the data management cookbook · introduction the amount of data available in your company and in...

THEDATA MANAGEMENTCOOKBOOKA POCKET GUIDE FOR IMPLEMENTATION OF DATA MANAGEMENT

Irina Steenbeek

“All roads lead to data.”

Copyright © 2018 Irina Steenbeek. All right reserved.

Published by Data Crossroads.www.datacrossroads.com

First edition, 2018.

All rights reserved. This book or any portion thereof may not be reproduced or used in any manner whatsoever without the express written permission of the publisher except for the

use of brief quotations in a book review.

Book design by Natalia Zhuravska, Dreamalizer.

ISBN: 1984149938ISBN-13: 978-1984149930

Table of contents

Introduction 4

Setting up the table 6 FIND YOUR DRIVER

First course: apéritif DEFINE YOUR DATA NEEDS 8 Second course: appetizers DIVIDE TASKS AND RESPONSIBILITIES 10

Third course: entreeORGANIZE YOUR DATA HOUSE 12 Part 1: Understand your data 12 Part 2: Locate your data 14 Part3:Managedataflow 16 Part 4: Improve data quality 18

Fourth course: main courseASSESS THE GAPS 20

Fifth course: dessertKEEP GOING 22

EXTRA MATERIALS:

Case study: Data Quality 26

Step-by-step checklist 28

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IntroductionThe amount of data available in your company and in the business environment grows exponentially. Thus the task of keeping the data in control becomes more and more difficultwithtime.

A lot of companies realize that data is an important asset and has to be managed ac-cordingly. They would also like to get value from data. Everyone wants to be ‘data-driv-en’ these days. What lies beneath this idea, is the wish to make the decision-making processeasierandmoreeffective.Itmeansdeliveringtherequireddataofacceptablequality to the decision makers when and where they need it. In short: a lot of companies understand the vital necessity of proper management of their data. The main question now is: how put this into practice?

Knowing the potential of your data, and managing it correctly is the key to a suc-cessful business. As a result of well-implemented data management, you will be able toreducerisksandcosts,increaseefficiency,ensurebusinesscontinuityandsuccessfulgrowth.

We propose a 5-step system which will guarantee successful implementation of data management.

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Inthisbook,weinviteyouforafive-coursedinner.Duringeachcoursewewillexplainthe steps of our 5-step system one by one.

The content and the order of the steps are a result of practical experience, but as any recipe, they are more of a guideline than a list of strict instructions. Tastes (and busi-nesses)differ,sofeelfreetochooseandadjust,makeiterations,orevenskipsteps,ifthatfitsyourpurpose.

A FEW THINGS YOU SHOULD KNOW ABOUT COOKING DATA

1. There are several recognizable international guidelines on setting up data manage-ment. These are:

• Data Management Body of Knowledge, 2nd edition (DAMA-DMBOK 2), by DAMA International1;

• Data Management Capability Assessment Model (DCAM), by EDM Council2;

• The Open Group Architecture Framework, version 9.1 (TOGAF 9.1), by The Open Group3.

2. The good news is: you do not have to read all of them in detail. Our method contains the essence of the above-mentioned guidelines. It covers the required feasible mini-mumofinformationthatyouneedforaneffectiveimplementationofdatamanage-ment in your company.

3. Regardless of their size, most companies are dealing with the same limited list of ur-gent tasks in the area of data management.

4.Youneedtodefinewhatdatamanagementfunctionsarefeasibleandwhichmatchyourcompany’sprofileandrequirements.Thereisnooneparticularapproachthathasbeenwidelyacceptedbythedatamanagementcommunity.Youneedtofindyourownway, with our support, of course!

Good luck, or should we say: ‘bon appétit’!

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Before diving into the development of a data management function, let us align our understanding of the basics.

TOGAF 9.1 stipulates that ‘business function delivers business capabilities closely aligned to the organization’4.Italsodefines‘capability’as‘anabilitythatorganization,person, or system possesses. Capabilities…typically…require a combination of organi-zation, people, processes and technology to achieve’5.

So the following conclusion derives from these statements: data management is a business function, and in order for it to operate properly, you need to combine organi-zation, processes, technology, and data all together.

First things firstTo activate the growth of your business, you have to understand where exactly you needtofocusyourenergyon.Thekeyistochoosethemostimportantandinfluentialbusiness areas for generating the overall success.For sure, data management business function already exists in your organization, in some formal or informal shape.

