How data management helps implement your data strategy

Ah, data, the new “oil” of our time. Organisations increasingly see its value, but they are still figuring out how to manage and use it in an efficient and effective way. According to the leader’s data manifesto, a significant number of companies are thriving “with small-scale analytics, governance, quality and other efforts”. 

At the same time, “examples of fundamental, lasting, company-wide change without committed leadership and the involvement of everyone at all levels of the organisation” are yet to be found. A 2019 report from PwC’s Strategy& states that 79% of companies still don’t have a Chief Data Officer (CDO), but more than two thirds talk more about data than five years ago. 

This said, we believe, quite confidently, that data is bringing more changes than we expected 20 years ago, and the really deep changes — technological, regulatory, among others — are still coming. All the while, data is becoming more and more important and increasingly embedded in our daily lives.

And today, as it is the intangible asset of our time and it’s progressively being treated as such,  it’s undeniable that data must be at the centre of any mindset shift across an organisation and become an integral part of its DNA.

But the gap between theory and practice is still wide. Once you’ve defined your data strategy, the real challenge begins — how to implement it. And that’s what this blog is about. We will define what data management is, deep dive into some of its most critical knowledge areas and explain how it can help you implement your data strategy.  

What is Data Management

According to the Data Management Body of Knowledge (DMBoK), data management is the development, execution and supervision of plans, policies, programmes and practices that deliver, control, protect and enhance the value of data and information assets throughout their lifecycles.

The DMBoK identifies 11 knowledge areas, including data architecture, data quality, data security, and meta-data. At the centre of all these functions is data governance, which provides direction and oversight for data management by establishing a system of decision rights over data that is valuable for the organisation.

Click to enlarge | Source: The DAMA-DMBOK2 Data Management Framework (The DAMA Wheel), Page 36
Data explosion and what it means for business

The “Digital Industrial Revolution” began with the rise of technology, and its everyday usage by people all around the globe shifted entire systems. Digital is delivering increasingly powerful tools and approaches to create value in the world; and data is one of them.

This digital revolution, through the emergence of the internet in the 90s, triggered a data explosion, and more recent technologies have multiplied the speed of data accumulation exponentially. Examples of such technologies include modern software, data analytics capabilities or customer-focused innovation capabilities that are at the heart of digital transformation.

Volume of data/information created, captured, copied, and consumed worldwide from 2010 to 2024 1 ZB = 1 000 000 millions GB
Click to enlarge | Source(s): IDC; Seagate; Statista estimates; ID 871513

There isn’t a better quote to describe what we mean than the one by Eric Schmidt, former Executive Chairman at Google: “There were 5 exabytes of information created between the dawn of civilization through 2003, but that much information is now created every two days.”

Today, you can see, and even experience, the direct consequences of data explosion everywhere. For instance, through the creation of new jobs and roles such as data scientist or chief data officer; the emergence of new business and IT challenges linked to the business channel shift to cloud, mobile, or 5G; the rise of new technological opportunities such as artificial intelligence, internet of things, or blockchain. And of course, there’s also the new challenges revolving around innovation, infrastructure and institutions.

To master this data explosion, organisations need to develop a structured approach.To get there, here’s some key findings to consider:

  • A structured format: the World Economic Forum reports that only 10% of all data is collected in a format that allows easy analysis and sharing. This means that the other 90% needs to be processed, cleansed or adapted to a formal format;
  • Breaking the silo: just a few years ago, most aspects related to data management belonged to the IT domain. Organisations are moving from the statisticians and data scientist silo to a more inclusive business user-case where user categorisation is necessary;
  • Accessibility: most non-sensitive data must be accessible. However, keep in mind you need to put in place measures to avoid data misinterpretation, assess security risks,  maintain data integrity, and ensure the protection of personal data.

These key points mean that it’s imperative to think, at least, of two aspects. First, governance of user interaction with data. To achieve this, businesses want to create a framework for data acquisition, management and archiving, while following security guidelines as well as ensuring their business remains compliant. Second, skills development, by putting in place data training and data upskilling programmes addressing users with different data needs.

The worldwide data explosion opens up a window of possibilities for businesses, meaning previously intuition-based decisions can now team up with data-driven ones to find the right balance that helps achieve goals through proper data organisation.

Why data requires a new kind of organisation

As we mentioned in our previous article about this subject, the key idea of a data strategy is to treat data as an asset. It seems quite straightforward, but remember that pretty much anything can be “data”; the trick is to define which data has value to you.

That’s why organisations need to be structured. Implementing a framework such as a data management programme, or a transformation programme, or by developing an operating model such as a data office, will help them make that decision, but also manage the challenges created by the data explosion.

