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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. 

Source: PwC Luxembourg