Usually, the idea to set up a formal data management function does not

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Setting up the tableFIND YOUR DRIVER

just‘comeup’,itcomesfromalongprocessofthinking,analyzingandevaluatingtheneeds of the company. This all leads to a certain point in time when necessity of setting up or extending a formal implementation of this function cannot be overlooked any longer.

Very often, this realization comes along with some urgent challenges. These chal-lenges are to some extent the business drivers we are talking about.

Choosing the right tablewareProbably you already have a few ideas in mind about what the main drivers for your company might be, but let us assist you in structuring these thoughts. It is crucial to have a clear goal before you start ‘cooking’.

1. Create a list of all possible drivers. Think of:• regulatory compliance (i.e. GDPR requirements);• data quality issues;• business intelligence and data warehousing activities;• big data perspective;• predictive analytics;• impact analysis of changes in software and reporting practices;• business continuity.

2. Investigate the most attractive selling points of your data management plan. These could be:

• costefficiencyreduction;• risk reduction;• improvementoforganizationalefficiencyandproductivity;• protection and improvement of the organization’s reputation.

3. Minimize the list to 1-2 most important drivers.

4.Lookfor‘sponsors’fortheidea(s)amongstinfluentialstakeholders.

5. Sell your ideas to your management.

Afterfinishingthisveryfirststep,youshouldhaveaprettyclearideaofthemaindriversand goals for the data management initiative. And hopefully you have already gotten acquainted with a few main stakeholders, which will come in very handy during the fol-lowing steps.

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First course:

apéritif

DEFINE DATA NEEDS

Ingredientsdata stakeholders

data challenges

data requirements

data quality

All business units within your company deal with data in one way or another. They produce data, transform and transport it, and most im-portantly - they use it for making decisions. In every business, data is the main means of com-munication.

PreparationBEFORE YOU START

Every company deals with some issues around data. It isoftendifficulttofindvolunteerswhowould wanttotake on the responsibility for managing this. Uncertainty in such responsibilities will result in a waste of time and resources.

INSTRUCTIONS

1.Identify all stakeholders within your company. They are your main partners in making your data managea-ble. Usually the main stakeholders are top management, IT,finance,sales,production,andotherbusinessunits.

2.Collectandaligntherequirementsofdifferentstake-holders.Donotbesurprisedifallofyouhavedifferentneeds and requirements for the data, its quality, the fre-quency of its delivery and the tools and devices involved in data delivery.

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RESULTS

Asaresultofyoureffortsyouwill:• knowwhothemostinfluentialstakeholdersareandhowtoapproachthem;• haveimprovedefficiencyofyourbusinesspartneringcommunication;• be able to protect your future business and information needs and requirements.

DELIVERABLES

1. Stakeholders’ map and assessment.

2. Communication approach.

3. Business and data requirements.

It is advisable to revise data requirements at least once a year. This has several reasons, but mainly because almost every year you might receive new requirements from regu-lators, thus your management reports will require regular updates.

You should organize revision before the the start of the budgeting cycle. By this time, you will have better insight in additional investments regarding new technologies, ap-plications,projects,developmentsofreporting,etc.

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Second course:

appetizer

DIVIDE TASKS AND RESPONSIBILITIES

Ingredientsdata management

principles

data governance

data owner

shared responsibility

rules, roles, and tasks

Data management is a shared responsibility between data professionals and others stake-holders6. You need to establish data govern-ance rules, starting with defining the main data management principles.


Thefinancedepartmentoftenseesdataastheirrespon-sibility, although you know now that every other depart-ment also has their own data needs. Assigning a data owner and defining a clear set of accountabilities,willresultinmoreefficientdataprocessing.

INSTRUCTIONS

1. Set up data management principles. There are a lot of principles which your company can adapt and imple-ment. For data management, you need to choose those which meet your company goals and culture.

2. Agree on governance rules and procedures. This ac-tionwillallowyou todefineaccountable functionsperspecificdatamanagementtask.Datagovernancecon-sists of:

• roles and responsibilities;

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• datamanagementtasksassignedtospecificroles;• processes and procedures; • governance bodies.

Thestepisiterative.Duringthefirststage,whenthedatamanagementfunctionisnotfully designed, you will comprise a preliminary list of tasks and allocate responsibilities. Later on you might need to revise this list.