One fundamental question that will arise immediately will be about the overall role of the IT department. You must realise that data is bringing a shift across the organisation, with agile and citizen-led initiatives, which need a well-defined governance. If you recall, data governance is at the centre of data management. And governance should be led outside technical solutions. Therefore, we believe the IT department should only be a stakeholder of a data initiative, at the same level as the business. And that data initiative should be led by a dedicated data-driven structure.

How to develop a Data Management Programme

One of the first fundamental actions to implement a data strategy is to develop a data management programme. But, what is a programme? It’s usually defined as a temporary organisation created to coordinate a set of related projects, and it can be seen as an umbrella under which projects can be coordinated.

A data management programme will have several objectives, such as remaining aligned with the organisation data strategy, leading data changes, delivering coherent data capabilities, learning from data-related experience (because a programme equals a learning organisation), just to name a few.

Overall, the programme will show the benefits of applying data management’s best practices, which are deeply embedded in the processes and decisions across an organisation, to provide data insights that help make informed decisions.

Perform a Data Maturity Assessment

Then, once the programme is put in place, it’s time to roll-out a Data Management Maturity Assessment (DMMA) to determine where the organisation stands and what are its objectives.

Its primary goal is to evaluate the current state of critical data management activities to help an organisation identify, prioritise, and enforce improvement opportunities. Usually, the evaluation places the organisation on the maturity scale by clarifying specific strengths and weaknesses. The DMMA frequently defines five or six levels of maturity, each with its own characteristics that span from non-existent (or ad hoc) to optimised (or high performance). Additionally, by defining targets of maturity to reach, the DMMA helps to assess the gap and the effort of transformation, and to conduct the necessary changes.

Many vendors have developed their own DMMA models. For the sake of example, let’s introduce the Data Maturity Model from the Capability Maturity Model Integration (CMMI), in which data management can be addressed across five areas: data strategy, data governance, data quality, platform & architecture, and data operations. These areas work together to create an effective data management programme.

In sum, measurement exercises such as the DMMA typically drive significant change by providing a roadmap for improvement. So, when an organisation implements or revamps a data management programme, which involves transforming processes, methods, and tools, this will, in turn, lead to a deep organisational and cultural transformation. 

How to set up a Data Office

Today, numerous organisations are facing increasing data-related challenges, including bigger data volume and variety to manage, more complex processes to put in place, and more data to capture, just to name a few. These challenges increase the complexity for data management. 

And, as the data landscape keeps evolving, organisations have to remain flexible to fulfil the data needs of the consumers and their businesses. Given this context, a higher number of businesses are setting up data offices, which have to answer fundamental questions about decision-making, data ownership, accountability, responsibility and collaboration.

Defining the target operating model entails a complex set of activities. Before engaging stakeholders in the data management processes, you will need to define whether your data office will be a new organisation altogether or just the improvement of an existing one. 

Remember that successful organisations don’t evolve randomly and that without strategy, change is merely substitution, not evolution. The data office has to fit with the company culture, the existing operating model and the strategic vision. 

Now, a small parenthesis on what we mean by operating model. It’s a framework that articulates roles, responsibilities, and decision-making processes, and describes how people and functions will work together in your data office. A truthful data office contributes to accountability by ensuring that the right functions are represented within the organisation. Moreover, it facilitates communication and provides a process for resolving issues.

When organising your data office, there are several operating models you can choose from — from decentralised to federated, or something in between. The general model we witness the most is the central data office, with one federated data office function by business unit, geography, or by purpose. 

Additionally, although there is no rule as to whom the Chief Data Officer should report to, the CDO should stand at the top management of the organisation and be sufficiently independent from IT and Business Lines to leverage significantly on strategic decisions, but serving business strategy and technology innovation… Today, many organisations are leaning towards aligning data with business functions other than IT, and combining data and analytics roles such as chief data and analytics officer. 

A data office has to solve many challenges, including discovering and developing the potential of the data (generated by the organisation or its clients), establishing the data management functions, policies, and procedures; and partnering with business and IT leaders to govern the data efficiently. 

Moreover, a data office should be a cornerstone of digital transformation and process optimisation through the creation of a firm-wide data process and the use of new technologies to unleash data potential while complying with the data management key principles.

Data Management knowledge areas key principles

We have seen what data management is and its importance in the creation of a data management structure such as a data office. Now, let’s focus on the business-as-usual activities of a data office, which involve dealing with numerous data management knowledge areas. We are going to dive into some of the most critical of them — data governance, data quality, data operations, and data architecture — and explore their key principles to keep in mind when dealing with data.

  • Data Governance

Data Governance is defined as the exercise of authority and control over the management of data assets. It guides all other data management functions. Its purpose is to ensure that data is managed properly, according to policies and best practices.

