course description

This course prepares students to take the all new DAMA CDMP Industry professional certification.

Note: the certification test is optional with a cost of additional 300 € + VAT. The attendees can take the examination online at the course on the afternoon of day-3. When you also want the certification test, enter ”and test” for additional details (”lisätietoja”).

Overview

This 3-day course addresses all the Information Management disciplines as defined in the DAMA body of knowledge (DMBoK) & introduces the “new” discipline of “Data Integration” introduced in DMBoK 2.0.  Taught by an industry recognized DAMA DMBoK(2.0) author and CDMP(Fellow) this course provides a solid foundation across all of the disciplines across the complete Information Management spectrum.  By attending the course, delegates will get a firm grounding of the core Information Management concepts and illustrate their practical application with real examples of how Information Architecture is applied.

Additionally this course provides a solid foundation for students wishing to consider proceeding to take the DAMA CDMP Industry professional certification.  This course is designed and taught by the author of “Data Modelling for the Business” an industry recognized DAMA DMBoK(2.0) author, DAMA CDMP (Fellow), VP Professional Development for DAMA International, past President of DAMA UK, author & examiner for the professional CDMP certification and recipient of the DAMA Lifetime Achievement Award for Data Management Excellence.

Learning Objectives

Gain familiarity with the CDMP examination format, types of questions and the most appropriate way of answering them.  Understand and revise the major syllabus points.  Practice taking the examinations to pass the CDMP examinations and gain recognition for your professional experience.

There are 4 levels of accomplishment for the CDMP certification, Associate, Practitioner and Master, and Fellow.

Associate Level:

  • Recommended 2 years relevant Data Professional work experience
  • 1 examination (DM Fundamentals).  Pass mark is 60%

Practitioner Level:

  • Recommended 3-5 years relevant Data Professional work experience
  • 3 examinations (DM Fundamentals + 2 electives).
  • Pass mark for all 3 exams 70%

Master Level:

  • Required 10+ years relevant Data Professional work experience
  • 3 examinations (DM Fundamentals + 2 electives).
  • Pass mark for all 3 exams  80%
  • Written case study reviewed by CDMP Fellow

Fellow:

  • By appointment
  • Completion of all previous levels,
  • Recognised exceptional works in the Data Management field
  • 25+ years’ experience

The 2 elective exams must be chosen from the following set:

  • Data Governance and Stewardship
  • Data & Information Quality
  • Data Modelling
  • Data Warehousing & Business Intelligence
  • Data Integration & Interoperability

Course Outline

CDMP Overview:  What is the CDMP, what are the levels and how can you progress from one level to the next.

Introduction to the DMBoK:   What is the DMBoK, its intended purpose and audience of the DMBoK.  Changes in DMBoK 2.0, relationship of the DMBoK with other frameworks (TOGAF / COBIT etc.).  DAMA CDMP professional certification overview & CDMP exam coverage by DMBoK section.

Data Governance:  Why Data Governance is at the heart of successful IM.  A typical DG reference model.  DG roles & responsibilities, the role of the DGO & its relationship with the PMO.  How to get started with Data Governance.

Data Quality Management:  The Dimensions of Data Quality, policies, procedures, metrics, technology and resources for ensuring Data Quality is measured and ultimately continually improved.  DQ reference model.  Capabilities & functionality of tools to support Data Quality management.

Master & Reference Data Management:  Differences between Reference & Master Data. Identification and management of Master Data across the enterprise.  4 generic MDM architectures & their suitability in different cases.  MDM maturity assessment to consider business procedures for MDM and the provision and appropriateness of MDM solutions per major data subject area.  How to incrementally implement MDM to align with business priorities.

Data Warehousing & BI Management:  Provision of Business Intelligence (BI) to the enterprise and the manner in which data consumed by BI solutions and the resulting reports are managed.  Particularly important if the data is replicated into a Data Warehouse.  Types of BI, DW and Analytics.

Data Modelling:   Types & levels of data models.  How to develop data models.  Use and exploitation of data models, ranging from Enterprise, through Conceptual to Logical, Physical and Dimensional.  Maturity assessment to consider the way in which models are utilized in the enterprise and their integration in the Software Development Life Cycle (SDLC).

Metadata Management:   Types and uses of metadata.  Sources of metadata.  Metadata standards & guidelines. Provision of metadata repositories and the means of providing business user access and glossaries from these.

Data Architecture Management:   Approaches, plans, considerations and guidelines for provision of Data Integration and access.  Consideration of P2P, ETL, CDC, Hub & Spoke, Service-orientated Architecture (SOA), Data Virtualization and assessment of their suitability for the particular use cases.

Data Lifecycle Management:  Proactive planning for the management of Data across its entire lifecycle from inception through, acquisition, provisioning, exploitation eventually to destruction.  This IM discipline and its maturity assessment determine how well this is planned for and accomplished.

Data Security & Privacy:  Identification of threats and the adoption of defences to prevent unauthorized access, use or loss of data and particularly abuse of personal data.  Exploration of threat categories, defence mechanisms & approaches, and implications of security & privacy breaches.

Regulatory Compliance:  The polices and assurance processes that the enterprise is required to meet.  Adapting to the changing legal and regulatory requirements related to information and data.  Assessing the approach to regulatory compliance & understanding the sanctions of non-compliance.

Data Risk Management:   Identification of risks (not just security) to data and its use, together with risk mitigation, controls and reporting.

Data Integration & Interoperability:   Different approaches for data integration.  Modes of data interoperability.  Challenges, advantages & risks of different Data Integration & Interoperability approaches..

 

Examinations

Participants can decide which level of examination most adequately meets their need & sit the appropriate sessions.

Audience

Practitioners who seek Professional recognition and certification for Information Management including:

  • Business Intelligence & Data Warehouse developers & architects
  • Data Modellers
  • Developers
  • Data Architects
  • Data Analysts
  • Enterprise Architects
  • Solution Architects
  • Application Architects
  • Information Architects
  • Business Analysts
  • Database Administrators
  • Project / Programme Managers
  • IT Consultants
  • Data Governance Managers
  • Data Quality Managers
  • Information Quality Practitioners

 

This course prepares students to take the all new DAMA CDMP Industry professional certification.

Note: the certification test is optional with a cost of additional 300 € + VAT. The attendees can take the examination online at the course on the afternoon of day-3. When you also want the certification test, enter ”and test” for additional details (”lisätietoja”).

 

 

Ota yhteyttä, mikäli kiinnostuit ohjelman koulutuksista.

Myös yrityskohtaiset toteutukset ovat mahdollisia.

Liiketoimintajohtaja, koulutukset
Petri Hakkarainen
petri.hakkarainen (at) arihovi.com
040-7690674

Toimitusjohtaja
Hannu Järvi
0400-687991
hannu.jarvi (at) arihovi.com