3. Identify the function responsible for data management. Although data management is a shared responsibility, you still need to decide who will have the ultimate account-ability for the coordination of the tasks. There is no agreed vision on the place of the data management function in the company. Some companies consider it an IT function, someassignitdirectlytotheCIO(ifpresent),othersgivetheresponsibilitytothefinan-cial business unit. It is your company’s choice. Themost significant challenge for theperson in this role, isbalancing the (often

contradicting) interestsofdifferentstakeholdersandguidingallofthemtocommonsuccess.

4. Identify data-related roles, including the owners’ accountabilities. Aside from ‘data owners’, you must have heard of ‘system owners’, ‘process owners’, ‘product owners’, etc. The distinction between such functions is not always clear. It is crucial to align re-sponsibilities of various roles and to assign them to business functions.

RESULTS

As soon as the data management principles, rules and roles are set up and agreed upon, you will:

• know who you can approach to discuss your data-related (including data quality) issues and solutions;

• be aware of all the concerns the main data stakeholders have;• be sure that all the issues with data will be resolved according to an agreed proce-

dure.

DELIVERABLES

1. Data management principles.

2. Data policy, governance bodies, procedures, roles, responsibilities.

3. Matrix: data management tasks vs roles.

4. Matrix: roles vs functions.

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Third course:

entree

ORGANIZE YOUR DATA HOUSE

Ingredientsprofessional language

internal and external communication

business glossary

reportflow

Clear communication between your colleagues and business partners is crucial in any business. You want to spend less time explaining what you mean and avoid any unnecessary duplica-tions in your reports. How do you achieve this?


Speaking the same language with your co-workers and external parties is not as easily achieved as it seems. Usually there are two issues which you can encounter while communicating on professional level:

• You use the same term as your colleague does, but itmeanssomethingentirelydifferent.

• You use different termswhich allmean the samething.

This is often the case with internal, as well as external communication, and is also reflected on the qualityof data and reports that are being exchanged. The re-ports received by the stakeholders might not always be as comprehensible for them as you intend. And this, in its turn, can have an undesirable effect on their deci-sion-making process.

This issue creates problems with reconciliation of re-portsandfiguresfromdifferentdepartmentsandbuild-ing enterprise DWH and BI solution.

Part 1: UNDERSTAND YOUR DATA

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INSTRUCTIONS

1. Create a business glossary by analysing:• company policies;• regulations relevant for your business;• main reports.

2.Agreewithstakeholdersonthedefinitionsofterms.

3. Create a catalogue of main management reports circulating in your company.

4.Createareportflow.

RESULTS

By now, you should be:• aware which and how many reports contain duplicate information;• able to optimize the number of your reports and remove the duplicate informa-

tion;• abletoreducetheeffortsonreconciliationreportsfromdifferentdepartments;• abletocommunicatemoreproductivelyandefficientlywithyourco-workersand

business partners.

DELIVERABLES

1. Company’s business glossary.

2. Report catalogue.

3.Reportflow.

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Third course:

entree


Ingredientscritical data elements

applications

a ‘golden’ source

Your dream is that pressing one button will make all required information magically ap-pear on your screen. Unfortunately, such a tool does not exist yet. It is not about the tooling though: the key to easy access to data is know-ing its exact location.


Youhavealreadyidentifiedhowmanyreportsarecircu-lating in your company. Not all the data in these reports is equally critical to your business. Now it is time to min-imizeyoureffortstoafeasibleminimumbyidentifyingcritical data elements that constitute the reports. It is also time to establish how many systems and applica-tions are in use in your company.

INSTRUCTIONS

The main task is to identify the location of the most im-portant elements within the applications involved in data processing, and pinpoint the ‘golden’ sources where the data elements were initially put into processing.

1. Identify critical data elements that have the biggest influenceonyourworkresults.

Part 2: LOCATE YOUR DATA

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2. Catalogue all of the main applications, services, and interfaces.

3. Map applications and critical data elements.

4. Identify the ‘golden’ sources for critical data elements.

5. Extend the list of data elements and repeat actions 1 to 4.

RESULTS

By now, you should know how to:• searchforthesourcesofinformationfasterandmoreefficiently;• save time on reconciliation of reports as you can acquire data from the ‘golden’

sources;• decrease the number of processes, reduce duplicate applications, and save mon-

ey in the process.