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Some of its key principles are:

  • Be compliant with the regulation (such as GDPR) and the organisation’s data policies. For example, when personal data are planned to be processed in a new project, the right function body (such as the Data Protection Office) should perform an analysis (such as  Data Protection Risk Assessment).
  • Apply the principle of minimisation to better control data usage. Some examples are data minimisation (data must be limited to its core purpose), privacy by design (the ability to embed privacy into IT systems and business practices) or privacy by default (procedures safeguarding confidentiality, integrity and availability of personal data).
  • Involve Data Owners and set up data access authorisation. For example, your client owns its data and they should provide their authorisation if you plan to use their data for a different purpose than the initial one. And your internal data should be owned by data owners identified by business domain.

  • Data Quality

Data Quality management is defined as the planning, implementation and control of activities that ensure data is fit for consumption and meet the needs of data consumers. Data Quality issues are almost always underestimated. Poor quality data is very frequent and many organisations fail to define what makes data fit for purpose. 

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Some of its key principles are:

  • Develop data quality continuous improvement. For example, data offices often rely on a data quality team which ensures a high level of data quality in internal systems and third party data.
  • Foster data accuracy when referring to data systems of an organisation such as a business glossary or a reference and master data systems. For example, terms and definitions should be checked and corrected in a Data Governance Catalogue, a correct business glossary should define the right business rules and policies, and master data should represent the authoritative, most accurate data available about key business entities.
  • Foster the use of patterns and methodology when using Data & Analytics tools. For example, ensure that the analytics functionalities of the D&A tools won’t generate wrong results, or manage the quality of data traceability and patterns usage.

  • Data Operations

Data Operations management is defined as the design, implementation, and support of stored data to maximise its value throughout its lifecycle, from acquisition to disposal.

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Some of its key principles are:

  • Improve data collection processes and monitoring. For example, ensuring that data collected, imported, or exported are compliant with Information Security rules and the Data Governance principles;
  • Harmonise data collection best practices and tools, as only 10% of data is collected in a format that allows easy analysis and sharing. Therefore, it’s time consuming and risky to deal with the remaining  90% and it’s always a good idea to focus on added value tasks (such as cleansing automation, automated extraction, among others);
  • Get agility in data transformation and visualisation for business decisions. For example, as data management should improve decision-making, business intelligence should be able to present information in a way that is easy to read and understand with the help of reporting or dashboarding.

  • Data Architecture

Data Architecture management is defined as the identification and design of the data needs of an organisation through the maintenance of the master blueprints meeting those needs. Usually, data architecture teams guide data integration, control data assets, and align data investments with business strategy. 

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Some of its key principles are:

  • Support the Enterprise Data Strategy. For example, by aligning the architecture with the business priorities, by coordinating and helping to integrate disparate data sources or by managing data sources.
  • Monitor and verify the integration of data-implementation best practices into information systems. For example, by identifying the relevant data sources and meta-data to fit with new data flow needs or supporting to document data models and develop data lineage.
  • Ensure the capability to answer data requests through the organisation. For example, the European GDPR allows Data Subject Access Request (a submission by an individual to a business asking to know what personal information they have or they are using) on personal data. Data Architecture should be able to technically provide it.
  • Advise on the best data architecture according to use and context, check the compliance related to the use of the data or document the architecture to allow a better understanding of the information.

We hope that it’s clear by now that data management is fundamentally linked to the recent data explosion, which, in turn, stems from the digital revolution. While we are still at the beginning of a major shift about how to use data effectively with the help of data management, we are convinced that’s the right path to success or, at least, a good first step.

With this blog, we want to emphasise that good implementation of a data strategy will lead your company to structural, technological and organisational changes. Whether you build a data office or develop a data management programme, you will have to rely on several data management knowledge areas such as data governance, data architecture or data quality, to succeed.

A journey of a thousand miles begins with a single step. Your data journey will be supported by a clearly defined and efficient data management structure. Besides, you will need to embark every member of your organisation on this technological and cultural adventure through a data-driven organisation. And, for that, you will need to put in place a data culture with the help of change management. But that’s a story for another time.

What we think
Bernard de Villepin, Senior Data Advisor and Business Coach at PwC Luxembourg
Bernard de Villepin, Senior Data Advisor and Business Coach at PwC Luxembourg

Mastering Data Management activities will greatly enhance the value of your data. Mastering your data value will enhance your decision-making. And mastering your decision-making will improve your business performance.

Philippe Delcambre, Data Management Senior Manager at PwC Luxembourg
Philippe Delcambre, Data Management Senior Manager at PwC Luxembourg

Implementing a Data Strategy means translating top management decisions into operational processes. But this transformation cannot work without the investment of each individual in applying data management key principles, adopting a data culture and a sense of accountability in business as usual data handling.

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