DELIVERABLES

1. Catalogues of: • (critical) data elements; • applications;• ‘golden’ sources.

2. Matrices: • application vs (critical) data elements;• ‘golden’ sources vs (critical) data elements.

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Third course:

entree


Ingredientsdataflow

data transformation

data life cycle

data lineage

Once in a while you internal audit or external regulators request you to explain how you de-rive to certain data in your reports. The same data can lead to different results, depending on transformation it undergoes along the way. The key to knowing your data is understanding these transformations and the way it travels starting from the source to the end user.


You and your colleagues from other departments use thesamedata,butsometimesitleadstodifferentout-comes.Whoisright?Itcostsalotoftimeandefforttoinvestigate what causes these issues, but such investiga-tions still happen almost daily. Due to new regulations (i.e., GDPR), the amount of such tasks will only grow.Supporting the data flow information is one of the

most complicated data-related tasks that many compa-nies deal with. The bigger the company the more com-plex and costly the solution will be. Every company has tofindafeasiblesolutiondependingonitssizeandre-sources.

The success lies not in the right software solution. The keyistogetallthestaffinvolvedinsharingtheirinfor-mation and be willing to make it maintainable.

Part 3: MANAGE THE DATA FLOW

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INSTRUCTIONS

1.Documentorfindatechnicalsolutionforthepresentationofthedataflow.Datalin-eage describes the changes that data undergoes from source to its end user. The main components of data lineage are business processes, application landscape, business roles, and technical metadata.

2. There are three solutions possible for documentation of data lineage:

Solution 1: Data lineage by design. This requires highly automated software solutions that exist on the market. There are severalprovidersthatoffersuchsolutions.

Solution 2: Descriptive data lineage. You analyze and describe your business processes, applications, aggregated data, and controls. The most important challenges are:

• centralization of the information to make it available to all stakeholders;• involvement of all stakeholders in the process;• making this process maintainable.

Solution 3: A combination of solutions 1 and 2.

RESULTS

Makingdataflowinformationavailablewillresultin:• saving a lot of time not having to investigate data quality (and other)issues;• decreased operational risk due to the decreased amount of issues with data qual-

ity;• increasedworkefficiencyofyourstaff.

DELIVERABLES

The main deliverables will depend on the type of the solution you have chosen. As a minimum you have to ensure centrally located and well-described documentation that links the following components of data lineage:

• business processes;• business roles;• applications;• (critical) data elements;• business rules and controls.

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Third course:

entree


Ingredientsdata quality

manual corrections

data quality dimensions

measurement criteria

The saying ‘garbage in, garbage out’ is a pre-cise description of the relationship between the quality of your data and the information on which decisions are based. You need to en-sure the quality of your data in order to make your business grow and prosper.


Correcting errors made during data input or processing costs a lot of time and energy, it means extra hours for the staff doing manual adjustments, repeated correc-tionsatleastonamonthlybasis,etc.Youwantyourstaffto focus on the analysis of the data, instead of investing their time in endless corrections.

INSTRUCTIONS

1.Definewhowillberesponsiblefordataqualitywithinbusiness units. Unless, of course, you have already done it while setting up data governance (Step 2). If you have - well done, now you can focus on the next points!

2.Definecriticaldataelementsforwhichyouwillexam-ine and improve data quality.

3.Definethemeasurementcriteriafordataquality.

Part 4: IMRPOVE DATA QUALITY

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There are several data quality dimensions such as: accuracy, completeness, consisten-cy, timeliness, etc.7Forthemaximumbenefitofyourcompany,youneedtoprioritizethecriteriawhichreflecttheneedsofyourbusinessthemost.

4.Definetechniquesyouwishtouseforthe‘root–cause’analysis.Therecanbealotofdifferentreasonswhyyourdataisofinsufficientquality.Someofthecommonsourcesof such issues are:

• data entry process; • data processing, including manual operations; • system (mal)functions.8

5. Set up a data quality issue log.

6. Start executing internal data quality initiatives.

RESULTSWorking with data of better quality, you will:

• reduce operational risk;• improve the decision making process;• saveresourcesonreconciliationsandfixingreportingissues.

DELIVERABLES

1. A list of data quality responsibilities assigned to business functions.

2. A catalogue of data issues.

3. Data quality issues resolution plan.

4. Data quality issues resolution business process embedded in daily activities.

5. Fine-tuned analysis techniques for data quality issues.

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Fourth course:

main course

ASSESS THE GAPS

Ingredientsbusiness function

data management capability

gap analysis

roadmap

It is highly probable that some of data manage-ment functions already exist in your company. Think, for example of information security. At this point you probably have a plan for further development. Now try to evaluate your current position. Once you know where you stand, you can start making changes towards your goal.


It is important to be on the same page with your busi-ness partners regarding feasibility and necessity of the changes. Also, you need to know how to achieve re-quiredresultsusingminimalresourcesandeffort.

INSTRUCTIONS

You have to make a gap analysis between the current sit-uation and the situation ‘to be’. The gap analysis consists of the following steps:

1.Definewhichcurrentbusinessfunctionsinyourcom-pany are related to data-management.

2. Finalize the list of the data management functions you wish to develop (the revision we were talking about on p. 13).

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3. Find the gaps between ‘now’ and ‘to be’. You can use the check-list in the end of this book (Appendix 2) to assess which data management capabilities are in need of further development.

4. Outline a roadmap to close these gaps.

RESULTS

As a result, you and your company’s management will have a clear strategic vision on:• which data management capabilities your company needs;• how long it will take to reach the desired situation and what it will cost;• whatyourcompanywillgainastheresultofthiseffort.

DELIVERABLES

1. An overview of the required data management functions within your company.

2. A gap analysis between the current and desired future situation.

3. A roadmap.

4.Afilled-inchecklistfordatacapabilities(seeAppendix2).

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Fifth course:

dessert

KEEP GOING

Ingredientsmaintenance and

development

You already have an approved roadmap and an idea of what your company can achieve. It is time to put theory into practice!


All the preliminary preparation is done, stop dreaming and start doing.

INSTRUCTIONSThere are various approaches you can choose from. You canworkonprojectbase,oryoucangoAgile-style.Itdoes not matter which approach you will choose, as long as you keep the following key success factors in mind:

• Data management needs to be set up as a busi-ness function.

• Data management is a shared responsibility: the stafffromdifferentdepartmentsneedtobein-volved on a daily basis.

• Top management has to be the main sponsor and supporter of the implementation.

• Once set up, it requires permanent maintenance and development.

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• Sometasksofdatamanagement(i.e.dataquality,dataflow)areongoingpro-cesses.

• There are not too many widely experienced data management specialists on the market.Itwouldbewisetotrainanddevelopyourownstaff.

• You need to concentrate on small deliverables that can immediately improve your current processes and deliver results.

RESULTS

As a result of this last step, you will:• getaclearvisiononthetaskstobedoneinadifferenttimeperspective;• knowwhichdeliverablesandresultsyouhavetorequestfromyourstaff;• know when you can expect improvements in your daily work, which investments

and which reduction of costs you need to plan.

DELIVERABLES

The main deliverable is a clear operational model for data management in your compa-ny.

23

24

Works citedDAMA International. DAMA Guide to the Data Managemen Body of Knowledge, 1st edition. Technics Publications, 2010.

DAMA International. DAMA-DMBOK: Data Management Body of Knowledge, 2nd edition. Technics Publications, 2017.

EDM Council. “Data Management Capability Assessment Model, DCAM 1.2.2. (Asses-sor’s Guide)” EDM Council, 23 Jan. 2018, www.edmcouncil.org/dcam.

The Open Group. “TOGAF Version 9.1”, The Open Group Standard no. G116, 2011.

Notes1. DAMA International. DAMA-DMBOK: Data Management Body of Knowledge, 2nd edition. Technics Publications, 2017.

2. EDM Council. “Data Management Capability Assessment Model, DCAM 1.2.2. (As-sessor’s Guide)” EDM Council, 23 Jan. 2018, www.edmcouncil.org/dcam.

3. The Open Group. “TOGAF Version 9.1”, The Open Group Standard no. G116, 2011.

4. TOGAF 9.1, 23.

5. TOGAF 9.1, 23.

6. DAMA-DMBOK 1, 5.

7. DAMA-DMBOK2, 465.

8. DAMA-DMBOK2, 467-469.

Extramaterials

CASE STUDY: DATA QUALITY

STEP-BY-STEPCHECKLIST

Define data needs1.Definemainstakeholdersthathaveconcernsregardingdataquality(DQ)issues.

2. Identify communication strategies with the stakeholders.

3. Document stakeholders’ data-related needs and requirements.

Divide tasks and responsibilities4.Definethescopeofyourcompanytobeinvolved.

5. Capture data (management) principles as the basis for DQ governance.

6. Make self-assessment of current data management capabilities.

7. Identify required DQ management processes, procedures, tasks, and roles.

8.Developoradjustdatamanagementroadmap,strategy,policywithregardtoDQ tasks.

Organize your data house 9. List reports that should be in scope.

10. Identify critical data domains and elements.

11. List reports involved in the scope.

12. Create a DQ issues log.

13.Specifydefinitionsofcriticaldataelementsbyputtingtheminto

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Case study

DRIVER: DATA QUALITY

Appendix 1

the business glossary and or by creating conceptual or logical data models.

14. Prepare an action plan for DQ issues resolution according to the DQ governance procedures.

15. In order to execute root-cause analysis, you need to document data lineage for the critical data elements. This process can be broken down into the following steps:

a. identifying related business processes;b. creating a catalogue of applications;c. identifying location of data elements in applications by documenting database

metadata;d. documenting business rules;e. analyzing existing business processes and data quality controls.

16.Identifydataqualityrequirementsofdifferentdatausers.

17. Align your activities with main stakeholders, especially IT.

18. Clean historical data if needed.

19. Develop data quality checks and controls, based on DQ requirements.

20.Adjustexistingdataprocessingflows.

Assess the gaps21. Re-assess the initial plans through gap analysis.

22. Based on gap analysis, verify the feasibility of the chosen approach.

Take action23.Realizeyourstrategyforfit-for-purposedatadeliverywiththerequiredlevelofquality.

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DRIVER

Regulatory compliance, i.e. GDPR

Data quality issues

Implementation of advanced analytics techniques

Improvementofbusinessandfinancialplanningandforecasting

Other,

DATA NEEDS

Stakeholder map

Stakeholder assessment

Stakeholder communication approach

Business needs and requirement

Data needs and requirements

TASKS & RESPONSIBILITIES

Data management principles

Data policy

Data governance roles and responsibilities

Data governance procedures

Data management tasks vs roles

Data roles vs organizational functions

28

Check listAppendix 2

29

DATA HOUSE

Company business glossary

Report catalogue

Reportflow

Catalogue of (critical) data elements

Data models (conceptual and logical)

Catalogue of applications

Data dictionary (physical data model)

Catalogue of ‘golden’ sources

Matrix application vs (critical) data element

Matrix ‘golden’ source vs (critical) data element

Descriptive data lineage (including business processes, roles, applications, data elements, business rules and controls)

List of data quality responsibilities assigned to business functions

Catalogue of data issues

Data quality resolution plan

Data quality issues resolution process

Fine-tuned data quality issues analysis techniques

THE GAPS

Gap analysis between existing and desired (future) data management functions and tasks

Roadmap on data management development

FINAL PRODUCT

Implemented operational data management function

A lot of companies realize that data is an invaluable asset and has to be managed ac-cordingly. They would also like to get value from data. Everyone wants to be ‘data-driv-en’ these days. What lies beneath this idea, is the wish to make the decision-making process easier and more effective. It means delivering the required data of accept-able quality to the relevant decision makers when and where they need it. In short: a lot of companies have the necessity to manage their data properly. The main question is: how do you put this in practice?

Knowing the potential of your data, and managing it correctly is the key to an effective and successful business. As a result of well-implemented data management, you will be able to reduce risks and costs, increase efficiency, ensure business continuity and successful growth.

In this book, we invite you for a five-course dinner. During each course we will explain the steps of our 5-step programme which guarantees successful implementation of data management.

ABOUT THE AUTHORDr. Irina Steenbeek is a dedicated and tena-cious Senior Data Management, Finance and IT Professional with 15+ years of extensive experience. Her areas of expertise are data management, software implementation, fi-nancial and business control, project man-agement, business process re-engineering, and management consulting and training.

Throughout the years, she has worked for var-ious medium and large multinational organi-zations, among which The World Bank, ABN AMRO Bank, Amsterdam Trade Bank, and In-ternational Card Services (ICS).

In 2016 she has founded Data Crossroads - a consulting agency in the area of data management and predictive analytics. She has developed several models for imple-mentation of data management which are based on industry reference guidelines and are universal for every business. Her approach is highly customizable, and en-sures effective results in any type of organization. Data Crossroads connects ex-perts within various industries in order to ensure the highest quality of consultation to all clients.

ISBN: 1984149938

ISBN-13: 978-1984149930

the data management cookbook · introduction the amount of data available in your company and in...

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